{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study\n",
    "Authors list:<br>\n",
    "[Frank Soboczenski](https://h21k.github.io/) (1),<br>\n",
    "[Thomas Trikalinos](https://vivo.brown.edu/display/ttrikali) (2),<br>\n",
    "[Joel Kuiper](https://joelkuiper.eu/) (3),<br>\n",
    "[Randolph G Bias](https://www.ischool.utexas.edu/~rbias/site/) (4),<br>\n",
    "[Byron C Wallace](http://www.byronwallace.com/) (5),<br>\n",
    "[Iain J Marshall](https://kclpure.kcl.ac.uk/portal/iain.marshall.html) (1)<br>\n",
    "\n",
    "(1) School of Population Health and Life Sciences, King's College London, London, UK<br>\n",
    "(2) Center for Evidence-based Medicine, Brown University, Providence, USA<br>\n",
    "(3) Vortext Systems, Groningen, Netherlands<br>\n",
    "(4) School of Information, University of Texas at Austin, Austin, USA<br>\n",
    "(5) College of Computer and Information Science, Northeastern University, Boston, USA\n",
    "<br>\n",
    "\n",
    "The System used and evaluated in this study can be found here: [RobotReviewer User Study](https://github.com/h21k/robotreviewer3/tree/ux)\n",
    "\n",
    "# 1. Main Analysis \n",
    " This Notebook is unsing an <b>R Kernel!</b><br>\n",
    " First setting up the R environment:\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1"
      ],
      "text/latex": [
       "1"
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      "text/markdown": [
       "1"
      ],
      "text/plain": [
       "[1] 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set specific working directory uncomment to refer to the data files\n",
    "#setwd(\"~/Desktop/R_STUFF\")\n",
    "\n",
    "# load performance improvement libraries & enable just in time compiler\n",
    "library(compiler)\n",
    "enableJIT(1)\n",
    "\n",
    "#some environmental variables (decimals)\n",
    "options=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] TRUE\n",
       "\n",
       "[[2]]\n",
       "[1] TRUE\n",
       "\n",
       "[[3]]\n",
       "[1] TRUE\n",
       "\n",
       "[[4]]\n",
       "[1] TRUE\n",
       "\n",
       "[[5]]\n",
       "[1] TRUE\n",
       "\n",
       "[[6]]\n",
       "[1] TRUE\n",
       "\n",
       "[[7]]\n",
       "[1] TRUE\n",
       "\n",
       "[[8]]\n",
       "[1] TRUE\n",
       "\n",
       "[[9]]\n",
       "[1] TRUE\n",
       "\n",
       "[[10]]\n",
       "[1] TRUE\n",
       "\n",
       "[[11]]\n",
       "[1] TRUE\n",
       "\n",
       "[[12]]\n",
       "[1] TRUE\n",
       "\n",
       "[[13]]\n",
       "[1] TRUE\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check if required packages are there - if not the script will install them!\n",
    "requiredPackages = c('rcompanion','gdata','compiler','car','lsr','sft','nlme', 'lme4', 'bibtex', 'psych', 'likert', 'ggplot2', 'tidyverse')\n",
    "for(p in requiredPackages){\n",
    "  if(!require(p,character.only = TRUE)) install.packages(p);\n",
    "  library(p,character.only = TRUE);\n",
    "}\n",
    "#load all the libraries    \n",
    "lapply(requiredPackages, require, character.only = TRUE);\n",
    "\n",
    "# !!!! NOTE: run this cell twice for decluttering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the data \n",
    "data <- read.xls(\"data/TimeAnalysis2_1.xlsx\", verbose=FALSE, na.strings=c(\"NA\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ParticipantNo</th><th scope=col>ID</th><th scope=col>PDF</th><th scope=col>Order</th><th scope=col>Time</th><th scope=col>Condition</th><th scope=col>CExperience</th><th scope=col>Tasks</th><th scope=col>NReviews</th><th scope=col>AMeanNReviews</th><th scope=col>⋯</th><th scope=col>AC2</th><th scope=col>AL2</th><th scope=col>AC3</th><th scope=col>AL3</th><th scope=col>AC4</th><th scope=col>AL4</th><th scope=col>Comments</th><th scope=col>TotalAdded</th><th scope=col>TotalDeleted</th><th scope=col>TotalSubmitted</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                                         </td><td>-JjAzwjQakZk-3kIbeMfw                     </td><td>U2PXr4iMBf8Kt-leQI8Ii                     </td><td>1                                         </td><td>435                                       </td><td>1                                         </td><td>1                                         </td><td>9                                         </td><td>10+                                       </td><td>12.5                                      </td><td>⋯                                         </td><td>Yes                                       </td><td>Yes                                       </td><td>Yes                                       </td><td>Yes                                       </td><td>Yes                                       </td><td>Yes                                       </td><td>C1:2added, C2:2added,C3:3added,C4:1added  </td><td>8                                         </td><td>0                                         </td><td>20                                        </td></tr>\n",
       "\t<tr><td>1                                         </td><td>-JjAzwjQakZk-3kIbeMfw                     </td><td>4gIe5sni3WJJCzNglSdo8                     </td><td>2                                         </td><td>582                                       </td><td>0                                         </td><td>1                                         </td><td>9                                         </td><td>10+                                       </td><td>12.5                                      </td><td>⋯                                         </td><td>Yes                                       </td><td>No                                        </td><td>Yes                                       </td><td>No                                        </td><td>Yes                                       </td><td>No                                        </td><td>C1:1added, C2:1added, C3:1added, C4:2added</td><td>5                                         </td><td>0                                         </td><td> 5                                        </td></tr>\n",
       "\t<tr><td>1                                         </td><td>-JjAzwjQakZk-3kIbeMfw                     </td><td>iHbGgWtrKNfksdoj9Hxv9                     </td><td>3                                         </td><td>297                                       </td><td>1                                         </td><td>1                                         </td><td>9                                         </td><td>10+                                       </td><td>12.5                                      </td><td>⋯                                         </td><td>No                                        </td><td>Yes                                       </td><td>Yes                                       </td><td>Yes                                       </td><td>Yes                                       </td><td>Yes                                       </td><td>C1:1added, C2:1added, C4:1added           </td><td>3                                         </td><td>0                                         </td><td>15                                        </td></tr>\n",
       "\t<tr><td>1                                         </td><td>-JjAzwjQakZk-3kIbeMfw                     </td><td>cnDXl97I_WoVUqyIH0HbQ                     </td><td>4                                         </td><td>610                                       </td><td>0                                         </td><td>1                                         </td><td>9                                         </td><td>10+                                       </td><td>12.5                                      </td><td>⋯                                         </td><td>No                                        </td><td>No                                        </td><td>No                                        </td><td>No                                        </td><td>Yes                                       </td><td>No                                        </td><td>C1:1added, C2:0added, C3:0added           </td><td>1                                         </td><td>0                                         </td><td> 1                                        </td></tr>\n",
       "\t<tr><td>2                                         </td><td>hNQKTiKtHSY_BmxLyZb9Q                     </td><td>XOCbiBddVQK3BYy1Ox4lI                     </td><td>1                                         </td><td>306                                       </td><td>1                                         </td><td>1                                         </td><td>8                                         </td><td>5to10                                     </td><td> 7.5                                      </td><td>⋯                                         </td><td>No                                        </td><td>Yes                                       </td><td>No                                        </td><td>Yes                                       </td><td>No                                        </td><td>Yes                                       </td><td>C1:1added                                 </td><td>1                                         </td><td>0                                         </td><td>16                                        </td></tr>\n",
       "\t<tr><td>2                                         </td><td>hNQKTiKtHSY_BmxLyZb9Q                     </td><td>oOAy7INgRLumTV3vZWtVM                     </td><td>2                                         </td><td>127                                       </td><td>1                                         </td><td>1                                         </td><td>8                                         </td><td>5to10                                     </td><td> 7.5                                      </td><td>⋯                                         </td><td>No                                        </td><td>Yes                                       </td><td>No                                        </td><td>Yes                                       </td><td>No                                        </td><td>Yes                                       </td><td>                                          </td><td>0                                         </td><td>0                                         </td><td>15                                        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllll}\n",
       " ParticipantNo & ID & PDF & Order & Time & Condition & CExperience & Tasks & NReviews & AMeanNReviews & ⋯ & AC2 & AL2 & AC3 & AL3 & AC4 & AL4 & Comments & TotalAdded & TotalDeleted & TotalSubmitted\\\\\n",
       "\\hline\n",
       "\t 1                                          & -JjAzwjQakZk-3kIbeMfw                      & U2PXr4iMBf8Kt-leQI8Ii                      & 1                                          & 435                                        & 1                                          & 1                                          & 9                                          & 10+                                        & 12.5                                       & ⋯                                          & Yes                                        & Yes                                        & Yes                                        & Yes                                        & Yes                                        & Yes                                        & C1:2added, C2:2added,C3:3added,C4:1added   & 8                                          & 0                                          & 20                                        \\\\\n",
       "\t 1                                          & -JjAzwjQakZk-3kIbeMfw                      & 4gIe5sni3WJJCzNglSdo8                      & 2                                          & 582                                        & 0                                          & 1                                          & 9                                          & 10+                                        & 12.5                                       & ⋯                                          & Yes                                        & No                                         & Yes                                        & No                                         & Yes                                        & No                                         & C1:1added, C2:1added, C3:1added, C4:2added & 5                                          & 0                                          &  5                                        \\\\\n",
       "\t 1                                          & -JjAzwjQakZk-3kIbeMfw                      & iHbGgWtrKNfksdoj9Hxv9                      & 3                                          & 297                                        & 1                                          & 1                                          & 9                                          & 10+                                        & 12.5                                       & ⋯                                          & No                                         & Yes                                        & Yes                                        & Yes                                        & Yes                                        & Yes                                        & C1:1added, C2:1added, C4:1added            & 3                                          & 0                                          & 15                                        \\\\\n",
       "\t 1                                          & -JjAzwjQakZk-3kIbeMfw                      & cnDXl97I\\_WoVUqyIH0HbQ                    & 4                                          & 610                                        & 0                                          & 1                                          & 9                                          & 10+                                        & 12.5                                       & ⋯                                          & No                                         & No                                         & No                                         & No                                         & Yes                                        & No                                         & C1:1added, C2:0added, C3:0added            & 1                                          & 0                                          &  1                                        \\\\\n",
       "\t 2                                          & hNQKTiKtHSY\\_BmxLyZb9Q                    & XOCbiBddVQK3BYy1Ox4lI                      & 1                                          & 306                                        & 1                                          & 1                                          & 8                                          & 5to10                                      &  7.5                                       & ⋯                                          & No                                         & Yes                                        & No                                         & Yes                                        & No                                         & Yes                                        & C1:1added                                  & 1                                          & 0                                          & 16                                        \\\\\n",
       "\t 2                                          & hNQKTiKtHSY\\_BmxLyZb9Q                    & oOAy7INgRLumTV3vZWtVM                      & 2                                          & 127                                        & 1                                          & 1                                          & 8                                          & 5to10                                      &  7.5                                       & ⋯                                          & No                                         & Yes                                        & No                                         & Yes                                        & No                                         & Yes                                        &                                            & 0                                          & 0                                          & 15                                        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "ParticipantNo | ID | PDF | Order | Time | Condition | CExperience | Tasks | NReviews | AMeanNReviews | ⋯ | AC2 | AL2 | AC3 | AL3 | AC4 | AL4 | Comments | TotalAdded | TotalDeleted | TotalSubmitted | \n",
       "|---|---|---|---|---|---|\n",
       "| 1                                          | -JjAzwjQakZk-3kIbeMfw                      | U2PXr4iMBf8Kt-leQI8Ii                      | 1                                          | 435                                        | 1                                          | 1                                          | 9                                          | 10+                                        | 12.5                                       | ⋯                                          | Yes                                        | Yes                                        | Yes                                        | Yes                                        | Yes                                        | Yes                                        | C1:2added, C2:2added,C3:3added,C4:1added   | 8                                          | 0                                          | 20                                         | \n",
       "| 1                                          | -JjAzwjQakZk-3kIbeMfw                      | 4gIe5sni3WJJCzNglSdo8                      | 2                                          | 582                                        | 0                                          | 1                                          | 9                                          | 10+                                        | 12.5                                       | ⋯                                          | Yes                                        | No                                         | Yes                                        | No                                         | Yes                                        | No                                         | C1:1added, C2:1added, C3:1added, C4:2added | 5                                          | 0                                          |  5                                         | \n",
       "| 1                                          | -JjAzwjQakZk-3kIbeMfw                      | iHbGgWtrKNfksdoj9Hxv9                      | 3                                          | 297                                        | 1                                          | 1                                          | 9                                          | 10+                                        | 12.5                                       | ⋯                                          | No                                         | Yes                                        | Yes                                        | Yes                                        | Yes                                        | Yes                                        | C1:1added, C2:1added, C4:1added            | 3                                          | 0                                          | 15                                         | \n",
       "| 1                                          | -JjAzwjQakZk-3kIbeMfw                      | cnDXl97I_WoVUqyIH0HbQ                      | 4                                          | 610                                        | 0                                          | 1                                          | 9                                          | 10+                                        | 12.5                                       | ⋯                                          | No                                         | No                                         | No                                         | No                                         | Yes                                        | No                                         | C1:1added, C2:0added, C3:0added            | 1                                          | 0                                          |  1                                         | \n",
       "| 2                                          | hNQKTiKtHSY_BmxLyZb9Q                      | XOCbiBddVQK3BYy1Ox4lI                      | 1                                          | 306                                        | 1                                          | 1                                          | 8                                          | 5to10                                      |  7.5                                       | ⋯                                          | No                                         | Yes                                        | No                                         | Yes                                        | No                                         | Yes                                        | C1:1added                                  | 1                                          | 0                                          | 16                                         | \n",
       "| 2                                          | hNQKTiKtHSY_BmxLyZb9Q                      | oOAy7INgRLumTV3vZWtVM                      | 2                                          | 127                                        | 1                                          | 1                                          | 8                                          | 5to10                                      |  7.5                                       | ⋯                                          | No                                         | Yes                                        | No                                         | Yes                                        | No                                         | Yes                                        |                                            | 0                                          | 0                                          | 15                                         | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  ParticipantNo ID                    PDF                   Order Time\n",
       "1 1             -JjAzwjQakZk-3kIbeMfw U2PXr4iMBf8Kt-leQI8Ii 1     435 \n",
       "2 1             -JjAzwjQakZk-3kIbeMfw 4gIe5sni3WJJCzNglSdo8 2     582 \n",
       "3 1             -JjAzwjQakZk-3kIbeMfw iHbGgWtrKNfksdoj9Hxv9 3     297 \n",
       "4 1             -JjAzwjQakZk-3kIbeMfw cnDXl97I_WoVUqyIH0HbQ 4     610 \n",
       "5 2             hNQKTiKtHSY_BmxLyZb9Q XOCbiBddVQK3BYy1Ox4lI 1     306 \n",
       "6 2             hNQKTiKtHSY_BmxLyZb9Q oOAy7INgRLumTV3vZWtVM 2     127 \n",
       "  Condition CExperience Tasks NReviews AMeanNReviews ⋯ AC2 AL2 AC3 AL3 AC4 AL4\n",
       "1 1         1           9     10+      12.5          ⋯ Yes Yes Yes Yes Yes Yes\n",
       "2 0         1           9     10+      12.5          ⋯ Yes No  Yes No  Yes No \n",
       "3 1         1           9     10+      12.5          ⋯ No  Yes Yes Yes Yes Yes\n",
       "4 0         1           9     10+      12.5          ⋯ No  No  No  No  Yes No \n",
       "5 1         1           8     5to10     7.5          ⋯ No  Yes No  Yes No  Yes\n",
       "6 1         1           8     5to10     7.5          ⋯ No  Yes No  Yes No  Yes\n",
       "  Comments                                   TotalAdded TotalDeleted\n",
       "1 C1:2added, C2:2added,C3:3added,C4:1added   8          0           \n",
       "2 C1:1added, C2:1added, C3:1added, C4:2added 5          0           \n",
       "3 C1:1added, C2:1added, C4:1added            3          0           \n",
       "4 C1:1added, C2:0added, C3:0added            1          0           \n",
       "5 C1:1added                                  1          0           \n",
       "6                                            0          0           \n",
       "  TotalSubmitted\n",
       "1 20            \n",
       "2  5            \n",
       "3 15            \n",
       "4  1            \n",
       "5 16            \n",
       "6 15            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quick check on the data\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Short comments on the data:<br>\n",
    "<b>ID</b> = individual participant<br>\n",
    "<b>PDF</b> = document ID<br>\n",
    "<b>Order</b> = document order of appearance<br>\n",
    "<b>Time</b> = Time spent on one document in seconds<br>\n",
    "<b>Condition</b> = Independent variable (1 = Machine learning recommendations (MLR) present, 0 = no MLR)<br>\n",
    "<b>CExperience</b> = Experience with Cochrance Risk of Bias Tool? (1 = Yes, 0 = No)<br>\n",
    "<b>Tasks</b> = Number of tasks (in a systematic review) performed (9 = max)<br>\n",
    "<b>NReviews</b> = Number of systematic reviews performed<br>\n",
    "<b>AMeanNReviews</b> = Artithmetic Mean number of systematic reviews performed (0=0, 1-5=3, 5-10=7.5, 10+=12.5)<br>\n",
    "<b>Error</b> = reported errors (ignore for now)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 Data Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IAAAQIECBBoTGDsBdLbMp/3St6X7LfE3NZ3kA5ITk62SU5KNAIECBAgQIAAAQIE\nGhMY+1Xs6mp0uyZHJU9Kzk/q0t4XJZcn2yV1Fbu9kt2S65MXJ59LNAIECBAgQIAAAQIEGhMY\ne4FUF184Nnl/cnRSV6qbPpN0dfZdkByTHJecl2xsq4/01SXDazmk3S6ddk9eM6SzPgQIECBA\ngAABAgQILI/A2AukTq2uZHf4ZKPOGtXvH22d1A/HXpZs6lbFTp212nLgA1e/OyV/nlw38D66\nESBAgAABAgQIECCwiQXGXiDtHa+fSL7ac6uP1lWWs52TB7/HDE+wf/qemtR3oTQCBAgQIECA\nAAECBFZIYOwXaXhlXL+SHJ8MPZuzQlPhaQkQIECAAAECBAgQWGmBsRdIne8RWanfQ6pLfmsE\nCBAgQIAAAQIECBBYVKClAmnPCHwh+avk7otq2EmAAAECBAgQIECAQNMCrRRI78ks3zf55+S3\nk/9KPpz8QtKKQYaqESBAgAABAgQIECCwlEBLxcH3AvGU5FeSM5PHJx9Mqlh6afLYpH4LSSNA\ngAABAgQIECBAoFGBlgqkbopPzMq9kwOStydVFNVvJH00uSCpH5Gt7yxpBAgQIECAAAECBAg0\nJjD2y3wvNZ2fzY2V5ye/lNw/+ZmkiidnkoKgESBAgAABAgQIEGhNoOUCqZvr+qHYt3Qbk2WL\nZ9amCGwSIECAAAECBAgQaE9AIbD4nN+4+G57CRAgQIAAAQIECBAYs8DYC6TnZPK2S+oskUaA\nAAECBAgQIECAAIElBcb+EbsfZfQVjQABAgQIECBAgAABAusVGPsZpPUC6ECAAAECBAgQIECA\nAIFOQIHUSVgSIECAAAECBAgQINC8gAKp+ZcAAAIECBAgQIAAAQIEOgEFUidhSYAAAQIECBAg\nQIBA8wIKpOZfAgAIECBAgAABAgQIEOgEFEidhCUBAgQIECBAgAABAs0LKJCafwkAIECAAAEC\nBAgQIECgE1AgdRKWBAgQIECAAAECBAg0L6BAav4lAIAAAQIECBAgQIAAgU5AgdRJWBIgQIAA\nAQIECBAg0LyAAqn5lwAAAgQIECBAgAABAgQ6AQVSJ2FJgAABAgQIECBAgEDzAgqk5l8CAAgQ\nIECAAAECBAgQ6AQUSJ2EJQECBAgQIECAAAECzQsokJp/CQAgQIAAAQIECBAgQKATUCB1EpYE\nCBAgQIAAAQIECDQvoEBq/iUAgAABAgQIECBAgACBTkCB1ElYEiBAgAABAgQIECDQvIACqfmX\nAAACBAgQIECAAAECBDoBBVInYUmAAAECBAgQIECAQPMCCqTmXwIACBAgQIAAAQIECBDoBBRI\nnYQlAQIECBAgQIAAAQLNCyiQmn8JACBAgAABAgQIECBAoBNQIHUSlgQIECBAgAABAgQINC+g\nQGr+JQCAAAECBAgQIECAAIFOQIHUSVgSIECAAAECBAgQINC8gAKp+ZcAAAIECBAgQIAAAQIE\nOgEFUidhSYAAAQIECBAgQIBA8wIKpOZfAgAIECBAgAABAgQIEOgEFEidhCUBAgQIECBAgAAB\nAs0LKJCafwkAIECAAAECBAgQIECgE1AgdRKWBAgQIECAAAECBAg0L6BAav4lAIAAAQIECBAg\nQIAAgU5AgdRJWBIgQIAAAQIECBAg0LyAAqn5lwAAAgQIECBAgAABAgQ6AQVSJ2FJgAABAgQI\nECBAgEDzAgqk5l8CAAgQIECAAAECBAgQ6AQUSJ2EJQECBAgQIECAAAECzQsokJp/CQAgQIAA\nAQIECBAgQKATUCB1EpYECBAgQIAAAQIECDQvoEBq/iUAgAABAgQIECBAgACBTkCB1ElYEiBA\ngAABAgQIECDQvIACqfmXAAACBAgQIECAAAECBDoBBVInYUmAAAECBAgQIECAQPMCCqTmXwIA\nCBAgQIAAAQIECBDoBBRInYQlAQIECBAgQIAAAQLNCyiQmn8JACBAgAABAgQIECBAoBNQIHUS\nlgQIECBAgAABAgQINC+gQGr+JQCAAAECBAgQIECAAIFOQIHUSVgSIECAAAECBAgQINC8gAKp\n+ZcAAAIECBAgQIAAAQIEOgEFUidhSYAAAQIECBAgQIBA8wIKpOZfAgAIECBAgAABAgQIEOgE\nFEidhCUBAgQIECBAgAABAs0LKJCafwkAIECAAAECBAgQIECgE1AgdRKWBAgQIECAAAECBAg0\nL7B5gwI7ZMzbJ1slVyaXJlclGgECBAgQIECAAAECjQu0cgbpgZnnE5LvJxcn30nOTNYmVSSd\nlRyf7JJoBAgQIECAAAECBAg0KtDCGaRXZG6PnMzvuVmellSRVIVRnUnaMdkzOSI5NHl+cmKi\nESBAgAABAgQIECDQmMDYC6TDMp9VHJ2cvCw5PVmsbZadByTHJO9K1iSnJhoBAgQIECBAgAAB\nAg0JjP0jdodkLs9Oarmu4qim+6bklOTg5IrkGYlGgAABAgQIECBAgEBjAmMvkPbJfNZH6q4Z\nOK+XpN8ZyR4D++tGgAABAgQIECBAgMCIBMZeIF2Yudo32WLgnNUV7qqoqgs4aAQIECBAgAAB\nAgQINCYw9gLpbZnPeyXvS/ZbYm677yDVd5W2SU5aoq+bCBAgQIAAAQIECBAYqcDYL9JQV6Pb\nNTkqeVJyfrI2uSi5PNkuqavY7ZXsllyfvDj5XKIRIECAAAECBAgQINCYwNgLpLr4wrHJ+5Oj\nkwOT6TNJV2ffBUldwe645LxEI0CAAAECBAgQIECgQYGxF0jdlNaV7A6fbNRZo/r9o62T+uHY\ny5JN3e6cB3xzsuXAB65j0ggQIECAAAECBAgQWGGBVgqkPnN9tK6ynK1+hLaunje0QKqr5j1k\nOQ/IYxMgQIAAAQIECBAgsH6BFgukvsrtsnG35AfJpf0bNnK9CqRXz/AY+6fvr83QX1cCBAgQ\nIECAAAECBJZBYOxXsSuyukjD3yZvqY1Jq4/Y/U1yVfJfSV204YykLtCgESBAgAABAgQIECDQ\nqMDYzyDtnHk9PamPsJ0ymeP6TaRPJA9Kbkw+ldQZpIcmr03ukfxOUrdpBAgQIECAAAECBAg0\nJDD2M0gvzVxWcfQHycGTeX1ellUcvSmp2x6VHJbcM3ld8tzkMYlGgAABAgQIECBAgEBjAmMv\nkOq7Pd9J/iy5ZjK3B2RZ3zeqs0TfneyrxbXJi5K6zPdjE40AAQIECBAgQIAAgcYExl4g1UcI\nv5z0Py53Q7bPTa5Lplv1uyCps0kaAQIECBAgQIAAAQKNCYz9O0j/nvn85WSnpC7EUO2U5InJ\nLsl/J/1Wv1/04GSWK9D172999QpUUbxv8rnVO4RlPfIv5tFfuKzP4MEJECBAgAABAnMgMPYC\n6YQY1+Wzv5JUofSZ5O+SpyXvSX4lqTNG1R6Q1L7rk39KtLYEds9wt0w+1NawB422LmDy84N6\n6kSAAAECBAgQIDD3AlUg/TCpj899NXlzUhdoqO363tF/Jt9Lbkpq328mt3ar70rV89cb9OVs\nb82DV7RbCtSZxe4s4y1vbXvPczL8M9smMHoCBAgQIECgFYGxfwep5rF+/+iuyZ8mOyTPSJ6d\nbJZskfxMcofk3ck+SRVPGgECBAgQIECAAAECDQqM/SN23ZTWGaK61Hfldkl912iP5OpkbVJX\ntdMIECBAgAABAgQIEGhcoJUCqT/NdRW78yfp77dOgAABAgQIECBAgEDjAi18xK7xKTZ8AgQI\nECBAgAABAgSGCiiQhkrpR4AAAQIECBAgQIDA6AUUSKOfYgMkQIAAAQIECBAgQGCogAJpqJR+\nBAgQIECAAAECBAiMXkCBNPopNkACBAgQIECAAAECBIYKKJCGSulHgAABAgQIECBAgMDoBRRI\no59iAyRAgAABAgQIECBAYKiAAmmolH4ECBAgQIAAAQIECIxeQIE0+ik2QAIECBAgQIAAAQIE\nhgookIZK6UeAAAECBAgQIECAwOgFFEijn2IDJECAAAECBAgQIEBgqIACaaiUfgQIECBAgAAB\nAgQIjF5AgTT6KTZAAgQIECBAgAABAgSGCiiQhkrpR4AAAQIECBAgQIDA6AUUSKOfYgMkQIAA\nAQIECBAgQGCogAJpqJR+BAgQIECAAAECBAiMXkCBNPopNkACBAgQIECAAAECBIYKKJCGSulH\ngAABAgQIECBAgMDoBRRIo59iAyRAgAABAgQIECBAYKiAAmmolH4ECBAgQIAAAQIECIxeQIE0\n+ik2QAIECBAgQIAAAQIEhgookIZK6UeAAAECBAgQIECAwOgFFEijn2IDJECAAAECBAgQIEBg\nqIACaaiUfgQIECBAgAABAgQIjF5AgTT6KTZAAgQIECBAgAABAgSGCiiQhkrpR4AAAQIECBAg\nQIDA6AUUSKOfYgMkQIAAAQIECBAgQGCogAJpqJR+BAgQIECAAAECBAiMXkCBNPopNkACBAgQ\nIECAAAECBIYKKJCGSulHgAABAgQIECBAgMDoBRRIo59iAyRAgAABAgQIECBAYKiAAmmolH4E\nCBAgQIAAAQIECIxeQIE0+ik2QAIECBAgQIAAAQIEhgookIZK6UeAAAECBAgQIECAwOgFFEij\nn2IDJECAAAECBAgQIEBgqIACaaiUfgQIECBAgAABAgQIjF5AgTT6KTZAAgQIECBAgAABAgSG\nCiiQhkrpR4AAAQIECBAgQIDA6AUUSKOfYgMkQIAAAQIECBAgQGCogAJpqJR+BAgQIECAAAEC\nBAiMXkCBNPopNkACBAgQIECAAAECBIYKKJCGSulHgAABAgQIECBAgMDoBRRIo59iAyRAgAAB\nAgQIECBAYKiAAmmolH4ECBAgQIAAAQIECIxeQIE0+ik2QAIECBAgQIAAAQIEhgookIZK6UeA\nAAECBAgQIECAwOgFFEijn2IDJECAAAECBAgQIEBgqIACaaiUfgQIECBAgAABAgQIjF5AgTT6\nKTZAAgQIECBAgAABAgSGCiiQhkrpR4AAAQIECBAgQIDA6AUUSKOfYgMkQIAAAQIECBAgQGCo\ngAJpqJR+BAgQIECAAAECBAiMXkCBNPopNkACBAgQIECAAAECBIYKKJCGSulHgAABAgQIECBA\ngMDoBRRIo59iAyRAgAABAgQIECBAYKiAAmmolH4ECBAgQIAAAQIECIxeYPPRj/CWA9whu7ZP\ntkquTC5Nrko0AgQIECBAgAABAgQaF2jlDNIDM88nJN9PLk6+k5yZrE2qSDorOT7ZJdEIECBA\ngAABAgQIEGhUoIUzSK/I3B45md9zszwtqSKpCqM6k7RjsmdyRHJo8vzkxEQjQIAAAQIECBAg\nQKAxgbEXSIdlPqs4Ojl5WXJ6sljbLDsPSI5J3pWsSU5NNAIECBAgQIAAAQIEGhIY+0fsDslc\nnp3Ucl3FUU33TckpycHJFckzEo0AAQIECBAgQIAAgcYExl4g7ZP5rI/UXTNwXi9JvzOSPQb2\n140AAQIECBAgQIAAgREJjL1AujBztW+yxcA5qyvcVVFVF3DQCBAgQIAAAQIECBBoTGDsBdLb\nMp/3St6X7LfE3HbfQarvKm2TnLREXzcRIECAAAECBAgQIDBSgbFfpKGuRrdrclTypOT8ZG1y\nUXJ5sl1SV7HbK9ktuT55cfK5RCNAgAABAgQIECBAoDGBsRdIdfGFY5P3J0cnBybTZ5Kuzr4L\nkrqC3XHJecmmaFWYDf1o386b4gk9BgECBAgQIECAAAECGycw9gKp06kr2R0+2aizRvX7R1sn\n9cOxlyWbut0jD/hfm/pBPR4BAgQIECBAgAABAssr0EqB1Fesj9ZVqtVH6+qMUhVK30x+mGyK\n9u08yF2ToWeQHpC+7000AgQIECBAgAABAgRWUGDsBdJzYvvI5DeSfvFzv2y/OXlw0rU6k/TH\nyWuTG7qdG7E8Z4b77jJDX10JECBAgAABAgQIEFgmgbFfxa7ODtVH67bs+d0l659Jqjj6UnJ8\n8vfJlcmfJH+eaAQIECBAgAABAgQINCgw9jNIi01pFUH1HaTfTd7Q61CX935T8qLkw8nHEo0A\nAQIECBAgQIAAgYYExn4GabGpfHh2/lvSL46qX13N7tlJXQL80YlGgAABAgQIECBAgEBjAi0W\nSHUVu6+tY57re0pnJvddx+12EyBAgAABAgQIECAwYoEWC6R/z3zWRRoWaztl50OSCxe70T4C\nBAgQIECAAAECBMYt0EqBVB+pe1fye8mpSV2g4ReTftszG/Wxu7qgw6f7N1gnQIAAAQIECBAg\nQKANgbFfpKEutnDHpH5n6JcnyeLmVsXQBybrv5DlSUl5VAFVV7XTCBAgQIAAAQIECBBoTGDs\nBdI/Zj4r1erKdVUoddmsdk7a7bKs7x9VYVRXsbsp0QgQIECAAAECBAgQaExg7AVSfzrrh2Dr\no3OLfXzuo9lf3z+6LtEIECBAgAABAgQIEGhUoKUCaakprrNHGgECBAgQIECAAAECjQu0cpGG\nxqfZ8AkQIECAAAECBAgQGCKgQBqipA8BAgQIECBAgAABAk0IKJCamGaDJECAAAECBAgQIEBg\niIACaYiSPgQIECBAgAABAgQINCGgQGpimg2SAAECBAgQIECAAIEhAgqkIUr6ECBAgAABAgQI\nECDQhIACqYlpNkgCBAgQIECAAAECBIYIKJCGKOlDgAABAgQIECBAgEATAgqkJqbZIAkQIECA\nAAECBAgQGCKgQBqipA8BAgQIECBAgAABAk0IKJCamGaDJECAAAECBAgQIEBgiIACaYiSPgQI\nECBAgAABAgQINCGgQGpimg2SAAECBAgQIECAAIEhAgqkIUr6ECBAgAABAgQIECDQhIACqYlp\nNkgCBAgQIECAAAECBIYIKJCGKOlDgAABAgQIECBAgEATAgqkJqbZIAkQIECAAAECBAgQGCKg\nQBqipA8BAgQIECBAgAABAk0IKJCamGaDJECAAAECBAgQIEBgiIACaYiSPgQIECBAgAABAgQI\nNCGgQGpimg2SAAECBAgQIECAAIEhAgqkIUr6ECBAgAABAgQIECDQhIACqYlpNkgCBAgQIECA\nAAECBIYIKJCGKOlDgAABAgQIECBAgEATAgqkJqbZIAkQIECAAAECBAgQGCKgQBqipA8BAgQI\nECBAgAABAk0IKJCamGaDJECAAAECBAgQIEBgiIACaYiSPgQIECBAgAABAgQINCGgQGpimg2S\nAAECBAgQIECAAIEhAgqkIUr6ECBAgAABAgQIECDQhIACqYlpNkgCBAgQIECAAAECBIYIKJCG\nKOlDgAABAgQIECBAgEATAgqkJqbZIAkQIECAAAECBAgQGCKgQBqipA8BAgQIECBAgAABAk0I\nKJCamGaDJECAAAECBAgQIEBgiIACaYiSPgQIECBAgAABAgQINCGgQGpimg2SAAECBAgQIECA\nAIEhAgqkIUr6ECBAgAABAgQIECDQhIACqYlpNkgCBAgQIECAAAECBIYIKJCGKOlDgAABAgQI\nECBAgEATAgqkJqbZIAkQIECAAAECBAgQGCKgQBqipA8BAgQIECBAgAABAk0IKJCamGaDJECA\nAAECBAgQIEBgiIACaYiSPgQIECBAgAABAgQINCGgQGpimg2SAAECBAgQIECAAIEhAgqkIUr6\nECBAgAABAgQIECDQhIACqYlpNkgCBAgQIECAAAECBIYIKJCGKOlDgAABAgQIECBAgEATAgqk\nJqbZIAkQIECAAAECBAgQGCKgQBqipA8BAgQIECBAgAABAk0IKJCamGaDJECAAAECBAgQIEBg\niIACaYiSPgQIECBAgAABAgQINCEwa4H0+qg8OdmiCR2DJECAAAECBAgQIECgKYFZC6QnROek\n5PzkL5MHJBoBAgQIECBAgAABAgRGITBrgbR/Rv3C5LzkBcmXk68ktW/XRCNAgAABAgQIECBA\ngMCqFZi1QPp+Rnpcsm9y3+TPkp2TY5M6q/T+5CmJj+AFQSNAgAABAgQIECBAYHUJzFog9Uf3\n9Wy8JNkzOSip7yc9LPmn5ILkL5J7JhoBAgQIECBAgAABAgRWhcDGFEjdAO+elQOTRyb1Mbub\nkjrTVB+7OzN5RTJPbYcczF2Tn072SO6QaAQIECBAgAABAgQIELjNhhZIu8Tud5MvJN9K/ijZ\nabK8R5b3Sapw+ufkyORZyUq2B+bJT0iqcLs4+U5Sxdva5MrkrOT4pMalESBAgAABAgQIECBA\nYJDAoen1oeS6pM4UXZ28M3lMslky3e6UHdXvHdM33IrbdQarjqFyTnJq8sHk3clHkiryLkzq\n9h8kv5zc2q0uflHPv+UyP/Fb8/gV7ZYCp2TXRbfcbU8EnpPUHxQ0AgQIECBAgACBKYGzs11v\n5E9Ljki2T5ZqdVZpTfKipTot422H5bHreKsQetASz1PFXX1M8ItJ9X94cms2BdKtqb34cymQ\nFnepvQqkddu4hQABAgQIEBiZwOYzjucN6f/hZOhfk+sv8ndNVqodkieuoq6W1yxxEFUU1Rvk\ng5M6y/SMpM40aQQIECBAgAABAgQINCRw2xnHWlemq+KoPlJ39959d8/62yf7e7tXfHWfHEGd\n7VqqOOof5CXZOCOpizdoBAgQIECAAAECBAg0JjBrgVSF0AeSjyX79az2zvqvTvb/UW//Sq9e\nmAPYNxn6u0x1hbsqqoaeIUtXjQABAgQIECBAgACBsQjMWiAdk4E/PqmP2v1rD+GzWa+Pp30m\neXlya3+HJ0+5aHtb9t4reV/SL+imO9d3kA5ITk62SU5KNAIECBAgQIAAAQIEGhOY5TtIVUQ8\nOfm/ye8u4vTR7PuP5Lzk6ck8fIfnxBxH/TbTUcmTkvOTtclFyeXJdsmOyV7Jbsn1yYuTzyUa\nAQIECBAgQIAAAQKNCcxSIP1EbG6ffHwJo/pI25eSPZfoc2veVBdfODZ5f3J0Uleqmz6TVJcq\nvyA5JjkuqQJvY1udhfrtZMuBD3SXgf10I0CAAAECBAgQIEBgGQVmKZDqjMu3kgcscTz1XZ/6\nPtK8nYGpK9kdPjnuOmu0fbJ1Uj8ce1myqdsd84BPTMpjSNt2SCd9CBAgQIAAAQIECBBYXoFZ\nCqQ6kk8mv5mckvx90m/1Jv91yS5JXcRhXlsVepWu1fHW7zVV8Xdjt3Mjl3VG6qAZHmP/9D11\nhv66EiBAgAABAgQIECCwDAKzFkivyDHUVeFOTF6Z/GdyabJ78tCkrgL3jqR+mHW1tP+dA/39\npIqki1fLQTtOAgQIECBAgAABAgQ2vcCsBVJ9JO1RSZ0pOig5JKmLN1Rbm7w0eVNtzEmrS3bf\nYT3H0v3m0UPSrzuzVN9DqvFoBAgQIECAAAECBAg0JDBrgVQ0Vya/PjGq7/LsmZyTdMXF5Ka5\nWLw9R3H/gUdSl/ju2quycmS3YUmAAAECBAgQIECAQBsCG1Ig9WXqAgdf6++Ys/W/zfHUVezq\nggwfSL6RTLc6I1YfD6yzYj+c3DhvF5mYHJYFAQIECBAgQIAAAQLLKbAhBdKjc0C/muya1GW/\nu4/YZfXH7a1Ze9uPt1ZupQqkzyT1namfS+riEfUjt3X57679aVaqQKozRr6D1KlYEiBAgAAB\nAgQIEGhQYNYC6akx+ocBTp8e0OfW6vL1PFEVQEcn9TtH9YOxv5acn2gECBAgQIAAAQIECBD4\nscBtf7w2bOXV6XZV8itJXbmuCqzFUmdj5qldk4Opq9U9NvmZpD4W+PREI0CAAAECBAgQIECA\nwI8FZjmDVFeDu2dyfFIfWVuN7RM56LqyXX30rn7H6ReTuuiERoAAAQIECBAgQIAAgZvP/gxl\nqAsY1JXq6gzSam6X5OCflnwwqe8jbZdoBAgQIECAAAECBAgQuM0sH7G7MV713aLDk1nuN6/M\n9YO2dQnwf0w+lVyXaAQIECBAgAABAgQINCwwy0fsiuk3k88lVVT8ZbImWeyM0tXZ310yO6tz\n29bkyA6b26NzYAQIECBAgAABAgQI3KoCs54Jqt8S2jV5SlJnk85JfrBIXpJ9GgECBAgQIECA\nAAECBFaVwKxnkL6c0V0wYITfGNBHFwIECBAgQIAAAQIECMyVwKwF0m/N1dE7GAIECBAgQIAA\nAQIECGxCgVk/Ytd/6ttn437JfpOddRlwjQABAgQIECBAgAABAqtWYEMKpD0z2vckdXGGM5LX\nJtXemRyVbFUbGgECBAgQIECAAAECBFabwKwfsdstAzw92Smp7xltk3Rts6y8LDkkeXDyo0Qj\nQIAAAQIECBAgQIDAqhGY9QzS6zKy+mjdAcm9kyqWunZoVo5O7pM8s9tpSYAAAQIECBAgQIAA\ngdUiMGuB9JgM7K+Szy4ywBuy78jksuRhi9xuFwECBAgQIECAAAECBOZaYJYCabuMZIfkm0uM\n6Lrc9vVJvyW6uYkAAQIECBAgQIAAAQLzJzBLgXR5Dv+7yUOWGEYVUfURuzOX6OMmAgQIECBA\ngAABAgQIzKXALAVSDeAjybOT5yXbJv12x2y8Pdk++Wj/BusECBAgQIAAAQIECBBYDQKzFki/\nl0FdkLw+OT95eLJ3clJyVvLk5K3JxxONAAECBAgQIECAAAECq0pg1gLp0ozuQcnxydbJnZLd\nkyqMqj0/qTNMGgECBAgQIECAAAECBFadwKy/g1QD/EHy3OR3kr2SOydrkjqzpBEgQIAAAQIE\nCBAgQGDVCmxIgdQNti7rffYk3T5LAgQIECBAgAABAgQIrFqBWT9it2oH6sAJECBAgAABAgQI\nECCwPoFZzyDVd4/qe0fra+9Oh4pGgAABAgQIECBAgACBVSMwa4H0cxnZ3dYzurW5/dPr6eNm\nAgQIECBAgAABAgQIzJ3ArAXSAzOC6Y/l1fZPJvdNjk3qzFEtNQIECBAgQIAAAQIECKwqgVkL\npMvWMbqLsv+rydeTLyefST6QaAQIECBAgAABAgQIEFg1AtNngzb2wL+SBzgnqY/iaQQIECBA\ngAABAgQIEFhVApu6QNoqo98p2XVVKThYAgQIECBAgAABAgQIRGDWj9htnftstohcPc4uyVHJ\ntsmXEo0AAQIECBAgQIAAAQKrSmDWAuk/M7r1XcWufjz2jatKwcESIECAAAECBAgQIEAgArMW\nSKfkPt9aRO7G7Ls8OSM5IVnXxRxyk0aAAAECBAgQIECAAIH5FJi1QHrWfA7DUREgQIAAAQIE\nCBAgQGDjBTb1RRo2/og8AgECBAgQIECAAAECBFZIYNYzSMfnOO+0Acf6jtznfRtwP3chQIAA\nAQIECBAgQIDArSYwa4G0T47svkldqa7aDcmlyY7JYle3y+6b2xe6FUsCBAgQIECAAAECBAjM\nq8CsH7H71QzkquQDyb5JXfZ758ny8Vl+I6liqM4y1e8hdTk26xoBAgQIECBAgAABAgTmWmDW\nM0h/l9F8OXlKUleu69q1WTk5+Y/km8kvJX+baAQIECBAgAABAgQIEFg1ArOcQdoqo3pY8s6k\nXxz1B7s2G19JHtnfaZ0AAQIECBAgQIAAAQKrQWCWAun6DOjKZI8lBrZFbrt7ctESfdxEgAAB\nAgQIECBAgACBuRSYpUCqCzJ8NHlp8tBFRrNN9nVXuauP22kECBAgQIAAAQIECBBYVQKzfgfp\nNRndI5K6EMMpSV2U4YrkJ5NHJ7sm9T2lDyYaAQIECBAgQIAAAQIEVpXArAXSGRndQ5I3Jwck\nByZd+15WnpOc0O2wJECAAAECBAgQIECAwGoSmLVAqrFdmNQlvevjeT+V1CW9z0rOT25KNAIE\nCBAgQIAAAQIECKxKgVm+gzQ9wLqqXV2U4UdJXb2uvoOkESBAgAABAgQIECBAYNUKbEiBtGdG\n+56kfjC2PnL32qTaO5OjkiqcNAIECBAgQIAAAQIECKw6gVk/YrdbRnh6slNSF2jonzXaLNsv\nSw5JHpzUmSWNAAECBAgQIECAAAECq0Zg1jNIr8vIbp/UBRrunVSx1LVDs3J0cp/kmd1OSwIE\nCBAgQIAAAQIECKwWgVkLpMdkYH+VfHaRAd6QfUcmlyUPW+R2uwgQIECAAAECBAgQIDDXArMU\nSNtlJDsk31xiRNfltq9P+i3RzU0ECBAgQIAAAQIECBCYP4FZCqTLc/jfTep3kNbVqoiqj9id\nua4O9hMgQIAAAQIECBAgQGBeBWYpkGoMH0menTwv2Tbptztm4+3J9slH+zdYJ0CAAAECBAgQ\nIECAwGoQmLVA+r0M6oLk9Un9MOzDk72Tk5L6sdgnJ29NPp5oBAgQIECAAAECBAgQWFUCsxZI\nl2Z0D0qOT7ZO7pTsnlRhVO35SZ1h0ggQIECAAAECBAgQILDqBGb9HaQa4A+S5ya/k+yV3DlZ\nk9SZJY0AAQIECBAgQIAAAQKrVmDWAqku8f3D5P9Prk/OniQLjQABAgQIECBAgAABAqtbYJaP\n2G2VodYPwD4xqeJII0CAAAECBAgQIECAwKgEZimQrs3Ir0i2STYblYLBECBAgAABAgQIECBA\nIAKzFEg3pf9TJmofyPJxyd2T+u2j6dTZJo0AAQIECBAgQIAAAQKrSmCWAqkG9tqkziDVx+xO\nTr6dXLZI/iD7NAIECBAgQIAAAQIECKwqgVkv0nBmRnfJgBF+c0AfXQgQIECAAAECBAgQIDBX\nArMWSM+eq6N3MAQIECBAgAABAgQIENiEAuv7iN2Bea5Hb8Ln81AECBAgQIAAAQIECBCYW4H1\nnUF6XY58++RuUyO4X7Z3Sj41tX81bO6Qg6wx1YUkrkwuTa5KNAIECBAgQIAAAQIEGhdY3xmk\ndfEclRs+ua4b53D/A3NMJyTfTy5OvpPU96nWJlUknZUcn+ySaAQIECBAgAABAgQINCqwvjNI\nY2B5RQZx5GQg52Z5WlJFUhVGdSZpx2TP5Ijk0OT5yYmJRoAAAQIECBAgQIBAYwJjL5AOy3xW\ncVSXJH9ZcnqyWKsfvj0gOSZ5V7ImOTXRCBAgQIAAAQIECBBoSGBDP2K3WogOyYGendRyXcVR\njaV+BPeU5ODkiuQZiUaAAAECBAgQIECAQGMCYy+Q9sl81kfqrhk4r/UbT2ckewzsrxsBAgQI\nECBAgAABAiMSGHuBdGHmat9ki4FzVle4q6KqLuCgESBAgAABAgQIECDQmMCQ7yBV0fCnUy73\nnmxP7++6fTQrH+s2VnD5tjz3O5P3JUcnX0gWa/UdpEckr022SU5KNAIECBAgQIAAAQIEGhMY\nUiDVld5+fx0u69p/dfrPQ4FUV6PbNTkqeVJyfrI2uSi5PNkuqavY7ZXsllyfvDj5XKIRIECA\nAAECBAgQINCYwPoKpLry2x03wKS+xzMPrS6+cGzy/qTOIB2Y7Jf0WxVzFyR1BbvjkvMSjQAB\nAgQIECBAgACBBgXWVyB9aCQmdSW7wydjqbNGdVZs66R+OPayZFO3u+QBP5xsMfCB61g0AgQI\nECBAgAABAgRWWGB9BdIKH96yPH19tK7Sta2yUh+xOyu5odu5kcvv5f5/kWw58HHuln4vGdhX\nNwIECBAgQIAAAQIElkmglQKpCpDHJVW41AUkrkzqO0evT2r/tkl91O4vk1cl1yUb067Nnd8y\nwwPsn74KpBnAdCVAgAABAgQIECCwHAItFEgvClydzekNrjgDAAAv90lEQVTamqw8NKli6NDk\n0uQjyQOSlyb3SJ6WaAQIECBAgAABAgQINCYw9t9B+vnMZ1184evJC5K6Ql19B+mzyVOTOmuz\ne/KEZO/kHUntr7NKGgECBAgQIECAAAECjQmM/QzSUzKfVyX7TZY1vXXBhv+brE2qeOq+d/Sj\nrD8nefwk/5KlRoAAAQIECBAgQIBAQwJjP4NUhdHJSRVJXfvXrFQx9MGkK466236YlTOTe3Y7\nLAkQIECAAAECBAgQaEdg7AXSJZnKKpL646yLMfxh8o1kut0xOx6S1O8iaQQIECBAgAABAgQI\nNCbQLxzGOPT6mNxdkrpIw517A6yP1r2ut12r9ZtFr0m2Sj6RaAQIECBAgAABAgQINCYw9gKp\nrlR3elIXaFiT7JAs1upqdvWdpN9KPpm8O9EIECBAgAABAgQIEGhMYOwFUn3X6BHJ0ckZSX3k\nbrF2h+ysH3Wts0p1RbubEo0AAQIECBAgQIAAgcYExl4g1XTWhRfqO0f120frau/NDTsndaap\niiqNAAECBAgQIECAAIEGBcZ+me+hU1pFlEaAAAECBAgQIECAQOMCLZxBanyKDZ8AAQIECBAg\nQIAAgaECCqShUvoRIECAAAECBAgQIDB6AQXS6KfYAAkQIECAAAECBAgQGCqgQBoqpR8BAgQI\nECBAgAABAqMXUCCNfooNkAABAgQIECBAgACBoQIKpKFS+hEgQIAAAQIECBAgMHoBBdLop9gA\nCRAgQIAAAQIECBAYKqBAGiqlHwECBAgQIECAAAECoxdQII1+ig2QAAECBAgQIECAAIGhAgqk\noVL6ESBAgAABAgQIECAwegEF0uin2AAJECBAgAABAgQIEBgqoEAaKqUfAQIECBAgQIAAAQKj\nF1AgjX6KDZAAAQIECBAgQIAAgaECCqShUvoRIECAAAECBAgQIDB6AQXS6KfYAAkQIECAAAEC\nBAgQGCqgQBoqpR8BAgQIECBAgAABAqMXUCCNfooNkAABAgQIECBAgACBoQIKpKFS+hEgQIAA\nAQIECBAgMHoBBdLop9gACRAgQIAAAQIECBAYKqBAGiqlHwECBAgQIECAAAECoxdQII1+ig2Q\nAAECBAgQIECAAIGhAgqkoVL6ESBAgAABAgQIECAwegEF0uin2AAJECBAgAABAgQIEBgqoEAa\nKqUfAQIECBAgQIAAAQKjF1AgjX6KDZAAAQIECBAgQIAAgaECCqShUvoRIECAAAECBAgQIDB6\nAQXS6KfYAAkQIECAAAECBAgQGCqgQBoqpR8BAgQIECBAgAABAqMXUCCNfooNkAABAgQIECBA\ngACBoQIKpKFS+hEgQIAAAQIECBAgMHoBBdLop9gACRAgQIAAAQIECBAYKqBAGiqlHwECBAgQ\nIECAAAECoxdQII1+ig2QAAECBAgQIECAAIGhAgqkoVL6ESBAgAABAgQIECAwegEF0uin2AAJ\nECBAgAABAgQIEBgqoEAaKqUfAQIECBAgQIAAAQKjF1AgjX6KDZAAAQIECBAgQIAAgaECCqSh\nUvoRIECAAAECBAgQIDB6AQXS6KfYAAkQIECAAAECBAgQGCqgQBoqpR8BAgQIECBAgAABAqMX\nUCCNfooNkAABAgQIECBAgACBoQIKpKFS+hEgQIAAAQIECBAgMHoBBdLop9gACRAgQIAAAQIE\nCBAYKqBAGiqlHwECBAgQIECAAAECoxdQII1+ig2QAAECBAgQIECAAIGhAgqkoVL6ESBAgAAB\nAgQIECAwegEF0uin2AAJECBAgAABAgQIEBgqoEAaKqUfAQIECBAgQIAAAQKjF1AgjX6KDZAA\nAQIECBAgQIAAgaECCqShUvoRIECAAAECBAgQIDB6AQXS6KfYAAkQIECAAAECBAgQGCqgQBoq\npR8BAgQIECBAgAABAqMXUCCNfooNkAABAgQIECBAgACBoQIKpKFS+hEgQIAAAQIECBAgMHoB\nBdLop9gACRAgQIAAAQIECBAYKqBAGiqlHwECBAgQIECAAAECoxdQII1+ig2QAAECBAgQIECA\nAIGhApsP7TiifjtkLNsnWyVXJpcmVyUaAQIECBAgQIAAAQKNC7RyBumBmecTku8nFyffSc5M\n1iZVJJ2VHJ/skmgECBAgQIAAAQIECDQq0MIZpFdkbo+czO+5WZ6WVJFUhVGdSdox2TM5Ijk0\neX5yYqIRIECAAAECBAgQINCYwNgLpMMyn1UcnZy8LDk9Waxtlp0HJMck70rWJKcmGgECBAgQ\nIECAAAECDQmM/SN2h2Quz05qua7iqKb7puSU5ODkiuQZiUaAAAECBAgQIECAQGMCYy+Q9sl8\n1kfqrhk4r5ek3xnJHgP760aAAAECBAgQIECAwIgExl4gXZi52jfZYuCc1RXuqqiqCzhoBAgQ\nIECAAAECBAg0JjD2Aultmc97Je9L9ltibrvvINV3lbZJTlqir5sIECBAgAABAgQIEBipwNgv\n0lBXo9s1OSp5UnJ+sja5KLk82S6pq9jtleyWXJ+8OPlcohEgQIAAAQIECBAg0JjA2AukuvjC\nscn7k6OTA5PpM0lXZ98FSV3B7rjkvGRTtAflQYZ+tO/em+IJPQYBAgQIECBAgAABAhsnMPYC\nqdOpK9kdPtmos0b1+0dbJ/XDsZclm7rdPQ/4xWTsH2Hc1G4ejwABAgQIECBAgMCKCrRSIHXI\nVbDUR+sqi7XbZWcVUD9MfrRYh4H7zkq/KsDq8Ya0Oqv1qSEd9SFAgAABAgQIECBAYPkEWjjD\ncafw/UNycVKF0SeTn00Wa/fLzur3ksVunHHfdelfRdaQXDvjY+tOgAABAgQIECBAgMAyCIy9\nQNo2ZvVRt6cmdTanLtDwyOSUpL6TpBEgQIAAAQIECBAgQODHAmMvkP5PRnqX5MjkJ5O65PdD\nkv9IXpr8RaIRIECAAAECBAgQIEDgZoGxF0gPzyjrQgxHJVfcPOLb3Obfs6yr2X0meVFSRZRG\ngAABAgQIECBAgACB0V9lbY/McRVC9ftG/VZXrntickbyp0l9BE8jQIAAAQIECBAgQKBxgbGf\nQTon8/vYpK4oN93qgg1PSOp7SW9L1nXhhtykESBAgAABAgQIECDQgsDYC6SPZxLrN49ek+y+\nyISen30/l9TH7z6c/EKiESBAgAABAgQIECDQqMDYC6Q3ZF7/M6nvGp2XPD2Zbt/MjoOTG5P6\nrlK1zf5n4Z8ECBAgQIAAAQIECLQkMPYCqX6DqH6E9XXJucm6fm/oK7ntwcnJiUaAAAECBAgQ\nIECAQKMCmzcw7iszxhdMslRBeFb6PD6py4BXYaURIECAAAECBAgQINCYQAsFUn9K62N062v1\nw7IaAQIECBAgQIAAAQINCix1RqVBDkMmQIAAAQIECBAgQKBlAQVSy7Nv7AQIECBAgAABAgQI\nLBBQIC3gsEGAAAECBAgQIECAQMsCCqSWZ9/YCRAgQIAAAQIECBBYIKBAWsBhgwABAgQIECBA\ngACBlgUUSC3PvrETIECAAAECBAgQILBAQIG0gMMGAQIECBAgQIAAAQItCyiQWp59YydAgAAB\nAgQIECBAYIGAAmkBhw0CBAgQIECAAAECBFoWUCC1PPvGToAAAQIECBAgQIDAAgEF0gIOGwQI\nECBAgAABAgQItCygQGp59o2dAAECBAgQIECAAIEFAgqkBRw2CBAgQIAAAQIECBBoWUCB1PLs\nGzsBAgQIECBAgAABAgsEFEgLOGwQIECAAAECBAgQINCygAKp5dk3dgIECBAgQIAAAQIEFggo\nkBZw2CBAgAABAgQIECBAoGUBBVLLs2/sBAgQIECAAAECBAgsEFAgLeCwQYAAAQIECBAgQIBA\nywIKpJZn39gJECBAgAABAgQIEFggoEBawGGDAAECBAgQIECAAIGWBRRILc++sRMgQIAAAQIE\nCBAgsEBAgbSAwwYBAgQIECBAgAABAi0LKJBann1jJ0CAAAECBAgQIEBggYACaQGHDQIECBAg\nQIAAAQIEWhZQILU8+8ZOgAABAgQIECBAgMACAQXSAg4bBAgQIECAAAECBAi0LKBAann2jZ0A\nAQIECBAgQIAAgQUCCqQFHDYIECBAgAABAgQIEGhZQIHU8uwbOwECBAgQIECAAAECCwQUSAs4\nbBAgQIAAAQIECBAg0LKAAqnl2Td2AgQIECBAgAABAgQWCCiQFnDYIECAAAECBAgQIECgZQEF\nUsuzb+wECBAgQIAAAQIECCwQUCAt4LBBgAABAgQIECBAgEDLAgqklmff2AkQIECAAAECBAgQ\nWCCgQFrAYYMAAQIECBAgQIAAgZYFFEgtz76xEyBAgAABAgQIECCwQECBtIDDBgECBAgQIECA\nAAECLQsokFqefWMnQIAAAQIECBAgQGCBgAJpAYcNAgQIECBAgAABAgRaFlAgtTz7xk6AAAEC\nBAgQIECAwAIBBdICDhsECBAgQIAAAQIECLQsoEBqefaNnQABAgQIECBAgACBBQIKpAUcNggQ\nIECAAAECBAgQaFlAgdTy7Bs7AQIECBAgQIAAAQILBBRICzhsECBAgAABAgQIECDQsoACqeXZ\nN3YCBAgQIECAAAECBBYIKJAWcNggQIAAAQIECBAgQKBlAQVSy7Nv7AQIECBAgAABAgQILBDY\nfMGWDQIECNxSYMfs2il59S1vsicC5yVvJEGAAAECBAiMQ0CBNI55NAoCyylw7zx4FUn7L+eT\nrNLH3jnHfY9EgbRKJ9BhEyBAgACBaQEF0rSIbQIEpgU2y47rk8dO32D7Nk+IwXs5ECBAgAAB\nAuMR8B2k8cylkRAgQIAAAQIECBAgsJECCqSNBHR3AgQIECBAgAABAgTGI6BAGs9cGgkBAgQI\nECBAgAABAhspoEDaSEB3J0CAAAECBAgQIEBgPAItXqRhh0zf9slWyZXJpclViUaAAAECBAgQ\nIECAQOMCrZxBemDm+YTk+8nFyXeSM5O1SRVJZyXHJ7skGgECBAgQIECAAAECjQq0cAbpFZnb\nIyfze26WpyVVJFVhVGeS6vdd9kyOSA5Nnp+cmGgECBAgQIAAAQIECDQmMPYC6bDMZxVHJycv\nS05PFmv1Oy8HJMck70rWJKcmGgECBAgQIECAAAECDQmM/SN2h2Quz05qua7iqKb7puSU5ODk\niuQZiUaAAAECBAgQIECAQGMCYy+Q9sl81kfqrhk4r5ek3xnJHgP760aAAAECBAgQIECAwIgE\nxl4gXZi52jfZYuCc1RXuqqiqCzhoBAgQIECAAAECBAg0JjD2Aultmc97Je9L9ltibrvvINV3\nlbZJTlqir5sIECBAgAABAgQIEBipwNgv0lBXo9s1OSp5UnJ+sja5KLk82S6pq9jtleyWXJ+8\nOPlcohEgQIAAAQIECBAg0JjA2AukuvjCscn7k6OTA5PpM0lXZ98FSV3B7rjkvGRj2x3zAK9K\nthz4QHca2E83AgTmS6D+Xa+P8P71fB3W3BzNZTmSuoLojXNzRA6EAAECBAisR2DsBVI3/LqS\n3eGTjTprVL9/tHVSPxxb/wPf1K1cd0qGFkh1PBoBAqtP4B455O7f99V39Mt7xFU8Hpz8WVIX\nwNEIECBAgMCqEGilQOpPRn20rrKc7Qd58F+d4Qn2T9/HzNBfVwIE5kvgafN1OHNxNHXBmyqQ\nNAIECBAgsKoExn6RhlU1GQ6WAAECBAgQIECAAIGVFVAgray/ZydAgAABAgQIECBAYI4Exv4R\nu9+MdX3naNZ2au5QPzCrESBAgAABAgQIECDQkMDYC6Tfzlw+YAPm81W5jwJpA+DchQABAgQI\nECBAgMBqFhh7gfT4TM4/JXURhLrU95uTIe2bQzrpQ4AAAQIECBAgQIDAuATGXiB9N9P1qOTT\nSRVLRyZfTjQCBAgQIECAAAECBAjcQqCFizRck1H/xmTkr7+FgB0ECBAgQIAAAQIECBCYCLRQ\nINVQv568NKkLNtwv0QgQIECAAAECBAgQIHALgVYKpBr4MUn9cOHXakMjQIAAAQIECBAgQIDA\ntEBLBdL02G0TIECAAAECBAgQIEBggYACaQGHDQIECBAgQIAAAQIEWhZQILU8+8ZOgAABAgQI\nECBAgMACAQXSAg4bBAgQIECAAAECBAi0LKBAann2jZ0AAQIECBAgQIAAgQUCCqQFHDYIECBA\ngAABAgQIEGhZQIHU8uwbOwECBAgQIECAAAECCwQUSAs4bBAgQIAAAQIECBAg0LKAAqnl2Td2\nAgQIECBAgAABAgQWCCiQFnDYIECAAAECBAgQIECgZQEFUsuzb+wECBAgQIAAAQIECCwQUCAt\n4LBBgAABAgQIECBAgEDLAgqklmff2AkQIECAAAECBAgQWCCgQFrAYYMAAQIECBAgQIAAgZYF\nFEgtz76xEyBAgAABAgQIECCwQECBtIDDBgECBAgQIECAAAECLQsokFqefWMnQIAAAQIECBAg\nQGCBgAJpAYcNAgQIECBAgAABAgRaFlAgtTz7xk6AAAECBAgQIECAwAIBBdICDhsECBAgQIAA\nAQIECLQsoEBqefaNnQABAgQIECBAgACBBQIKpAUcNggQIECAAAECBAgQaFlAgdTy7Bs7AQIE\nCBAgQIAAAQILBBRICzhsECBAgAABAgQIECDQsoACqeXZN3YCBAgQIECAAAECBBYIKJAWcNgg\nQIAAAQIECBAgQKBlAQVSy7Nv7AQIECBAgAABAgQILBBQIC3gsEGAAAECBAgQIECAQMsCCqSW\nZ9/YCRAgQIAAAQIECBBYIKBAWsBhgwABAgQIECBAgACBlgUUSC3PvrETIECAAAECBAgQILBA\nQIG0gMMGAQIECBAgQIAAAQItCyiQWp59YydAgAABAgQIECBAYIGAAmkBhw0CBAgQIECAAAEC\nBFoWUCC1PPvGToAAAQIECBAgQIDAAgEF0gIOGwQIECBAgAABAgQItCywecuDN3YCBAgQWDaB\nHSaP/Oksb1i2Z1m9D/yjHPovJv+9eofgyAkQIDBOAQXSOOfVqAgQILDSAjtPDuCkLC9e6YOZ\ns+ffJsdzdLJbokCas8lxOAQIEFAgeQ0QIECAwHIKvDkPvmY5n2AVPnadXasCSSNAgACBORTw\nHaQ5nBSHRIAAAQIECBAgQIDAyggokFbG3bMSIECAAAECBAgQIDCHAgqkOZwUh0SAAAECBAgQ\nIECAwMoIKJBWxt2zEiBAgAABAgQIECAwhwIKpDmcFIdEgAABAgQIECBAgMDKCLiK3cq4e1YC\nBAgQIECAwKwCb80dHjbrnRrq/6qM9d0NjddQl0lAgbRMsB6WAAECBAgQILCJBao4+lLymU38\nuGN4uBdmEPcZw0CMYeUFFEgrPweOgAABAgQIECAwVKCKo+OHdm6o32ENjdVQl1nAd5CWGdjD\nEyBAgAABAgQIECCwegQUSKtnrhwpAQIECBAgQIAAAQLLLKBAWmZgD0+AAAECBAgQIECAwOoR\nUCCtnrlypAQIECBAgAABAgQILLOAAmmZgT08AQIECBAgQIAAAQKrR8BV7FbPXDlSAgQIECDQ\ngsCnM8i7tTDQDRjjnXOfn9mA+7Vwl3tmkA9OntnCYDdgjG/Iff5sA+7X5F0USE1Ou0ETIECA\nAIG5Fag3uX+TfHVuj3DlDuzNeeqdV+7p5/qZt83RfS85aq6PcmUO7og87b1X5qlX57MqkFbn\nvDlqAgQIECAwZoFPZHAfHvMAN3BsJ2zg/Vq5WxVI72hlsDOM8zEz9NU1Ar6D5GVAgAABAgQI\nECBAgACBiYACyUuBAAECBAgQIECAAAECEwEFkpcCAQIECBAgQIAAAQIEJgIKJC8FAgQIECBA\ngAABAgQITAQUSF4KBAgQIECAAAECBAgQmAgokLwUCBAgQIAAAQIECBAgMBFQIHkpECBAgAAB\nAgQIECBAYCKgQPJSIECAAAECBAgQIECAwERAgeSlQIAAAQIECBAgQIAAgYnA5g1K7JAxb59s\nlVyZXJpclWgECBAgQIAAAQIECDQu0MoZpAdmnk9Ivp9cnHwnOTNZm1SRdFZyfLJLohEgQIAA\nAQIECBAg0KhAC2eQXpG5PXIyv+dmeVpSRVIVRnUmacdkz+SI5NDk+cmJiUaAAAECBAgQIECA\nQGMCYy+QDst8VnF0cvKy5PRksbZZdh6QHJO8K1mTnJpoBAgQIECAAAECBAg0JFCFwZhbFTsP\nS+6dXDNgoPX9pHOSOoP03AH919Vl79zw78mW6+owtb8+6ljfiar+10/dtik362OGz0yu3ZQP\nOpLHKv+ahx+OZDybchj1urwdm0VJ649M5XP1ore2vbNeM/XvVf07dVPbFLcYff2/9/YJm1vQ\n3LyjbK5LlvP/h4s/8/zvLZsbEv8fv+Vclc2NyZD3e7e897j31H+L35o8e9zD3HSjG/sZpH1C\ndVoy9F+WS9L3jGSPZGPamtz5qckWAx+k/me5c7Lc/zOojxv+08Bjaq3bthlw5butDXzAeOs/\nrLsm5w3o21qXKgLqI7r1vUbtlgL3yK5v33K3PRG4Z/JfJBYVuFv21kfiqxDQFgrcJZv1feqh\n72sW3nvcW3fO8OrrExXtlgJfueUue1oV+NcM/BvJ0EKlziBdnvx5ohEgQIAAAQIECBAgQGBU\nAr+S0dRHOz6Q7LfEyOoMTn0H6QtJncX52UQjQIAAAQIECBAgQIDAqASq8HlRUr9zVIXS2uTz\nyYeSv58s6yN4FyR1+3XJCxKNAAECBAgQIECAAAECoxWoiyZUQXR+UoVQP1U81efAX5vUZ3s1\nAgQIECBAgAABAgQaFagzLK217TLg+v2jrZP6ouNliUaAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjcegKb3XpP\n5ZnmRGDLOTkOh0GAAAECBAgQ2JQC127KB/NY7QookNqa+89nuPu1NWSjJUCAAAECBBoReG/G\n+dRGxmqYyyiw+TI+toeeP4Gzc0gXJUfN36E5ojkWeHKO7enJ4XN8jA5t/gTunkN6R/K45Ir5\nOzxHNMcCn8ixvSw5bY6P0aHNn8BxOaR6n6MR2GgBBdJGE66qB6hTz/+d+J/Oqpq2FT/YfXIE\nP/K6WfF5WG0HcNXkgL+Y5SWr7eAd74oK3JhnPzPx/6oVnYZV9+SX54ivW3VH7YDnUuC2c3lU\nDooAAQIECBAgQIAAAQIrIKBAWgF0T0mAAAECBAgQIECAwHwKKJDmc14cFQECBAgQIECAAAEC\nKyCgQFoBdE9JgAABAgQIECBAgMB8CiiQ5nNeHBUBAgQIECBAgAABAisgoEBaAXRPSYAAAQIE\nCBAgQIDAfAookOZzXhwVAQIECBAgQIAAAQIrIKBAWgF0T0mAAAECBAgQIECAwHwKKJDmc14c\nFQECBAgQIECAAAECKyCw+Qo8p6dcOYFrV+6pPfMqFqjXjV8nX8UTuEKHXq+bm5LrV+j5Pe3q\nFfDfnNU7dyt55PW6qWgECBCYSWDH9K5oBGYR2DKd7zLLHfQlMBG4BwkCGyCwd+7jEy4bANf4\nXe6c8W/buIHhEyBAgMD/a+9egG2r6zqAD28EL4KCikThCBgB8qYgEkbSSjSQIYPRgBmCohJr\nGjBmKlSwxKbMSUezh5GQNRY+S7ABzTFTCCiUh8IMKggC8VIDriD0/d27VrPYs/e+Z13uumef\nvT//me9dr/9e678+688++3/W2gcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAwEoT2GylNVh710ugrvNhyaHJ48n9ibI4ArvlVI9JvjzllPv0\nkaHqTmmeTRtRYJsc68DkJ5Ptk+8kq5NxZci+8EM54JFJTe9JHkuU2RVYlabVz5kDkoeS7yXT\nSp/rO1Tdae2zbXkEXpbD7pzcPuHwQ/WFPu9lE5pmNQECK0lgjzT2puTJTm7I/K6JMv8C2+UU\nb0y+O+VU+/SRoepOaZ5NG1Hg5Bzr7qT7flEDpLPGtGHIvvCWHK8GRG07Hs/8OWPaYNVsCJyU\nZtybtNerpl9InpuMK32u71B1x7XLuuUVeGUOX33n8gnNGKov9Hkvm9A0qwkQWEkCm6Sxn0vq\nA87rk92T05OHk28k2ybK/ArskFO7LKkfOJMGSH36yFB15/cKrKwze3ma+0RyW3Jusk9SA6Ob\nk+pDv5S0Zci+UO2o412a1N2IQ5O2H78h88psCbw0zakB7C1J/XypfnNe8khS67ZKuqXP9R2q\nbrc95mdDYKc049tJ/bc/boA0VF/o8142G1JaQYDA0xY4M3uoN5tfGdlT/RAbt36kmsUVLPCa\ntP3OpK7z6mTSAKlPHxmqbpqnzIDAZ9KG6i+vGGnLIc36uvPclqH6Qj3ed1tyR1KPvLRly8zU\n+nrspru+3W66fAKfzKGr39RjvN3ygSzU+vpg25Y+13eoum1bTGdL4GNpzj1J9ZnRAdKQfaHP\ne9lsiWkNAQLrLfClvPLRpL5H0C312FX9du/q7krzcyPwczmT+iHzP8nPJ9cmkwZIffrIUHXT\nPGWZBTbN8a9KahA0bgBSd5HqLkG7bai+0Pbdt+dYo+VtWVH9evSD+Gg9yxtXoH7hdmFSv4nv\nlrrjWNfrrM7KPtd3qLqd5pidEYEz0o7qK8c107pj3C1D9oUv5UA+J3W1za8RqB+KynwKbJHT\n2j/5WvLgyCnWI3f1gWe/pOop8yVQH2QvSPZMPj7l1Pr0kaHqTmmeTRtRoB6tq0fZ9k5+MHLc\nrbO8c/L1pLYN2ReqDVVqsDZa2nUHj26wvKwCf5GjvympD7htqcFS3cWucsXayZp/+1zfoep2\nmmN2BgT2SBv+JHlPMjowaps3VF/o817WtsV0QQQ2X5DzXMTT3CEnXY+l3Dfh5O/P+npz2Cm5\nc0Idq1emwL+m2ZV1lT59pAZdS+1Pferqe+u6Ssu/vT78bpe8r2nKUP2m+sLzmmOMe9+q96wq\nu6yd+HcGBX4sbToxeVVSv4A7O6m7km3pc32Hqtu2xXT5Beoz6CVJPVJ7zpTmDNUX+ryX+Vk1\n5QLN4yYDpHm8qmvPqT7QVKnHrMaV9sPGtuM2WrcQAn36SPvb4aX0pz51FwJ6BZ/ka9P2309u\nSd6cVBmq36xr396z1vDP9D+/mdad3rTw1kxHv0syre+MXt+h6s404II17ryc7wHJ4cnDSd2t\nHleG6gvT9lvtGO2T49pm3ZwKeMRuTi9sTuvR5tQmXeP2uwSjj9PMr4gzGxXo00eGqjvaJsuz\nI3BqmnJxcm9ybPJIUmXIvjBt396z1vrP8r9vTeOen9QfBqpreW1yRtKWPtd3qLptW0yXV6AG\nRecmFyRXr6MpQ/WFafutJnnPWceFmefNkz48z/M5L8q5tX8u89kTTrhd/9CE7VbPv0CfPjJU\n3flXXplnWHeNPpDckdSfcb4pacuQfaF9jKV9f2qPWdN2nfesrspszVd/uTt5f3JisnnS/dPs\nfa7vUHXTJGWZBVbl+Bcn1yfvTLbpJLNrBia1bstaSBmqL/R5L1vbEv8ujIAB0vxe6voeyD1J\n+6Fi9Exrfd3SHv0DDqP1LM+vQJ8+MlTd+dVdmWdWX65/V/KWpH6re1hSf+ilW4bsC0v5IPSt\nbmPMz6xAffeo/kLYPskPN63sc32Hqts0xWQZBeqxuhcmNa1fePxvk/a7hz/dLF+UaZWh+kKf\n97K1LfHvwggYIM33pa7f+taXZnccOc2dsrxXck3iEbsRnAVb7NNHhqq7YOQze7r18+Cvk7OS\njyZHJXU3YFwZqi/UfqscuXbylH/bdVc9Za2F5RR4Zg5+a3LlhEY80az/XjPtc32HqjuhqVZv\nRIEa8PzZmLy3acM3m23td9iG7Au1b5+TGngTAosicHxO9Mlk9K/D/E6z/oRFgVjw86zvAUz6\n/yD16SND1V3wyzMzp39mWlLvF5cm7bP3kxo3ZF+4Pge9K9muc/BnZb4eh7kuqce2lNkRaH/R\nVncDuqXuPtYv4OqadUuf6ztU3W57zM+OwNZpSr0HXTamSUP1heObY/qcNAbdKgLzKlC/Eb4x\nqR9S5yd12/qCZrk+BCmLITBtgNSnjwxVdzGuwmyf5XPSvAeS+nByRVJ3kMal7hhUGbIvnJT9\nVzvqg3f9EucXkurD9TjMgYkyWwJHpDmPJfVI94XJ0cnZST06tToZHTj1ub5D1U2zlBkUmDZA\nGqov9Hkvm0EyTSJAYH0F6vG6TyX1qEN96Khcnjw/URZDYNoAqQT69JGh6i7GlZjdszw2TWvf\nH6ZNd+icwpB94XU5zv2dNtX8aZ1jm50tgfrl281Jt+/8R5b3m9DMPtd3qLoTmmb1MgpMGyBV\ns4bqC33ey5aRx6EJEBhCYFV2elBiYDSE7nzss08fGarufEgu1lkM1RfqD0bsnuydbLVYpCv2\nbHdJyw9Jtl/CGfS5vkPVXUIzVZkxgSH7Qp/3shlj0RwCBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nWKLAZkuspxoBAgQIEFhpAiekwXslN620hmsvAQIECCyfwObLd2hHJkCAAIE5E3hJzudFyRXJ\nd5bx3H48xz45OSZ5LNkl+VByb1Jl0+TYNXNL++eLqXZXcmhS+/rn5PuJQoAAAQIECBAgQIAA\ngYkC78qWJ5N9J9aYvmGLbD47+cXp1aZuPSNbn2hyZ6Z3J9WmGtD8bFJlq6TWLTXH1YtSPpzU\na3aqBYUAAQIE5lPAHaT5vK7OigABAitR4LVp9DuSX17Pxu+a19Ug7ZvJq5ITkxrQXJ58Nvlg\nsnNSd5VOTbrlwCycldTdr6rXLdc2CzV9RuLuUVfHPAECBOZMwABpzi6o0yFAgMACCxyRc986\n+cvkKx2Hz2f+o8l+yYuTG5KLkm55KAs1QLo5Gd3W1vvDdsaUAAECBOZXwABpfq+tMyNAgMCQ\nAnUn5WXJS5OvJp9IJpUds+HopAYn2ye3JjVouT5py09lplLl8OTx5CNJ+12mpexju9Sv8sDa\nyVP+rT/Y8HTLkdnBbsk/JI8mRyXVrn9MDknqHOvn6meSf0+q1DnXo311d+s/k3pt3dUaLXtk\nRXlW/a8nn026PllUCBAgQIAAAQIECBCYRYE906gahNQH/XuSemStHmv7dFLrut9BOjbL9ccR\nan3dpamBRc3/IDknacslman1bep7RDVYqLLUfeyduvX6ehSuvid0QXJ+spRS3zOq1757SuXR\n7yBdmrq3J7+X1Gvr0bu2/Wdm/jXJ6pH1H8ryaPntrKh6dc61vxocls/bkk0ShQABAgQIECBA\ngACBGRVYlXbdmNyXHJVUeWZycdIODtoBUt3RqTtAVffgZLNk26QGIzUgeDh5VtKW12Wm9nFa\nuyLTvvuox+tqHw8m1yV/nmyZrKus7wCpBjXfTX4m2SJ5RVIDwTq/+5M3JNsnuyVfS6pteyVt\neXVmat2/JS9oVq7K9O+SWn9Ks86EAAECBAgQIECAAIEZFKjBS31w/62Rtm2V5Tubbfs2234i\n08uS7oCn2bTmkbzazz7tikzHDZD67qPuuNQdma8mtf9KDWD+NKnB2aSyvgOk2v+vj+z0k1mu\n9W8dWf+7zfq6s9SWmzNTdQ9qVzTTamsNIMvUXaQGxYQAAQIbQ6CelVYIECBAgMBSBfZrKn5s\n5AV1x6TW/Wpn/RczX9+/aUvdYXlRsn/yvGblNs100qTvPmqw8cdN3pNpfTeovif0xqS+L1X/\nj6R6JHBDlmtGdvblLB+TXDWy/q5mue4QVak7Sy9ObkmqTS9JuuXqLFSb687St7obzBMgQIDA\ncAIGSMPZ2jMBAgTmUaD9ED/uA3t9D2m07J4VdUfnqKTm6+dO+1haZpd0d2R99/FA9n95cn5S\nf0jh1ckpST2GtyHLN0Z2Vt8fqvLg2sn//9uub1fs0czU9L/blWOmdf7jvMdUtYoAAQIEnq5A\n/aBSCBAgQIDAUgXuayrWI2B116hbNusuZP5Hky8kdcekBiofTP4rqTsjb05+LVlX6bOPuhu1\nb1L7r0FYW+qPJ7wvqQFS3UHa0AOk2v/6lEebF5XNH03ZwVembLOJAAECBDawgAHSBga1OwIE\nCMy5wDU5v+OTduDSPd32jki77qzM7JCcmlyUdMuezcLooKpbp+b77OP9qV/fYzo6uTLplnbA\ntKEfr+seo+/8rXlBPRJYjwBeMebFNZiru071HSqFAAECBDaSwKYb6TgOQ4AAAQLzIfC55jS6\nf6K7VtX3ZE5otrWTFzYzt7UrmukBmR7WzG/R2dYOXuruVFv67OMTzYtObl/cmb6+mR/9XlCn\nykaffSRH/HRyUPLKkaPvneWy/qukBlEKAQIECGwkAQOkjQTtMAQIEJgTgc/nPP4gOTap7/XU\nB/sakNT69i5NZteUK5vpOzM9KTk8eVNSg4LVSZVnr52s+feeZv43Mr0w2TXps49/Sf0bkvqe\n0buT3ZL6IwgfT+rO0nXJxckslTemMfWo3T8l5yUvT2rwWX/wou6unZa0A8fMKgQIECBAgAAB\nAgQIzJrAJmnQ25Pbk7q78XhySXJus1zfA6pSH/Dfm9T2qlf5dnJ6cnCzXN8Naks99v33SQ0I\nqm7dkeq7jx3zmk8l30/aY9bA7SNJ/c9jJ5XjsqHq18BqUvlwNlSddj+XNsvPGXnBBc36I0bW\nn9qsP3lkfT2uWHeL6nG6ts13ZP6URCFAgAABAgQIECBAYAUJ1If7Veto73bZvn/ygnXUazc/\nIzPPbReaad99VJv+NvmbpL4HtRLKNmlkOf1IUgNDhQABAgQIECBAgAABAhtMoO7knL/B9mZH\nBAgQIECAAAECBAgQWMECW6XtFYUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIDAQgn8H2SGZTCa13zEAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Histogram of data$Time”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first we'll look at data: the histogram\n",
    "hist(data$Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AdZV9L6IUCAQPcCdRblncm5SZ1xOSyp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\ngg==",
      "text/plain": [
       "Plot with title “Histogram of lot”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# However we chose to use a log representation (lot=log of time) of our data to account for \n",
    "lot <- log(data$Time)\n",
    "hist(lot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tShapiro-Wilk normality test\n",
       "\n",
       "data:  data$Time\n",
       "W = 0.69672, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we perform a shapiro-wilk test to establish if the data is parametric or not:\n",
    "shapiro.test(data$Time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Shapiro-Wilk test shows significance ($W=0.69$, $p<0.001$) which means it is highly unlikely that the data here was sampled from a normal distribution. Hence our data is non-parametric.\n",
    "\n",
    "As this analysis is looking for differences between the two groups (machine-leanring and No-machine-learning) and as the study followed a within-jubjects design, a <b>Wilcoxon Rank-Sum test</b> is the suitable test for an initial overview of the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alpha level of .05 was used for all statistical tests. A Shapiro-Wilk test showed that the timing data does not follow a normal distribution $W=0.69$, $p<0.001$. The subsequent Wilcoxon Rank-Sum test showed that there was a significant difference in time between the participants in the <b>semi-automated</b> (machine-learning) and the <b>manual</b> (non-machine learning) condition $W(164)=$  13530, $p<$ 0.001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon signed rank test with continuity correction\n",
       "\n",
       "data:  T_log and data$Condition\n",
       "V = 13530, p-value < 2.2e-16\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon signed rank test with continuity correction\n",
       "\n",
       "data:  T_log and data$Order\n",
       "V = 13530, p-value < 2.2e-16\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We used th log of the timeing data for all subsequent analyses \n",
    "T_log <- log(data$Time)\n",
    "# In this case the Wilcox test does not care about Log or not as it uses Rankings of bins\n",
    "# Wilcox text on the Log Time of the data \n",
    "wilcox.test(T_log, data$Condition, paired = TRUE)\n",
    "# We also looked at the Timing differences by documents\n",
    "#wilcox.test(T_log, data$PDF, paired = TRUE)\n",
    "# As well as differences in time in respect to the order \n",
    "wilcox.test(T_log, data$Order, paired = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jk8FhIgQIAAAQIECBAgMCYBBdKYzraxEiBAgAABAgQIECAwU0CBNJPHQgIECBAg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\nAAECBAgQIECAAAECBAgQIECAwF4t8P8BPL6R3nTTg0wAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Overall timing by condition”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We'll have a look at the boxplots now:\n",
    "# For timing data (Logarithmic scale) by Condition (ML or Non-ML)\n",
    "boxplot(T_log~data$Condition, xlab='Condition 0=Manual, 1=Semi-Automated', ylab=\"Time in Seconds (Log)\", main='Overall timing by condition')\n",
    "#boxplot(data$Time~data$Condition, xlab='Condition', ylab=\"Time in Seconds\", main=axis(1, at=0:1, labels=([\"N\", \"M\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8512VBMm7IafDCCCAAAII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\n+v9gdu6CJxZ9OAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# also by looking at the scatterplot\n",
    "scatterplot(T_log ~ Condition, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eyeballing...there is a slight downward tendencey of time towards the MLR (1.0) condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPaired t-test\n",
       "\n",
       "data:  T_log and data$Condition\n",
       "t = 73.028, df = 163, p-value < 2.2e-16\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 5.668718 5.983796\n",
       "sample estimates:\n",
       "mean of the differences \n",
       "               5.826257 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We also perfomed a t-test (paired because our within-participants design) to \n",
    "# check robustness\n",
    "t.test(T_log, data$Condition, paired = TRUE, alternative = \"two.sided\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The t-test also confirms the result is highly significant! \n",
    "The t-test shows that the two groups differ significatnly from each other $t(163) = 73.02, p < 0.001$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JM0RHMoqAgCCCCAAAIIIIBAZAILqz57RFYnqlOwAB2kgoEpHgEEEEAAAQQQQKCy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\nAhCAAAQgAAEIQAACEIAABOpH4P8D3i3fkUtYFSIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# before we carry on, lets have a look if there is a difference of timing in terms of the \n",
    "# documents. First the box plot:\n",
    "boxplot(T_log~data$PDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JYTUCAtJ2Q6AQIECBAgQIAAAQK9EVAg9eZQ21ECBAgQIECAAAECBJYTUCAtJ2Q6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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Overall Time by Order”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Also looking at the order here First the box plot:\n",
    "boxplot(data$Time~data$Order, xlab=\"Order of the Documents\", ylab=\"Time in Seconds\", main=\"Overall Time by Order\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This nicely shows the incease in time spent on a PDF from the 1st Document participants see to the 4th."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V2Ff9vkwxRvHNJHxe6HXe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\nbRA4ug2NHGEbHxzhvtk1Ar0EVu21AsvrJUAHqV7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      "text/plain": [
       "Plot with title “Order by Time Separated by Condition”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now the same plot in time (Log scale) and separated by condition \n",
    "boxplot(T_log~data$Order*data$Condition, xlab='0.X = Manual conditions, 1.X = Semi-Automated conditions', ylab=\"Time in Seconds (Log)\", main='Order by Time Separated by Condition')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a almost linear increase in the timing data on the non-machine learning side (1.0, 2.0, 3.0, 4.0) compared to the machine-learning side. Note, as this was a within-participants design fatigue effects are expected and can be seen here in form of the last plot 4.0 & 4.1. The timing in the machine-learning condition almost stays at a constant level except the expected last box. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPaired t-test\n",
       "\n",
       "data:  T_log and data$Order\n",
       "t = 44.277, df = 163, p-value < 2.2e-16\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 3.655618 3.996895\n",
       "sample estimates:\n",
       "mean of the differences \n",
       "               3.826257 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Again for robustness we also performed a t-test:\n",
    "t.test(T_log, data$Order, paired = TRUE, alternative = \"two.sided\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The t-test shows that the Order is significantly different in terms of timing data $t(163) = 44.28, p < 0.001$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Descriptives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# creating two groups (ML (1) and NonML (0) )\n",
    "Annotations <- subset(data, Condition=='1')\n",
    "NoAnnotations <- subset(data, Condition=='0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ParticipantNo</th><th scope=col>ID</th><th scope=col>PDF</th><th scope=col>Order</th><th scope=col>Time</th><th scope=col>Condition</th><th scope=col>CExperience</th><th scope=col>Tasks</th><th scope=col>NReviews</th><th scope=col>AMeanNReviews</th><th scope=col>⋯</th><th scope=col>AC2</th><th scope=col>AL2</th><th scope=col>AC3</th><th scope=col>AL3</th><th scope=col>AC4</th><th scope=col>AL4</th><th scope=col>Comments</th><th scope=col>TotalAdded</th><th scope=col>TotalDeleted</th><th scope=col>TotalSubmitted</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1                                                        </td><td>-JjAzwjQakZk-3kIbeMfw                                    </td><td>U2PXr4iMBf8Kt-leQI8Ii                                    </td><td>1                                                        </td><td>435                                                      </td><td>1                                                        </td><td>1                                                        </td><td>9                                                        </td><td>10+                                                      </td><td>12.5                                                     </td><td>⋯                                                        </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>C1:2added, C2:2added,C3:3added,C4:1added                 </td><td>8                                                        </td><td>0                                                        </td><td>20                                                       </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1                                                        </td><td>-JjAzwjQakZk-3kIbeMfw                                    </td><td>iHbGgWtrKNfksdoj9Hxv9                                    </td><td>3                                                        </td><td>297                                                      </td><td>1                                                        </td><td>1                                                        </td><td>9                                                        </td><td>10+                                                      </td><td>12.5                                                     </td><td>⋯                                                        </td><td>No                                                       </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>C1:1added, C2:1added, C4:1added                          </td><td>3                                                        </td><td>0                                                        </td><td>15                                                       </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2                                                        </td><td>hNQKTiKtHSY_BmxLyZb9Q                                    </td><td>XOCbiBddVQK3BYy1Ox4lI                                    </td><td>1                                                        </td><td>306                                                      </td><td>1                                                        </td><td>1                                                        </td><td>8                                                        </td><td>5to10                                                    </td><td> 7.5                                                     </td><td>⋯                                                        </td><td>No                                                       </td><td>Yes                                                      </td><td>No                                                       </td><td>Yes                                                      </td><td>No                                                       </td><td>Yes                                                      </td><td>C1:1added                                                </td><td>1                                                        </td><td>0                                                        </td><td>16                                                       </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2                                                        </td><td>hNQKTiKtHSY_BmxLyZb9Q                                    </td><td>oOAy7INgRLumTV3vZWtVM                                    </td><td>2                                                        </td><td>127                                                      </td><td>1                                                        </td><td>1                                                        </td><td>8                                                        </td><td>5to10                                                    </td><td> 7.5                                                     </td><td>⋯                                                        </td><td>No                                                       </td><td>Yes                                                      </td><td>No                                                       </td><td>Yes                                                      </td><td>No                                                       </td><td>Yes                                                      </td><td>                                                         </td><td>0                                                        </td><td>0                                                        </td><td>15                                                       </td></tr>\n",
       "\t<tr><th scope=row>10</th><td>3                                                        </td><td>pyTvUi2W85Nb9handPSvZ                                    </td><td>gD_JugCFB6D5iV-JIOI0p                                    </td><td>2                                                        </td><td>209                                                      </td><td>1                                                        </td><td>1                                                        </td><td>9                                                        </td><td>5to10                                                    </td><td> 7.5                                                     </td><td>⋯                                                        </td><td>No                                                       </td><td>Yes                                                      </td><td>No                                                       </td><td>Yes                                                      </td><td>No                                                       </td><td>Yes                                                      </td><td>                                                         </td><td>0                                                        </td><td>0                                                        </td><td>15                                                       </td></tr>\n",
       "\t<tr><th scope=row>12</th><td>3                                                        </td><td>pyTvUi2W85Nb9handPSvZ                                    </td><td>4gIe5sni3WJJCzNglSdo8                                    </td><td>4                                                        </td><td>756                                                      </td><td>1                                                        </td><td>1                                                        </td><td>9                                                        </td><td>5to10                                                    </td><td> 7.5                                                     </td><td>⋯                                                        </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>Yes                                                      </td><td>C1:1added all left;C2:1added all left,C3:1added,C4:1added</td><td>4                                                        </td><td>0                                                        </td><td>19                                                       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllll}\n",
       "  & ParticipantNo & ID & PDF & Order & Time & Condition & CExperience & Tasks & NReviews & AMeanNReviews & ⋯ & AC2 & AL2 & AC3 & AL3 & AC4 & AL4 & Comments & TotalAdded & TotalDeleted & TotalSubmitted\\\\\n",
       "\\hline\n",
       "\t1 & 1                                                         & -JjAzwjQakZk-3kIbeMfw                                     & U2PXr4iMBf8Kt-leQI8Ii                                     & 1                                                         & 435                                                       & 1                                                         & 1                                                         & 9                                                         & 10+                                                       & 12.5                                                      & ⋯                                                         & Yes                                                       & Yes                                                       & Yes                                                       & Yes                                                       & Yes                                                       & Yes                                                       & C1:2added, C2:2added,C3:3added,C4:1added                  & 8                                                         & 0                                                         & 20                                                       \\\\\n",
       "\t3 & 1                                                         & -JjAzwjQakZk-3kIbeMfw                                     & iHbGgWtrKNfksdoj9Hxv9                                     & 3                                                         & 297                                                       & 1                                                         & 1                                                         & 9                                                         & 10+                                                       & 12.5                                                      & ⋯                                                         & No                                                        & Yes                                                       & Yes                                                       & Yes                                                       & Yes                                                       & Yes                                                       & C1:1added, C2:1added, C4:1added                           & 3                                                         & 0                                                         & 15                                                       \\\\\n",
       "\t5 & 2                                                         & hNQKTiKtHSY\\_BmxLyZb9Q                                   & XOCbiBddVQK3BYy1Ox4lI                                     & 1                                                         & 306                                                       & 1                                                         & 1                                                         & 8                                                         & 5to10                                                     &  7.5                                                      & ⋯                                                         & No                                                        & Yes                                                       & No                                                        & Yes                                                       & No                                                        & Yes                                                       & C1:1added                                                 & 1                                                         & 0                                                         & 16                                                       \\\\\n",
       "\t6 & 2                                                         & hNQKTiKtHSY\\_BmxLyZb9Q                                   & oOAy7INgRLumTV3vZWtVM                                     & 2                                                         & 127                                                       & 1                                                         & 1                                                         & 8                                                         & 5to10                                                     &  7.5                                                      & ⋯                                                         & No                                                        & Yes                                                       & No                                                        & Yes                                                       & No                                                        & Yes                                                       &                                                           & 0                                                         & 0                                                         & 15                                                       \\\\\n",
       "\t10 & 3                                                         & pyTvUi2W85Nb9handPSvZ                                     & gD\\_JugCFB6D5iV-JIOI0p                                   & 2                                                         & 209                                                       & 1                                                         & 1                                                         & 9                                                         & 5to10                                                     &  7.5                                                      & ⋯                                                         & No                                                        & Yes                                                       & No                                                        & Yes                                                       & No                                                        & Yes                                                       &                                                           & 0                                                         & 0                                                         & 15                                                       \\\\\n",
       "\t12 & 3                                                         & pyTvUi2W85Nb9handPSvZ                                     & 4gIe5sni3WJJCzNglSdo8                                     & 4                                                         & 756                                                       & 1                                                         & 1                                                         & 9                                                         & 5to10                                                     &  7.5                                                      & ⋯                                                         & Yes                                                       & Yes                                                       & Yes                                                       & Yes                                                       & Yes                                                       & Yes                                                       & C1:1added all left;C2:1added all left,C3:1added,C4:1added & 4                                                         & 0                                                         & 19                                                       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ParticipantNo | ID | PDF | Order | Time | Condition | CExperience | Tasks | NReviews | AMeanNReviews | ⋯ | AC2 | AL2 | AC3 | AL3 | AC4 | AL4 | Comments | TotalAdded | TotalDeleted | TotalSubmitted | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 1                                                         | -JjAzwjQakZk-3kIbeMfw                                     | U2PXr4iMBf8Kt-leQI8Ii                                     | 1                                                         | 435                                                       | 1                                                         | 1                                                         | 9                                                         | 10+                                                       | 12.5                                                      | ⋯                                                         | Yes                                                       | Yes                                                       | Yes                                                       | Yes                                                       | Yes                                                       | Yes                                                       | C1:2added, C2:2added,C3:3added,C4:1added                  | 8                                                         | 0                                                         | 20                                                        | \n",
       "| 3 | 1                                                         | -JjAzwjQakZk-3kIbeMfw                                     | iHbGgWtrKNfksdoj9Hxv9                                     | 3                                                         | 297                                                       | 1                                                         | 1                                                         | 9                                                         | 10+                                                       | 12.5                                                      | ⋯                                                         | No                                                        | Yes                                                       | Yes                                                       | Yes                                                       | Yes                                                       | Yes                                                       | C1:1added, C2:1added, C4:1added                           | 3                                                         | 0                                                         | 15                                                        | \n",
       "| 5 | 2                                                         | hNQKTiKtHSY_BmxLyZb9Q                                     | XOCbiBddVQK3BYy1Ox4lI                                     | 1                                                         | 306                                                       | 1                                                         | 1                                                         | 8                                                         | 5to10                                                     |  7.5                                                      | ⋯                                                         | No                                                        | Yes                                                       | No                                                        | Yes                                                       | No                                                        | Yes                                                       | C1:1added                                                 | 1                                                         | 0                                                         | 16                                                        | \n",
       "| 6 | 2                                                         | hNQKTiKtHSY_BmxLyZb9Q                                     | oOAy7INgRLumTV3vZWtVM                                     | 2                                                         | 127                                                       | 1                                                         | 1                                                         | 8                                                         | 5to10                                                     |  7.5                                                      | ⋯                                                         | No                                                        | Yes                                                       | No                                                        | Yes                                                       | No                                                        | Yes                                                       |                                                           | 0                                                         | 0                                                         | 15                                                        | \n",
       "| 10 | 3                                                         | pyTvUi2W85Nb9handPSvZ                                     | gD_JugCFB6D5iV-JIOI0p                                     | 2                                                         | 209                                                       | 1                                                         | 1                                                         | 9                                                         | 5to10                                                     |  7.5                                                      | ⋯                                                         | No                                                        | Yes                                                       | No                                                        | Yes                                                       | No                                                        | Yes                                                       |                                                           | 0                                                         | 0                                                         | 15                                                        | \n",
       "| 12 | 3                                                         | pyTvUi2W85Nb9handPSvZ                                     | 4gIe5sni3WJJCzNglSdo8                                     | 4                                                         | 756                                                       | 1                                                         | 1                                                         | 9                                                         | 5to10                                                     |  7.5                                                      | ⋯                                                         | Yes                                                       | Yes                                                       | Yes                                                       | Yes                                                       | Yes                                                       | Yes                                                       | C1:1added all left;C2:1added all left,C3:1added,C4:1added | 4                                                         | 0                                                         | 19                                                        | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   ParticipantNo ID                    PDF                   Order Time\n",
       "1  1             -JjAzwjQakZk-3kIbeMfw U2PXr4iMBf8Kt-leQI8Ii 1     435 \n",
       "3  1             -JjAzwjQakZk-3kIbeMfw iHbGgWtrKNfksdoj9Hxv9 3     297 \n",
       "5  2             hNQKTiKtHSY_BmxLyZb9Q XOCbiBddVQK3BYy1Ox4lI 1     306 \n",
       "6  2             hNQKTiKtHSY_BmxLyZb9Q oOAy7INgRLumTV3vZWtVM 2     127 \n",
       "10 3             pyTvUi2W85Nb9handPSvZ gD_JugCFB6D5iV-JIOI0p 2     209 \n",
       "12 3             pyTvUi2W85Nb9handPSvZ 4gIe5sni3WJJCzNglSdo8 4     756 \n",
       "   Condition CExperience Tasks NReviews AMeanNReviews ⋯ AC2 AL2 AC3 AL3 AC4 AL4\n",
       "1  1         1           9     10+      12.5          ⋯ Yes Yes Yes Yes Yes Yes\n",
       "3  1         1           9     10+      12.5          ⋯ No  Yes Yes Yes Yes Yes\n",
       "5  1         1           8     5to10     7.5          ⋯ No  Yes No  Yes No  Yes\n",
       "6  1         1           8     5to10     7.5          ⋯ No  Yes No  Yes No  Yes\n",
       "10 1         1           9     5to10     7.5          ⋯ No  Yes No  Yes No  Yes\n",
       "12 1         1           9     5to10     7.5          ⋯ Yes Yes Yes Yes Yes Yes\n",
       "   Comments                                                  TotalAdded\n",
       "1  C1:2added, C2:2added,C3:3added,C4:1added                  8         \n",
       "3  C1:1added, C2:1added, C4:1added                           3         \n",
       "5  C1:1added                                                 1         \n",
       "6                                                            0         \n",
       "10                                                           0         \n",
       "12 C1:1added all left;C2:1added all left,C3:1added,C4:1added 4         \n",
       "   TotalDeleted TotalSubmitted\n",
       "1  0            20            \n",
       "3  0            15            \n",
       "5  0            16            \n",
       "6  0            15            \n",
       "10 0            15            \n",
       "12 0            19            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the data\n",
    "head(Annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Overall:  129454 \n",
      "Overall Mean Time:  789.3537 \n",
      "Overall SD Time:  794.4597 \n",
      "_____________________________________________ \n",
      "Time in Annotations:  61913 Time in NoAnnotations:  67541 \n",
      "Mean Annotations:  755.0366 Mean NoAnnotations:  823.6707 \n",
      "SD Annotations:  868.4611 SD NoAnnotations:  716.6 \n",
      "_____________________________________________ \n"
     ]
    }
   ],
   "source": [
    "cat(\"Time Overall: \", sum(data$Time), '\\n')\n",
    "cat(\"Overall Mean Time: \", mean(data$Time), '\\n')\n",
    "cat(\"Overall SD Time: \", sd(data$Time), '\\n')\n",
    "cat('_____________________________________________', '\\n')\n",
    "cat(\"Time in Annotations: \", sum(Annotations$Time), \"Time in NoAnnotations: \", sum(NoAnnotations$Time), '\\n') \n",
    "cat(\"Mean Annotations: \", mean(Annotations$Time), \"Mean NoAnnotations: \", mean(NoAnnotations$Time), '\\n') \n",
    "cat(\"SD Annotations: \", sd(Annotations$Time), \"SD NoAnnotations: \", sd(NoAnnotations$Time), '\\n') \n",
    "cat('_____________________________________________', '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forty-one participants were recruited. All except four had experience of at least one systematic review and all but eight were familiar with the Cochrane Risk of Bias tool. Twenty listed more than one task on how they contributed to previsous systematic reviews.\n",
    "\n",
    "A mean of 755 seconds (SD 868) were taken for <b>semi-automated</b> bias assesmsents and 824 seconds (SD 717) for <b>manual</b> assessments ( $p<0.001$). Participants spent in total 129454 seconds ($Mean$=789.35, $SD$=794.46) to complete the study together in both conditions <b>semi-automated</b> and <b>manual</b>.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Tukey Ladder of Powers\n",
    "The <b>Tukey Ladder of Powers</b> was used to transform the response variable (time) to come closer to a normal distribution. The resulting $\\lambda =$ -0.15 is close to 0 which confirms that the optimal transformation according to the Tukey Ladder of Powers is indeed a logarithmic operation. For the following mixed model analysis the timing data was therefore transformed to a logarithmic scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xhyf4/fYzUCBAgQIECAAAECBCZYoO4F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\nyRD4/7X2677DNFJmAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick look at the data \n",
    "plotNormalHistogram(data$Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    lambda      W Shapiro.p.value\n",
      "395  -0.15 0.9907          0.3626\n",
      "\n",
      "if (lambda >  0){TRANS = x ^ lambda} \n",
      "if (lambda == 0){TRANS = log(x)} \n",
      "if (lambda <  0){TRANS = -1 * x ^ lambda} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# cheching the data transformation:\n",
    "T_tuk = transformTukey(data$Time, plotit=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2rXrt7V1rbT/ZFGuRUrKqLbATUhgAACCHxJgA7Slzh4ggACCCDwhYB3oJYvCZ6/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\nSfp62izS2qkMgZgIMECKyYKgGwhsLJAdq9eHBtP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Hmnf/Zsw/TvM+1Yz5mfUzBHr16pW94oorOjY2289/vot76y27iKFzX/nKzMy3vvUF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\nkCEfueuue8zttNOi3Nx2XtLpp+/pnnqqbwmfZpZyCTBAKpes5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      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the transformed data\n",
    "plotNormalHistogram(T_tuk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ahs397P3X33YV6yVVVb3WWX/TsZiYcyy9S/1Kzv+k07Qo82qkRBAAEEEIiBAB2k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\ngg==",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now using the log of the data and plotting it:\n",
    "plotNormalHistogram(T_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <b>Tukey Ladder of Powers</b> was used to transform the response variable (time) to come closer to a normal distribution. The resulting $\\lambda =$ -0.15 is close to 0 which confirms that the optimal transformation according to the Tukey Ladder of Powers is indeed a logarithmic operation. For the following mixed model analysis the timing data was therefore transformed to a logarithmic scale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Linear Mixed Effects Model Analysis\n",
    "A linear mixed effects model was used to examine the associations between the  log transformed time response, the <b>semi-automatic</b> or <b>manual</b> condition, the order in which a document was randomly presented and self reported characteristics of the reviewers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  4.1 Primary model analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# first add the log time to the data frame\n",
    "data$logT <- log(data$Time)\n",
    "# create the primary model: Log(Time) by Condition as fixed effects and ID as random effect:\n",
    "ml.p = lmer(logT ~ Condition + (1 | ID), data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by REML ['lmerMod']\n",
       "Formula: logT ~ Condition + (1 | ID)\n",
       "   Data: data\n",
       "\n",
       "REML criterion at convergence: 374.2\n",
       "\n",
       "Scaled residuals: \n",
       "     Min       1Q   Median       3Q      Max \n",
       "-2.29905 -0.63291  0.02993  0.55999  2.47915 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev.\n",
       " ID       (Intercept) 0.2085   0.4566  \n",
       " Residual             0.4277   0.6540  \n",
       "Number of obs: 164, groups:  ID, 41\n",
       "\n",
       "Fixed effects:\n",
       "            Estimate Std. Error t value\n",
       "(Intercept)   6.4678     0.1015   63.73\n",
       "Condition    -0.2832     0.1021   -2.77\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "          (Intr)\n",
       "Condition -0.503"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(ml.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>(Intercept)</th><th scope=col>Condition</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>-JjAzwjQakZk-3kIbeMfw</th><td>6.342909  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>-O8MP5AR-esmcnFcENyoF</th><td>6.402884  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>-YoLEIFk1XZUJo3BgVmlp</th><td>6.723705  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>2K-K26QZH9xPJH9RVMltM</th><td>6.419611  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>2snvok14kLm_dRIdL67PF</th><td>6.223803  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>3CDOco99k3UGgia7A9LQo</th><td>7.275245  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>4dkYUV8wlkm_Q5miw0qCJ</th><td>6.865738  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>5h9rXuHepqDSmetOKXM0J</th><td>6.557449  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>5SAqbCbqZP_q7hVWTEJdL</th><td>5.615216  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>8arsi19w5sMwMmpkmM0sX</th><td>6.415119  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>C07qGfUuD1L-1IoLjtOXY</th><td>6.572443  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>C4xWyqDJrsHcIoHU5GtUG</th><td>7.029055  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>Ci3GWDLAOc5ANxkPpNOBc</th><td>6.409193  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>cs1qlmKFvdkFjUcXEkJgT</th><td>6.431466  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>FfUZhXrHdKSHZ3ON2_Dty</th><td>6.667310  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>fQoBVJ60uF5ehuYNAg4v2</th><td>6.417978  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>FXvg7iTf7P1bl-Z9dHKPO</th><td>6.431382  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>GR96RbbiPLL4gS3-ilxky</th><td>7.259352  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>hNQKTiKtHSY_BmxLyZb9Q</th><td>6.227614  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>j-6IWj13fqVcUXKjFCo7y</th><td>6.202480  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>jBFO8AR45EErIQ5grz5n-</th><td>6.324240  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>jYha2hNra2RfUfgXnVaj1</th><td>6.436849  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>KlKCr9O8A-8WK6gr2vK9k</th><td>6.455434  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>KtfB3jYg9jsg0osJkMWVG</th><td>5.920122  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>L-KBZ1ZC0lK2hQlDSUCgH</th><td>6.018265  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>lllK6Q_LLhHbw7bTJ1v51</th><td>6.135699  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>pyTvUi2W85Nb9handPSvZ</th><td>6.191651  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>Rh7QrcKwEzMEORahevriS</th><td>6.846711  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>S2oI3sQr3HnSJ7rgbaMJR</th><td>6.692770  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>sLUZSWii-lFp5oEFHCd7f</th><td>6.310985  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>to4PqjXHBIG3bKQhv9cHr</th><td>6.173613  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>tRdDoOfbjIQw3tUkGktky</th><td>6.736527  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>TTtTg_voG4fV-uDraAWC3</th><td>6.302266  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>UbIO7NW7RAUjVfe3_VJ48</th><td>6.232454  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>uq4HMVa0zTYYknq8_4SOc</th><td>7.464664  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>UyqDH9vL0gK918t2bEj0z</th><td>6.266225  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>V9_3tiOUC-jab7xnPK1HK</th><td>6.569870  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>WpFHCjeR-3YdyEJJmCT2Y</th><td>6.208212  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>ynh1N1_BZO8W4d0_gaMDy</th><td>6.345564  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>ZAj9Y0K4px17TAcPf3uYG</th><td>6.092636  </td><td>-0.2831514</td></tr>\n",
       "\t<tr><th scope=row>ZXpghXhauDD3Vpv76PGoj</th><td>6.966419  </td><td>-0.2831514</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & (Intercept) & Condition\\\\\n",
       "\\hline\n",
       "\t-JjAzwjQakZk-3kIbeMfw & 6.342909   & -0.2831514\\\\\n",
       "\t-O8MP5AR-esmcnFcENyoF & 6.402884   & -0.2831514\\\\\n",
       "\t-YoLEIFk1XZUJo3BgVmlp & 6.723705   & -0.2831514\\\\\n",
       "\t2K-K26QZH9xPJH9RVMltM & 6.419611   & -0.2831514\\\\\n",
       "\t2snvok14kLm\\_dRIdL67PF & 6.223803   & -0.2831514\\\\\n",
       "\t3CDOco99k3UGgia7A9LQo & 7.275245   & -0.2831514\\\\\n",
       "\t4dkYUV8wlkm\\_Q5miw0qCJ & 6.865738   & -0.2831514\\\\\n",
       "\t5h9rXuHepqDSmetOKXM0J & 6.557449   & -0.2831514\\\\\n",
       "\t5SAqbCbqZP\\_q7hVWTEJdL & 5.615216   & -0.2831514\\\\\n",
       "\t8arsi19w5sMwMmpkmM0sX & 6.415119   & -0.2831514\\\\\n",
       "\tC07qGfUuD1L-1IoLjtOXY & 6.572443   & -0.2831514\\\\\n",
       "\tC4xWyqDJrsHcIoHU5GtUG & 7.029055   & -0.2831514\\\\\n",
       "\tCi3GWDLAOc5ANxkPpNOBc & 6.409193   & -0.2831514\\\\\n",
       "\tcs1qlmKFvdkFjUcXEkJgT & 6.431466   & -0.2831514\\\\\n",
       "\tFfUZhXrHdKSHZ3ON2\\_Dty & 6.667310   & -0.2831514\\\\\n",
       "\tfQoBVJ60uF5ehuYNAg4v2 & 6.417978   & -0.2831514\\\\\n",
       "\tFXvg7iTf7P1bl-Z9dHKPO & 6.431382   & -0.2831514\\\\\n",
       "\tGR96RbbiPLL4gS3-ilxky & 7.259352   & -0.2831514\\\\\n",
       "\thNQKTiKtHSY\\_BmxLyZb9Q & 6.227614   & -0.2831514\\\\\n",
       "\tj-6IWj13fqVcUXKjFCo7y & 6.202480   & -0.2831514\\\\\n",
       "\tjBFO8AR45EErIQ5grz5n- & 6.324240   & -0.2831514\\\\\n",
       "\tjYha2hNra2RfUfgXnVaj1 & 6.436849   & -0.2831514\\\\\n",
       "\tKlKCr9O8A-8WK6gr2vK9k & 6.455434   & -0.2831514\\\\\n",
       "\tKtfB3jYg9jsg0osJkMWVG & 5.920122   & -0.2831514\\\\\n",
       "\tL-KBZ1ZC0lK2hQlDSUCgH & 6.018265   & -0.2831514\\\\\n",
       "\tlllK6Q\\_LLhHbw7bTJ1v51 & 6.135699   & -0.2831514\\\\\n",
       "\tpyTvUi2W85Nb9handPSvZ & 6.191651   & -0.2831514\\\\\n",
       "\tRh7QrcKwEzMEORahevriS & 6.846711   & -0.2831514\\\\\n",
       "\tS2oI3sQr3HnSJ7rgbaMJR & 6.692770   & -0.2831514\\\\\n",
       "\tsLUZSWii-lFp5oEFHCd7f & 6.310985   & -0.2831514\\\\\n",
       "\tto4PqjXHBIG3bKQhv9cHr & 6.173613   & -0.2831514\\\\\n",
       "\ttRdDoOfbjIQw3tUkGktky & 6.736527   & -0.2831514\\\\\n",
       "\tTTtTg\\_voG4fV-uDraAWC3 & 6.302266   & -0.2831514\\\\\n",
       "\tUbIO7NW7RAUjVfe3\\_VJ48 & 6.232454   & -0.2831514\\\\\n",
       "\tuq4HMVa0zTYYknq8\\_4SOc & 7.464664   & -0.2831514\\\\\n",
       "\tUyqDH9vL0gK918t2bEj0z & 6.266225   & -0.2831514\\\\\n",
       "\tV9\\_3tiOUC-jab7xnPK1HK & 6.569870   & -0.2831514\\\\\n",
       "\tWpFHCjeR-3YdyEJJmCT2Y & 6.208212   & -0.2831514\\\\\n",
       "\tynh1N1\\_BZO8W4d0\\_gaMDy & 6.345564   & -0.2831514\\\\\n",
       "\tZAj9Y0K4px17TAcPf3uYG & 6.092636   & -0.2831514\\\\\n",
       "\tZXpghXhauDD3Vpv76PGoj & 6.966419   & -0.2831514\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | (Intercept) | Condition | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| -JjAzwjQakZk-3kIbeMfw | 6.342909   | -0.2831514 | \n",
       "| -O8MP5AR-esmcnFcENyoF | 6.402884   | -0.2831514 | \n",
       "| -YoLEIFk1XZUJo3BgVmlp | 6.723705   | -0.2831514 | \n",
       "| 2K-K26QZH9xPJH9RVMltM | 6.419611   | -0.2831514 | \n",
       "| 2snvok14kLm_dRIdL67PF | 6.223803   | -0.2831514 | \n",
       "| 3CDOco99k3UGgia7A9LQo | 7.275245   | -0.2831514 | \n",
       "| 4dkYUV8wlkm_Q5miw0qCJ | 6.865738   | -0.2831514 | \n",
       "| 5h9rXuHepqDSmetOKXM0J | 6.557449   | -0.2831514 | \n",
       "| 5SAqbCbqZP_q7hVWTEJdL | 5.615216   | -0.2831514 | \n",
       "| 8arsi19w5sMwMmpkmM0sX | 6.415119   | -0.2831514 | \n",
       "| C07qGfUuD1L-1IoLjtOXY | 6.572443   | -0.2831514 | \n",
       "| C4xWyqDJrsHcIoHU5GtUG | 7.029055   | -0.2831514 | \n",
       "| Ci3GWDLAOc5ANxkPpNOBc | 6.409193   | -0.2831514 | \n",
       "| cs1qlmKFvdkFjUcXEkJgT | 6.431466   | -0.2831514 | \n",
       "| FfUZhXrHdKSHZ3ON2_Dty | 6.667310   | -0.2831514 | \n",
       "| fQoBVJ60uF5ehuYNAg4v2 | 6.417978   | -0.2831514 | \n",
       "| FXvg7iTf7P1bl-Z9dHKPO | 6.431382   | -0.2831514 | \n",
       "| GR96RbbiPLL4gS3-ilxky | 7.259352   | -0.2831514 | \n",
       "| hNQKTiKtHSY_BmxLyZb9Q | 6.227614   | -0.2831514 | \n",
       "| j-6IWj13fqVcUXKjFCo7y | 6.202480   | -0.2831514 | \n",
       "| jBFO8AR45EErIQ5grz5n- | 6.324240   | -0.2831514 | \n",
       "| jYha2hNra2RfUfgXnVaj1 | 6.436849   | -0.2831514 | \n",
       "| KlKCr9O8A-8WK6gr2vK9k | 6.455434   | -0.2831514 | \n",
       "| KtfB3jYg9jsg0osJkMWVG | 5.920122   | -0.2831514 | \n",
       "| L-KBZ1ZC0lK2hQlDSUCgH | 6.018265   | -0.2831514 | \n",
       "| lllK6Q_LLhHbw7bTJ1v51 | 6.135699   | -0.2831514 | \n",
       "| pyTvUi2W85Nb9handPSvZ | 6.191651   | -0.2831514 | \n",
       "| Rh7QrcKwEzMEORahevriS | 6.846711   | -0.2831514 | \n",
       "| S2oI3sQr3HnSJ7rgbaMJR | 6.692770   | -0.2831514 | \n",
       "| sLUZSWii-lFp5oEFHCd7f | 6.310985   | -0.2831514 | \n",
       "| to4PqjXHBIG3bKQhv9cHr | 6.173613   | -0.2831514 | \n",
       "| tRdDoOfbjIQw3tUkGktky | 6.736527   | -0.2831514 | \n",
       "| TTtTg_voG4fV-uDraAWC3 | 6.302266   | -0.2831514 | \n",
       "| UbIO7NW7RAUjVfe3_VJ48 | 6.232454   | -0.2831514 | \n",
       "| uq4HMVa0zTYYknq8_4SOc | 7.464664   | -0.2831514 | \n",
       "| UyqDH9vL0gK918t2bEj0z | 6.266225   | -0.2831514 | \n",
       "| V9_3tiOUC-jab7xnPK1HK | 6.569870   | -0.2831514 | \n",
       "| WpFHCjeR-3YdyEJJmCT2Y | 6.208212   | -0.2831514 | \n",
       "| ynh1N1_BZO8W4d0_gaMDy | 6.345564   | -0.2831514 | \n",
       "| ZAj9Y0K4px17TAcPf3uYG | 6.092636   | -0.2831514 | \n",
       "| ZXpghXhauDD3Vpv76PGoj | 6.966419   | -0.2831514 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                      (Intercept) Condition \n",
       "-JjAzwjQakZk-3kIbeMfw 6.342909    -0.2831514\n",
       "-O8MP5AR-esmcnFcENyoF 6.402884    -0.2831514\n",
       "-YoLEIFk1XZUJo3BgVmlp 6.723705    -0.2831514\n",
       "2K-K26QZH9xPJH9RVMltM 6.419611    -0.2831514\n",
       "2snvok14kLm_dRIdL67PF 6.223803    -0.2831514\n",
       "3CDOco99k3UGgia7A9LQo 7.275245    -0.2831514\n",
       "4dkYUV8wlkm_Q5miw0qCJ 6.865738    -0.2831514\n",
       "5h9rXuHepqDSmetOKXM0J 6.557449    -0.2831514\n",
       "5SAqbCbqZP_q7hVWTEJdL 5.615216    -0.2831514\n",
       "8arsi19w5sMwMmpkmM0sX 6.415119    -0.2831514\n",
       "C07qGfUuD1L-1IoLjtOXY 6.572443    -0.2831514\n",
       "C4xWyqDJrsHcIoHU5GtUG 7.029055    -0.2831514\n",
       "Ci3GWDLAOc5ANxkPpNOBc 6.409193    -0.2831514\n",
       "cs1qlmKFvdkFjUcXEkJgT 6.431466    -0.2831514\n",
       "FfUZhXrHdKSHZ3ON2_Dty 6.667310    -0.2831514\n",
       "fQoBVJ60uF5ehuYNAg4v2 6.417978    -0.2831514\n",
       "FXvg7iTf7P1bl-Z9dHKPO 6.431382    -0.2831514\n",
       "GR96RbbiPLL4gS3-ilxky 7.259352    -0.2831514\n",
       "hNQKTiKtHSY_BmxLyZb9Q 6.227614    -0.2831514\n",
       "j-6IWj13fqVcUXKjFCo7y 6.202480    -0.2831514\n",
       "jBFO8AR45EErIQ5grz5n- 6.324240    -0.2831514\n",
       "jYha2hNra2RfUfgXnVaj1 6.436849    -0.2831514\n",
       "KlKCr9O8A-8WK6gr2vK9k 6.455434    -0.2831514\n",
       "KtfB3jYg9jsg0osJkMWVG 5.920122    -0.2831514\n",
       "L-KBZ1ZC0lK2hQlDSUCgH 6.018265    -0.2831514\n",
       "lllK6Q_LLhHbw7bTJ1v51 6.135699    -0.2831514\n",
       "pyTvUi2W85Nb9handPSvZ 6.191651    -0.2831514\n",
       "Rh7QrcKwEzMEORahevriS 6.846711    -0.2831514\n",
       "S2oI3sQr3HnSJ7rgbaMJR 6.692770    -0.2831514\n",
       "sLUZSWii-lFp5oEFHCd7f 6.310985    -0.2831514\n",
       "to4PqjXHBIG3bKQhv9cHr 6.173613    -0.2831514\n",
       "tRdDoOfbjIQw3tUkGktky 6.736527    -0.2831514\n",
       "TTtTg_voG4fV-uDraAWC3 6.302266    -0.2831514\n",
       "UbIO7NW7RAUjVfe3_VJ48 6.232454    -0.2831514\n",
       "uq4HMVa0zTYYknq8_4SOc 7.464664    -0.2831514\n",
       "UyqDH9vL0gK918t2bEj0z 6.266225    -0.2831514\n",
       "V9_3tiOUC-jab7xnPK1HK 6.569870    -0.2831514\n",
       "WpFHCjeR-3YdyEJJmCT2Y 6.208212    -0.2831514\n",
       "ynh1N1_BZO8W4d0_gaMDy 6.345564    -0.2831514\n",
       "ZAj9Y0K4px17TAcPf3uYG 6.092636    -0.2831514\n",
       "ZXpghXhauDD3Vpv76PGoj 6.966419    -0.2831514"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# looking at coefficients by ID\n",
    "coef(ml.p)$ID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>(Intercept)</dt>\n",
       "\t\t<dd>6.46783243288911</dd>\n",
       "\t<dt>Condition</dt>\n",
       "\t\t<dd>-0.283151403792091</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[(Intercept)] 6.46783243288911\n",
       "\\item[Condition] -0.283151403792091\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "(Intercept)\n",
       ":   6.46783243288911Condition\n",
       ":   -0.283151403792091\n",
       "\n"
      ],
      "text/plain": [
       "(Intercept)   Condition \n",
       "  6.4678324  -0.2831514 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the mean values\n",
    "coef(summary(ml.p))[,\"Estimate\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing profile confidence intervals ...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>2.5 %</th><th scope=col>97.5 %</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>.sig01</th><td> 0.3074827 </td><td> 0.62121739</td></tr>\n",
       "\t<tr><th scope=row>.sigma</th><td> 0.5777147 </td><td> 0.74206725</td></tr>\n",
       "\t<tr><th scope=row>(Intercept)</th><td> 6.2682066 </td><td> 6.66745826</td></tr>\n",
       "\t<tr><th scope=row>Condition</th><td>-0.4840935 </td><td>-0.08220934</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & 2.5 \\% & 97.5 \\%\\\\\n",
       "\\hline\n",
       "\t.sig01 &  0.3074827  &  0.62121739\\\\\n",
       "\t.sigma &  0.5777147  &  0.74206725\\\\\n",
       "\t(Intercept) &  6.2682066  &  6.66745826\\\\\n",
       "\tCondition & -0.4840935  & -0.08220934\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 2.5 % | 97.5 % | \n",
       "|---|---|---|---|\n",
       "| .sig01 |  0.3074827  |  0.62121739 | \n",
       "| .sigma |  0.5777147  |  0.74206725 | \n",
       "| (Intercept) |  6.2682066  |  6.66745826 | \n",
       "| Condition | -0.4840935  | -0.08220934 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "            2.5 %      97.5 %     \n",
       ".sig01       0.3074827  0.62121739\n",
       ".sigma       0.5777147  0.74206725\n",
       "(Intercept)  6.2682066  6.66745826\n",
       "Condition   -0.4840935 -0.08220934"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# establishing the confidence intervalls:\n",
    "confint(ml.p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confidence intervals are -0.48 to -0.08 -> exp(-0.48)=62% to exp(-0.08)=92% )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.755783741455725"
      ],
      "text/latex": [
       "0.755783741455725"
      ],
      "text/markdown": [
       "0.755783741455725"
      ],
      "text/plain": [
       "[1] 0.7557837"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# main speed up = 100 - 0.75 = 25%\n",
    "exp(-.28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary model ($m_p$) took the log transformed time and the condition <b>semi-automated</b> or <b>manual</b>) as fixed effects and the individuals as random effect into account. Participants performing bias assessments in the <b>semi-automated</b> condition were on average 25% quicker than the participants in the <b>manual</b> condition (95% CI 62% to 92%).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.2 Exploratory Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now continuing with Exploratory Analisys (Likelihood Ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, model $m_1$ took also the Order as fixed effect and the document as random effect into account. whereas $m_2$ did \n",
    "only account for the document as random effect to examine the importance of the Order in the model. A close examination for $m_1$ \n",
    "showed that there was a random intercept for every PDF. The random effect variance of the documents is 0.02 (about 1/10th) as \n",
    "high as from the individuals (0.23). Hence, there is more variance across participants than there is across documents. \n",
    "The subsequent likelihood-ratio test showed that the Order was highly significant $\\chi^2$(1) = 42.26, $p<$ 0.001. \n",
    "Therefore the Order was kept in the following models.\\newline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# What is the effect of the PDF, Order, Condition and Person?\n",
    "ml.1 = lmer(logT ~ Order + Condition + (1|ID) + (1|PDF), data=data, REML=FALSE)\n",
    "ml.2 = lmer(logT ~ Condition + (1|ID) + (1|PDF), data=data, REML=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: logT ~ Order + Condition + (1 | ID) + (1 | PDF)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       "   338.2    356.8   -163.1    326.2      158 \n",
       "\n",
       "Scaled residuals: \n",
       "     Min       1Q   Median       3Q      Max \n",
       "-2.05956 -0.44213 -0.01847  0.60865  2.15485 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev.\n",
       " PDF      (Intercept) 0.02466  0.1571  \n",
       " ID       (Intercept) 0.23839  0.4883  \n",
       " Residual             0.27691  0.5262  \n",
       "Number of obs: 164, groups:  PDF, 46; ID, 41\n",
       "\n",
       "Fixed effects:\n",
       "            Estimate Std. Error t value\n",
       "(Intercept)  5.78065    0.13809   41.86\n",
       "Order        0.27151    0.03781    7.18\n",
       "Condition   -0.27389    0.08498   -3.22\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "          (Intr) Order \n",
       "Order     -0.691       \n",
       "Condition -0.319  0.012"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(ml.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: logT ~ Condition + (1 | ID) + (1 | PDF)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       "   378.5    394.0   -184.2    368.5      159 \n",
       "\n",
       "Scaled residuals: \n",
       "     Min       1Q   Median       3Q      Max \n",
       "-2.29806 -0.63552  0.02841  0.55830  2.49665 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance  Std.Dev. \n",
       " PDF      (Intercept) 2.306e-15 4.802e-08\n",
       " ID       (Intercept) 2.016e-01 4.490e-01\n",
       " Residual             4.243e-01 6.514e-01\n",
       "Number of obs: 164, groups:  PDF, 46; ID, 41\n",
       "\n",
       "Fixed effects:\n",
       "            Estimate Std. Error t value\n",
       "(Intercept)   6.4678     0.1005   64.38\n",
       "Condition    -0.2832     0.1017   -2.78\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "          (Intr)\n",
       "Condition -0.506"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(ml.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Df</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>logLik</th><th scope=col>deviance</th><th scope=col>Chisq</th><th scope=col>Chi Df</th><th scope=col>Pr(&gt;Chisq)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ml.2</th><td>5           </td><td>378.4641    </td><td>393.9634    </td><td>-184.2321   </td><td>368.4641    </td><td>      NA    </td><td>NA          </td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>ml.1</th><td>6           </td><td>338.1962    </td><td>356.7954    </td><td>-163.0981   </td><td>326.1962    </td><td>42.26793    </td><td> 1          </td><td>7.958708e-11</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & Df & AIC & BIC & logLik & deviance & Chisq & Chi Df & Pr(>Chisq)\\\\\n",
       "\\hline\n",
       "\tml.2 & 5            & 378.4641     & 393.9634     & -184.2321    & 368.4641     &       NA     & NA           &           NA\\\\\n",
       "\tml.1 & 6            & 338.1962     & 356.7954     & -163.0981    & 326.1962     & 42.26793     &  1           & 7.958708e-11\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Df | AIC | BIC | logLik | deviance | Chisq | Chi Df | Pr(>Chisq) | \n",
       "|---|---|\n",
       "| ml.2 | 5            | 378.4641     | 393.9634     | -184.2321    | 368.4641     |       NA     | NA           |           NA | \n",
       "| ml.1 | 6            | 338.1962     | 356.7954     | -163.0981    | 326.1962     | 42.26793     |  1           | 7.958708e-11 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     Df AIC      BIC      logLik    deviance Chisq    Chi Df Pr(>Chisq)  \n",
       "ml.2 5  378.4641 393.9634 -184.2321 368.4641       NA NA               NA\n",
       "ml.1 6  338.1962 356.7954 -163.0981 326.1962 42.26793  1     7.958708e-11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Likelihood-Ratio analysis (ANOVA) to see if the Order is important\n",
    "anova(ml.1, ml.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us (and confirms previous overall results) to keep the order: <br>\n",
    "The Likelihood-Ratio analysis (Anova) between $m_1$ and $m_2$ tells us that the order is highly significant ($p<0.001$).<br>\n",
    "That is an indication to keep the order in the model.<br>\n",
    "Let's again take a look at model $m_1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: logT ~ Order + Condition + (1 | ID) + (1 | PDF)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       "   338.2    356.8   -163.1    326.2      158 \n",
       "\n",
       "Scaled residuals: \n",
       "     Min       1Q   Median       3Q      Max \n",
       "-2.05956 -0.44213 -0.01847  0.60865  2.15485 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev.\n",
       " PDF      (Intercept) 0.02466  0.1571  \n",
       " ID       (Intercept) 0.23839  0.4883  \n",
       " Residual             0.27691  0.5262  \n",
       "Number of obs: 164, groups:  PDF, 46; ID, 41\n",
       "\n",
       "Fixed effects:\n",
       "            Estimate Std. Error t value\n",
       "(Intercept)  5.78065    0.13809   41.86\n",
       "Order        0.27151    0.03781    7.18\n",
       "Condition   -0.27389    0.08498   -3.22\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "          (Intr) Order \n",
       "Order     -0.691       \n",
       "Condition -0.319  0.012"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(ml.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a random intercept for every PDF. We can see that the random effect variance of PDF is about 1/10th as high as the variance from the ID. So there is more variance across people than there is across PDF (document) related.<br>\n",
    "Let's examine a model $m_3$ (removed the PDF random effect):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ml.3 = lmer(logT ~ Order + Condition + (1|ID), data=data, REML=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Df</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>logLik</th><th scope=col>deviance</th><th scope=col>Chisq</th><th scope=col>Chi Df</th><th scope=col>Pr(&gt;Chisq)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ml.3</th><td>5        </td><td>336.9199 </td><td>352.4193 </td><td>-163.4600</td><td>326.9199 </td><td>      NA </td><td>NA       </td><td>       NA</td></tr>\n",
       "\t<tr><th scope=row>ml.1</th><td>6        </td><td>338.1962 </td><td>356.7954 </td><td>-163.0981</td><td>326.1962 </td><td>0.723746 </td><td> 1       </td><td>0.3949179</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & Df & AIC & BIC & logLik & deviance & Chisq & Chi Df & Pr(>Chisq)\\\\\n",
       "\\hline\n",
       "\tml.3 & 5         & 336.9199  & 352.4193  & -163.4600 & 326.9199  &       NA  & NA        &        NA\\\\\n",
       "\tml.1 & 6         & 338.1962  & 356.7954  & -163.0981 & 326.1962  & 0.723746  &  1        & 0.3949179\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Df | AIC | BIC | logLik | deviance | Chisq | Chi Df | Pr(>Chisq) | \n",
       "|---|---|\n",
       "| ml.3 | 5         | 336.9199  | 352.4193  | -163.4600 | 326.9199  |       NA  | NA        |        NA | \n",
       "| ml.1 | 6         | 338.1962  | 356.7954  | -163.0981 | 326.1962  | 0.723746  |  1        | 0.3949179 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     Df AIC      BIC      logLik    deviance Chisq    Chi Df Pr(>Chisq)\n",
       "ml.3 5  336.9199 352.4193 -163.4600 326.9199       NA NA            NA \n",
       "ml.1 6  338.1962 356.7954 -163.0981 326.1962 0.723746  1     0.3949179 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# again likelihood-ratio analysis (ANOVA) now looking at the difference between m_1 and m_3 :\n",
    "anova(ml.1, ml.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model $m_3$ did not have the document as random effect in order to see if it had an influence. The following likelihood-ratio comparisons with $m_1$ showed that there was no significance which suggests that there is little variability from documents and by ignoring this does not detract from the explanatory potential of the model $\\chi^2$(1) = 0.72, $p=$ 0.39."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is <b>no significance!</b> This means we do not care so much for the PDF random effect.<br> \n",
    "In fact we could loose the PDF random effect without destroying the known universe.<br>\n",
    "So let's see the random effects coefficients:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$ID\n",
       "                      (Intercept)     Order  Condition\n",
       "-JjAzwjQakZk-3kIbeMfw    5.646676 0.2701334 -0.2765628\n",
       "-O8MP5AR-esmcnFcENyoF    5.715102 0.2701334 -0.2765628\n",
       "-YoLEIFk1XZUJo3BgVmlp    6.081139 0.2701334 -0.2765628\n",
       "2K-K26QZH9xPJH9RVMltM    5.734187 0.2701334 -0.2765628\n",
       "2snvok14kLm_dRIdL67PF    5.510783 0.2701334 -0.2765628\n",
       "3CDOco99k3UGgia7A9LQo    6.710410 0.2701334 -0.2765628\n",
       "4dkYUV8wlkm_Q5miw0qCJ    6.243188 0.2701334 -0.2765628\n",
       "5h9rXuHepqDSmetOKXM0J    5.891452 0.2701334 -0.2765628\n",
       "5SAqbCbqZP_q7hVWTEJdL    4.816425 0.2701334 -0.2765628\n",
       "8arsi19w5sMwMmpkmM0sX    5.729063 0.2701334 -0.2765628\n",
       "C07qGfUuD1L-1IoLjtOXY    5.908558 0.2701334 -0.2765628\n",
       "C4xWyqDJrsHcIoHU5GtUG    6.429523 0.2701334 -0.2765628\n",
       "Ci3GWDLAOc5ANxkPpNOBc    5.722301 0.2701334 -0.2765628\n",
       "cs1qlmKFvdkFjUcXEkJgT    5.747713 0.2701334 -0.2765628\n",
       "FfUZhXrHdKSHZ3ON2_Dty    6.016796 0.2701334 -0.2765628\n",
       "fQoBVJ60uF5ehuYNAg4v2    5.732324 0.2701334 -0.2765628\n",
       "FXvg7iTf7P1bl-Z9dHKPO    5.747617 0.2701334 -0.2765628\n",
       "GR96RbbiPLL4gS3-ilxky    6.692276 0.2701334 -0.2765628\n",
       "hNQKTiKtHSY_BmxLyZb9Q    5.515131 0.2701334 -0.2765628\n",
       "j-6IWj13fqVcUXKjFCo7y    5.486455 0.2701334 -0.2765628\n",
       "jBFO8AR45EErIQ5grz5n-    5.625375 0.2701334 -0.2765628\n",
       "jYha2hNra2RfUfgXnVaj1    5.753855 0.2701334 -0.2765628\n",
       "KlKCr9O8A-8WK6gr2vK9k    5.775059 0.2701334 -0.2765628\n",
       "KtfB3jYg9jsg0osJkMWVG    5.164302 0.2701334 -0.2765628\n",
       "L-KBZ1ZC0lK2hQlDSUCgH    5.276278 0.2701334 -0.2765628\n",
       "lllK6Q_LLhHbw7bTJ1v51    5.410262 0.2701334 -0.2765628\n",
       "pyTvUi2W85Nb9handPSvZ    5.474099 0.2701334 -0.2765628\n",
       "Rh7QrcKwEzMEORahevriS    6.221480 0.2701334 -0.2765628\n",
       "S2oI3sQr3HnSJ7rgbaMJR    6.045844 0.2701334 -0.2765628\n",
       "sLUZSWii-lFp5oEFHCd7f    5.610252 0.2701334 -0.2765628\n",
       "to4PqjXHBIG3bKQhv9cHr    5.453520 0.2701334 -0.2765628\n",
       "tRdDoOfbjIQw3tUkGktky    6.095768 0.2701334 -0.2765628\n",
       "TTtTg_voG4fV-uDraAWC3    5.600304 0.2701334 -0.2765628\n",
       "UbIO7NW7RAUjVfe3_VJ48    5.520653 0.2701334 -0.2765628\n",
       "uq4HMVa0zTYYknq8_4SOc    6.926525 0.2701334 -0.2765628\n",
       "UyqDH9vL0gK918t2bEj0z    5.559183 0.2701334 -0.2765628\n",
       "V9_3tiOUC-jab7xnPK1HK    5.905622 0.2701334 -0.2765628\n",
       "WpFHCjeR-3YdyEJJmCT2Y    5.492995 0.2701334 -0.2765628\n",
       "ynh1N1_BZO8W4d0_gaMDy    5.649705 0.2701334 -0.2765628\n",
       "ZAj9Y0K4px17TAcPf3uYG    5.361130 0.2701334 -0.2765628\n",
       "ZXpghXhauDD3Vpv76PGoj    6.358060 0.2701334 -0.2765628\n",
       "\n",
       "attr(,\"class\")\n",
       "[1] \"coef.mer\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef(ml.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: logT ~ Order + Condition + (1 | ID)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       "   336.9    352.4   -163.5    326.9      159 \n",
       "\n",
       "Scaled residuals: \n",
       "     Min       1Q   Median       3Q      Max \n",
       "-2.19339 -0.52439 -0.01335  0.53696  2.27474 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev.\n",
       " ID       (Intercept) 0.2320   0.4817  \n",
       " Residual             0.3027   0.5501  \n",
       "Number of obs: 164, groups:  ID, 41\n",
       "\n",
       "Fixed effects:\n",
       "            Estimate Std. Error t value\n",
       "(Intercept)  5.78920    0.13663   42.37\n",
       "Order        0.27013    0.03843    7.03\n",
       "Condition   -0.27656    0.08592   -3.22\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "          (Intr) Order \n",
       "Order     -0.707       \n",
       "Condition -0.322  0.011"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(ml.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variance of coefficients 0.23<br>\n",
    "Do different people have a different coefficient when it comes to the Machine learning (random slope)?<br>\n",
    "One option to examine this is to include the condition also as random effect. So there is an overall mean for the machine learning, but how variable does it seem to be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# including the condition also as random effect in model m_3\n",
    "ml.3 = lmer(logT ~ Order + Condition + (Condition | ID), data=data, REML=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: logT ~ Order + Condition + (Condition | ID)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       "   328.0    349.7   -157.0    314.0      157 \n",
       "\n",
       "Scaled residuals: \n",
       "    Min      1Q  Median      3Q     Max \n",
       "-2.3985 -0.4850  0.0052  0.5775  2.2962 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev. Corr\n",
       " ID       (Intercept) 0.1487   0.3856       \n",
       "          Condition   0.2000   0.4473   0.29\n",
       " Residual             0.2361   0.4859       \n",
       "Number of obs: 164, groups:  ID, 41\n",
       "\n",
       "Fixed effects:\n",
       "            Estimate Std. Error t value\n",
       "(Intercept)  5.76592    0.12264   47.01\n",
       "Order        0.27940    0.03678    7.60\n",
       "Condition   -0.27634    0.10314   -2.68\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "          (Intr) Order \n",
       "Order     -0.753       \n",
       "Condition -0.138  0.009"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(ml.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be variability in the slope. So different people seem to react differently to the machine learning condition. It seems that the residual variance has dropped between the first $m_3$ and updated $m_3$ models: It was .40 in the first $m_3$ now in the updated $m_3$ it is .24. Perhaps this model explains a bit more. Let's take a look at the coefficients:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>(Intercept)</th><th scope=col>Order</th><th scope=col>Condition</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>-JjAzwjQakZk-3kIbeMfw</th><td>5.645644   </td><td>0.2794002  </td><td>-0.31868291</td></tr>\n",
       "\t<tr><th scope=row>-O8MP5AR-esmcnFcENyoF</th><td>5.689063   </td><td>0.2794002  </td><td>-0.24591965</td></tr>\n",
       "\t<tr><th scope=row>-YoLEIFk1XZUJo3BgVmlp</th><td>6.018559   </td><td>0.2794002  </td><td>-0.21255577</td></tr>\n",
       "\t<tr><th scope=row>2K-K26QZH9xPJH9RVMltM</th><td>5.758266   </td><td>0.2794002  </td><td>-0.43458312</td></tr>\n",
       "\t<tr><th scope=row>2snvok14kLm_dRIdL67PF</th><td>5.533983   </td><td>0.2794002  </td><td>-0.37010571</td></tr>\n",
       "\t<tr><th scope=row>3CDOco99k3UGgia7A9LQo</th><td>6.471687   </td><td>0.2794002  </td><td> 0.25956400</td></tr>\n",
       "\t<tr><th scope=row>4dkYUV8wlkm_Q5miw0qCJ</th><td>6.085542   </td><td>0.2794002  </td><td> 0.09094654</td></tr>\n",
       "\t<tr><th scope=row>5h9rXuHepqDSmetOKXM0J</th><td>5.825058   </td><td>0.2794002  </td><td>-0.14658113</td></tr>\n",
       "\t<tr><th scope=row>5SAqbCbqZP_q7hVWTEJdL</th><td>5.065488   </td><td>0.2794002  </td><td>-1.00634718</td></tr>\n",
       "\t<tr><th scope=row>8arsi19w5sMwMmpkmM0sX</th><td>5.665417   </td><td>0.2794002  </td><td>-0.11211088</td></tr>\n",
       "\t<tr><th scope=row>C07qGfUuD1L-1IoLjtOXY</th><td>5.907933   </td><td>0.2794002  </td><td>-0.39198176</td></tr>\n",
       "\t<tr><th scope=row>C4xWyqDJrsHcIoHU5GtUG</th><td>6.114986   </td><td>0.2794002  </td><td> 0.61406152</td></tr>\n",
       "\t<tr><th scope=row>Ci3GWDLAOc5ANxkPpNOBc</th><td>5.721248   </td><td>0.2794002  </td><td>-0.33934149</td></tr>\n",
       "\t<tr><th scope=row>cs1qlmKFvdkFjUcXEkJgT</th><td>5.696203   </td><td>0.2794002  </td><td>-0.16163754</td></tr>\n",
       "\t<tr><th scope=row>FfUZhXrHdKSHZ3ON2_Dty</th><td>5.907440   </td><td>0.2794002  </td><td>-0.02371297</td></tr>\n",
       "\t<tr><th scope=row>fQoBVJ60uF5ehuYNAg4v2</th><td>5.799538   </td><td>0.2794002  </td><td>-0.59194148</td></tr>\n",
       "\t<tr><th scope=row>FXvg7iTf7P1bl-Z9dHKPO</th><td>5.716290   </td><td>0.2794002  </td><td>-0.23548329</td></tr>\n",
       "\t<tr><th scope=row>GR96RbbiPLL4gS3-ilxky</th><td>6.409160   </td><td>0.2794002  </td><td> 0.42701709</td></tr>\n",
       "\t<tr><th scope=row>hNQKTiKtHSY_BmxLyZb9Q</th><td>5.593803   </td><td>0.2794002  </td><td>-0.57432025</td></tr>\n",
       "\t<tr><th scope=row>j-6IWj13fqVcUXKjFCo7y</th><td>5.540374   </td><td>0.2794002  </td><td>-0.47586129</td></tr>\n",
       "\t<tr><th scope=row>jBFO8AR45EErIQ5grz5n-</th><td>5.589154   </td><td>0.2794002  </td><td>-0.18404910</td></tr>\n",
       "\t<tr><th scope=row>jYha2hNra2RfUfgXnVaj1</th><td>5.650462   </td><td>0.2794002  </td><td> 0.02656450</td></tr>\n",
       "\t<tr><th scope=row>KlKCr9O8A-8WK6gr2vK9k</th><td>5.710093   </td><td>0.2794002  </td><td>-0.11988919</td></tr>\n",
       "\t<tr><th scope=row>KtfB3jYg9jsg0osJkMWVG</th><td>5.223300   </td><td>0.2794002  </td><td>-0.40611143</td></tr>\n",
       "\t<tr><th scope=row>L-KBZ1ZC0lK2hQlDSUCgH</th><td>5.348445   </td><td>0.2794002  </td><td>-0.48501901</td></tr>\n",
       "\t<tr><th scope=row>lllK6Q_LLhHbw7bTJ1v51</th><td>5.418836   </td><td>0.2794002  </td><td>-0.28901104</td></tr>\n",
       "\t<tr><th scope=row>pyTvUi2W85Nb9handPSvZ</th><td>5.496255   </td><td>0.2794002  </td><td>-0.35622486</td></tr>\n",
       "\t<tr><th scope=row>Rh7QrcKwEzMEORahevriS</th><td>6.114006   </td><td>0.2794002  </td><td>-0.08672533</td></tr>\n",
       "\t<tr><th scope=row>S2oI3sQr3HnSJ7rgbaMJR</th><td>5.908495   </td><td>0.2794002  </td><td> 0.07077306</td></tr>\n",
       "\t<tr><th scope=row>sLUZSWii-lFp5oEFHCd7f</th><td>5.602679   </td><td>0.2794002  </td><td>-0.28475114</td></tr>\n",
       "\t<tr><th scope=row>to4PqjXHBIG3bKQhv9cHr</th><td>5.475240   </td><td>0.2794002  </td><td>-0.34898562</td></tr>\n",
       "\t<tr><th scope=row>tRdDoOfbjIQw3tUkGktky</th><td>6.095288   </td><td>0.2794002  </td><td>-0.44385065</td></tr>\n",
       "\t<tr><th scope=row>TTtTg_voG4fV-uDraAWC3</th><td>5.697105   </td><td>0.2794002  </td><td>-0.66402711</td></tr>\n",
       "\t<tr><th scope=row>UbIO7NW7RAUjVfe3_VJ48</th><td>5.689982   </td><td>0.2794002  </td><td>-0.90763414</td></tr>\n",
       "\t<tr><th scope=row>uq4HMVa0zTYYknq8_4SOc</th><td>6.665880   </td><td>0.2794002  </td><td> 0.28053689</td></tr>\n",
       "\t<tr><th scope=row>UyqDH9vL0gK918t2bEj0z</th><td>5.595343   </td><td>0.2794002  </td><td>-0.43080896</td></tr>\n",
       "\t<tr><th scope=row>V9_3tiOUC-jab7xnPK1HK</th><td>5.849366   </td><td>0.2794002  </td><td>-0.18756661</td></tr>\n",
       "\t<tr><th scope=row>WpFHCjeR-3YdyEJJmCT2Y</th><td>5.609388   </td><td>0.2794002  </td><td>-0.70630849</td></tr>\n",
       "\t<tr><th scope=row>ynh1N1_BZO8W4d0_gaMDy</th><td>5.831413   </td><td>0.2794002  </td><td>-0.98833027</td></tr>\n",
       "\t<tr><th scope=row>ZAj9Y0K4px17TAcPf3uYG</th><td>5.459037   </td><td>0.2794002  </td><td>-0.60249247</td></tr>\n",
       "\t<tr><th scope=row>ZXpghXhauDD3Vpv76PGoj</th><td>6.207454   </td><td>0.2794002  </td><td> 0.03368090</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       "  & (Intercept) & Order & Condition\\\\\n",
       "\\hline\n",
       "\t-JjAzwjQakZk-3kIbeMfw & 5.645644    & 0.2794002   & -0.31868291\\\\\n",
       "\t-O8MP5AR-esmcnFcENyoF & 5.689063    & 0.2794002   & -0.24591965\\\\\n",
       "\t-YoLEIFk1XZUJo3BgVmlp & 6.018559    & 0.2794002   & -0.21255577\\\\\n",
       "\t2K-K26QZH9xPJH9RVMltM & 5.758266    & 0.2794002   & -0.43458312\\\\\n",
       "\t2snvok14kLm\\_dRIdL67PF & 5.533983    & 0.2794002   & -0.37010571\\\\\n",
       "\t3CDOco99k3UGgia7A9LQo & 6.471687    & 0.2794002   &  0.25956400\\\\\n",
       "\t4dkYUV8wlkm\\_Q5miw0qCJ & 6.085542    & 0.2794002   &  0.09094654\\\\\n",
       "\t5h9rXuHepqDSmetOKXM0J & 5.825058    & 0.2794002   & -0.14658113\\\\\n",
       "\t5SAqbCbqZP\\_q7hVWTEJdL & 5.065488    & 0.2794002   & -1.00634718\\\\\n",
       "\t8arsi19w5sMwMmpkmM0sX & 5.665417    & 0.2794002   & -0.11211088\\\\\n",
       "\tC07qGfUuD1L-1IoLjtOXY & 5.907933    & 0.2794002   & -0.39198176\\\\\n",
       "\tC4xWyqDJrsHcIoHU5GtUG & 6.114986    & 0.2794002   &  0.61406152\\\\\n",
       "\tCi3GWDLAOc5ANxkPpNOBc & 5.721248    & 0.2794002   & -0.33934149\\\\\n",
       "\tcs1qlmKFvdkFjUcXEkJgT & 5.696203    & 0.2794002   & -0.16163754\\\\\n",
       "\tFfUZhXrHdKSHZ3ON2\\_Dty & 5.907440    & 0.2794002   & -0.02371297\\\\\n",
       "\tfQoBVJ60uF5ehuYNAg4v2 & 5.799538    & 0.2794002   & -0.59194148\\\\\n",
       "\tFXvg7iTf7P1bl-Z9dHKPO & 5.716290    & 0.2794002   & -0.23548329\\\\\n",
       "\tGR96RbbiPLL4gS3-ilxky & 6.409160    & 0.2794002   &  0.42701709\\\\\n",
       "\thNQKTiKtHSY\\_BmxLyZb9Q & 5.593803    & 0.2794002   & -0.57432025\\\\\n",
       "\tj-6IWj13fqVcUXKjFCo7y & 5.540374    & 0.2794002   & -0.47586129\\\\\n",
       "\tjBFO8AR45EErIQ5grz5n- & 5.589154    & 0.2794002   & -0.18404910\\\\\n",
       "\tjYha2hNra2RfUfgXnVaj1 & 5.650462    & 0.2794002   &  0.02656450\\\\\n",
       "\tKlKCr9O8A-8WK6gr2vK9k & 5.710093    & 0.2794002   & -0.11988919\\\\\n",
       "\tKtfB3jYg9jsg0osJkMWVG & 5.223300    & 0.2794002   & -0.40611143\\\\\n",
       "\tL-KBZ1ZC0lK2hQlDSUCgH & 5.348445    & 0.2794002   & -0.48501901\\\\\n",
       "\tlllK6Q\\_LLhHbw7bTJ1v51 & 5.418836    & 0.2794002   & -0.28901104\\\\\n",
       "\tpyTvUi2W85Nb9handPSvZ & 5.496255    & 0.2794002   & -0.35622486\\\\\n",
       "\tRh7QrcKwEzMEORahevriS & 6.114006    & 0.2794002   & -0.08672533\\\\\n",
       "\tS2oI3sQr3HnSJ7rgbaMJR & 5.908495    & 0.2794002   &  0.07077306\\\\\n",
       "\tsLUZSWii-lFp5oEFHCd7f & 5.602679    & 0.2794002   & -0.28475114\\\\\n",
       "\tto4PqjXHBIG3bKQhv9cHr & 5.475240    & 0.2794002   & -0.34898562\\\\\n",
       "\ttRdDoOfbjIQw3tUkGktky & 6.095288    & 0.2794002   & -0.44385065\\\\\n",
       "\tTTtTg\\_voG4fV-uDraAWC3 & 5.697105    & 0.2794002   & -0.66402711\\\\\n",
       "\tUbIO7NW7RAUjVfe3\\_VJ48 & 5.689982    & 0.2794002   & -0.90763414\\\\\n",
       "\tuq4HMVa0zTYYknq8\\_4SOc & 6.665880    & 0.2794002   &  0.28053689\\\\\n",
       "\tUyqDH9vL0gK918t2bEj0z & 5.595343    & 0.2794002   & -0.43080896\\\\\n",
       "\tV9\\_3tiOUC-jab7xnPK1HK & 5.849366    & 0.2794002   & -0.18756661\\\\\n",
       "\tWpFHCjeR-3YdyEJJmCT2Y & 5.609388    & 0.2794002   & -0.70630849\\\\\n",
       "\tynh1N1\\_BZO8W4d0\\_gaMDy & 5.831413    & 0.2794002   & -0.98833027\\\\\n",
       "\tZAj9Y0K4px17TAcPf3uYG & 5.459037    & 0.2794002   & -0.60249247\\\\\n",
       "\tZXpghXhauDD3Vpv76PGoj & 6.207454    & 0.2794002   &  0.03368090\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | (Intercept) | Order | Condition | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| -JjAzwjQakZk-3kIbeMfw | 5.645644    | 0.2794002   | -0.31868291 | \n",
       "| -O8MP5AR-esmcnFcENyoF | 5.689063    | 0.2794002   | -0.24591965 | \n",
       "| -YoLEIFk1XZUJo3BgVmlp | 6.018559    | 0.2794002   | -0.21255577 | \n",
       "| 2K-K26QZH9xPJH9RVMltM | 5.758266    | 0.2794002   | -0.43458312 | \n",
       "| 2snvok14kLm_dRIdL67PF | 5.533983    | 0.2794002   | -0.37010571 | \n",
       "| 3CDOco99k3UGgia7A9LQo | 6.471687    | 0.2794002   |  0.25956400 | \n",
       "| 4dkYUV8wlkm_Q5miw0qCJ | 6.085542    | 0.2794002   |  0.09094654 | \n",
       "| 5h9rXuHepqDSmetOKXM0J | 5.825058    | 0.2794002   | -0.14658113 | \n",
       "| 5SAqbCbqZP_q7hVWTEJdL | 5.065488    | 0.2794002   | -1.00634718 | \n",
       "| 8arsi19w5sMwMmpkmM0sX | 5.665417    | 0.2794002   | -0.11211088 | \n",
       "| C07qGfUuD1L-1IoLjtOXY | 5.907933    | 0.2794002   | -0.39198176 | \n",
       "| C4xWyqDJrsHcIoHU5GtUG | 6.114986    | 0.2794002   |  0.61406152 | \n",
       "| Ci3GWDLAOc5ANxkPpNOBc | 5.721248    | 0.2794002   | -0.33934149 | \n",
       "| cs1qlmKFvdkFjUcXEkJgT | 5.696203    | 0.2794002   | -0.16163754 | \n",
       "| FfUZhXrHdKSHZ3ON2_Dty | 5.907440    | 0.2794002   | -0.02371297 | \n",
       "| fQoBVJ60uF5ehuYNAg4v2 | 5.799538    | 0.2794002   | -0.59194148 | \n",
       "| FXvg7iTf7P1bl-Z9dHKPO | 5.716290    | 0.2794002   | -0.23548329 | \n",
       "| GR96RbbiPLL4gS3-ilxky | 6.409160    | 0.2794002   |  0.42701709 | \n",
       "| hNQKTiKtHSY_BmxLyZb9Q | 5.593803    | 0.2794002   | -0.57432025 | \n",
       "| j-6IWj13fqVcUXKjFCo7y | 5.540374    | 0.2794002   | -0.47586129 | \n",
       "| jBFO8AR45EErIQ5grz5n- | 5.589154    | 0.2794002   | -0.18404910 | \n",
       "| jYha2hNra2RfUfgXnVaj1 | 5.650462    | 0.2794002   |  0.02656450 | \n",
       "| KlKCr9O8A-8WK6gr2vK9k | 5.710093    | 0.2794002   | -0.11988919 | \n",
       "| KtfB3jYg9jsg0osJkMWVG | 5.223300    | 0.2794002   | -0.40611143 | \n",
       "| L-KBZ1ZC0lK2hQlDSUCgH | 5.348445    | 0.2794002   | -0.48501901 | \n",
       "| lllK6Q_LLhHbw7bTJ1v51 | 5.418836    | 0.2794002   | -0.28901104 | \n",
       "| pyTvUi2W85Nb9handPSvZ | 5.496255    | 0.2794002   | -0.35622486 | \n",
       "| Rh7QrcKwEzMEORahevriS | 6.114006    | 0.2794002   | -0.08672533 | \n",
       "| S2oI3sQr3HnSJ7rgbaMJR | 5.908495    | 0.2794002   |  0.07077306 | \n",
       "| sLUZSWii-lFp5oEFHCd7f | 5.602679    | 0.2794002   | -0.28475114 | \n",
       "| to4PqjXHBIG3bKQhv9cHr | 5.475240    | 0.2794002   | -0.34898562 | \n",
       "| tRdDoOfbjIQw3tUkGktky | 6.095288    | 0.2794002   | -0.44385065 | \n",
       "| TTtTg_voG4fV-uDraAWC3 | 5.697105    | 0.2794002   | -0.66402711 | \n",
       "| UbIO7NW7RAUjVfe3_VJ48 | 5.689982    | 0.2794002   | -0.90763414 | \n",
       "| uq4HMVa0zTYYknq8_4SOc | 6.665880    | 0.2794002   |  0.28053689 | \n",
       "| UyqDH9vL0gK918t2bEj0z | 5.595343    | 0.2794002   | -0.43080896 | \n",
       "| V9_3tiOUC-jab7xnPK1HK | 5.849366    | 0.2794002   | -0.18756661 | \n",
       "| WpFHCjeR-3YdyEJJmCT2Y | 5.609388    | 0.2794002   | -0.70630849 | \n",
       "| ynh1N1_BZO8W4d0_gaMDy | 5.831413    | 0.2794002   | -0.98833027 | \n",
       "| ZAj9Y0K4px17TAcPf3uYG | 5.459037    | 0.2794002   | -0.60249247 | \n",
       "| ZXpghXhauDD3Vpv76PGoj | 6.207454    | 0.2794002   |  0.03368090 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                      (Intercept) Order     Condition  \n",
       "-JjAzwjQakZk-3kIbeMfw 5.645644    0.2794002 -0.31868291\n",
       "-O8MP5AR-esmcnFcENyoF 5.689063    0.2794002 -0.24591965\n",
       "-YoLEIFk1XZUJo3BgVmlp 6.018559    0.2794002 -0.21255577\n",
       "2K-K26QZH9xPJH9RVMltM 5.758266    0.2794002 -0.43458312\n",
       "2snvok14kLm_dRIdL67PF 5.533983    0.2794002 -0.37010571\n",
       "3CDOco99k3UGgia7A9LQo 6.471687    0.2794002  0.25956400\n",
       "4dkYUV8wlkm_Q5miw0qCJ 6.085542    0.2794002  0.09094654\n",
       "5h9rXuHepqDSmetOKXM0J 5.825058    0.2794002 -0.14658113\n",
       "5SAqbCbqZP_q7hVWTEJdL 5.065488    0.2794002 -1.00634718\n",
       "8arsi19w5sMwMmpkmM0sX 5.665417    0.2794002 -0.11211088\n",
       "C07qGfUuD1L-1IoLjtOXY 5.907933    0.2794002 -0.39198176\n",
       "C4xWyqDJrsHcIoHU5GtUG 6.114986    0.2794002  0.61406152\n",
       "Ci3GWDLAOc5ANxkPpNOBc 5.721248    0.2794002 -0.33934149\n",
       "cs1qlmKFvdkFjUcXEkJgT 5.696203    0.2794002 -0.16163754\n",
       "FfUZhXrHdKSHZ3ON2_Dty 5.907440    0.2794002 -0.02371297\n",
       "fQoBVJ60uF5ehuYNAg4v2 5.799538    0.2794002 -0.59194148\n",
       "FXvg7iTf7P1bl-Z9dHKPO 5.716290    0.2794002 -0.23548329\n",
       "GR96RbbiPLL4gS3-ilxky 6.409160    0.2794002  0.42701709\n",
       "hNQKTiKtHSY_BmxLyZb9Q 5.593803    0.2794002 -0.57432025\n",
       "j-6IWj13fqVcUXKjFCo7y 5.540374    0.2794002 -0.47586129\n",
       "jBFO8AR45EErIQ5grz5n- 5.589154    0.2794002 -0.18404910\n",
       "jYha2hNra2RfUfgXnVaj1 5.650462    0.2794002  0.02656450\n",
       "KlKCr9O8A-8WK6gr2vK9k 5.710093    0.2794002 -0.11988919\n",
       "KtfB3jYg9jsg0osJkMWVG 5.223300    0.2794002 -0.40611143\n",
       "L-KBZ1ZC0lK2hQlDSUCgH 5.348445    0.2794002 -0.48501901\n",
       "lllK6Q_LLhHbw7bTJ1v51 5.418836    0.2794002 -0.28901104\n",
       "pyTvUi2W85Nb9handPSvZ 5.496255    0.2794002 -0.35622486\n",
       "Rh7QrcKwEzMEORahevriS 6.114006    0.2794002 -0.08672533\n",
       "S2oI3sQr3HnSJ7rgbaMJR 5.908495    0.2794002  0.07077306\n",
       "sLUZSWii-lFp5oEFHCd7f 5.602679    0.2794002 -0.28475114\n",
       "to4PqjXHBIG3bKQhv9cHr 5.475240    0.2794002 -0.34898562\n",
       "tRdDoOfbjIQw3tUkGktky 6.095288    0.2794002 -0.44385065\n",
       "TTtTg_voG4fV-uDraAWC3 5.697105    0.2794002 -0.66402711\n",
       "UbIO7NW7RAUjVfe3_VJ48 5.689982    0.2794002 -0.90763414\n",
       "uq4HMVa0zTYYknq8_4SOc 6.665880    0.2794002  0.28053689\n",
       "UyqDH9vL0gK918t2bEj0z 5.595343    0.2794002 -0.43080896\n",
       "V9_3tiOUC-jab7xnPK1HK 5.849366    0.2794002 -0.18756661\n",
       "WpFHCjeR-3YdyEJJmCT2Y 5.609388    0.2794002 -0.70630849\n",
       "ynh1N1_BZO8W4d0_gaMDy 5.831413    0.2794002 -0.98833027\n",
       "ZAj9Y0K4px17TAcPf3uYG 5.459037    0.2794002 -0.60249247\n",
       "ZXpghXhauDD3Vpv76PGoj 6.207454    0.2794002  0.03368090"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coefficients by ID\n",
    "coef(ml.3)$ID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HftG5npjvn9zJ/XLH89891Nc22rG9xu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FHkPjQp1Tn6P9ZipXKTYpo/jzn694EOTy\nmOInVrZ38Y38KOVuxU/K8spdqnQfGansq7jPxFJNf4qf+QPaabHiz/wg5U+K2/Znn1fGqtKf\ng/uzz8/nni2TVDFS8efmc5mi+PPywPATij9n26bb+XOMpezzj9fyhA7wYjjIvHgwpggggAAC\nCCCAAALNEfANpG8e31b+rKwK834Skr5qNFHLC8I6b7cyzLvuYCUtvml+UHGb3s431J53m+MV\nl5MUP1lwveN5ZytlJ2Wd4sFGWryvt71E8atGnvd2nj6lDFdmhWW/NuX6VxWfeyzVPkGK22en\nbust5dbsCi37KZgHQz9L1nk7n4dfg/ONt+f9BKmodGqFt5mcbHBuqCt6ghQ39WdhD9+4u4xW\n3NZ1XuhluUj7/X3Y92RNr0za8aDU7fuz7K74M80WO/p8vX93/Sl+5tO03R/C9vGz9bV+VsmW\n76rC7Xo7D5o971cQPU2fIMXPZlfVj1Gy/TH2v3Q7bbahNOL891bLv1Xi/+5u07yPkxZfwz1p\nBfMIIIAAAggggAAC5Qlsp6ZmK/6p+McV38juoHxJ8Y3YVxQX1/lJgp/ofETpULytb5h9c/uC\n4p+8u7j+IcWDiNimZjf8Ls8yTf3KWbyBd72P/6ZnknKY5n387M1vvFn2jeyNyl8ouymdirf3\nYOgOxfv7GHFg4ScEsfRmgORr+qTiG2Zf76PKSCWv3KPKRcmKCZr3dfv8ZoXpSE2LSryWyckG\n8Tp6GiB5YObj7Bv2LWOAlJzGJrOLVOPjjd1kTfcVtfSn9DP3U81Dlf7KicobyivKACWWT2vG\n5/RfSqw/QvPxMygaIGmTDcWDtufCfJxkB0iNPP94zKKpr819jIIAAggggAACCCDQAIGj1KZv\nuL6VadsDgpnKDMVPPc5WvN3XlGw5RxVed1FY4QGUl28Py+lkWlh3ZlI5W/PZAZIHRm7jQ8l2\nno03y76B9eAulgM14+399GtwrNTU17FWmZvU9WaA5IGY24+5RPN2ySu+MfcAblCy8j2a9yDt\ndSW2MUfzH1SypVMV3mZysqLaAdIPwr5Tw76NHiD5evwUZ+twvGontfSn+JkvUuMe9KbFAyZb\neZtY3DcWKx7EpOWLWvC2ZQyQGnn+6TnnzfsaGCDlyVCHAAK9Fujo9Z7siAACCGx5AhPDJU3P\nXJpvwo5QjlZ8AzxBcflhZdLl3xvDkgcpLh6AuNyt+MY1jV+xc4nbVpY2/Tc+AfHNbl7xq1Z+\nehDLY2HGT6f8hCcWX8eflHSwEtfVMl2ujUcoBynfV/yE7feKX6nLlmdV4YHZu5MV8zR/urJX\nqLO3B002OiXUlTHZJTTiJ2nNKL7W/sqQGg9WS3+KTT+sGQ9207IoLAwOU1//nsoM5bVQFyce\noJZVGnX+ZZ0f7SCAAAI1CTBAqomLjRFAYAsXiDd6RQORePnjNOPBRt52S1Tvm1E/rXAZU5n0\n+6amczO5OayL24bFTSZxcPH8JmsqFX/M1Pt1PpcVlUmXf+O6LpU1Lvj6fO2/U/z7OH6Vzed4\njJIt8ZzjNaTrPdByOU2ZtGGuX79vh2kZk78MjSwso7Eq2vCgxSXvWitr8v+tpT/FFvxUKFv8\nBMsl/rd9v8rixv8j2LC4YbJE/6aD6nRdrfONOv9az4PtEUAAgVIE4pdoKY3RCAIIILCZC/jp\nkMuAyqTwXz+R8FOR7GtL3sGvPW2vxJvVOD1DdUcW5HzVd1fWh5U7F2yUfZJQsFnDqq8LLR+f\nc4R4znFgtre2yRsQ3q96D7iGh2hSV/FTMh/Hg4BFSjPKI+EgfhrWXfm8Vvqp30fCRrX0p9hu\n7BNxOW+6LFQOzFnp/lvWPUCjzj/ntKlCAAEEGi9Q1pdj48+UIyCAAAKNF3g6HCI+eUiP6EGM\nXwUbpSwIK/KeFLxT63zzGZ/qPBW29atuMzPxEwdvm/c0QNUby0thzm23qpynA/uJz+ScE4g3\n66tz1sVz9jV4wLJIuV7JK7GdOFDN26bauq9oQw8MOpVmDSDvDSfnvrJrmM9O+qvi04r7zrNh\nZS39KexS1eRxbeWnffEzSHfyQLXo98bS7aqZb9T5V3NstkEAAQRKF+govUUaRAABBDZfgTt0\n6n517guZS/DvdPiG+0jlBcWvlLl8WfEAJy0XhoWfhultmrpN128d6uLkas3cpRwaKwqmC0P9\nuIL1zaier4P4aVDWxsc+y/+oeACYLfHm3NfgAajb+YAySknLaC28T/HA0q9/9bbY+EzFfzjA\nT4/+VWlWeUAH+oXiwcetSnYA4sHRVcoE5T7FT81caulPlT2q+9dP7Xwc99sDMrt8MbNctLhO\nK/KeQKXbN+r802MwjwACCCCAAAIIINAigWt1XA9ofq6coPhm+yHFdelN5fRQ5xviKcqJSqz7\ngebTgZOfmHj/e5XTFW9/gxKPo9mNZbbm3ty4VJk5SBNv+71M/fhQ75vutPhG3Nv/Jq0M84s0\njQMuVx2ieNsrvBBKbHdurNDU1xMHkDM0/zHlJOVOxfvfouSVFap8Klnxd5pfr/gc3Ib3PUfx\nH4/w/FQlLZ1acP3kpPLcUPeYpr45d/w5zFGWK97+deVYJS2jteB116WVmo8GHuDUW/ZQA48q\nPo7P5cfKWcqlynzF9b723ZS0xL7TU3+Kn032M3db0xS3f7gXQtlL05cUv273OcV/aOS7yhrF\n/ewnSiwe1Hn/9OnXb0Pdf2rqz84lb7tGnX/liMX/+nzvKV7NGgQQQAABBBBAAIF6BfwE4qvK\nasU3X45vdLNPTvx0wDek6XZPa/kyJR0caXHD73qcp6kHC7FNDxJ887y7kpbZWvCNa1rc3otK\n9ga+6Ga5EQMkn89g5TuKzy9ex6uat5c9smUfVXi7f8+s8I3282FdbGeplj14zJZOVXibycmK\nc0Nd3NdTe/rJ0xzFxxulZEszBkg+5s7K5YqvKT1HD0q+qQxTsqXa/lT0mbu9aYqPlw6QXH+g\n4qd7axWvX6xMUlYpPQ2QvF0cvM7TvEveAKmR5185av6/vh4GSPk21CKAAAIIIIAAAqUKdKg1\n31CPUzzgKCoevPh3lvyT+mrKCG00QfFgo5bipyy+GfTNbqvLDjqB/ZWxSva1wfTcPBh4Tdk7\nrUzm7eBr8tTejS5FAyQf91TFN/5lFl/TcOUgZaRSzTXW2p/UbNVlJ205puqtu27oJ17bd63K\nXWrk+ecdkAFSngp1CCCAAAIIIIBAHxDwT+j9qpqfOm0OZahOcrVySTcn64Gnb3C7G2R1s3vN\nq4oGSNuqJb8+mH1KWPMB2KHpAgyQmk7OARFAAAEEEEAAgfYR+LBO5S3FT27avXxNJ+jXAnfs\n4UT9VKNZJQ6QfqcD+o9u+HegXPZUpil++kFpfwE/ffXn5zBAav/PizNEAAEEEEAAAQQaKjBD\nrV/T0CPU3/gANeHfv/lk/U2V2oJfhfRftov5Vamt01izBPw6a/wMPf1Nsw7McRBAAAEEEEAA\nAQTaT2CYTsm/s9POxU+N/Hs3PJFp50+Jc0MAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQACBLUjg/wEL2SqjBc4plAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Histogram of coef(ml.3)$ID[, \"Condition\"]”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram about those conditions:\n",
    "hist(coef(ml.3)$ID[,\"Condition\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there seems to be some people who do it faster and some slower so there seems to be some heterogeneity. Model $m_4$ adds the random effect per person:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ml.4 = lmer(logT ~ Order + Condition + (Condition|ID), data=data, REML=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Df</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>logLik</th><th scope=col>deviance</th><th scope=col>Chisq</th><th scope=col>Chi Df</th><th scope=col>Pr(&gt;Chisq)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ml.4</th><td>7        </td><td>328.0184 </td><td>349.7175 </td><td>-157.0092</td><td>314.0184 </td><td>NA       </td><td>NA       </td><td>NA       </td></tr>\n",
       "\t<tr><th scope=row>ml.3</th><td>7        </td><td>328.0184 </td><td>349.7175 </td><td>-157.0092</td><td>314.0184 </td><td> 0       </td><td> 0       </td><td> 1       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & Df & AIC & BIC & logLik & deviance & Chisq & Chi Df & Pr(>Chisq)\\\\\n",
       "\\hline\n",
       "\tml.4 & 7         & 328.0184  & 349.7175  & -157.0092 & 314.0184  & NA        & NA        & NA       \\\\\n",
       "\tml.3 & 7         & 328.0184  & 349.7175  & -157.0092 & 314.0184  &  0        &  0        &  1       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Df | AIC | BIC | logLik | deviance | Chisq | Chi Df | Pr(>Chisq) | \n",
       "|---|---|\n",
       "| ml.4 | 7         | 328.0184  | 349.7175  | -157.0092 | 314.0184  | NA        | NA        | NA        | \n",
       "| ml.3 | 7         | 328.0184  | 349.7175  | -157.0092 | 314.0184  |  0        |  0        |  1        | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     Df AIC      BIC      logLik    deviance Chisq Chi Df Pr(>Chisq)\n",
       "ml.4 7  328.0184 349.7175 -157.0092 314.0184 NA    NA     NA        \n",
       "ml.3 7  328.0184 349.7175 -157.0092 314.0184  0     0      1        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# again likelihood-ratio (ANOVA) to see the differences between models:\n",
    "anova(ml.4, ml.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not significant: $\\chi^2$(0) = 0.0, $p=$ 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Do we need the Order if we have the condition as a random effect?\n",
    "ml.5 = lmer(logT ~ Condition + (Condition|ID), data=data, REML=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Df</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>logLik</th><th scope=col>deviance</th><th scope=col>Chisq</th><th scope=col>Chi Df</th><th scope=col>Pr(&gt;Chisq)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ml.5</th><td>6           </td><td>372.7248    </td><td>391.3240    </td><td>-180.3624   </td><td>360.7248    </td><td>      NA    </td><td>NA          </td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>ml.4</th><td>7           </td><td>328.0184    </td><td>349.7175    </td><td>-157.0092   </td><td>314.0184    </td><td>46.70645    </td><td> 1          </td><td>8.245712e-12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & Df & AIC & BIC & logLik & deviance & Chisq & Chi Df & Pr(>Chisq)\\\\\n",
       "\\hline\n",
       "\tml.5 & 6            & 372.7248     & 391.3240     & -180.3624    & 360.7248     &       NA     & NA           &           NA\\\\\n",
       "\tml.4 & 7            & 328.0184     & 349.7175     & -157.0092    & 314.0184     & 46.70645     &  1           & 8.245712e-12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Df | AIC | BIC | logLik | deviance | Chisq | Chi Df | Pr(>Chisq) | \n",
       "|---|---|\n",
       "| ml.5 | 6            | 372.7248     | 391.3240     | -180.3624    | 360.7248     |       NA     | NA           |           NA | \n",
       "| ml.4 | 7            | 328.0184     | 349.7175     | -157.0092    | 314.0184     | 46.70645     |  1           | 8.245712e-12 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     Df AIC      BIC      logLik    deviance Chisq    Chi Df Pr(>Chisq)  \n",
       "ml.5 6  372.7248 391.3240 -180.3624 360.7248       NA NA               NA\n",
       "ml.4 7  328.0184 349.7175 -157.0092 314.0184 46.70645  1     8.245712e-12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Differences in models:\n",
    "anova(ml.4, ml.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, significant! The order is still important: $\\chi^2$(1) = 46.70, $p<$ 0.001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: logT ~ Order + Condition + (Condition | ID)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       "   328.0    349.7   -157.0    314.0      157 \n",
       "\n",
       "Scaled residuals: \n",
       "    Min      1Q  Median      3Q     Max \n",
       "-2.3985 -0.4850  0.0052  0.5775  2.2962 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev. Corr\n",
       " ID       (Intercept) 0.1487   0.3856       \n",
       "          Condition   0.2000   0.4473   0.29\n",
       " Residual             0.2361   0.4859       \n",
       "Number of obs: 164, groups:  ID, 41\n",
       "\n",
       "Fixed effects:\n",
       "            Estimate Std. Error t value\n",
       "(Intercept)  5.76592    0.12264   47.01\n",
       "Order        0.27940    0.03678    7.60\n",
       "Condition   -0.27634    0.10314   -2.68\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "          (Intr) Order \n",
       "Order     -0.753       \n",
       "Condition -0.138  0.009"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# examining model m_4\n",
    "summary(ml.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: logT ~ Condition + (Condition | ID)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       "   372.7    391.3   -180.4    360.7      158 \n",
       "\n",
       "Scaled residuals: \n",
       "     Min       1Q   Median       3Q      Max \n",
       "-2.34127 -0.62213  0.05227  0.57998  2.21132 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev. Corr\n",
       " ID       (Intercept) 0.1069   0.3270       \n",
       "          Condition   0.1603   0.4003   0.52\n",
       " Residual             0.3708   0.6090       \n",
       "Number of obs: 164, groups:  ID, 41\n",
       "\n",
       "Fixed effects:\n",
       "            Estimate Std. Error t value\n",
       "(Intercept)  6.46783    0.08444   76.60\n",
       "Condition   -0.28315    0.11381   -2.49\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "          (Intr)\n",
       "Condition -0.298"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# examining model m_5\n",
    "summary(ml.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, both of the models have the same variance with different sign (indication for colinearty?). However, the order is not exactly random. Each prticipant has two documents of each. It is a random ordering. Each participant will have for sure two ML and two Non-ML conditions / documents.<br>\n",
    "\n",
    "After we remove the order in model $m_5$ the condition variance is roughly the same.\n",
    "Before was -27 now is -28.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Reviewer Jugements & Annotations Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Descriptives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________TOTAL DATA:______________________ \n",
      "Total Annotations added: 486 \n",
      "Mean Total Annotations added: 2.963415 \n",
      "SD Total Annotations added: 3.033333 \n",
      "_____________________________________________ \n",
      "Total Annotations deleted: 127 \n",
      "Mean Total Annotations deleted: 0.7743902 \n",
      "SD Total Annotations deleted: 1.957607 \n",
      "\n",
      "____________MACHINE LEARNING:________________ \n",
      "Total Annotations added: 103 \n",
      "Mean Total Annotations added: 1.256098 \n",
      "SD Total Annotations added: 1.755492 \n",
      "_____________________________________________ \n",
      "Total Annotations deleted: 127 \n",
      "Mean Total Annotations deleted: 1.54878 \n",
      "SD Total Annotations deleted: 2.549037 \n",
      "\n",
      "____________NON-MACHINE LEARNING: ___________ \n",
      "Total Annotations added: 383 \n",
      "Mean Total Annotations added: 4.670732 \n",
      "SD Total Annotations added: 3.087429 \n",
      "_____________________________________________ \n",
      "Total Annotations deleted: 0 \n",
      "Mean Total Annotations deleted: 0 \n",
      "SD Total Annotations deleted: 0 \n"
     ]
    }
   ],
   "source": [
    "# Some descriptitves\n",
    "# TOTAL first (Annotations & NonAnnotations together)\n",
    "cat('____________TOTAL DATA:______________________', '\\n')\n",
    "cat(\"Total Annotations added:\", sum(data$TotalAdded), '\\n')\n",
    "cat(\"Mean Total Annotations added:\", mean(data$TotalAdded), '\\n')\n",
    "cat(\"SD Total Annotations added:\", sd(data$TotalAdded), '\\n')\n",
    "cat('_____________________________________________', '\\n')\n",
    "\n",
    "cat(\"Total Annotations deleted:\", sum(data$TotalDeleted), '\\n')\n",
    "cat(\"Mean Total Annotations deleted:\", mean(data$TotalDeleted), '\\n')\n",
    "cat(\"SD Total Annotations deleted:\", sd(data$TotalDeleted), '\\n')\n",
    "cat('\\n')\n",
    "\n",
    "\n",
    "cat('____________MACHINE LEARNING:________________', '\\n')\n",
    "\n",
    "# Now only for Machine-Learning Condition\n",
    "cat(\"Total Annotations added:\", sum(Annotations$TotalAdded), '\\n')\n",
    "cat(\"Mean Total Annotations added:\", mean(Annotations$TotalAdded), '\\n')\n",
    "cat(\"SD Total Annotations added:\", sd(Annotations$TotalAdded), '\\n')\n",
    "cat('_____________________________________________', '\\n')\n",
    "\n",
    "cat(\"Total Annotations deleted:\", sum(Annotations$TotalDeleted), '\\n')\n",
    "cat(\"Mean Total Annotations deleted:\", mean(Annotations$TotalDeleted), '\\n')\n",
    "cat(\"SD Total Annotations deleted:\", sd(Annotations$TotalDeleted), '\\n')\n",
    "cat('\\n')\n",
    "\n",
    "cat('____________NON-MACHINE LEARNING: ___________', '\\n')\n",
    "\n",
    "# And for the Non-Machine-Learning Condition\n",
    "cat(\"Total Annotations added:\", sum(NoAnnotations$TotalAdded), '\\n')\n",
    "cat(\"Mean Total Annotations added:\", mean(NoAnnotations$TotalAdded), '\\n')\n",
    "cat(\"SD Total Annotations added:\", sd(NoAnnotations$TotalAdded), '\\n')\n",
    "cat('_____________________________________________', '\\n')\n",
    "\n",
    "cat(\"Total Annotations deleted:\", sum(NoAnnotations$TotalDeleted), '\\n')\n",
    "cat(\"Mean Total Annotations deleted:\", mean(NoAnnotations$TotalDeleted), '\\n')\n",
    "cat(\"SD Total Annotations deleted:\", sd(NoAnnotations$TotalDeleted), '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Descriptives Self-Reported characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# using a subset of the above main data here:\n",
    "data_s <- read.xls(\"data/subset_selfreported.xlsx\", verbose=FALSE, na.strings=c(\"NA\"))\n",
    "# and filtering out douplicates\n",
    "data_s2 <- unique(data_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ID</th><th scope=col>CExperience</th><th scope=col>Tasks</th><th scope=col>NReviews</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>-JjAzwjQakZk-3kIbeMfw</td><td>1                    </td><td>9                    </td><td>10+                  </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>hNQKTiKtHSY_BmxLyZb9Q</td><td>1                    </td><td>8                    </td><td>5to10                </td></tr>\n",
       "\t<tr><th scope=row>9</th><td>pyTvUi2W85Nb9handPSvZ</td><td>1                    </td><td>9                    </td><td>5to10                </td></tr>\n",
       "\t<tr><th scope=row>13</th><td>2K-K26QZH9xPJH9RVMltM</td><td>1                    </td><td>9                    </td><td>10+                  </td></tr>\n",
       "\t<tr><th scope=row>17</th><td>8arsi19w5sMwMmpkmM0sX</td><td>1                    </td><td>9                    </td><td>10+                  </td></tr>\n",
       "\t<tr><th scope=row>21</th><td>GR96RbbiPLL4gS3-ilxky</td><td>1                    </td><td>9                    </td><td>1to5                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & ID & CExperience & Tasks & NReviews\\\\\n",
       "\\hline\n",
       "\t1 & -JjAzwjQakZk-3kIbeMfw & 1                     & 9                     & 10+                  \\\\\n",
       "\t5 & hNQKTiKtHSY\\_BmxLyZb9Q & 1                       & 8                       & 5to10                  \\\\\n",
       "\t9 & pyTvUi2W85Nb9handPSvZ & 1                     & 9                     & 5to10                \\\\\n",
       "\t13 & 2K-K26QZH9xPJH9RVMltM & 1                     & 9                     & 10+                  \\\\\n",
       "\t17 & 8arsi19w5sMwMmpkmM0sX & 1                     & 9                     & 10+                  \\\\\n",
       "\t21 & GR96RbbiPLL4gS3-ilxky & 1                     & 9                     & 1to5                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ID | CExperience | Tasks | NReviews | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 | -JjAzwjQakZk-3kIbeMfw | 1                     | 9                     | 10+                   | \n",
       "| 5 | hNQKTiKtHSY_BmxLyZb9Q | 1                     | 8                     | 5to10                 | \n",
       "| 9 | pyTvUi2W85Nb9handPSvZ | 1                     | 9                     | 5to10                 | \n",
       "| 13 | 2K-K26QZH9xPJH9RVMltM | 1                     | 9                     | 10+                   | \n",
       "| 17 | 8arsi19w5sMwMmpkmM0sX | 1                     | 9                     | 10+                   | \n",
       "| 21 | GR96RbbiPLL4gS3-ilxky | 1                     | 9                     | 1to5                  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   ID                    CExperience Tasks NReviews\n",
       "1  -JjAzwjQakZk-3kIbeMfw 1           9     10+     \n",
       "5  hNQKTiKtHSY_BmxLyZb9Q 1           8     5to10   \n",
       "9  pyTvUi2W85Nb9handPSvZ 1           9     5to10   \n",
       "13 2K-K26QZH9xPJH9RVMltM 1           9     10+     \n",
       "17 8arsi19w5sMwMmpkmM0sX 1           9     10+     \n",
       "21 GR96RbbiPLL4gS3-ilxky 1           9     1to5    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quick check on the data\n",
    "head(data_s2)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
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      "text/html": [
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       "\t<dt>0%</dt>\n",
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       "\t\t<dd>6</dd>\n",
       "\t<dt>50%</dt>\n",
       "\t\t<dd>8</dd>\n",
       "\t<dt>75%</dt>\n",
       "\t\t<dd>9</dd>\n",
       "\t<dt>100%</dt>\n",
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       "</dl>\n"
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       "\\item[75\\textbackslash{}\\%] 9\n",
       "\\item[100\\textbackslash{}\\%] 9\n",
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       "0%\n",
       ":   125%\n",
       ":   650%\n",
       ":   875%\n",
       ":   9100%\n",
       ":   9\n",
       "\n"
      ],
      "text/plain": [
       "  0%  25%  50%  75% 100% \n",
       "   1    6    8    9    9 "
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     "metadata": {},
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   ],
   "source": [
    "# Calculating Descriptive Self-reported characteristics data:\n",
    "NumberOfReviews0 <- sum(data_s2$NReviews=='0')\n",
    "NumberOfReviews1_5 <- sum(data_s2$NReviews=='1to5')\n",
    "NumberOfReviews5_0 <- sum(data_s2$NReviews=='5to10')\n",
    "NumberOfReviews10plus <- sum(data_s2$NReviews=='10+')\n",
    "NumberOfReviews0 \n",
    "NumberOfReviews1_5 \n",
    "NumberOfReviews5_0\n",
    "NumberOfReviews10plus\n",
    "\n",
    "NumberOfReviews0_percentage <- NumberOfReviews0/41\n",
    "NumberOfReviews1_5_percentage <- NumberOfReviews1_5/41\n",
    "NumberOfReviews5_0_percentage <- NumberOfReviews5_0/41\n",
    "NumberOfReviews10plus_percentage <- NumberOfReviews10plus/41\n",
    "NumberOfReviews0_percentage \n",
    "NumberOfReviews1_5_percentage\n",
    "NumberOfReviews5_0_percentage\n",
    "NumberOfReviews10plus_percentage\n",
    "\n",
    "TotalSumOfPeopleWithCochraneExperience <- sum(data_s2$CExperience=='1')\n",
    "TotalSumOfPeopleWithoutCochraneExperience <- sum(data_s2$CExperience=='0')\n",
    "TotalSumOfPeopleWithCochraneExperience\n",
    "TotalSumOfPeopleWithoutCochraneExperience\n",
    "\n",
    "Percentage_TotalSumOfPeopleWithCochraneExperience <- TotalSumOfPeopleWithCochraneExperience/41\n",
    "Percentage_TotalSumOfPeopleWithoutCochraneExperience <- TotalSumOfPeopleWithoutCochraneExperience/41\n",
    "Percentage_TotalSumOfPeopleWithCochraneExperience\n",
    "Percentage_TotalSumOfPeopleWithoutCochraneExperience\n",
    "\n",
    "MedianOfTasksPerfomed <- median(data_s2$Tasks)\n",
    "IRQofTasksPerformed <- quantile(data_s2$Tasks)\n",
    "MedianOfTasksPerfomed\n",
    "IRQofTasksPerformed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Jugdement Agrement Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the data \n",
    "data_agreement <- read.xls(\"data/agreement.xlsx\", verbose=FALSE, na.strings=c(\"NA\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# creating two groups (ML (1) and NonML (0) )\n",
    "ML <- subset(data_agreement, Condition=='1')\n",
    "NoML <- subset(data_agreement, Condition=='0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>82</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " n\\\\\n",
       "\\hline\n",
       "\t 82\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "n | \n",
       "|---|\n",
       "| 82 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  n \n",
       "1 82"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking how many datapoints in one column:\n",
    "count(ML)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RSG changed: 7 RSG %: 0.08536585 \n",
      "AC changed: 7 AC %: 0.08536585 \n",
      "BPP changed: 6 BPP %: 0.07317073 \n",
      "BOA changed: 7 BOA %: 0.08536585 \n",
      "Overall changed: 27 RSG %: 0.08231707 \n"
     ]
    }
   ],
   "source": [
    "# calculating the changed data and percentiles:\n",
    "RSG <- sum(ML$Changed)\n",
    "cat(\"RSG changed:\", RSG, \"RSG %:\", RSG/82, '\\n')\n",
    "AC <- sum(ML$Changed.1)\n",
    "cat(\"AC changed:\", AC, \"AC %:\", AC/82, '\\n')\n",
    "BPP <- sum(ML$Changed.2)\n",
    "cat(\"BPP changed:\", BPP, \"BPP %:\", BPP/82, '\\n')\n",
    "BOA <- sum(ML$Changed.3)\n",
    "cat(\"BOA changed:\", BOA, \"BOA %:\", BOA/82, '\\n')\n",
    "Overall <- RSG+AC+BPP+BOA\n",
    "cat(\"Overall changed:\", Overall, \"RSG %:\", Overall/328, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Annotations Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "14.6341463414634"
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       "[1] 14.63415"
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     "output_type": "display_data"
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    {
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       "4.67073170731707"
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       "4.67073170731707"
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       "[1] 4.670732"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overall mean annotations\n",
    "data <- read.xls(\"data/TimeAnalysis2_1.xlsx\", verbose=FALSE, na.strings=c(\"NA\"))\n",
    "data2 <- subset(data, Condition=='1')\n",
    "data3 <- subset(data, Condition=='0')\n",
    "\n",
    "mean(data2$TotalSubmitted)\n",
    "mean(data3$TotalSubmitted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
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       "[1] 0.5609756"
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       "[1] 0.4146341"
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       "[1] 0.02439024"
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      "text/html": [
       "0.621951219512195"
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       "[1] 0.6219512"
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       "[1] 0.03658537"
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       "0.634146341463415"
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       "[1] 0.6341463"
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       "[1] 0.3292683"
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       "0.646341463414634"
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       "[1] 0.2926829"
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       "[1] 0.06097561"
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       "[1] 0.6158537"
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    {
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       "[1] 0.3445122"
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    {
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   ],
   "source": [
    "##############\n",
    "# annotations\n",
    "\n",
    "data <- read.xls(\"data/annotationschanged.xlsx\", verbose=FALSE, na.strings=c(\"NA\"))\n",
    "data2 <- subset(data, Condition=='1')\n",
    "data3 <- subset(data, Condition=='0')\n",
    "\n",
    "\n",
    "\n",
    "RSG1 <- sum(data2$Changed1=='0')\n",
    "RSG2 <- sum(data2$Changed1=='1')\n",
    "RSG3 <- sum(data2$Changed1=='2')\n",
    "RSG1/82\n",
    "RSG2/82\n",
    "RSG3/82\n",
    "\n",
    "AC1 <- sum(data2$Changed2=='0')\n",
    "AC2 <- sum(data2$Changed2=='1')\n",
    "AC3 <- sum(data2$Changed2=='2')\n",
    "AC1/82\n",
    "AC2/82\n",
    "AC3/82\n",
    "\n",
    "BPP1 <- sum(data2$Changed3=='0')\n",
    "BPP2 <- sum(data2$Changed3=='1')\n",
    "BPP3 <- sum(data2$Changed3=='2')\n",
    "BPP1/82\n",
    "BPP2/82\n",
    "BPP3/82\n",
    "\n",
    "BOA1 <- sum(data2$Changed4=='0')\n",
    "BOA2 <- sum(data2$Changed4=='1')\n",
    "BOA3 <- sum(data2$Changed4=='2')\n",
    "BOA1/82\n",
    "BOA2/82\n",
    "BOA3/82\n",
    "\n",
    "TotalUnchanged <- RSG1+AC1+BPP1+BOA1\n",
    "TotalUnchanged/328\n",
    "\n",
    "TotalChangedML <- RSG2+AC2+BPP2+BOA2\n",
    "TotalChangedML/328\n",
    "\n",
    "TotalChangedNoML <- RSG3+AC3+BPP3+BOA3\n",
    "TotalChangedNoML/328"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. QUESTIONNAIRE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the data:\n",
    "dataq <- read.xls(\"data/UXData1.xlsx\", verbose=FALSE, na.strings=c(\"NA\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Data Preparation (cleaning and structuring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subsetting\n",
    "data_rel <- as.data.frame(dataq)\n",
    "ss2 <- c(1,3,4, 25:44)\n",
    "data_q2 <- subset(data_rel, select=ss2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ParticipantNo</th><th scope=col>Sequence</th><th scope=col>Condition</th><th scope=col>CapacityReview</th><th scope=col>NoOfTasksPerformed</th><th scope=col>HowManyReviews</th><th scope=col>CochraneRoBExp</th><th scope=col>UseFrequently</th><th scope=col>Complex</th><th scope=col>EasyToUse</th><th scope=col>⋯</th><th scope=col>UseQuickly</th><th scope=col>Cumbersome</th><th scope=col>Confident</th><th scope=col>NeededLearn</th><th scope=col>TextHelpful</th><th scope=col>DifficultToNav</th><th scope=col>ImproveQuality</th><th scope=col>Irrelevant</th><th scope=col>Confused</th><th scope=col>ContinueUse</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                                                                                                                                                                                                </td><td>1010                                                                                                                                                                                             </td><td>A                                                                                                                                                                                                </td><td>develop questions; planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review</td><td> 9                                                                                                                                                                                               </td><td>10+                                                                                                                                                                                              </td><td>Yes                                                                                                                                                                                              </td><td> 5                                                                                                                                                                                               </td><td> 1                                                                                                                                                                                               </td><td> 4                                                                                                                                                                                               </td><td>⋯                                                                                                                                                                                                </td><td> 5                                                                                                                                                                                               </td><td> 1                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td><td> 1                                                                                                                                                                                               </td><td> 4                                                                                                                                                                                               </td><td> 1                                                                                                                                                                                               </td><td> 4                                                                                                                                                                                               </td><td> 3                                                                                                                                                                                               </td><td> 2                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td></tr>\n",
       "\t<tr><td>1                                                                                                                                                                                                </td><td>1010                                                                                                                                                                                             </td><td>NOA                                                                                                                                                                                              </td><td>                                                                                                                                                                                                 </td><td>NA                                                                                                                                                                                               </td><td>                                                                                                                                                                                                 </td><td>                                                                                                                                                                                                 </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>⋯                                                                                                                                                                                                </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td></tr>\n",
       "\t<tr><td>2                                                                                                                                                                                                </td><td>1100                                                                                                                                                                                             </td><td>A                                                                                                                                                                                                </td><td>planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review                   </td><td> 8                                                                                                                                                                                               </td><td>5to10                                                                                                                                                                                            </td><td>Yes                                                                                                                                                                                              </td><td> 5                                                                                                                                                                                               </td><td> 1                                                                                                                                                                                               </td><td> 4                                                                                                                                                                                               </td><td>⋯                                                                                                                                                                                                </td><td> 5                                                                                                                                                                                               </td><td> 2                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td><td> 2                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td><td> 1                                                                                                                                                                                               </td><td> 4                                                                                                                                                                                               </td><td> 3                                                                                                                                                                                               </td><td> 1                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td></tr>\n",
       "\t<tr><td>2                                                                                                                                                                                                </td><td>1100                                                                                                                                                                                             </td><td>NOA                                                                                                                                                                                              </td><td>                                                                                                                                                                                                 </td><td>NA                                                                                                                                                                                               </td><td>                                                                                                                                                                                                 </td><td>                                                                                                                                                                                                 </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>⋯                                                                                                                                                                                                </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td></tr>\n",
       "\t<tr><td>3                                                                                                                                                                                                </td><td> 101                                                                                                                                                                                             </td><td>A                                                                                                                                                                                                </td><td>develop questions; planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review</td><td> 9                                                                                                                                                                                               </td><td>5to10                                                                                                                                                                                            </td><td>Yes                                                                                                                                                                                              </td><td> 5                                                                                                                                                                                               </td><td> 4                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td><td>⋯                                                                                                                                                                                                </td><td> 5                                                                                                                                                                                               </td><td> 2                                                                                                                                                                                               </td><td> 2                                                                                                                                                                                               </td><td> 2                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td><td> 1                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td><td> 2                                                                                                                                                                                               </td><td> 2                                                                                                                                                                                               </td><td> 5                                                                                                                                                                                               </td></tr>\n",
       "\t<tr><td>3                                                                                                                                                                                                </td><td> 101                                                                                                                                                                                             </td><td>NOA                                                                                                                                                                                              </td><td>                                                                                                                                                                                                 </td><td>NA                                                                                                                                                                                               </td><td>                                                                                                                                                                                                 </td><td>                                                                                                                                                                                                 </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>⋯                                                                                                                                                                                                </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td><td>NA                                                                                                                                                                                               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllll}\n",
       " ParticipantNo & Sequence & Condition & CapacityReview & NoOfTasksPerformed & HowManyReviews & CochraneRoBExp & UseFrequently & Complex & EasyToUse & ⋯ & UseQuickly & Cumbersome & Confident & NeededLearn & TextHelpful & DifficultToNav & ImproveQuality & Irrelevant & Confused & ContinueUse\\\\\n",
       "\\hline\n",
       "\t 1                                                                                                                                                                                                 & 1010                                                                                                                                                                                              & A                                                                                                                                                                                                 & develop questions; planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review &  9                                                                                                                                                                                                & 10+                                                                                                                                                                                               & Yes                                                                                                                                                                                               &  5                                                                                                                                                                                                &  1                                                                                                                                                                                                &  4                                                                                                                                                                                                & ⋯                                                                                                                                                                                                 &  5                                                                                                                                                                                                &  1                                                                                                                                                                                                &  5                                                                                                                                                                                                &  1                                                                                                                                                                                                &  4                                                                                                                                                                                                &  1                                                                                                                                                                                                &  4                                                                                                                                                                                                &  3                                                                                                                                                                                                &  2                                                                                                                                                                                                &  5                                                                                                                                                                                               \\\\\n",
       "\t 1                                                                                                                                                                                                 & 1010                                                                                                                                                                                              & NOA                                                                                                                                                                                               &                                                                                                                                                                                                   & NA                                                                                                                                                                                                &                                                                                                                                                                                                   &                                                                                                                                                                                                   & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & ⋯                                                                                                                                                                                                 & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                               \\\\\n",
       "\t 2                                                                                                                                                                                                 & 1100                                                                                                                                                                                              & A                                                                                                                                                                                                 & planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review                    &  8                                                                                                                                                                                                & 5to10                                                                                                                                                                                             & Yes                                                                                                                                                                                               &  5                                                                                                                                                                                                &  1                                                                                                                                                                                                &  4                                                                                                                                                                                                & ⋯                                                                                                                                                                                                 &  5                                                                                                                                                                                                &  2                                                                                                                                                                                                &  5                                                                                                                                                                                                &  2                                                                                                                                                                                                &  5                                                                                                                                                                                                &  1                                                                                                                                                                                                &  4                                                                                                                                                                                                &  3                                                                                                                                                                                                &  1                                                                                                                                                                                                &  5                                                                                                                                                                                               \\\\\n",
       "\t 2                                                                                                                                                                                                 & 1100                                                                                                                                                                                              & NOA                                                                                                                                                                                               &                                                                                                                                                                                                   & NA                                                                                                                                                                                                &                                                                                                                                                                                                   &                                                                                                                                                                                                   & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & ⋯                                                                                                                                                                                                 & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                               \\\\\n",
       "\t 3                                                                                                                                                                                                 &  101                                                                                                                                                                                              & A                                                                                                                                                                                                 & develop questions; planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review &  9                                                                                                                                                                                                & 5to10                                                                                                                                                                                             & Yes                                                                                                                                                                                               &  5                                                                                                                                                                                                &  4                                                                                                                                                                                                &  5                                                                                                                                                                                                & ⋯                                                                                                                                                                                                 &  5                                                                                                                                                                                                &  2                                                                                                                                                                                                &  2                                                                                                                                                                                                &  2                                                                                                                                                                                                &  5                                                                                                                                                                                                &  1                                                                                                                                                                                                &  5                                                                                                                                                                                                &  2                                                                                                                                                                                                &  2                                                                                                                                                                                                &  5                                                                                                                                                                                               \\\\\n",
       "\t 3                                                                                                                                                                                                 &  101                                                                                                                                                                                              & NOA                                                                                                                                                                                               &                                                                                                                                                                                                   & NA                                                                                                                                                                                                &                                                                                                                                                                                                   &                                                                                                                                                                                                   & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & ⋯                                                                                                                                                                                                 & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                                & NA                                                                                                                                                                                               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "ParticipantNo | Sequence | Condition | CapacityReview | NoOfTasksPerformed | HowManyReviews | CochraneRoBExp | UseFrequently | Complex | EasyToUse | ⋯ | UseQuickly | Cumbersome | Confident | NeededLearn | TextHelpful | DifficultToNav | ImproveQuality | Irrelevant | Confused | ContinueUse | \n",
       "|---|---|---|---|---|---|\n",
       "| 1                                                                                                                                                                                                 | 1010                                                                                                                                                                                              | A                                                                                                                                                                                                 | develop questions; planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review |  9                                                                                                                                                                                                | 10+                                                                                                                                                                                               | Yes                                                                                                                                                                                               |  5                                                                                                                                                                                                |  1                                                                                                                                                                                                |  4                                                                                                                                                                                                | ⋯                                                                                                                                                                                                 |  5                                                                                                                                                                                                |  1                                                                                                                                                                                                |  5                                                                                                                                                                                                |  1                                                                                                                                                                                                |  4                                                                                                                                                                                                |  1                                                                                                                                                                                                |  4                                                                                                                                                                                                |  3                                                                                                                                                                                                |  2                                                                                                                                                                                                |  5                                                                                                                                                                                                | \n",
       "| 1                                                                                                                                                                                                 | 1010                                                                                                                                                                                              | NOA                                                                                                                                                                                               |                                                                                                                                                                                                   | NA                                                                                                                                                                                                |                                                                                                                                                                                                   |                                                                                                                                                                                                   | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | ⋯                                                                                                                                                                                                 | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | \n",
       "| 2                                                                                                                                                                                                 | 1100                                                                                                                                                                                              | A                                                                                                                                                                                                 | planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review                    |  8                                                                                                                                                                                                | 5to10                                                                                                                                                                                             | Yes                                                                                                                                                                                               |  5                                                                                                                                                                                                |  1                                                                                                                                                                                                |  4                                                                                                                                                                                                | ⋯                                                                                                                                                                                                 |  5                                                                                                                                                                                                |  2                                                                                                                                                                                                |  5                                                                                                                                                                                                |  2                                                                                                                                                                                                |  5                                                                                                                                                                                                |  1                                                                                                                                                                                                |  4                                                                                                                                                                                                |  3                                                                                                                                                                                                |  1                                                                                                                                                                                                |  5                                                                                                                                                                                                | \n",
       "| 2                                                                                                                                                                                                 | 1100                                                                                                                                                                                              | NOA                                                                                                                                                                                               |                                                                                                                                                                                                   | NA                                                                                                                                                                                                |                                                                                                                                                                                                   |                                                                                                                                                                                                   | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | ⋯                                                                                                                                                                                                 | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | \n",
       "| 3                                                                                                                                                                                                 |  101                                                                                                                                                                                              | A                                                                                                                                                                                                 | develop questions; planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review |  9                                                                                                                                                                                                | 5to10                                                                                                                                                                                             | Yes                                                                                                                                                                                               |  5                                                                                                                                                                                                |  4                                                                                                                                                                                                |  5                                                                                                                                                                                                | ⋯                                                                                                                                                                                                 |  5                                                                                                                                                                                                |  2                                                                                                                                                                                                |  2                                                                                                                                                                                                |  2                                                                                                                                                                                                |  5                                                                                                                                                                                                |  1                                                                                                                                                                                                |  5                                                                                                                                                                                                |  2                                                                                                                                                                                                |  2                                                                                                                                                                                                |  5                                                                                                                                                                                                | \n",
       "| 3                                                                                                                                                                                                 |  101                                                                                                                                                                                              | NOA                                                                                                                                                                                               |                                                                                                                                                                                                   | NA                                                                                                                                                                                                |                                                                                                                                                                                                   |                                                                                                                                                                                                   | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | ⋯                                                                                                                                                                                                 | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | NA                                                                                                                                                                                                | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  ParticipantNo Sequence Condition\n",
       "1 1             1010     A        \n",
       "2 1             1010     NOA      \n",
       "3 2             1100     A        \n",
       "4 2             1100     NOA      \n",
       "5 3              101     A        \n",
       "6 3              101     NOA      \n",
       "  CapacityReview                                                                                                                                                                                   \n",
       "1 develop questions; planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review\n",
       "2                                                                                                                                                                                                  \n",
       "3 planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review                   \n",
       "4                                                                                                                                                                                                  \n",
       "5 develop questions; planning methods or write and publish protocols; develop and run search; select studies; collect data; assess RoB; analyse data; interprete findings; write and publish review\n",
       "6                                                                                                                                                                                                  \n",
       "  NoOfTasksPerformed HowManyReviews CochraneRoBExp UseFrequently Complex\n",
       "1  9                 10+            Yes             5             1     \n",
       "2 NA                                               NA            NA     \n",
       "3  8                 5to10          Yes             5             1     \n",
       "4 NA                                               NA            NA     \n",
       "5  9                 5to10          Yes             5             4     \n",
       "6 NA                                               NA            NA     \n",
       "  EasyToUse ⋯ UseQuickly Cumbersome Confident NeededLearn TextHelpful\n",
       "1  4        ⋯  5          1          5         1           4         \n",
       "2 NA        ⋯ NA         NA         NA        NA          NA         \n",
       "3  4        ⋯  5          2          5         2           5         \n",
       "4 NA        ⋯ NA         NA         NA        NA          NA         \n",
       "5  5        ⋯  5          2          2         2           5         \n",
       "6 NA        ⋯ NA         NA         NA        NA          NA         \n",
       "  DifficultToNav ImproveQuality Irrelevant Confused ContinueUse\n",
       "1  1              4              3          2        5         \n",
       "2 NA             NA             NA         NA       NA         \n",
       "3  1              4              3          1        5         \n",
       "4 NA             NA             NA         NA       NA         \n",
       "5  1              5              2          2        5         \n",
       "6 NA             NA             NA         NA       NA         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the data\n",
    "head(data_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ParticipantNo</th><th scope=col>Condition</th><th scope=col>UseFrequently</th><th scope=col>Complex</th><th scope=col>EasyToUse</th><th scope=col>NeedSupport</th><th scope=col>WellIntegrated</th><th scope=col>Inconsistency</th><th scope=col>UseQuickly</th><th scope=col>Cumbersome</th><th scope=col>Confident</th><th scope=col>NeededLearn</th><th scope=col>TextHelpful</th><th scope=col>DifficultToNav</th><th scope=col>ImproveQuality</th><th scope=col>Irrelevant</th><th scope=col>Confused</th><th scope=col>ContinueUse</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1  </td><td>A  </td><td> 5 </td><td> 1 </td><td> 4 </td><td> 1 </td><td> 4 </td><td> 2 </td><td> 5 </td><td> 1 </td><td> 5 </td><td> 1 </td><td> 4 </td><td> 1 </td><td> 4 </td><td> 3 </td><td> 2 </td><td> 5 </td></tr>\n",
       "\t<tr><td>1  </td><td>NOA</td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n",
       "\t<tr><td>2  </td><td>A  </td><td> 5 </td><td> 1 </td><td> 4 </td><td> 1 </td><td> 5 </td><td> 1 </td><td> 5 </td><td> 2 </td><td> 5 </td><td> 2 </td><td> 5 </td><td> 1 </td><td> 4 </td><td> 3 </td><td> 1 </td><td> 5 </td></tr>\n",
       "\t<tr><td>2  </td><td>NOA</td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n",
       "\t<tr><td>3  </td><td>A  </td><td> 5 </td><td> 4 </td><td> 5 </td><td> 2 </td><td> 5 </td><td> 2 </td><td> 5 </td><td> 2 </td><td> 2 </td><td> 2 </td><td> 5 </td><td> 1 </td><td> 5 </td><td> 2 </td><td> 2 </td><td> 5 </td></tr>\n",
       "\t<tr><td>3  </td><td>NOA</td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td><td>NA </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       " ParticipantNo & Condition & UseFrequently & Complex & EasyToUse & NeedSupport & WellIntegrated & Inconsistency & UseQuickly & Cumbersome & Confident & NeededLearn & TextHelpful & DifficultToNav & ImproveQuality & Irrelevant & Confused & ContinueUse\\\\\n",
       "\\hline\n",
       "\t 1   & A   &  5  &  1  &  4  &  1  &  4  &  2  &  5  &  1  &  5  &  1  &  4  &  1  &  4  &  3  &  2  &  5 \\\\\n",
       "\t 1   & NOA & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA \\\\\n",
       "\t 2   & A   &  5  &  1  &  4  &  1  &  5  &  1  &  5  &  2  &  5  &  2  &  5  &  1  &  4  &  3  &  1  &  5 \\\\\n",
       "\t 2   & NOA & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA \\\\\n",
       "\t 3   & A   &  5  &  4  &  5  &  2  &  5  &  2  &  5  &  2  &  2  &  2  &  5  &  1  &  5  &  2  &  2  &  5 \\\\\n",
       "\t 3   & NOA & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA  & NA \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "ParticipantNo | Condition | UseFrequently | Complex | EasyToUse | NeedSupport | WellIntegrated | Inconsistency | UseQuickly | Cumbersome | Confident | NeededLearn | TextHelpful | DifficultToNav | ImproveQuality | Irrelevant | Confused | ContinueUse | \n",
       "|---|---|---|---|---|---|\n",
       "| 1   | A   |  5  |  1  |  4  |  1  |  4  |  2  |  5  |  1  |  5  |  1  |  4  |  1  |  4  |  3  |  2  |  5  | \n",
       "| 1   | NOA | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | \n",
       "| 2   | A   |  5  |  1  |  4  |  1  |  5  |  1  |  5  |  2  |  5  |  2  |  5  |  1  |  4  |  3  |  1  |  5  | \n",
       "| 2   | NOA | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | \n",
       "| 3   | A   |  5  |  4  |  5  |  2  |  5  |  2  |  5  |  2  |  2  |  2  |  5  |  1  |  5  |  2  |  2  |  5  | \n",
       "| 3   | NOA | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | NA  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  ParticipantNo Condition UseFrequently Complex EasyToUse NeedSupport\n",
       "1 1             A          5             1       4         1         \n",
       "2 1             NOA       NA            NA      NA        NA         \n",
       "3 2             A          5             1       4         1         \n",
       "4 2             NOA       NA            NA      NA        NA         \n",
       "5 3             A          5             4       5         2         \n",
       "6 3             NOA       NA            NA      NA        NA         \n",
       "  WellIntegrated Inconsistency UseQuickly Cumbersome Confident NeededLearn\n",
       "1  4              2             5          1          5         1         \n",
       "2 NA             NA            NA         NA         NA        NA         \n",
       "3  5              1             5          2          5         2         \n",
       "4 NA             NA            NA         NA         NA        NA         \n",
       "5  5              2             5          2          2         2         \n",
       "6 NA             NA            NA         NA         NA        NA         \n",
       "  TextHelpful DifficultToNav ImproveQuality Irrelevant Confused ContinueUse\n",
       "1  4           1              4              3          2        5         \n",
       "2 NA          NA             NA             NA         NA       NA         \n",
       "3  5           1              4              3          1        5         \n",
       "4 NA          NA             NA             NA         NA       NA         \n",
       "5  5           1              5              2          2        5         \n",
       "6 NA          NA             NA             NA         NA       NA         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ss3 <- c(1, 3, 8:23)\n",
    "clean_q2 <- subset(data_q2, select=ss3)\n",
    "head(clean_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_q2 <- subset(clean_q2, Condition==\"A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ParticipantNo</th><th scope=col>Condition</th><th scope=col>UseFrequently</th><th scope=col>Complex</th><th scope=col>EasyToUse</th><th scope=col>NeedSupport</th><th scope=col>WellIntegrated</th><th scope=col>Inconsistency</th><th scope=col>UseQuickly</th><th scope=col>Cumbersome</th><th scope=col>Confident</th><th scope=col>NeededLearn</th><th scope=col>TextHelpful</th><th scope=col>DifficultToNav</th><th scope=col>ImproveQuality</th><th scope=col>Irrelevant</th><th scope=col>Confused</th><th scope=col>ContinueUse</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>A</td><td>5</td><td>1</td><td>4</td><td>1</td><td>4</td><td>2</td><td>5</td><td>1</td><td>5</td><td>1</td><td>4</td><td>1</td><td>4</td><td>3</td><td>2</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2</td><td>A</td><td>5</td><td>1</td><td>4</td><td>1</td><td>5</td><td>1</td><td>5</td><td>2</td><td>5</td><td>2</td><td>5</td><td>1</td><td>4</td><td>3</td><td>1</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>3</td><td>A</td><td>5</td><td>4</td><td>5</td><td>2</td><td>5</td><td>2</td><td>5</td><td>2</td><td>2</td><td>2</td><td>5</td><td>1</td><td>5</td><td>2</td><td>2</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>4</td><td>A</td><td>5</td><td>2</td><td>5</td><td>4</td><td>5</td><td>2</td><td>5</td><td>5</td><td>4</td><td>2</td><td>5</td><td>2</td><td>4</td><td>2</td><td>2</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>5</td><td>A</td><td>5</td><td>2</td><td>5</td><td>2</td><td>5</td><td>1</td><td>4</td><td>1</td><td>4</td><td>1</td><td>3</td><td>1</td><td>3</td><td>3</td><td>2</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>6</td><td>A</td><td>5</td><td>3</td><td>3</td><td>3</td><td>2</td><td>4</td><td>5</td><td>2</td><td>1</td><td>1</td><td>2</td><td>5</td><td>5</td><td>1</td><td>2</td><td>5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & ParticipantNo & Condition & UseFrequently & Complex & EasyToUse & NeedSupport & WellIntegrated & Inconsistency & UseQuickly & Cumbersome & Confident & NeededLearn & TextHelpful & DifficultToNav & ImproveQuality & Irrelevant & Confused & ContinueUse\\\\\n",
       "\\hline\n",
       "\t1 & 1 & A & 5 & 1 & 4 & 1 & 4 & 2 & 5 & 1 & 5 & 1 & 4 & 1 & 4 & 3 & 2 & 5\\\\\n",
       "\t3 & 2 & A & 5 & 1 & 4 & 1 & 5 & 1 & 5 & 2 & 5 & 2 & 5 & 1 & 4 & 3 & 1 & 5\\\\\n",
       "\t5 & 3 & A & 5 & 4 & 5 & 2 & 5 & 2 & 5 & 2 & 2 & 2 & 5 & 1 & 5 & 2 & 2 & 5\\\\\n",
       "\t7 & 4 & A & 5 & 2 & 5 & 4 & 5 & 2 & 5 & 5 & 4 & 2 & 5 & 2 & 4 & 2 & 2 & 5\\\\\n",
       "\t9 & 5 & A & 5 & 2 & 5 & 2 & 5 & 1 & 4 & 1 & 4 & 1 & 3 & 1 & 3 & 3 & 2 & 4\\\\\n",
       "\t11 & 6 & A & 5 & 3 & 3 & 3 & 2 & 4 & 5 & 2 & 1 & 1 & 2 & 5 & 5 & 1 & 2 & 5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ParticipantNo | Condition | UseFrequently | Complex | EasyToUse | NeedSupport | WellIntegrated | Inconsistency | UseQuickly | Cumbersome | Confident | NeededLearn | TextHelpful | DifficultToNav | ImproveQuality | Irrelevant | Confused | ContinueUse | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 1 | A | 5 | 1 | 4 | 1 | 4 | 2 | 5 | 1 | 5 | 1 | 4 | 1 | 4 | 3 | 2 | 5 | \n",
       "| 3 | 2 | A | 5 | 1 | 4 | 1 | 5 | 1 | 5 | 2 | 5 | 2 | 5 | 1 | 4 | 3 | 1 | 5 | \n",
       "| 5 | 3 | A | 5 | 4 | 5 | 2 | 5 | 2 | 5 | 2 | 2 | 2 | 5 | 1 | 5 | 2 | 2 | 5 | \n",
       "| 7 | 4 | A | 5 | 2 | 5 | 4 | 5 | 2 | 5 | 5 | 4 | 2 | 5 | 2 | 4 | 2 | 2 | 5 | \n",
       "| 9 | 5 | A | 5 | 2 | 5 | 2 | 5 | 1 | 4 | 1 | 4 | 1 | 3 | 1 | 3 | 3 | 2 | 4 | \n",
       "| 11 | 6 | A | 5 | 3 | 3 | 3 | 2 | 4 | 5 | 2 | 1 | 1 | 2 | 5 | 5 | 1 | 2 | 5 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   ParticipantNo Condition UseFrequently Complex EasyToUse NeedSupport\n",
       "1  1             A         5             1       4         1          \n",
       "3  2             A         5             1       4         1          \n",
       "5  3             A         5             4       5         2          \n",
       "7  4             A         5             2       5         4          \n",
       "9  5             A         5             2       5         2          \n",
       "11 6             A         5             3       3         3          \n",
       "   WellIntegrated Inconsistency UseQuickly Cumbersome Confident NeededLearn\n",
       "1  4              2             5          1          5         1          \n",
       "3  5              1             5          2          5         2          \n",
       "5  5              2             5          2          2         2          \n",
       "7  5              2             5          5          4         2          \n",
       "9  5              1             4          1          4         1          \n",
       "11 2              4             5          2          1         1          \n",
       "   TextHelpful DifficultToNav ImproveQuality Irrelevant Confused ContinueUse\n",
       "1  4           1              4              3          2        5          \n",
       "3  5           1              4              3          1        5          \n",
       "5  5           1              5              2          2        5          \n",
       "7  5           2              4              2          2        5          \n",
       "9  3           1              3              3          2        4          \n",
       "11 2           5              5              1          2        5          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(clean_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_q2_A <- subset(clean_q2, Condition=='A')\n",
    "clean_q2_NOA <- subset(clean_q2, Condition=='NOA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ParticipantNo</th><th scope=col>Condition</th><th scope=col>UseFrequently</th><th scope=col>Complex</th><th scope=col>EasyToUse</th><th scope=col>NeedSupport</th><th scope=col>WellIntegrated</th><th scope=col>Inconsistency</th><th scope=col>UseQuickly</th><th scope=col>Cumbersome</th><th scope=col>Confident</th><th scope=col>NeededLearn</th><th scope=col>TextHelpful</th><th scope=col>DifficultToNav</th><th scope=col>ImproveQuality</th><th scope=col>Irrelevant</th><th scope=col>Confused</th><th scope=col>ContinueUse</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>A</td><td>5</td><td>1</td><td>4</td><td>1</td><td>4</td><td>2</td><td>5</td><td>1</td><td>5</td><td>1</td><td>4</td><td>1</td><td>4</td><td>3</td><td>2</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2</td><td>A</td><td>5</td><td>1</td><td>4</td><td>1</td><td>5</td><td>1</td><td>5</td><td>2</td><td>5</td><td>2</td><td>5</td><td>1</td><td>4</td><td>3</td><td>1</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>3</td><td>A</td><td>5</td><td>4</td><td>5</td><td>2</td><td>5</td><td>2</td><td>5</td><td>2</td><td>2</td><td>2</td><td>5</td><td>1</td><td>5</td><td>2</td><td>2</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>4</td><td>A</td><td>5</td><td>2</td><td>5</td><td>4</td><td>5</td><td>2</td><td>5</td><td>5</td><td>4</td><td>2</td><td>5</td><td>2</td><td>4</td><td>2</td><td>2</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>5</td><td>A</td><td>5</td><td>2</td><td>5</td><td>2</td><td>5</td><td>1</td><td>4</td><td>1</td><td>4</td><td>1</td><td>3</td><td>1</td><td>3</td><td>3</td><td>2</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>6</td><td>A</td><td>5</td><td>3</td><td>3</td><td>3</td><td>2</td><td>4</td><td>5</td><td>2</td><td>1</td><td>1</td><td>2</td><td>5</td><td>5</td><td>1</td><td>2</td><td>5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & ParticipantNo & Condition & UseFrequently & Complex & EasyToUse & NeedSupport & WellIntegrated & Inconsistency & UseQuickly & Cumbersome & Confident & NeededLearn & TextHelpful & DifficultToNav & ImproveQuality & Irrelevant & Confused & ContinueUse\\\\\n",
       "\\hline\n",
       "\t1 & 1 & A & 5 & 1 & 4 & 1 & 4 & 2 & 5 & 1 & 5 & 1 & 4 & 1 & 4 & 3 & 2 & 5\\\\\n",
       "\t3 & 2 & A & 5 & 1 & 4 & 1 & 5 & 1 & 5 & 2 & 5 & 2 & 5 & 1 & 4 & 3 & 1 & 5\\\\\n",
       "\t5 & 3 & A & 5 & 4 & 5 & 2 & 5 & 2 & 5 & 2 & 2 & 2 & 5 & 1 & 5 & 2 & 2 & 5\\\\\n",
       "\t7 & 4 & A & 5 & 2 & 5 & 4 & 5 & 2 & 5 & 5 & 4 & 2 & 5 & 2 & 4 & 2 & 2 & 5\\\\\n",
       "\t9 & 5 & A & 5 & 2 & 5 & 2 & 5 & 1 & 4 & 1 & 4 & 1 & 3 & 1 & 3 & 3 & 2 & 4\\\\\n",
       "\t11 & 6 & A & 5 & 3 & 3 & 3 & 2 & 4 & 5 & 2 & 1 & 1 & 2 & 5 & 5 & 1 & 2 & 5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ParticipantNo | Condition | UseFrequently | Complex | EasyToUse | NeedSupport | WellIntegrated | Inconsistency | UseQuickly | Cumbersome | Confident | NeededLearn | TextHelpful | DifficultToNav | ImproveQuality | Irrelevant | Confused | ContinueUse | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 1 | A | 5 | 1 | 4 | 1 | 4 | 2 | 5 | 1 | 5 | 1 | 4 | 1 | 4 | 3 | 2 | 5 | \n",
       "| 3 | 2 | A | 5 | 1 | 4 | 1 | 5 | 1 | 5 | 2 | 5 | 2 | 5 | 1 | 4 | 3 | 1 | 5 | \n",
       "| 5 | 3 | A | 5 | 4 | 5 | 2 | 5 | 2 | 5 | 2 | 2 | 2 | 5 | 1 | 5 | 2 | 2 | 5 | \n",
       "| 7 | 4 | A | 5 | 2 | 5 | 4 | 5 | 2 | 5 | 5 | 4 | 2 | 5 | 2 | 4 | 2 | 2 | 5 | \n",
       "| 9 | 5 | A | 5 | 2 | 5 | 2 | 5 | 1 | 4 | 1 | 4 | 1 | 3 | 1 | 3 | 3 | 2 | 4 | \n",
       "| 11 | 6 | A | 5 | 3 | 3 | 3 | 2 | 4 | 5 | 2 | 1 | 1 | 2 | 5 | 5 | 1 | 2 | 5 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   ParticipantNo Condition UseFrequently Complex EasyToUse NeedSupport\n",
       "1  1             A         5             1       4         1          \n",
       "3  2             A         5             1       4         1          \n",
       "5  3             A         5             4       5         2          \n",
       "7  4             A         5             2       5         4          \n",
       "9  5             A         5             2       5         2          \n",
       "11 6             A         5             3       3         3          \n",
       "   WellIntegrated Inconsistency UseQuickly Cumbersome Confident NeededLearn\n",
       "1  4              2             5          1          5         1          \n",
       "3  5              1             5          2          5         2          \n",
       "5  5              2             5          2          2         2          \n",
       "7  5              2             5          5          4         2          \n",
       "9  5              1             4          1          4         1          \n",
       "11 2              4             5          2          1         1          \n",
       "   TextHelpful DifficultToNav ImproveQuality Irrelevant Confused ContinueUse\n",
       "1  4           1              4              3          2        5          \n",
       "3  5           1              4              3          1        5          \n",
       "5  5           1              5              2          2        5          \n",
       "7  5           2              4              2          2        5          \n",
       "9  3           1              3              3          2        4          \n",
       "11 2           5              5              1          2        5          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(clean_q2_A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Qualitative Analysis (likert scales diagram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t41 obs. of  18 variables:\n",
      " $ ParticipantNo : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Condition     : Factor w/ 2 levels \"A\",\"NOA\": 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ UseFrequently : int  5 5 5 5 5 5 4 1 2 4 ...\n",
      " $ Complex       : int  1 1 4 2 2 3 1 3 2 1 ...\n",
      " $ EasyToUse     : int  4 4 5 5 5 3 5 4 2 4 ...\n",
      " $ NeedSupport   : int  1 1 2 4 2 3 2 1 1 1 ...\n",
      " $ WellIntegrated: int  4 5 5 5 5 2 4 4 4 4 ...\n",
      " $ Inconsistency : int  2 1 2 2 1 4 3 1 3 1 ...\n",
      " $ UseQuickly    : int  5 5 5 5 4 5 4 3 3 4 ...\n",
      " $ Cumbersome    : int  1 2 2 5 1 2 2 4 2 1 ...\n",
      " $ Confident     : int  5 5 2 4 4 1 4 2 3 4 ...\n",
      " $ NeededLearn   : int  1 2 2 2 1 1 2 2 3 1 ...\n",
      " $ TextHelpful   : int  4 5 5 5 3 2 3 5 4 3 ...\n",
      " $ DifficultToNav: int  1 1 1 2 1 5 2 3 1 1 ...\n",
      " $ ImproveQuality: int  4 4 5 4 3 5 3 3 5 2 ...\n",
      " $ Irrelevant    : int  3 3 2 2 3 1 4 2 2 4 ...\n",
      " $ Confused      : int  2 1 2 2 2 2 3 2 2 4 ...\n",
      " $ ContinueUse   : int  5 5 5 5 4 5 4 4 2 4 ...\n"
     ]
    }
   ],
   "source": [
    "str(clean_q2_A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# need to change this to factors!\n",
    "clean_q2_A$UseFrequently = factor(clean_q2_A$UseFrequently,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$Complex = factor(clean_q2_A$Complex,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$EasyToUse = factor(clean_q2_A$EasyToUse,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$NeedSupport = factor(clean_q2_A$NeedSupport,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$WellIntegrated = factor(clean_q2_A$WellIntegrated,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$Inconsistency = factor(clean_q2_A$Inconsistency,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$UseQuickly = factor(clean_q2_A$UseQuickly,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$Cumbersome = factor(clean_q2_A$Cumbersome,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$Confident = factor(clean_q2_A$Confident,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$NeededLearn = factor(clean_q2_A$NeededLearn,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$TextHelpful = factor(clean_q2_A$TextHelpful,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$DifficultToNav = factor(clean_q2_A$DifficultToNav,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$ImproveQuality = factor(clean_q2_A$ImproveQuality,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$Irrelevant = factor(clean_q2_A$Irrelevant,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$Confused = factor(clean_q2_A$Confused,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)\n",
    "\n",
    "clean_q2_A$ContinueUse = factor(clean_q2_A$ContinueUse,\n",
    "                                  levels = c(\"1\", \"2\", \"3\", \"4\", \"5\"), ordered = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t41 obs. of  18 variables:\n",
      " $ ParticipantNo : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Condition     : Factor w/ 2 levels \"A\",\"NOA\": 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ UseFrequently : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 5 5 5 5 5 5 4 1 2 4 ...\n",
      " $ Complex       : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 1 1 4 2 2 3 1 3 2 1 ...\n",
      " $ EasyToUse     : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 4 4 5 5 5 3 5 4 2 4 ...\n",
      " $ NeedSupport   : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 1 1 2 4 2 3 2 1 1 1 ...\n",
      " $ WellIntegrated: Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 4 5 5 5 5 2 4 4 4 4 ...\n",
      " $ Inconsistency : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 2 1 2 2 1 4 3 1 3 1 ...\n",
      " $ UseQuickly    : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 5 5 5 5 4 5 4 3 3 4 ...\n",
      " $ Cumbersome    : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 1 2 2 5 1 2 2 4 2 1 ...\n",
      " $ Confident     : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 5 5 2 4 4 1 4 2 3 4 ...\n",
      " $ NeededLearn   : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 1 2 2 2 1 1 2 2 3 1 ...\n",
      " $ TextHelpful   : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 4 5 5 5 3 2 3 5 4 3 ...\n",
      " $ DifficultToNav: Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 1 1 1 2 1 5 2 3 1 1 ...\n",
      " $ ImproveQuality: Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 4 4 5 4 3 5 3 3 5 2 ...\n",
      " $ Irrelevant    : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 3 3 2 2 3 1 4 2 2 4 ...\n",
      " $ Confused      : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 2 1 2 2 2 2 3 2 2 4 ...\n",
      " $ ContinueUse   : Ord.factor w/ 5 levels \"1\"<\"2\"<\"3\"<\"4\"<..: 5 5 5 5 4 5 4 4 2 4 ...\n"
     ]
    }
   ],
   "source": [
    "# checkl again\n",
    "str(clean_q2_A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " ParticipantNo Condition UseFrequently Complex   EasyToUse NeedSupport\n",
       " Min.   : 1    A  :41    1   : 1       1   :24   1   : 3   1   :25    \n",
       " 1st Qu.:11    NOA: 0    2   : 5       2   :10   2   : 1   2   :11    \n",
       " Median :21              3   : 8       3   : 4   3   : 6   3   : 2    \n",
       " Mean   :21              4   :10       4   : 1   4   :13   4   : 1    \n",
       " 3rd Qu.:31              5   :15       5   : 0   5   :16   5   : 0    \n",
       " Max.   :41              NA's: 2       NA's: 2   NA's: 2   NA's: 2    \n",
       " WellIntegrated Inconsistency UseQuickly Cumbersome Confident NeededLearn\n",
       " 1   : 1        1   :15       1   : 0    1   :20    1   : 2   1   :17    \n",
       " 2   : 2        2   :13       2   : 2    2   :11    2   : 5   2   :15    \n",
       " 3   : 7        3   : 7       3   : 2    3   : 5    3   : 8   3   : 3    \n",
       " 4   :17        4   : 4       4   :17    4   : 2    4   :12   4   : 2    \n",
       " 5   :12        5   : 0       5   :18    5   : 1    5   :12   5   : 2    \n",
       " NA's: 2        NA's: 2       NA's: 2    NA's: 2    NA's: 2   NA's: 2    \n",
       " TextHelpful DifficultToNav ImproveQuality Irrelevant Confused  ContinueUse\n",
       " 1   : 1     1   :24        1   : 0        1   : 4    1   :12   1   : 0    \n",
       " 2   : 4     2   : 7        2   : 6        2   :12    2   :16   2   : 5    \n",
       " 3   :12     3   : 4        3   :13        3   :11    3   : 2   3   : 7    \n",
       " 4   :12     4   : 3        4   :11        4   : 8    4   : 8   4   :12    \n",
       " 5   :10     5   : 1        5   : 9        5   : 4    5   : 1   5   :15    \n",
       " NA's: 2     NA's: 2        NA's: 2        NA's: 2    NA's: 2   NA's: 2    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(clean_q2_A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " UseFrequently Complex   EasyToUse NeedSupport WellIntegrated Inconsistency\n",
       " 1   : 1       1   :24   1   : 3   1   :25     1   : 1        1   :15      \n",
       " 2   : 5       2   :10   2   : 1   2   :11     2   : 2        2   :13      \n",
       " 3   : 8       3   : 4   3   : 6   3   : 2     3   : 7        3   : 7      \n",
       " 4   :10       4   : 1   4   :13   4   : 1     4   :17        4   : 4      \n",
       " 5   :15       5   : 0   5   :16   5   : 0     5   :12        5   : 0      \n",
       " NA's: 2       NA's: 2   NA's: 2   NA's: 2     NA's: 2        NA's: 2      \n",
       " UseQuickly Cumbersome Confident NeededLearn TextHelpful DifficultToNav\n",
       " 1   : 0    1   :20    1   : 2   1   :17     1   : 1     1   :24       \n",
       " 2   : 2    2   :11    2   : 5   2   :15     2   : 4     2   : 7       \n",
       " 3   : 2    3   : 5    3   : 8   3   : 3     3   :12     3   : 4       \n",
       " 4   :17    4   : 2    4   :12   4   : 2     4   :12     4   : 3       \n",
       " 5   :18    5   : 1    5   :12   5   : 2     5   :10     5   : 1       \n",
       " NA's: 2    NA's: 2    NA's: 2   NA's: 2     NA's: 2     NA's: 2       \n",
       " ImproveQuality Irrelevant Confused  ContinueUse\n",
       " 1   : 0        1   : 4    1   :12   1   : 0    \n",
       " 2   : 6        2   :12    2   :16   2   : 5    \n",
       " 3   :13        3   :11    3   : 2   3   : 7    \n",
       " 4   :11        4   : 8    4   : 8   4   :12    \n",
       " 5   : 9        5   : 4    5   : 1   5   :15    \n",
       " NA's: 2        NA's: 2    NA's: 2   NA's: 2    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#need to remove the 'out of level columns'\n",
    "ss4 <- c(3:18)\n",
    "final_data <- subset(clean_q2_A, select=ss4)\n",
    "summary(final_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results <- likert(final_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QPCCZdct27dclwIo0Npoj67dOkSdtxxx1r3suEavOiii8Iee+wR7rvvvvD222+H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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Legend: 5 = Strongly agree 1 = Stronly disagree\n",
    "plot(results, type='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z8VhrTNmjVLZ/1iGBi9UgzRYvIC9rmip4HCjG9euJ7Z00LCmGhh1KhR2iNGrxy9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VPxRIdLiYNIR9P+J1GFky++27kYdEeeDg/ujUoYs5nDznHc1xjbqMKIq2YZqNOn6k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\njpJPrI/EBVTve92T+2Hx35WrV1C8VBFJVHZs3YVGzo2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QqlnVSwSixlY4KAQUAkGNgNIgBTWiqjyFgEJAIaAQUAgoBBQCCgGFgELAbhFQ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\n4TVOXI0/QhZE063Vq1frFx3LttWKbN6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AQBQUAQEAQcDAEhSA72wKS6goAgIAgI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TUGqb/JJnz55h0qRJmnTYmtb169dPm+pR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\nRGCpmjZ0u+TO8rWk0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ECROUogvPE8wV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\nAiRAAiRAAiRAAiRAAk5G4P+BYjrPK2yT6QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alternative hearmap graph\n",
    "plot(results, \n",
    "     type=\"heat\",\n",
    "           low.color = \"white\", \n",
    "           high.color = \"blue\",\n",
    "           text.color = \"black\", \n",
    "           text.size = 4, \n",
    "           wrap = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ASyisDatWvly1/+stx0002ydetWaW5ulnvvvVcuuugiOx6qxkAgzZ8/f6huz/uS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mtnwI1hB36CKZnM78LrF6sbfPG5suX58z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\nt6iQnjChsG00UgKpVI1nOSRAAiRAAiRAAiRAAiRAAiTQEwK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3SRUSrHIQWGFhhREpsGrB4gXrV7FiNbu2STUdwZoGhyF/JrBUyDox60QW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\nQAACEIAABCAAgSYS+H/zrou3/BLX+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checkng the distributions\n",
    "plot(results,\n",
    "     type=\"density\",\n",
    "           facet = TRUE, \n",
    "           bw = 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jason Bryer, J. and Speerschneider, K. Package ‘likert’. cran.r-project.org/web/packages/likert/likert.pdf."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.3 System Usability Score Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ParticipantNo</th><th scope=col>UseFrequently</th><th scope=col>Complex</th><th scope=col>EasyToUse</th><th scope=col>NeedSupport</th><th scope=col>WellIntegrated</th><th scope=col>Inconsistency</th><th scope=col>UseQuickly</th><th scope=col>Cumbersome</th><th scope=col>Confident</th><th scope=col>NeededLearn</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>5</td><td>1</td><td>4</td><td>1</td><td>4</td><td>2</td><td>5</td><td>1</td><td>5</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2</td><td>5</td><td>1</td><td>4</td><td>1</td><td>5</td><td>1</td><td>5</td><td>2</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>3</td><td>5</td><td>4</td><td>5</td><td>2</td><td>5</td><td>2</td><td>5</td><td>2</td><td>2</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>4</td><td>5</td><td>2</td><td>5</td><td>4</td><td>5</td><td>2</td><td>5</td><td>5</td><td>4</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>5</td><td>5</td><td>2</td><td>5</td><td>2</td><td>5</td><td>1</td><td>4</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>6</td><td>5</td><td>3</td><td>3</td><td>3</td><td>2</td><td>4</td><td>5</td><td>2</td><td>1</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & ParticipantNo & UseFrequently & Complex & EasyToUse & NeedSupport & WellIntegrated & Inconsistency & UseQuickly & Cumbersome & Confident & NeededLearn\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 5 & 1 & 4 & 1 & 4 & 2 & 5 & 1 & 5 & 1\\\\\n",
       "\t3 & 2 & 5 & 1 & 4 & 1 & 5 & 1 & 5 & 2 & 5 & 2\\\\\n",
       "\t5 & 3 & 5 & 4 & 5 & 2 & 5 & 2 & 5 & 2 & 2 & 2\\\\\n",
       "\t7 & 4 & 5 & 2 & 5 & 4 & 5 & 2 & 5 & 5 & 4 & 2\\\\\n",
       "\t9 & 5 & 5 & 2 & 5 & 2 & 5 & 1 & 4 & 1 & 4 & 1\\\\\n",
       "\t11 & 6 & 5 & 3 & 3 & 3 & 2 & 4 & 5 & 2 & 1 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ParticipantNo | UseFrequently | Complex | EasyToUse | NeedSupport | WellIntegrated | Inconsistency | UseQuickly | Cumbersome | Confident | NeededLearn | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 1 | 5 | 1 | 4 | 1 | 4 | 2 | 5 | 1 | 5 | 1 | \n",
       "| 3 | 2 | 5 | 1 | 4 | 1 | 5 | 1 | 5 | 2 | 5 | 2 | \n",
       "| 5 | 3 | 5 | 4 | 5 | 2 | 5 | 2 | 5 | 2 | 2 | 2 | \n",
       "| 7 | 4 | 5 | 2 | 5 | 4 | 5 | 2 | 5 | 5 | 4 | 2 | \n",
       "| 9 | 5 | 5 | 2 | 5 | 2 | 5 | 1 | 4 | 1 | 4 | 1 | \n",
       "| 11 | 6 | 5 | 3 | 3 | 3 | 2 | 4 | 5 | 2 | 1 | 1 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   ParticipantNo UseFrequently Complex EasyToUse NeedSupport WellIntegrated\n",
       "1  1             5             1       4         1           4             \n",
       "3  2             5             1       4         1           5             \n",
       "5  3             5             4       5         2           5             \n",
       "7  4             5             2       5         4           5             \n",
       "9  5             5             2       5         2           5             \n",
       "11 6             5             3       3         3           2             \n",
       "   Inconsistency UseQuickly Cumbersome Confident NeededLearn\n",
       "1  2             5          1          5         1          \n",
       "3  1             5          2          5         2          \n",
       "5  2             5          2          2         2          \n",
       "7  2             5          5          4         2          \n",
       "9  1             4          1          4         1          \n",
       "11 4             5          2          1         1          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r1 <- c(1, 3:12)\n",
    "sus <- subset(clean_q2_A, select=r1)\n",
    "head(sus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ParticipantNo</th><th scope=col>UseFrequently</th><th scope=col>Complex</th><th scope=col>EasyToUse</th><th scope=col>NeedSupport</th><th scope=col>WellIntegrated</th><th scope=col>Inconsistency</th><th scope=col>UseQuickly</th><th scope=col>Cumbersome</th><th scope=col>Confident</th><th scope=col>NeededLearn</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 1</td><td>5 </td><td>1 </td><td>4 </td><td>1 </td><td>4 </td><td>2 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 2</td><td>5 </td><td>1 </td><td>4 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>2 </td><td>5 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>5</th><td> 3</td><td>5 </td><td>4 </td><td>5 </td><td>2 </td><td>5 </td><td>2 </td><td>5 </td><td>2 </td><td>2 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>7</th><td> 4</td><td>5 </td><td>2 </td><td>5 </td><td>4 </td><td>5 </td><td>2 </td><td>5 </td><td>5 </td><td>4 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>9</th><td> 5</td><td>5 </td><td>2 </td><td>5 </td><td>2 </td><td>5 </td><td>1 </td><td>4 </td><td>1 </td><td>4 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>11</th><td> 6</td><td>5 </td><td>3 </td><td>3 </td><td>3 </td><td>2 </td><td>4 </td><td>5 </td><td>2 </td><td>1 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>13</th><td> 7</td><td>4 </td><td>1 </td><td>5 </td><td>2 </td><td>4 </td><td>3 </td><td>4 </td><td>2 </td><td>4 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>15</th><td> 8</td><td>1 </td><td>3 </td><td>4 </td><td>1 </td><td>4 </td><td>1 </td><td>3 </td><td>4 </td><td>2 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>17</th><td> 9</td><td>2 </td><td>2 </td><td>2 </td><td>1 </td><td>4 </td><td>3 </td><td>3 </td><td>2 </td><td>3 </td><td>3 </td></tr>\n",
       "\t<tr><th scope=row>19</th><td>10</td><td>4 </td><td>1 </td><td>4 </td><td>1 </td><td>4 </td><td>1 </td><td>4 </td><td>1 </td><td>4 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>21</th><td>11</td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>23</th><td>12</td><td>3 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>4 </td><td>1 </td><td>4 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>25</th><td>13</td><td>3 </td><td>1 </td><td>3 </td><td>1 </td><td>4 </td><td>1 </td><td>4 </td><td>3 </td><td>3 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>27</th><td>14</td><td>3 </td><td>3 </td><td>3 </td><td>1 </td><td>4 </td><td>1 </td><td>5 </td><td>2 </td><td>5 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>29</th><td>15</td><td>2 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>2 </td><td>4 </td><td>1 </td><td>5 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>31</th><td>16</td><td>3 </td><td>1 </td><td>4 </td><td>2 </td><td>5 </td><td>1 </td><td>4 </td><td>1 </td><td>3 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>17</td><td>2 </td><td>2 </td><td>4 </td><td>1 </td><td>2 </td><td>2 </td><td>4 </td><td>3 </td><td>3 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>18</td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>3 </td><td>3 </td><td>5 </td><td>1 </td><td>5 </td><td>4 </td></tr>\n",
       "\t<tr><th scope=row>37</th><td>19</td><td>5 </td><td>2 </td><td>3 </td><td>2 </td><td>4 </td><td>4 </td><td>4 </td><td>2 </td><td>4 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>39</th><td>20</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>41</th><td>21</td><td>3 </td><td>1 </td><td>4 </td><td>2 </td><td>3 </td><td>3 </td><td>4 </td><td>1 </td><td>3 </td><td>3 </td></tr>\n",
       "\t<tr><th scope=row>43</th><td>22</td><td>3 </td><td>3 </td><td>3 </td><td>2 </td><td>3 </td><td>4 </td><td>2 </td><td>3 </td><td>5 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>45</th><td>23</td><td>3 </td><td>2 </td><td>4 </td><td>1 </td><td>3 </td><td>1 </td><td>4 </td><td>2 </td><td>4 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>47</th><td>24</td><td>4 </td><td>1 </td><td>5 </td><td>1 </td><td>4 </td><td>2 </td><td>5 </td><td>1 </td><td>4 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>49</th><td>25</td><td>4 </td><td>1 </td><td>1 </td><td>1 </td><td>1 </td><td>4 </td><td>4 </td><td>2 </td><td>3 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>51</th><td>26</td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>2 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>53</th><td>27</td><td>5 </td><td>1 </td><td>5 </td><td>2 </td><td>4 </td><td>2 </td><td>5 </td><td>1 </td><td>5 </td><td>3 </td></tr>\n",
       "\t<tr><th scope=row>55</th><td>28</td><td>4 </td><td>2 </td><td>4 </td><td>1 </td><td>4 </td><td>2 </td><td>4 </td><td>4 </td><td>2 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>57</th><td>29</td><td>4 </td><td>1 </td><td>1 </td><td>1 </td><td>4 </td><td>1 </td><td>4 </td><td>1 </td><td>5 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>59</th><td>30</td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>61</th><td>31</td><td>2 </td><td>2 </td><td>4 </td><td>3 </td><td>4 </td><td>2 </td><td>4 </td><td>2 </td><td>1 </td><td>5 </td></tr>\n",
       "\t<tr><th scope=row>63</th><td>32</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>65</th><td>33</td><td>4 </td><td>1 </td><td>1 </td><td>2 </td><td>3 </td><td>3 </td><td>4 </td><td>1 </td><td>2 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>67</th><td>34</td><td>5 </td><td>1 </td><td>4 </td><td>2 </td><td>4 </td><td>2 </td><td>5 </td><td>2 </td><td>4 </td><td>5 </td></tr>\n",
       "\t<tr><th scope=row>69</th><td>35</td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>4 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>71</th><td>36</td><td>4 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>5 </td><td>1 </td><td>3 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>73</th><td>37</td><td>3 </td><td>2 </td><td>4 </td><td>1 </td><td>3 </td><td>3 </td><td>4 </td><td>3 </td><td>4 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>75</th><td>38</td><td>4 </td><td>1 </td><td>3 </td><td>1 </td><td>4 </td><td>1 </td><td>5 </td><td>1 </td><td>4 </td><td>1 </td></tr>\n",
       "\t<tr><th scope=row>77</th><td>39</td><td>5 </td><td>1 </td><td>5 </td><td>2 </td><td>5 </td><td>2 </td><td>5 </td><td>1 </td><td>4 </td><td>4 </td></tr>\n",
       "\t<tr><th scope=row>79</th><td>40</td><td>2 </td><td>2 </td><td>4 </td><td>1 </td><td>3 </td><td>3 </td><td>2 </td><td>3 </td><td>2 </td><td>2 </td></tr>\n",
       "\t<tr><th scope=row>81</th><td>41</td><td>4 </td><td>1 </td><td>5 </td><td>1 </td><td>4 </td><td>2 </td><td>5 </td><td>1 </td><td>3 </td><td>1 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & ParticipantNo & UseFrequently & Complex & EasyToUse & NeedSupport & WellIntegrated & Inconsistency & UseQuickly & Cumbersome & Confident & NeededLearn\\\\\n",
       "\\hline\n",
       "\t1 &  1 & 5  & 1  & 4  & 1  & 4  & 2  & 5  & 1  & 5  & 1 \\\\\n",
       "\t3 &  2 & 5  & 1  & 4  & 1  & 5  & 1  & 5  & 2  & 5  & 2 \\\\\n",
       "\t5 &  3 & 5  & 4  & 5  & 2  & 5  & 2  & 5  & 2  & 2  & 2 \\\\\n",
       "\t7 &  4 & 5  & 2  & 5  & 4  & 5  & 2  & 5  & 5  & 4  & 2 \\\\\n",
       "\t9 &  5 & 5  & 2  & 5  & 2  & 5  & 1  & 4  & 1  & 4  & 1 \\\\\n",
       "\t11 &  6 & 5  & 3  & 3  & 3  & 2  & 4  & 5  & 2  & 1  & 1 \\\\\n",
       "\t13 &  7 & 4  & 1  & 5  & 2  & 4  & 3  & 4  & 2  & 4  & 2 \\\\\n",
       "\t15 &  8 & 1  & 3  & 4  & 1  & 4  & 1  & 3  & 4  & 2  & 2 \\\\\n",
       "\t17 &  9 & 2  & 2  & 2  & 1  & 4  & 3  & 3  & 2  & 3  & 3 \\\\\n",
       "\t19 & 10 & 4  & 1  & 4  & 1  & 4  & 1  & 4  & 1  & 4  & 1 \\\\\n",
       "\t21 & 11 & 5  & 1  & 5  & 1  & 5  & 1  & 5  & 1  & 5  & 1 \\\\\n",
       "\t23 & 12 & 3  & 1  & 5  & 1  & 5  & 1  & 4  & 1  & 4  & 1 \\\\\n",
       "\t25 & 13 & 3  & 1  & 3  & 1  & 4  & 1  & 4  & 3  & 3  & 1 \\\\\n",
       "\t27 & 14 & 3  & 3  & 3  & 1  & 4  & 1  & 5  & 2  & 5  & 1 \\\\\n",
       "\t29 & 15 & 2  & 1  & 5  & 1  & 5  & 2  & 4  & 1  & 5  & 1 \\\\\n",
       "\t31 & 16 & 3  & 1  & 4  & 2  & 5  & 1  & 4  & 1  & 3  & 2 \\\\\n",
       "\t33 & 17 & 2  & 2  & 4  & 1  & 2  & 2  & 4  & 3  & 3  & 2 \\\\\n",
       "\t35 & 18 & 5  & 1  & 5  & 1  & 3  & 3  & 5  & 1  & 5  & 4 \\\\\n",
       "\t37 & 19 & 5  & 2  & 3  & 2  & 4  & 4  & 4  & 2  & 4  & 1 \\\\\n",
       "\t39 & 20 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t41 & 21 & 3  & 1  & 4  & 2  & 3  & 3  & 4  & 1  & 3  & 3 \\\\\n",
       "\t43 & 22 & 3  & 3  & 3  & 2  & 3  & 4  & 2  & 3  & 5  & 1 \\\\\n",
       "\t45 & 23 & 3  & 2  & 4  & 1  & 3  & 1  & 4  & 2  & 4  & 2 \\\\\n",
       "\t47 & 24 & 4  & 1  & 5  & 1  & 4  & 2  & 5  & 1  & 4  & 2 \\\\\n",
       "\t49 & 25 & 4  & 1  & 1  & 1  & 1  & 4  & 4  & 2  & 3  & 2 \\\\\n",
       "\t51 & 26 & 5  & 1  & 5  & 1  & 5  & 2  & 5  & 1  & 5  & 1 \\\\\n",
       "\t53 & 27 & 5  & 1  & 5  & 2  & 4  & 2  & 5  & 1  & 5  & 3 \\\\\n",
       "\t55 & 28 & 4  & 2  & 4  & 1  & 4  & 2  & 4  & 4  & 2  & 2 \\\\\n",
       "\t57 & 29 & 4  & 1  & 1  & 1  & 4  & 1  & 4  & 1  & 5  & 2 \\\\\n",
       "\t59 & 30 & 5  & 1  & 5  & 1  & 5  & 1  & 5  & 1  & 5  & 1 \\\\\n",
       "\t61 & 31 & 2  & 2  & 4  & 3  & 4  & 2  & 4  & 2  & 1  & 5 \\\\\n",
       "\t63 & 32 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t65 & 33 & 4  & 1  & 1  & 2  & 3  & 3  & 4  & 1  & 2  & 1 \\\\\n",
       "\t67 & 34 & 5  & 1  & 4  & 2  & 4  & 2  & 5  & 2  & 4  & 5 \\\\\n",
       "\t69 & 35 & 5  & 1  & 5  & 1  & 4  & 1  & 5  & 1  & 5  & 1 \\\\\n",
       "\t71 & 36 & 4  & 1  & 5  & 1  & 5  & 1  & 5  & 1  & 3  & 2 \\\\\n",
       "\t73 & 37 & 3  & 2  & 4  & 1  & 3  & 3  & 4  & 3  & 4  & 2 \\\\\n",
       "\t75 & 38 & 4  & 1  & 3  & 1  & 4  & 1  & 5  & 1  & 4  & 1 \\\\\n",
       "\t77 & 39 & 5  & 1  & 5  & 2  & 5  & 2  & 5  & 1  & 4  & 4 \\\\\n",
       "\t79 & 40 & 2  & 2  & 4  & 1  & 3  & 3  & 2  & 3  & 2  & 2 \\\\\n",
       "\t81 & 41 & 4  & 1  & 5  & 1  & 4  & 2  & 5  & 1  & 3  & 1 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ParticipantNo | UseFrequently | Complex | EasyToUse | NeedSupport | WellIntegrated | Inconsistency | UseQuickly | Cumbersome | Confident | NeededLearn | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 |  1 | 5  | 1  | 4  | 1  | 4  | 2  | 5  | 1  | 5  | 1  | \n",
       "| 3 |  2 | 5  | 1  | 4  | 1  | 5  | 1  | 5  | 2  | 5  | 2  | \n",
       "| 5 |  3 | 5  | 4  | 5  | 2  | 5  | 2  | 5  | 2  | 2  | 2  | \n",
       "| 7 |  4 | 5  | 2  | 5  | 4  | 5  | 2  | 5  | 5  | 4  | 2  | \n",
       "| 9 |  5 | 5  | 2  | 5  | 2  | 5  | 1  | 4  | 1  | 4  | 1  | \n",
       "| 11 |  6 | 5  | 3  | 3  | 3  | 2  | 4  | 5  | 2  | 1  | 1  | \n",
       "| 13 |  7 | 4  | 1  | 5  | 2  | 4  | 3  | 4  | 2  | 4  | 2  | \n",
       "| 15 |  8 | 1  | 3  | 4  | 1  | 4  | 1  | 3  | 4  | 2  | 2  | \n",
       "| 17 |  9 | 2  | 2  | 2  | 1  | 4  | 3  | 3  | 2  | 3  | 3  | \n",
       "| 19 | 10 | 4  | 1  | 4  | 1  | 4  | 1  | 4  | 1  | 4  | 1  | \n",
       "| 21 | 11 | 5  | 1  | 5  | 1  | 5  | 1  | 5  | 1  | 5  | 1  | \n",
       "| 23 | 12 | 3  | 1  | 5  | 1  | 5  | 1  | 4  | 1  | 4  | 1  | \n",
       "| 25 | 13 | 3  | 1  | 3  | 1  | 4  | 1  | 4  | 3  | 3  | 1  | \n",
       "| 27 | 14 | 3  | 3  | 3  | 1  | 4  | 1  | 5  | 2  | 5  | 1  | \n",
       "| 29 | 15 | 2  | 1  | 5  | 1  | 5  | 2  | 4  | 1  | 5  | 1  | \n",
       "| 31 | 16 | 3  | 1  | 4  | 2  | 5  | 1  | 4  | 1  | 3  | 2  | \n",
       "| 33 | 17 | 2  | 2  | 4  | 1  | 2  | 2  | 4  | 3  | 3  | 2  | \n",
       "| 35 | 18 | 5  | 1  | 5  | 1  | 3  | 3  | 5  | 1  | 5  | 4  | \n",
       "| 37 | 19 | 5  | 2  | 3  | 2  | 4  | 4  | 4  | 2  | 4  | 1  | \n",
       "| 39 | 20 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | \n",
       "| 41 | 21 | 3  | 1  | 4  | 2  | 3  | 3  | 4  | 1  | 3  | 3  | \n",
       "| 43 | 22 | 3  | 3  | 3  | 2  | 3  | 4  | 2  | 3  | 5  | 1  | \n",
       "| 45 | 23 | 3  | 2  | 4  | 1  | 3  | 1  | 4  | 2  | 4  | 2  | \n",
       "| 47 | 24 | 4  | 1  | 5  | 1  | 4  | 2  | 5  | 1  | 4  | 2  | \n",
       "| 49 | 25 | 4  | 1  | 1  | 1  | 1  | 4  | 4  | 2  | 3  | 2  | \n",
       "| 51 | 26 | 5  | 1  | 5  | 1  | 5  | 2  | 5  | 1  | 5  | 1  | \n",
       "| 53 | 27 | 5  | 1  | 5  | 2  | 4  | 2  | 5  | 1  | 5  | 3  | \n",
       "| 55 | 28 | 4  | 2  | 4  | 1  | 4  | 2  | 4  | 4  | 2  | 2  | \n",
       "| 57 | 29 | 4  | 1  | 1  | 1  | 4  | 1  | 4  | 1  | 5  | 2  | \n",
       "| 59 | 30 | 5  | 1  | 5  | 1  | 5  | 1  | 5  | 1  | 5  | 1  | \n",
       "| 61 | 31 | 2  | 2  | 4  | 3  | 4  | 2  | 4  | 2  | 1  | 5  | \n",
       "| 63 | 32 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | \n",
       "| 65 | 33 | 4  | 1  | 1  | 2  | 3  | 3  | 4  | 1  | 2  | 1  | \n",
       "| 67 | 34 | 5  | 1  | 4  | 2  | 4  | 2  | 5  | 2  | 4  | 5  | \n",
       "| 69 | 35 | 5  | 1  | 5  | 1  | 4  | 1  | 5  | 1  | 5  | 1  | \n",
       "| 71 | 36 | 4  | 1  | 5  | 1  | 5  | 1  | 5  | 1  | 3  | 2  | \n",
       "| 73 | 37 | 3  | 2  | 4  | 1  | 3  | 3  | 4  | 3  | 4  | 2  | \n",
       "| 75 | 38 | 4  | 1  | 3  | 1  | 4  | 1  | 5  | 1  | 4  | 1  | \n",
       "| 77 | 39 | 5  | 1  | 5  | 2  | 5  | 2  | 5  | 1  | 4  | 4  | \n",
       "| 79 | 40 | 2  | 2  | 4  | 1  | 3  | 3  | 2  | 3  | 2  | 2  | \n",
       "| 81 | 41 | 4  | 1  | 5  | 1  | 4  | 2  | 5  | 1  | 3  | 1  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   ParticipantNo UseFrequently Complex EasyToUse NeedSupport WellIntegrated\n",
       "1   1            5             1       4         1           4             \n",
       "3   2            5             1       4         1           5             \n",
       "5   3            5             4       5         2           5             \n",
       "7   4            5             2       5         4           5             \n",
       "9   5            5             2       5         2           5             \n",
       "11  6            5             3       3         3           2             \n",
       "13  7            4             1       5         2           4             \n",
       "15  8            1             3       4         1           4             \n",
       "17  9            2             2       2         1           4             \n",
       "19 10            4             1       4         1           4             \n",
       "21 11            5             1       5         1           5             \n",
       "23 12            3             1       5         1           5             \n",
       "25 13            3             1       3         1           4             \n",
       "27 14            3             3       3         1           4             \n",
       "29 15            2             1       5         1           5             \n",
       "31 16            3             1       4         2           5             \n",
       "33 17            2             2       4         1           2             \n",
       "35 18            5             1       5         1           3             \n",
       "37 19            5             2       3         2           4             \n",
       "39 20            NA            NA      NA        NA          NA            \n",
       "41 21            3             1       4         2           3             \n",
       "43 22            3             3       3         2           3             \n",
       "45 23            3             2       4         1           3             \n",
       "47 24            4             1       5         1           4             \n",
       "49 25            4             1       1         1           1             \n",
       "51 26            5             1       5         1           5             \n",
       "53 27            5             1       5         2           4             \n",
       "55 28            4             2       4         1           4             \n",
       "57 29            4             1       1         1           4             \n",
       "59 30            5             1       5         1           5             \n",
       "61 31            2             2       4         3           4             \n",
       "63 32            NA            NA      NA        NA          NA            \n",
       "65 33            4             1       1         2           3             \n",
       "67 34            5             1       4         2           4             \n",
       "69 35            5             1       5         1           4             \n",
       "71 36            4             1       5         1           5             \n",
       "73 37            3             2       4         1           3             \n",
       "75 38            4             1       3         1           4             \n",
       "77 39            5             1       5         2           5             \n",
       "79 40            2             2       4         1           3             \n",
       "81 41            4             1       5         1           4             \n",
       "   Inconsistency UseQuickly Cumbersome Confident NeededLearn\n",
       "1  2             5          1          5         1          \n",
       "3  1             5          2          5         2          \n",
       "5  2             5          2          2         2          \n",
       "7  2             5          5          4         2          \n",
       "9  1             4          1          4         1          \n",
       "11 4             5          2          1         1          \n",
       "13 3             4          2          4         2          \n",
       "15 1             3          4          2         2          \n",
       "17 3             3          2          3         3          \n",
       "19 1             4          1          4         1          \n",
       "21 1             5          1          5         1          \n",
       "23 1             4          1          4         1          \n",
       "25 1             4          3          3         1          \n",
       "27 1             5          2          5         1          \n",
       "29 2             4          1          5         1          \n",
       "31 1             4          1          3         2          \n",
       "33 2             4          3          3         2          \n",
       "35 3             5          1          5         4          \n",
       "37 4             4          2          4         1          \n",
       "39 NA            NA         NA         NA        NA         \n",
       "41 3             4          1          3         3          \n",
       "43 4             2          3          5         1          \n",
       "45 1             4          2          4         2          \n",
       "47 2             5          1          4         2          \n",
       "49 4             4          2          3         2          \n",
       "51 2             5          1          5         1          \n",
       "53 2             5          1          5         3          \n",
       "55 2             4          4          2         2          \n",
       "57 1             4          1          5         2          \n",
       "59 1             5          1          5         1          \n",
       "61 2             4          2          1         5          \n",
       "63 NA            NA         NA         NA        NA         \n",
       "65 3             4          1          2         1          \n",
       "67 2             5          2          4         5          \n",
       "69 1             5          1          5         1          \n",
       "71 1             5          1          3         2          \n",
       "73 3             4          3          4         2          \n",
       "75 1             5          1          4         1          \n",
       "77 2             5          1          4         4          \n",
       "79 3             2          3          2         2          \n",
       "81 2             5          1          3         1          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "sus_ready <- read.csv(\"data/sus_calculation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Participant</th><th scope=col>q1</th><th scope=col>q2</th><th scope=col>q3</th><th scope=col>q4</th><th scope=col>q5</th><th scope=col>q6</th><th scope=col>q7</th><th scope=col>q8</th><th scope=col>q9</th><th scope=col>q10</th><th scope=col>SUS.Score</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1   </td><td>5   </td><td>1   </td><td>4   </td><td>1   </td><td>4   </td><td>2   </td><td>5   </td><td>1   </td><td>5   </td><td>1   </td><td>92.5</td></tr>\n",
       "\t<tr><td>2   </td><td>5   </td><td>1   </td><td>4   </td><td>1   </td><td>5   </td><td>1   </td><td>5   </td><td>2   </td><td>5   </td><td>2   </td><td>92.5</td></tr>\n",
       "\t<tr><td>3   </td><td>5   </td><td>4   </td><td>5   </td><td>2   </td><td>5   </td><td>2   </td><td>5   </td><td>2   </td><td>2   </td><td>2   </td><td>75.0</td></tr>\n",
       "\t<tr><td>4   </td><td>5   </td><td>2   </td><td>5   </td><td>4   </td><td>5   </td><td>2   </td><td>5   </td><td>5   </td><td>4   </td><td>2   </td><td>72.5</td></tr>\n",
       "\t<tr><td>5   </td><td>5   </td><td>2   </td><td>5   </td><td>2   </td><td>5   </td><td>1   </td><td>4   </td><td>1   </td><td>4   </td><td>1   </td><td>90.0</td></tr>\n",
       "\t<tr><td>6   </td><td>5   </td><td>3   </td><td>3   </td><td>3   </td><td>2   </td><td>4   </td><td>5   </td><td>2   </td><td>1   </td><td>1   </td><td>57.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " Participant & q1 & q2 & q3 & q4 & q5 & q6 & q7 & q8 & q9 & q10 & SUS.Score\\\\\n",
       "\\hline\n",
       "\t 1    & 5    & 1    & 4    & 1    & 4    & 2    & 5    & 1    & 5    & 1    & 92.5\\\\\n",
       "\t 2    & 5    & 1    & 4    & 1    & 5    & 1    & 5    & 2    & 5    & 2    & 92.5\\\\\n",
       "\t 3    & 5    & 4    & 5    & 2    & 5    & 2    & 5    & 2    & 2    & 2    & 75.0\\\\\n",
       "\t 4    & 5    & 2    & 5    & 4    & 5    & 2    & 5    & 5    & 4    & 2    & 72.5\\\\\n",
       "\t 5    & 5    & 2    & 5    & 2    & 5    & 1    & 4    & 1    & 4    & 1    & 90.0\\\\\n",
       "\t 6    & 5    & 3    & 3    & 3    & 2    & 4    & 5    & 2    & 1    & 1    & 57.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Participant | q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | SUS.Score | \n",
       "|---|---|---|---|---|---|\n",
       "| 1    | 5    | 1    | 4    | 1    | 4    | 2    | 5    | 1    | 5    | 1    | 92.5 | \n",
       "| 2    | 5    | 1    | 4    | 1    | 5    | 1    | 5    | 2    | 5    | 2    | 92.5 | \n",
       "| 3    | 5    | 4    | 5    | 2    | 5    | 2    | 5    | 2    | 2    | 2    | 75.0 | \n",
       "| 4    | 5    | 2    | 5    | 4    | 5    | 2    | 5    | 5    | 4    | 2    | 72.5 | \n",
       "| 5    | 5    | 2    | 5    | 2    | 5    | 1    | 4    | 1    | 4    | 1    | 90.0 | \n",
       "| 6    | 5    | 3    | 3    | 3    | 2    | 4    | 5    | 2    | 1    | 1    | 57.5 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Participant q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 SUS.Score\n",
       "1 1           5  1  4  1  4  2  5  1  5  1   92.5     \n",
       "2 2           5  1  4  1  5  1  5  2  5  2   92.5     \n",
       "3 3           5  4  5  2  5  2  5  2  2  2   75.0     \n",
       "4 4           5  2  5  4  5  2  5  5  4  2   72.5     \n",
       "5 5           5  2  5  2  5  1  4  1  4  1   90.0     \n",
       "6 6           5  3  3  3  2  4  5  2  1  1   57.5     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(sus_ready)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UV+3PoT88fvsHzjG99oy/eGAIHaCMQA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\ntxG0slzRAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g <- c(2:11)\n",
    "ggplot(data=sus_ready, aes(x=Participant, y=SUS.Score, group=1)) +\n",
    "  geom_point()+\n",
    "  geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "77.6923076923077"
      ],
      "text/latex": [
       "77.6923076923077"
      ],
      "text/markdown": [
       "77.6923076923077"
      ],
      "text/plain": [
       "[1] 77.69231"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(sus_ready$SUS.Score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual SUS calculation is in <b>sus_calculation.xlsx</b><br>\n",
    "SUS: $60-70=ok$, $70-80=good$, $80+ =excellent$"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}