{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: daltoolbox\n",
      "\n",
      "Registered S3 method overwritten by 'quantmod':\n",
      "  method            from\n",
      "  as.zoo.data.frame zoo \n",
      "\n",
      "\n",
      "Attaching package: ‘daltoolbox’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    transform\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DAL ToolBox\n",
    "# version 1.1.727\n",
    "\n",
    "source(\"https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R\")\n",
    "\n",
    "#loading DAL\n",
    "load_library(\"daltoolbox\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: ggplot2\n",
      "\n",
      "Loading required package: RColorBrewer\n",
      "\n"
     ]
    }
   ],
   "source": [
    "load_library(\"ggplot2\")\n",
    "load_library(\"RColorBrewer\")\n",
    "\n",
    "#color palette\n",
    "colors <- brewer.pal(4, 'Set1')\n",
    "\n",
    "# setting the font size for all charts\n",
    "font <- theme(text = element_text(size=16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>setosa</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n",
       "\t2 & 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n",
       "\t3 & 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n",
       "\t4 & 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n",
       "\t5 & 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n",
       "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | Sepal.Length &lt;dbl&gt; | Sepal.Width &lt;dbl&gt; | Petal.Length &lt;dbl&gt; | Petal.Width &lt;dbl&gt; | Species &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n",
       "| 2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n",
       "| 3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n",
       "| 4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n",
       "| 5 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n",
       "| 6 | 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n",
       "\n"
      ],
      "text/plain": [
       "  Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
       "1 5.1          3.5         1.4          0.2         setosa \n",
       "2 4.9          3.0         1.4          0.2         setosa \n",
       "3 4.7          3.2         1.3          0.2         setosa \n",
       "4 4.6          3.1         1.5          0.2         setosa \n",
       "5 5.0          3.6         1.4          0.2         setosa \n",
       "6 5.4          3.9         1.7          0.4         setosa "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#iris dataset for the example\n",
    "head(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: dplyr\n",
      "\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Species</th><th scope=col>Sepal.Length</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>setosa    </td><td>5.006</td></tr>\n",
       "\t<tr><td>versicolor</td><td>5.936</td></tr>\n",
       "\t<tr><td>virginica </td><td>6.588</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 2\n",
       "\\begin{tabular}{ll}\n",
       " Species & Sepal.Length\\\\\n",
       " <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t setosa     & 5.006\\\\\n",
       "\t versicolor & 5.936\\\\\n",
       "\t virginica  & 6.588\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 2\n",
       "\n",
       "| Species &lt;fct&gt; | Sepal.Length &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| setosa     | 5.006 |\n",
       "| versicolor | 5.936 |\n",
       "| virginica  | 6.588 |\n",
       "\n"
      ],
      "text/plain": [
       "  Species    Sepal.Length\n",
       "1 setosa     5.006       \n",
       "2 versicolor 5.936       \n",
       "3 virginica  6.588       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_library(\"dplyr\")\n",
    "\n",
    "data <- iris |> group_by(Species) |> summarize(Sepal.Length=mean(Sepal.Length))\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lollipop plot\n",
    "\n",
    "The lollipop graph has the same goal as a bar graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lMB+6E4m801NXU8+2xcXR33dU+7PiWv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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grf <- plot_lollipop(data, colors=colors[1], max_value_gap=0.2) + font\n",
    "plot(grf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grf <- plot_lollipop(data, colors=colors[1], max_value_gap=0.1) + font + coord_flip() \n",
    "plot(grf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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