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    "Find this project in [Github: UberHowley/SPOC-File-Processing](https://github.com/UberHowley/spoc-file-processing)\n",
    "\n",
    "---\n",
    "\n",
    "# Discussion Forums in a Small Private Online Course (SPOC)\n",
    "In previous work [detailed here](http://www.irishowley.com/website/pMOOChelpers.html) I found that up- and down-voting had a significant negative effect on the number of peer helpers MOOC students invited to their help seeking thread. Results from a previous survey experiment also suggested that framing tasks with a learning-oriented (or value-emphasized) instruction decreased evaluation anxiety, which is the hypothesized mechanism through which voting inhibits help seeking. Following on this work, I wished to investigate **how voting impacts help seeking in more traditional online course discussion forums** and **if we can alleviate any potential costs of effective participation while still maintaining the benefits of voting in forums**? \n",
    "\n",
    "So this experiment took place in a Small Private Online Course (SPOC) with a more naturalistic up/downvoting setup in a disucssion forum and used prompts that emphasized the value of participating in forums.\n",
    "\n",
    "*I used Python to clean the logfile data, gensim to assign automated [LDA] topics to each message board post, and pandas to perform statistical analyses in order to answer my research questions.*\n",
    "\n",
    "## Research Questions\n",
    "Our QuickHelper system was designed to advise students on which peers they might want to help them, but also to answer  theory-based questions about student motivation and decision-making in the face of common interactional archetypes employed in MOOCs. This yielded the following research questions:\n",
    "1. What is the effect of no/up/downvoting on {learning, posting comments, posting quality comments, help seeking}\n",
    "1. What is the effect of neutral/goal prompting on {learning, posting comments, posting quality comments, help seeking}\n",
    "1. Is there an interaction between votingXprompting on {learning, posting comments, posting quality comments, help seeking}\n",
    "1. What is the relationship between {posting, posting quality, help seeking} and learning?\n",
    "1. Which prompts are the most successful at getting a student response? (Does this change over time?) What about the highest quality responses?\n",
    "1. What behavior features correlate strongly with learning or exam scores? (i.e., can we predict student performance with behaviors in the first couple of weeks?)\n",
    "  1. Is there a correlation between pageviews and performance?\n",
    "  1. What is the most viewed lecture? etc.\n",
    "  1. What lecture is viewed the most by non-students? etc.\n",
    "\n",
    "## Experimental Setup\n",
    "The parallel computing course in which this experiment took place met in-person twice per week for 1:20, but all lecture slides (of which there were 28 sets) were posted [online](http://15418.courses.cs.cmu.edu/spring2015/). Each slide had its own discussion forum-like comments beneath it, as shown below. Students enrolled in the course were required to \"individually contribute one interesting comment per lecture using the course web site\" for a 5% participation grade. The experiment began in the TODO week of the course, and ran for TODO weeks.\n",
    "![SPOC Discussion Forum](http://www.irishowley.com/website/img/proj_spoc_forum.jpg)\n",
    "\n",
    "Approximately 70 consenting students are included in this 3X2 experiment.\n",
    "\n",
    "### Voting\n",
    "There were three voting conditions in this dimension: no voting, upvoting only, and downvoting, as shown below. Each student was assigned to one of these conditions, and they only ever saw one voting condition. There was the issue of cross-contamination of voting conditions, if one student looked at a peer's screen, but the instructors did not see any physical evidence of this cross contamination until the last week of the course.\n",
    "![SPOC Voting Conditions](http://www.irishowley.com/website/img/proj_spoc_voting.jpg)\n",
    "\n",
    "### Prompting\n",
    "Students were also assigned to only one of two prompting conditions: positive (learning-oriented/value-emphasis) or neutral. These prompts were in the form of an email sent through the system, that would have a preset \"welcome prompt\" followed by a customizable \"instructional prompt.\" The welcome prompt was either value-emphasis or neutral in tone, and the instructional prompt was either a general 'restate' request or a more specific in nature, prompting the student to answer a specific question. The welcome prompts were predefined and not customizable by the sender of the prompts, while the instructional portion was customizable.  In order to maintain the utility of the email prompots, instructor-customization had to be supported.\n",
    "\n",
    "<table><tr><td><b>Neutral Welcome Prompt</b></td><td><b>Positive Welcome Prompt</b></td></tr>\n",
    "<tr><td><ul><li>This is a discussion you can participate in.</li>\n",
    "<li>Here is a discussion you can participate in.</li>\n",
    "<li>This is a thread you can participate in.</li>\n",
    "<li>Here’s a thread you can comment on.</li>\n",
    "<li>Here’s a conversation you can comment on.</li>\n",
    "<li>Here’s a conversation you can participate in.</li>\n",
    "<li>Here is a discussion in which you can participate.</li>\n",
    "<li>This is an opportunity to engage in the class discussion.</li>\n",
    "<li>Here is an opportunity for you to engage in the class discussion.</li>\n",
    "<li>Here’s a chance to engage in the discussion.</li>\n",
    "<li>Here’s a chance to jump into the discussion.</li>\n",
    "<li>Here’s an opportunity to jump in.</li>\n",
    "<li>This is a thread you can respond to.</li>\n",
    "<li>Here is a discussion you can respond to.</li>\n",
    "<li>Here’s a conversation to jump in on.</li>\n",
    "<li>Here’s a conversation you can respond to.</li></ul>\n",
    "</td><td><ul><li>Participating in class discussions increases exposure to new ideas.</li>\n",
    "<li>Our class discussion forums increase exposure to new ideas.</li>\n",
    "<li>Exposure to new ideas is one benefit to the class discussion forums.</li>\n",
    "<li>Take some time to explore some new ideas in the class forums.</li>\n",
    "<li>Writing down your thoughts will help you think through complex ideas.</li>\n",
    "<li>Class discussions help you understand concepts, not just memorize them.</li>\n",
    "<li>Participating in class discussions will help you understand the concepts, and not just memorize them.</li>\n",
    "<li>Contributing to the course discussion forums is a good way to learn new things.</li>\n",
    "<li>Participation in course discussions will increase your learning.</li>\n",
    "<li>Expanding on others’ ideas is a great way to learn new things.</li>\n",
    "<li>Participating in the class discussions online will help you learn the concepts better.</li>\n",
    "<li>Asynchronous discussions allow for more thought-processing time.</li>\n",
    "<li>Class discussion forums get you to think through your thoughts.</li>\n",
    "<li>Participation from all students is key to everyone’s learning.</li>\n",
    "<li>Expanding on others’ ideas is a great way to check your own understanding.</li>\n",
    "<li>Writing things down is a great way to check your own understanding.</li></ul>\n",
    "</td></tr></table>\n",
    "\n",
    "<table><tr><td><b>Context - Restate (customizable)</b></td><td><b>Context - Question (customizable)</b></td></tr>\n",
    "<li><tr><td><ul><li>Try to describe what was said on this slide in your own words.</li>\n",
    "<li>Summarize the main point of this slide.</li>\n",
    "<li>Summarize the topic of [Insert concept related to slide here].</li>\n",
    "<li>Summarize, in your own words, what @NAME was trying to say here.</li>\n",
    "<li>Restate, in your own words, what I was trying to explain on this slide.</li>\n",
    "<li>Restate, in your own words, the idea of [Insert concept related to slide here].</li>\n",
    "<li>Restate, in your own words, what @NAME was trying to say here.</li>\n",
    "<li>Try restating, in your own words, what @NAME was saying here.</li>\n",
    "<li>Try and describe the main idea in this slide.</li>\n",
    "<li>Can you summarize the discussion happening here?</li></ul>\n",
    "</td><td><ul><li>Can you answer the question in this discussion?</li>\n",
    "<li>Try to answer the question in this discussion.</li>\n",
    "<li>What’s your answer to this question?</li>\n",
    "<li>How would you answer this question?</li></ul>\n",
    "</td></tr></table>\n",
    "\n",
    "A course instructor might decide to prompt some students to answer another students' question, as this is a valuable learning activity. When a course instructor decides to send an email prompt to a student, the system would randomly select two students. The instructor would select and customize the instructional prompt and upon submitting, the system would send an email to two students, with an appropriate version of a randomly selected welcome prompt. An example email prompt is shown below:\n",
    "![SPOC Email Prompt Example](http://www.irishowley.com/website/img/proj_spoc_prompting.jpg)\n",
    "\n",
    "\n",
    "## Processing Logfiles\n",
    "Processing the logfiles mostly involves: (1) ensuring that only consenting students are included in the dataset, (2) that each comment is assigned an LDA topic, (3) that student names are removed from the dataset, (4) removing course instructors from the dataset, (5) removing spaces from column headers, and (6) removing approximately 241 comments from the logfiles that were posted to a lecture slide more than 2-3 weeks in the past (i.e., attempted cramming for an increased participation grade).\n",
    "\n",
    "## Statistical Analysis\n",
    "I used the pandas and statsmodels libraries to run descriptive statistics, generate plots to better understand the data, and answer our research questions (from above). The main effects of interest were the categorical condition variables, `voting` (updownvote, upvote, novote) & `prompts` (positive, neutral) and a variety of scalar dependent variables (`number of comments`, `comment quality`, `help seeking`, and `learning`)."
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    "# necessary libraries/setup\n",
    "%matplotlib inline\n",
    "import utilsSPOC as utils  # separate file that contains all the constants we need\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# need a few more libraries for ANOVA analysis\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "# importing seaborns for its factorplot\n",
    "import seaborn as sns  \n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.set_context(\"notebook\")\n",
    "\n",
    "data_comments = pd.io.parsers.read_csv(\"150512_spoc_comments_lda.csv\", encoding=\"utf8\")\n",
    "data_prompts = pd.io.parsers.read_csv(\"150512_spoc_prompts_mod.csv\", encoding=\"utf8\")\n",
    "data = pd.io.parsers.read_csv(\"150512_spoc_full_data_mod.csv\", encoding=\"utf-8-sig\")\n",
    "conditions = [utils.COL_VOTING, utils.COL_PROMPTS]  # all our categorical IVs of interest\n",
    "covariate = utils.COL_NUM_PROMPTS  # number of prompts received needs to be controlled for\n",
    "outcome = utils.COL_NUM_LEGIT_COMMENTS"
   ]
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   "source": [
    "### Descriptive Statistics\n",
    "Descriptive statistics showed that the mean number of comments students posted was 19 and the median was 17. Students also saw a mean and median number of '1' email prompts."
   ]
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    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumTimesPrompted</th>\n",
       "      <th>num_punctual_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.169231</td>\n",
       "      <td>10.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.024226</td>\n",
       "      <td>9.270767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "       NumTimesPrompted  num_punctual_comments\n",
       "count         65.000000              65.000000\n",
       "mean           1.169231              10.076923\n",
       "std            1.024226               9.270767\n",
       "min            0.000000               0.000000\n",
       "25%            0.000000               1.000000\n",
       "50%            1.000000               9.000000\n",
       "75%            2.000000              16.000000\n",
       "max            5.000000              39.000000"
      ]
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   "source": [
    "df = data[[covariate, outcome]]\n",
    "df.describe()\n",
    "# note: the log-processing removes the Prompting condition of any student who did not see a prompt"
   ]
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kVbr55ps48KR/Ye6Chb0OZTUP3HcXpxwMO+64U69DkbSeM5mUJEkah7kLFrLZ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\nSart/wNtMasX1X/VowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb96c128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of num comments\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(121)\n",
    "comments_hist = data[outcome]\n",
    "ax1 = comments_hist.plot(kind='hist', title=\"Histogram \"+outcome, by=outcome)\n",
    "ax1.locator_params(axis='x')\n",
    "ax1.set_xlabel(outcome)\n",
    "ax1.set_ylabel(\"Count\")\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "prompts_hist = data[covariate]\n",
    "ax2 = prompts_hist.plot(kind='hist', title=\"Histogram \"+covariate, by=covariate)\n",
    "ax2.locator_params(axis='x')\n",
    "ax2.set_xlabel(covariate)\n",
    "ax2.set_ylabel(\"Count\")\n",
    "\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jim\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0, 25)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJBXC4l6SJEkqhMW9JEmSVAiLe0mSJKkQFveSJElSISzuJUmSpEJY3EuSJEmFsLiXJEmS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\nMFfzZVyzRhIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa10c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting num prompts per day\n",
    "sns.set_context(\"poster\")\n",
    "plot_prompts = data_prompts[[utils.COL_ENCOURAGEMENT_TYPE, utils.COL_TSTAMP]]\n",
    "plot_prompts[utils.COL_TSTAMP] = pd.to_datetime(plot_prompts[utils.COL_TSTAMP])\n",
    "plot_prompts.set_index(utils.COL_TSTAMP)\n",
    "date_counts = plot_prompts[utils.COL_TSTAMP].value_counts()\n",
    "date_plot = date_counts.resample('d',how=sum).plot(title=\"Total Prompts By Date\",legend=None, kind='bar')\n",
    "date_plot.set(ylim=(0, 25))\n",
    "# TODO: need to find a way to drop time part of timestamp..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the independent variables, we see a even random assignment to `Condition`. Initially, the assignment to `EncouragementType` wasn't perfectly even (41 vs. 27), but when we removed the prompting condition assignment of any student who did not receive any prompts, the distribution becomes more even."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Condition  NumTimesPrompted  num_punctual_comments\n",
      "Condition                                                     \n",
      "NOVOTE             22          1.318182              10.727273\n",
      "UPDOWNVOTE         24          1.000000              10.625000\n",
      "UPVOTE             19          1.210526               8.631579\n",
      "                   EncouragementType  NumTimesPrompted  num_punctual_comments\n",
      "EncouragementType                                                            \n",
      "NEUTRAL                           25          1.400000               7.720000\n",
      "NO_PROMPT                         18          0.000000              12.555556\n",
      "POSITIVE                          22          1.863636              10.727273\n"
     ]
    }
   ],
   "source": [
    "df = data[conditions+[covariate, outcome]]\n",
    "for cond in conditions:\n",
    "    print(pd.concat([df.groupby(cond)[cond].count(), df.groupby(cond)[covariate].mean(), df.groupby(cond)[outcome].mean()], axis=1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answering Our Research Questions\n",
    "The research questions require a bit of statistics to answer. In the case of a single factor with two levels we use a t-test to determine if the independent variable in question has a significant effect on the outcome variable. In the case of a single factor with more than two levels, we use a one-way Analysis of Variance (ANOVA). With more than one factor we use a two-way ANOVA. These are all essentially similar linear models (ordinary least squares), with slightly different equations or statistics for determining significance. \n",
    "\n",
    "#### What is the effect of no/up/downvoting on {learning, posting comments, posting quality comments, help seeking}\n",
    "*(VotingCondition --> numComments)*\n",
    "\n",
    "To answer this question, we run a one-way ANOVA (since there are more than 2 levels to this one factor). Since the p-value is above 0.05 (i.e., 0.72) we must accept the null hypothesis that there is no difference between voting conditions on number of posted comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              df       sum_sq    mean_sq        F    PR(>F)\n",
      "C(Condition)   2    56.205696  28.102848  0.32003  0.727319\n",
      "Residual      62  5444.409689  87.813059      NaN       NaN\n",
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     num_punctual_comments   R-squared:                       0.010\n",
      "Model:                               OLS   Adj. R-squared:                 -0.022\n",
      "Method:                    Least Squares   F-statistic:                    0.3200\n",
      "Date:                   Wed, 15 Jul 2015   Prob (F-statistic):              0.727\n",
      "Time:                           00:03:07   Log-Likelihood:                -236.14\n",
      "No. Observations:                     65   AIC:                             478.3\n",
      "Df Residuals:                         62   BIC:                             484.8\n",
      "Df Model:                              2                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                     10.7273      1.998      5.369      0.000         6.734    14.721\n",
      "C(Condition)[T.UPDOWNVOTE]    -0.1023      2.766     -0.037      0.971        -5.631     5.427\n",
      "C(Condition)[T.UPVOTE]        -2.0957      2.935     -0.714      0.478        -7.962     3.771\n",
      "==============================================================================\n",
      "Omnibus:                        6.185   Durbin-Watson:                   1.951\n",
      "Prob(Omnibus):                  0.045   Jarque-Bera (JB):                6.090\n",
      "Skew:                           0.749   Prob(JB):                       0.0476\n",
      "Kurtosis:                       2.930   Cond. No.                         3.72\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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1EnL+JyL2AC7IzMdG3VuSpGmiSWPJwPFkw3UScl4HvAcgIlrbhjJz5kxDLEmS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\nkCNJkhrJkCNJkhrp/wMXa9X/WVfEwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa45c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond = utils.COL_VOTING\n",
    "df = data[[cond, outcome]].dropna()\n",
    "\n",
    "cond_lm = ols(outcome + \" ~ C(\" + cond + \")\", data=df).fit()\n",
    "anova_table = anova_lm(cond_lm)\n",
    "print(anova_table)\n",
    "print(cond_lm.summary())\n",
    "\n",
    "# boxplot \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax = df.boxplot(outcome, cond, ax=plt.gca())\n",
    "ax.set_xlabel(cond)\n",
    "ax.set_ylabel(outcome)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Does showing expertise information about potential helpers increase the number of helpers the student invites to her question thread? \n",
    "*(EncouragementType + numPrompts --> numComments)*\n",
    "\n",
    "We run an ANCOVA (since we need to control for the number of prompts each student saw) and see that the p-value is once again not significant (i.e., 0.23)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      df       sum_sq     mean_sq         F    PR(>F)\n",
      "C(EncouragementType)   2   258.767304  129.383652  1.512520  0.228502\n",
      "NumTimesPrompted       1    23.798525   23.798525  0.278209  0.599791\n",
      "Residual              61  5218.049556   85.541796       NaN       NaN\n",
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     num_punctual_comments   R-squared:                       0.051\n",
      "Model:                               OLS   Adj. R-squared:                  0.005\n",
      "Method:                    Least Squares   F-statistic:                     1.101\n",
      "Date:                   Wed, 15 Jul 2015   Prob (F-statistic):              0.356\n",
      "Time:                           00:03:11   Log-Likelihood:                -234.76\n",
      "No. Observations:                     65   AIC:                             477.5\n",
      "Df Residuals:                         61   BIC:                             486.2\n",
      "Df Model:                              3                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "=====================================================================================================\n",
      "                                        coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "Intercept                             6.4852      2.984      2.174      0.034         0.519    12.451\n",
      "C(EncouragementType)[T.NO_PROMPT]     6.0704      3.695      1.643      0.106        -1.319    13.459\n",
      "C(EncouragementType)[T.POSITIVE]      2.5983      2.813      0.924      0.359        -3.026     8.223\n",
      "NumTimesPrompted                      0.8820      1.672      0.527      0.600        -2.462     4.226\n",
      "==============================================================================\n",
      "Omnibus:                        9.478   Durbin-Watson:                   1.841\n",
      "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.086\n",
      "Skew:                           0.855   Prob(JB):                       0.0106\n",
      "Kurtosis:                       3.656   Cond. No.                         7.41\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
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l7ZsqU5IkaYA3A5QkSa1kyJEkSa1kyJEkSa1kyJEkSa1kyJEkSa1kyJEkSa1kyJEkSa1k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\naiVDjiRJaqX/D9TmysDeLWhjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d81860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond = utils.COL_PROMPTS\n",
    "df = data[[cond, covariate, outcome]].dropna()\n",
    "\n",
    "cond_lm = ols(outcome + \" ~ C(\" + cond + \") + \" + covariate, data=df).fit()\n",
    "anova_table = anova_lm(cond_lm)\n",
    "print(anova_table)\n",
    "print(cond_lm.summary())\n",
    "\n",
    "# boxplot \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax = df.boxplot(outcome, cond, ax=plt.gca())\n",
    "ax.set_xlabel(cond)\n",
    "ax.set_ylabel(outcome)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there an interaction between voting X prompting on {learning, posting comments, posting quality comments, help seeking}\n",
    "*Condition X EncouragementType + numPrompts--> numComments*\n",
    "\n",
    "The OLS output shows that neither the additive model nor the interactive/multiplicative model are significant (p = 0.53 vs. p = 0.47). The AIC scores are quite similar so we'd select the one with the lower AIC score as being a better fit to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "= = = = = = = = num_punctual_comments ~ C(Condition) + C(EncouragementType) + NumTimesPrompted = = = = = = = =\n",
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     num_punctual_comments   R-squared:                       0.065\n",
      "Model:                               OLS   Adj. R-squared:                 -0.014\n",
      "Method:                    Least Squares   F-statistic:                    0.8260\n",
      "Date:                   Wed, 15 Jul 2015   Prob (F-statistic):              0.536\n",
      "Time:                           00:03:27   Log-Likelihood:                -234.27\n",
      "No. Observations:                     65   AIC:                             480.5\n",
      "Df Residuals:                         59   BIC:                             493.6\n",
      "Df Model:                              5                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "=====================================================================================================\n",
      "                                        coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "Intercept                             6.5921      3.661      1.800      0.077        -0.734    13.918\n",
      "C(Condition)[T.UPDOWNVOTE]            0.4712      2.819      0.167      0.868        -5.170     6.112\n",
      "C(Condition)[T.UPVOTE]               -2.1386      2.927     -0.731      0.468        -7.995     3.718\n",
      "C(EncouragementType)[T.NO_PROMPT]     6.5192      3.798      1.716      0.091        -1.081    14.119\n",
      "C(EncouragementType)[T.POSITIVE]      2.4994      2.841      0.880      0.383        -3.185     8.184\n",
      "NumTimesPrompted                      1.0987      1.734      0.634      0.529        -2.370     4.568\n",
      "==============================================================================\n",
      "Omnibus:                        9.615   Durbin-Watson:                   1.904\n",
      "Prob(Omnibus):                  0.008   Jarque-Bera (JB):                9.256\n",
      "Skew:                           0.864   Prob(JB):                      0.00977\n",
      "Kurtosis:                       3.659   Cond. No.                         8.46\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "= = = = = = = = num_punctual_comments ~ C(Condition) * C(EncouragementType) + NumTimesPrompted = = = = = = = =\n",
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     num_punctual_comments   R-squared:                       0.138\n",
      "Model:                               OLS   Adj. R-squared:                 -0.003\n",
      "Method:                    Least Squares   F-statistic:                    0.9754\n",
      "Date:                   Wed, 15 Jul 2015   Prob (F-statistic):              0.470\n",
      "Time:                           00:03:28   Log-Likelihood:                -231.66\n",
      "No. Observations:                     65   AIC:                             483.3\n",
      "Df Residuals:                         55   BIC:                             505.1\n",
      "Df Model:                              9                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "================================================================================================================================\n",
      "                                                                   coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "--------------------------------------------------------------------------------------------------------------------------------\n",
      "Intercept                                                        4.6678      4.203      1.111      0.272        -3.755    13.091\n",
      "C(Condition)[T.UPDOWNVOTE]                                       4.2664      4.436      0.962      0.340        -4.624    13.157\n",
      "C(Condition)[T.UPVOTE]                                           2.0318      4.808      0.423      0.674        -7.604    11.667\n",
      "C(EncouragementType)[T.NO_PROMPT]                                8.1655      5.660      1.443      0.155        -3.178    19.509\n",
      "C(EncouragementType)[T.POSITIVE]                                 8.5283      4.770      1.788      0.079        -1.032    18.088\n",
      "C(Condition)[T.UPDOWNVOTE]:C(EncouragementType)[T.NO_PROMPT]    -0.4331      6.959     -0.062      0.951       -14.379    13.513\n",
      "C(Condition)[T.UPVOTE]:C(EncouragementType)[T.NO_PROMPT]        -6.6985      7.202     -0.930      0.356       -21.131     7.734\n",
      "C(Condition)[T.UPDOWNVOTE]:C(EncouragementType)[T.POSITIVE]    -10.9197      6.426     -1.699      0.095       -23.797     1.958\n",
      "C(Condition)[T.UPVOTE]:C(EncouragementType)[T.POSITIVE]         -6.0041      6.955     -0.863      0.392       -19.943     7.934\n",
      "NumTimesPrompted                                                 0.5548      1.749      0.317      0.752        -2.951     4.060\n",
      "==============================================================================\n",
      "Omnibus:                        6.251   Durbin-Watson:                   1.878\n",
      "Prob(Omnibus):                  0.044   Jarque-Bera (JB):                5.391\n",
      "Skew:                           0.598   Prob(JB):                       0.0675\n",
      "Kurtosis:                       3.748   Cond. No.                         20.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\n",
      "= = num_punctual_comments ~ C(Condition) + C(EncouragementType) + NumTimesPrompted ANOVA = = \n",
      "= = vs. num_punctual_comments ~ C(Condition) * C(EncouragementType) + NumTimesPrompted = =\n",
      "   df_resid          ssr  df_diff     ss_diff         F    Pr(>F)\n",
      "0        59  5140.763092        0         NaN       NaN       NaN\n",
      "1        55  4743.521015        4  397.242076  1.151482  0.342267\n"
     ]
    }
   ],
   "source": [
    "col_names = [utils.COL_VOTING, utils.COL_PROMPTS, covariate, outcome]\n",
    "factor_groups = data[col_names].dropna()\n",
    "\n",
    "formula = outcome + \" ~ C(\" + col_names[0] + \") + C(\" + col_names[1] + \") + \" + covariate\n",
    "formula_interaction = outcome + \" ~ C(\" + col_names[0] + \") * C(\" + col_names[1] + \") + \" + covariate\n",
    "\n",
    "print(\"= = = = = = = = \" + formula + \" = = = = = = = =\")\n",
    "lm = ols(formula, data=factor_groups).fit()  # linear model: AIC 418\n",
    "print(lm.summary())\n",
    "\n",
    "print(\"\\n= = = = = = = = \" + formula_interaction + \" = = = = = = = =\")\n",
    "lm_interaction = ols(formula_interaction, data=factor_groups).fit()  # interaction linear model: AIC 418.1\n",
    "print(lm_interaction.summary())\n",
    "\n",
    "# We can test if they're significantly different with an ANOVA (neither is sig. so not necessary)\n",
    "print(\"= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\")\n",
    "print(\"= = \" + formula + \" ANOVA = = \")\n",
    "print(\"= = vs. \" + formula_interaction + \" = =\")\n",
    "print(anova_lm(lm, lm_interaction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\n",
      "= = NumComments ~ C(Condition) ANOVA = = \n",
      "= = vs. NumComments ~ C(Condition) + C(EncouragementType) + NumTimesPrompted = =\n",
      "   df_resid           ssr  df_diff     ss_diff         F    Pr(>F)\n",
      "0        62  17408.632376        0         NaN       NaN       NaN\n",
      "1        59  16497.225960        3  911.406417  1.086505  0.361914\n",
      "= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\n",
      "= = NumComments ~ C(EncouragementType) + NumTimesPrompted = = \n",
      "= = vs. NumComments ~ C(Condition) + C(EncouragementType) + NumTimesPrompted = =\n",
      "   df_resid           ssr  df_diff    ss_diff         F    Pr(>F)\n",
      "0        61  16530.305736        0        NaN       NaN       NaN\n",
      "1        59  16497.225960        2  33.079776  0.059153  0.942619\n"
     ]
    }
   ],
   "source": [
    "# These are ANOVA tests for determining if different models are significantly different from each other.\n",
    "# Although we know from the previous step that none of these are signficant, so these tests aren't necessary.\n",
    "# The ANOVA output provides the F-statistics which are necessary for reporting results.\n",
    "# From: http://statsmodels.sourceforge.net/devel/examples/generated/example_interactions.html\n",
    "\n",
    "# Tests whether the LM of just Condition is significantly different from the additive LM\n",
    "print(\"= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\")\n",
    "f_just_first = outcome + \" ~ C(\" + col_names[0] + \")\"\n",
    "print(\"= = \" + f_just_first + \" ANOVA = = \")\n",
    "print(\"= = vs. \" + formula + \" = =\")\n",
    "print(anova_lm(ols(f_just_first, data=factor_groups).fit(), ols(formula, data=factor_groups).fit()))\n",
    "\n",
    "# Testing whether the LM of just EncouragementType is significantly different from the additive LM\n",
    "print(\"= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\")\n",
    "f_just_second = outcome + \" ~ C(\" + col_names[1] + \") + \" + covariate\n",
    "print(\"= = \" + f_just_second + \" = = \")\n",
    "print(\"= = vs. \" + formula + \" = =\")\n",
    "print(anova_lm(ols(f_just_second, data=factor_groups).fit(), ols(formula, data=factor_groups).fit()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9d8d160>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa2aa3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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LOH3EW4GNuQ13fxHoTKxGIiJlJs40mD8E9gWWAL3Ap4B64FYAd789yQqqj1hEJoo9mQaz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\niRYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9db15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZzXL3ZsI49juA4wmBvAC4J9r2ZuDHZnaYu3dH28wCtwPfMLOp7p77MD0X+IGZ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\nIilTEIuIpOx/AOoq5IIkzpnPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa2eacc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9Rp3UexKRriuZxLjd3X+T8khEmthRtoslxS/zyq41VNY2H563T1Zvpg27kIuL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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d28860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting\n",
    "from statsmodels.graphics.api import interaction_plot\n",
    "sns.set_context(\"notebook\")\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = sns.factorplot(x=col_names[1], y=outcome, data=factor_groups, kind='point', ci=95)\n",
    "ax1.set(ylim=(0, 45))\n",
    "ax2 = sns.factorplot(x=col_names[0], y=outcome, data=factor_groups, kind='point', ci=95)\n",
    "ax2.set(ylim=(0, 45))\n",
    "ax3 = sns.factorplot(x=col_names[1], hue=col_names[0], y=outcome, data=factor_groups, kind='point', ci=95)\n",
    "ax3.set(ylim=(0, 45))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This last interaction plot is interesting, and suggests that we might want to look at the \"downvote\" condition compared to the \"non-downvote\" conditions, a split which is captured in our 'COL_NEG_VOTE' column. Neither model appears significant, and an additional test shows neither model performs statistically better than the other (p = 0.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sample code for calculating new column reformulation\n",
    "def get_neg_vote(voting_cond)\n",
    "        if voting_cond == utils.COND_VOTE_NONE or voting_cond == utils.COND_VOTE_UP:\n",
    "            return utils.COND_OTHER\n",
    "        elif voting_cond == utils.COND_VOTE_BOTH:\n",
    "            return utils.COND_VOTE_BOTH\n",
    "        else:\n",
    "            return \"\"\n",
    "\n",
    "# if you're missing the log processing, you can get a 'negativeVoting' column with something like this code:\n",
    "data[utils.COL_NEG_VOTE] = data[utils.COL_VOTING].apply(lambda x: get_neg_vote(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "= = = = = = = = num_punctual_comments ~ C(negativeVoting) + C(EncouragementType) + NumTimesPrompted = = = = = = = =\n",
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     num_punctual_comments   R-squared:                       0.057\n",
      "Model:                               OLS   Adj. R-squared:                 -0.006\n",
      "Method:                    Least Squares   F-statistic:                    0.9061\n",
      "Date:                   Wed, 15 Jul 2015   Prob (F-statistic):              0.466\n",
      "Time:                           00:03:48   Log-Likelihood:                -234.57\n",
      "No. Observations:                     65   AIC:                             479.1\n",
      "Df Residuals:                         60   BIC:                             490.0\n",
      "Df Model:                              4                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "=========================================================================================================\n",
      "                                            coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "---------------------------------------------------------------------------------------------------------\n",
      "Intercept                                 5.5690      3.370      1.653      0.104        -1.172    12.310\n",
      "C(negativeVoting)[T.UPDOWNVOTE_GROUP]     1.4667      2.459      0.597      0.553        -3.451     6.385\n",
      "C(EncouragementType)[T.NO_PROMPT]         6.4977      3.783      1.717      0.091        -1.070    14.065\n",
      "C(EncouragementType)[T.POSITIVE]          2.5425      2.829      0.899      0.372        -3.117     8.202\n",
      "NumTimesPrompted                          1.1174      1.727      0.647      0.520        -2.337     4.571\n",
      "==============================================================================\n",
      "Omnibus:                        9.249   Durbin-Watson:                   1.857\n",
      "Prob(Omnibus):                  0.010   Jarque-Bera (JB):                8.876\n",
      "Skew:                           0.863   Prob(JB):                       0.0118\n",
      "Kurtosis:                       3.548   Cond. No.                         8.04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "= = = = = = = = num_punctual_comments ~ C(negativeVoting) * C(EncouragementType) + NumTimesPrompted = = = = = = = =\n",
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     num_punctual_comments   R-squared:                       0.113\n",
      "Model:                               OLS   Adj. R-squared:                  0.021\n",
      "Method:                    Least Squares   F-statistic:                     1.233\n",
      "Date:                   Wed, 15 Jul 2015   Prob (F-statistic):              0.303\n",
      "Time:                           00:03:48   Log-Likelihood:                -232.57\n",
      "No. Observations:                     65   AIC:                             479.1\n",
      "Df Residuals:                         58   BIC:                             494.4\n",
      "Df Model:                              6                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "===========================================================================================================================================\n",
      "                                                                              coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Intercept                                                                   5.4942      3.550      1.548      0.127        -1.612    12.601\n",
      "C(negativeVoting)[T.UPDOWNVOTE_GROUP]                                       3.3447      3.788      0.883      0.381        -4.238    10.927\n",
      "C(EncouragementType)[T.NO_PROMPT]                                           5.0058      4.429      1.130      0.263        -3.859    13.871\n",
      "C(EncouragementType)[T.POSITIVE]                                            5.8349      3.532      1.652      0.104        -1.236    12.905\n",
      "C(negativeVoting)[T.UPDOWNVOTE_GROUP]:C(EncouragementType)[T.NO_PROMPT]     2.8219      5.948      0.474      0.637        -9.084    14.728\n",
      "C(negativeVoting)[T.UPDOWNVOTE_GROUP]:C(EncouragementType)[T.POSITIVE]     -8.2502      5.541     -1.489      0.142       -19.343     2.842\n",
      "NumTimesPrompted                                                            0.6342      1.725      0.368      0.714        -2.819     4.087\n",
      "==============================================================================\n",
      "Omnibus:                        5.871   Durbin-Watson:                   1.880\n",
      "Prob(Omnibus):                  0.053   Jarque-Bera (JB):                4.992\n",
      "Skew:                           0.633   Prob(JB):                       0.0824\n",
      "Kurtosis:                       3.493   Cond. No.                         13.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\n",
      "= = num_punctual_comments ~ C(negativeVoting) + C(EncouragementType) + NumTimesPrompted ANOVA = = \n",
      "= = vs. num_punctual_comments ~ C(negativeVoting) * C(EncouragementType) + NumTimesPrompted = =\n",
      "   df_resid          ssr  df_diff     ss_diff         F    Pr(>F)\n",
      "0        60  5187.280502        0         NaN       NaN       NaN\n",
      "1        58  4878.188603        2  309.091899  1.837499  0.168363\n"
     ]
    },
    {
     "data": {
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HIgLURGtYufsDCosWsmqPN/qak3qNYnrBFM7MP430tPQ2uW9ORg7Xjr6cC4ee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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3be550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aCyCldD5wHvAJ4E3AVODGlNKUutdYkiRJUlMYMIyklKYCbwfOj4jPR8SNwBuA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\n2V4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3a3710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_names = [utils.COL_NEG_VOTE, utils.COL_PROMPTS, covariate, outcome]\n",
    "factor_groups = data[col_names].dropna()\n",
    "\n",
    "formula = outcome + \" ~ C(\" + col_names[0] + \") + C(\" + col_names[1] + \") + \" + covariate\n",
    "formula_interaction = outcome + \" ~ C(\" + col_names[0] + \") * C(\" + col_names[1] + \") + \" + covariate\n",
    "\n",
    "print(\"= = = = = = = = \" + formula + \" = = = = = = = =\")\n",
    "lm = ols(formula, data=factor_groups).fit()  # linear model: AIC 416\n",
    "print(lm.summary())\n",
    "\n",
    "print(\"\\n= = = = = = = = \" + formula_interaction + \" = = = = = = = =\")\n",
    "lm_interaction = ols(formula_interaction, data=factor_groups).fit()  # interaction linear model: AIC 414\n",
    "print(lm_interaction.summary())\n",
    "\n",
    "# We can test if they're significantly different with an ANOVA (neither is sig. so not necessary)\n",
    "print(\"= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\")\n",
    "print(\"= = \" + formula + \" ANOVA = = \")\n",
    "print(\"= = vs. \" + formula_interaction + \" = =\")\n",
    "print(anova_lm(lm, lm_interaction))\n",
    "\n",
    "# plotting\n",
    "sns.set_context(\"notebook\") \n",
    "ax3 = sns.factorplot(x=col_names[1], hue=col_names[0], y=outcome, data=factor_groups, kind='point', ci=95)\n",
    "ax3.set(ylim=(0, 45))\n",
    "sns.set_context(\"poster\")  # larger plots\n",
    "ax4 = factor_groups.boxplot(return_type='axes', column=outcome, by=[col_names[0], col_names[1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "This analysis so far has only looked at `number of comments` as the dependent variable, of which there does not appear to be any significant effect. However, `comment quality`, `help seeking`, and `learning` are all important dependent variables to examine as well. This analysis is still in progress."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Modeling\n",
    "I used gensim to automatically apply topics to each forum comment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count               655\n",
      "unique               15\n",
      "top       like*hardware\n",
      "freq                 73\n",
      "Name: LDAtopic, dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9d825c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RziRX0H3IDjQHeCx5+OCh5Mr0YjIY7iAH0n4ySDYnO+mdtdw76+OtI6KfHAwf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d82ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# basic descriptive statistics\n",
    "data_topic = data_comments[[utils.COL_LDA]]\n",
    "data_help = data_comments[[utils.COL_HELP]]\n",
    "\n",
    "print(data_topic[utils.COL_LDA].describe())\n",
    "\n",
    "# histogram of LDA topics\n",
    "sns.set_context(\"notebook\") \n",
    "sns.factorplot(utils.COL_LDA, data=data_topic, size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also graph the number of comments over time, and (TODO) graph these comments by topic over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jim\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\Jim\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day         LDAtopic     \n",
      "2015-05-02  will*can         1\n",
      "2015-05-03  case*larger      1\n",
      "2015-05-04  better*always    1\n",
      "            will*can         1\n",
      "2015-05-08  like*hardware    1\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0, 100)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1FjyvkvGXm+Qq9yfknX6i3LSd6DJHhbP2i10GAAAAQA1UEvbvkzQhNwtakmSM6ZNbqCRa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac316a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# num comments of each topic type by date\n",
    "data_topic = data_comments[[utils.COL_LDA, utils.COL_TIMESTAMP]]\n",
    "data_topic[utils.COL_TIMESTAMP] = pd.to_datetime(data_topic[utils.COL_TIMESTAMP])\n",
    "data_topic = data_topic.set_index(utils.COL_TIMESTAMP)\n",
    "data_topic['day'] = data_topic.index.date\n",
    "counts = data_topic.groupby(['day', utils.COL_LDA]).agg(len)\n",
    "print(counts.tail())\n",
    "\n",
    "# plotting\n",
    "sns.set_context(\"poster\")\n",
    "data_topic = data_comments[[utils.COL_LDA, utils.COL_TIMESTAMP]]\n",
    "data_topic[utils.COL_TIMESTAMP] = pd.to_datetime(data_topic[utils.COL_TIMESTAMP])\n",
    "data_topic.set_index(utils.COL_TIMESTAMP)\n",
    "date_counts = data_topic[utils.COL_TIMESTAMP].value_counts()\n",
    "date_plot = date_counts.resample('d',how=sum).plot(title=\"Total Comments By Date\",legend=None)\n",
    "date_plot.set(ylim=(0, 100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Questions\n",
    "Basic descriptive statistics and bar graphs are simple enough to acquire, but what we might want to know is **what topic is the most popular to write about** and **what topic are students requesting the most help on**? To do this, we need to find out how many comments there are on a particular comment, and how many of these comments are help requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jim\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Jim\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Jim\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "def count_instances(df,uid,column): \n",
    "    \"\"\"\n",
    "    Return dataframe of total counts for each unique value in COLUMN, by UID\n",
    "    :param df: the dataframe containing the data of interest\n",
    "    :param uid: the unique index/ID to group the counts by (i.e., author user id)\n",
    "    :param column: the column containing the things to count (i.e., help requests)\n",
    "    :return: True if the string contains the letter 'y'\n",
    "    \"\"\"\n",
    "    df['tot_comments'] = 1\n",
    "    for item in df[column].unique():\n",
    "        colname = \"help_requests\" if item == \"True\" else \"other_comments\"\n",
    "        df['num_%s' % colname] = df[column].apply(lambda value: 1 if value == str(item) else 0)\n",
    "        \n",
    "    new_data = df.groupby(uid).sum()\n",
    "    \n",
    "    \"\"\"#calculating percents\n",
    "    cols = [col for col in new_data.columns if 'num' in col]\n",
    "    for col in cols:\n",
    "        new_data[col.replace('num','percent')] = new_data[col] / new_data['tot_comments'] * 100\n",
    "    \"\"\"    \n",
    "    return new_data\n",
    "\n",
    "data_topic = data_comments[[utils.COL_LDA, utils.COL_TIMESTAMP, utils.COL_HELP]]\n",
    "\n",
    "df_by_topic = count_instances(data_topic, utils.COL_LDA, utils.COL_HELP)  # calculate number help requests per topic\n",
    "\n",
    "data_help = data_comments[[utils.COL_ID, utils.COL_HELP]]\n",
    "data_help[utils.COL_HELP] = data_help[utils.COL_HELP].astype('str')\n",
    "df_help_counts = count_instances(data_help, utils.COL_ID, utils.COL_HELP)  # calculate number help requests per user\n",
    "\n",
    "# merging our help counts table with original data\n",
    "data[utils.COL_ID] = data[utils.COL_ID].astype('str')\n",
    "indexed_data = data.set_index([utils.COL_ID])\n",
    "combined_df = pd.concat([indexed_data, df_help_counts], axis=1, join_axes=[indexed_data.index])\n",
    "\n",
    "# Replace NaN in help_requests/other_comments cols with '0'\n",
    "combined_df[\"num_help_requests\"].fillna(value=0, inplace=True)\n",
    "combined_df[\"num_other_comments\"].fillna(value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Help Seeking\n",
    "I used an extremely naive set of rules to determine if a comment was a help request, such as identifying if a question mark is included in the message (or the word 'question', or 'struggle' or 'stuck', etc) I identified which entries are most likely help requests and which are not. In the future, a coding scheme should be developed to determine what kinds of help are being sought."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# example code - does not need to execute\n",
    "def is_help_topic(sentence):\n",
    "    if \"help\" in sentence or \"question\" in sentence or \"?\" in sentence or \"dunno\" in sentence or \"n't know\" in sentence:\n",
    "        return True\n",
    "    if \"confus\" in sentence or \"struggl\" in sentence or \"lost\" in sentence or \"stuck\" in sentence or \"know how\" in sentence:\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "# if you're missing the log processing, you can get an 'isHelpSeeking' column with something like this code:\n",
    "data_comments[utils.COL_HELP] = data_comments[utils.COL_COMMENT].apply(lambda x: is_help_topic(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nsns.set_context(\"poster\")\\ndata_by_date = data[[utils.COL_HELP, utils.COL_TIMESTAMP]]\\ndata_by_date[utils.COL_TIMESTAMP] = pd.to_datetime(data_by_date[utils.COL_TIMESTAMP])\\ndata_by_date.set_index(utils.COL_TIMESTAMP)\\ndate_counts = data_by_date[utils.COL_TIMESTAMP].value_counts()\\ndate_plot = date_counts.resample(\\'d\\',how=sum).plot(title=\"Total Help Requests By Date\",legend=None)\\ndate_plot.set(ylim=(0, 100))\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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QEX8K3JSZ909FxyRJGvdsvMy8BCAidgGeD8wepd1tPemZJGnGG3dIRcQvAZ8GfmOMZhsZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c359e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of help requests\n",
    "sns.factorplot(utils.COL_HELP, data=data_help, size=6)\n",
    "data_help.describe()\n",
    "\n",
    "# plotting\n",
    "\"\"\"\n",
    "sns.set_context(\"poster\")\n",
    "data_by_date = data[[utils.COL_HELP, utils.COL_TIMESTAMP]]\n",
    "data_by_date[utils.COL_TIMESTAMP] = pd.to_datetime(data_by_date[utils.COL_TIMESTAMP])\n",
    "data_by_date.set_index(utils.COL_TIMESTAMP)\n",
    "date_counts = data_by_date[utils.COL_TIMESTAMP].value_counts()\n",
    "date_plot = date_counts.resample('d',how=sum).plot(title=\"Total Help Requests By Date\",legend=None)\n",
    "date_plot.set(ylim=(0, 100))\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, instead of using 'numComments' as our outcome variable, we can use 'numHelpRequests' as the outcome variable. But first we have to calculate that for each user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jim\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\Jim\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "labels ['tot_comments'] not contained in axis",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-3eb8b6e3ae09>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# merging our help counts table with original data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mdf_help_counts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"tot_comments\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOL_ID\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOL_ID\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'str'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mindexed_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOL_ID\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, level, inplace)\u001b[0m\n\u001b[0;32m   1584\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1585\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1586\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1587\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1588\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\pandas\\core\\index.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels)\u001b[0m\n\u001b[0;32m   2342\u001b[0m         \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2343\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2344\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'labels %s not contained in axis'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2345\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: labels ['tot_comments'] not contained in axis"
     ]
    }
   ],
   "source": [
    "data_help = data_comments[[utils.COL_AUTHOR, utils.COL_HELP]]\n",
    "data_help.rename(columns={utils.COL_AUTHOR:utils.COL_ID}, inplace=True)  #rename so we can merge later\n",
    "data_help[utils.COL_ID] = data_help[utils.COL_ID].astype('str')\n",
    "data_help[utils.COL_HELP] = data_help[utils.COL_HELP].astype('str')\n",
    "\n",
    "# merging our help counts table with original data\n",
    "df_help_counts.drop(\"tot_comments\", axis=1, inplace=True)\n",
    "data[utils.COL_ID] = data[utils.COL_ID].astype('str')\n",
    "indexed_data = data.set_index([utils.COL_ID])\n",
    "combined_df = pd.concat([indexed_data, df_help_counts], axis=1, join_axes=[indexed_data.index])\n",
    "\n",
    "# Replace NaN in help_requests/other_comments cols with '0'\n",
    "combined_df[\"num_help_requests\"].fillna(value=0, inplace=True)\n",
    "combined_df[\"num_other_comments\"].fillna(value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "= = = = = = = = num_help_requests ~ C(negativeVoting) + C(EncouragementType) + NumTimesPrompted = = = = = = = =\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "negative dimensions are not allowed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-64db1f3c8af3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"= = = = = = = = \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mformula\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\" = = = = = = = =\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mlm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mols\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformula\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfactor_groups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# linear model: AIC 416\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36mfrom_formula\u001b[1;34m(cls, formula, data, subset, *args, **kwargs)\u001b[0m\n\u001b[0;32m    145\u001b[0m         (endog, exog), missing_idx = handle_formula_data(data, None, formula,\n\u001b[0;32m    146\u001b[0m                                                          \u001b[0mdepth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meval_env\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m                                                          missing=missing)\n\u001b[0m\u001b[0;32m    148\u001b[0m         kwargs.update({'missing_idx': missing_idx,\n\u001b[0;32m    149\u001b[0m                        'missing': missing})\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\statsmodels\\formula\\formulatools.py\u001b[0m in \u001b[0;36mhandle_formula_data\u001b[1;34m(Y, X, formula, depth, missing)\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_using_pandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m             result = dmatrices(formula, Y, depth, return_type='dataframe',\n\u001b[1;32m---> 65\u001b[1;33m                                NA_action=na_action)\n\u001b[0m\u001b[0;32m     66\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m             result = dmatrices(formula, Y, depth, return_type='dataframe',\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\patsy\\highlevel.py\u001b[0m in \u001b[0;36mdmatrices\u001b[1;34m(formula_like, data, eval_env, NA_action, return_type)\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[0meval_env\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mEvalEnvironment\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meval_env\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreference\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    296\u001b[0m     (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,\n\u001b[1;32m--> 297\u001b[1;33m                                       NA_action, return_type)\n\u001b[0m\u001b[0;32m    298\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlhs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mPatsyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"model is missing required outcome variables\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\patsy\\highlevel.py\u001b[0m in \u001b[0;36m_do_highlevel_design\u001b[1;34m(formula_like, data, eval_env, NA_action, return_type)\u001b[0m\n\u001b[0;32m    150\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m     builders = _try_incr_builders(formula_like, data_iter_maker, eval_env,\n\u001b[1;32m--> 152\u001b[1;33m                                   NA_action)\n\u001b[0m\u001b[0;32m    153\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mbuilders\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m         return build_design_matrices(builders, data,\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\patsy\\highlevel.py\u001b[0m in \u001b[0;36m_try_incr_builders\u001b[1;34m(formula_like, data_iter_maker, eval_env, NA_action)\u001b[0m\n\u001b[0;32m     55\u001b[0m                                        formula_like.rhs_termlist],\n\u001b[0;32m     56\u001b[0m                                       \u001b[0mdata_iter_maker\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m                                       NA_action)\n\u001b[0m\u001b[0;32m     58\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\patsy\\build.py\u001b[0m in \u001b[0;36mdesign_matrix_builders\u001b[1;34m(termlists, data_iter_maker, NA_action)\u001b[0m\n\u001b[0;32m    678\u001b[0m         result = _make_term_column_builders(termlist,\n\u001b[0;32m    679\u001b[0m                                             \u001b[0mnum_column_counts\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 680\u001b[1;33m                                             cat_levels_contrasts)\n\u001b[0m\u001b[0;32m    681\u001b[0m         \u001b[0mnew_term_order\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mterm_to_column_builders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    682\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_term_order\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtermlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\patsy\\build.py\u001b[0m in \u001b[0;36m_make_term_column_builders\u001b[1;34m(terms, num_column_counts, cat_levels_contrasts)\u001b[0m\n\u001b[0;32m    607\u001b[0m                         coded = code_contrast_matrix(factor_coding[factor],\n\u001b[0;32m    608\u001b[0m                                                      \u001b[0mlevels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontrast\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 609\u001b[1;33m                                                      default=Treatment)\n\u001b[0m\u001b[0;32m    610\u001b[0m                         \u001b[0mcat_contrasts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfactor\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcoded\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    611\u001b[0m                 column_builder = _ColumnBuilder(builder_factors,\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\patsy\\contrasts.py\u001b[0m in \u001b[0;36mcode_contrast_matrix\u001b[1;34m(intercept, levels, contrast, default)\u001b[0m\n\u001b[0;32m    574\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontrast\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode_with_intercept\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 576\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontrast\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode_without_intercept\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    577\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\patsy\\contrasts.py\u001b[0m in \u001b[0;36mcode_without_intercept\u001b[1;34m(self, levels)\u001b[0m\n\u001b[0;32m    170\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m             \u001b[0mreference\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_level\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreference\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m         \u001b[0meye\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meye\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m         contrasts = np.vstack((eye[:reference, :],\n\u001b[0;32m    174\u001b[0m                                 \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jim\\Anaconda\\lib\\site-packages\\numpy\\lib\\twodim_base.py\u001b[0m in \u001b[0;36meye\u001b[1;34m(N, M, k, dtype)\u001b[0m\n\u001b[0;32m    229\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mM\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    230\u001b[0m         \u001b[0mM\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 231\u001b[1;33m     \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    232\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mM\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: negative dimensions are not allowed"
     ]
    }
   ],
   "source": [
    "outcome = \"num_help_requests\"\n",
    "col_names = [utils.COL_NEG_VOTE, utils.COL_PROMPTS, covariate, outcome]\n",
    "factor_groups = combined_df[col_names].dropna()\n",
    "\n",
    "formula = outcome + \" ~ C(\" + col_names[0] + \") + C(\" + col_names[1] + \") + \" + covariate\n",
    "formula_interaction = outcome + \" ~ C(\" + col_names[0] + \") * C(\" + col_names[1] + \") + \" + covariate\n",
    "\n",
    "print(\"= = = = = = = = \" + formula + \" = = = = = = = =\")\n",
    "lm = ols(formula, data=factor_groups).fit()  # linear model: AIC 416\n",
    "print(lm.summary())\n",
    "\n",
    "print(\"\\n= = = = = = = = \" + formula_interaction + \" = = = = = = = =\")\n",
    "lm_interaction = ols(formula_interaction, data=factor_groups).fit()  # interaction linear model: AIC 414\n",
    "print(lm_interaction.summary())\n",
    "\n",
    "# We can test if they're significantly different with an ANOVA (neither is sig. so not necessary)\n",
    "print(\"= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\")\n",
    "print(\"= = \" + formula + \" ANOVA = = \")\n",
    "print(\"= = vs. \" + formula_interaction + \" = =\")\n",
    "print(anova_lm(lm, lm_interaction))\n",
    "\n",
    "# plotting\n",
    "sns.set_context(\"notebook\") \n",
    "ax3 = sns.factorplot(x=col_names[1], hue=col_names[0], y=outcome, data=factor_groups, kind='point', ci=95)\n",
    "ax3.set(ylim=(0, 45))\n",
    "sns.set_context(\"poster\")  # larger plots\n",
    "ax4 = factor_groups.boxplot(return_type='axes', column=outcome, by=[col_names[0], col_names[1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "In conclusion, ...TODO: there's still more analysis to do..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Documentation\n",
    "Find this project in [Github: UberHowley/SPOC-File-Processing](https://github.com/UberHowley/spoc-file-processing)\n",
    "\n",
    "\n"
   ]
  }
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