{
 "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": [
    "## Pie chart\n",
    "A pie chart is a circular statistical graphic, which is divided into slices to illustrate numerical proportion. \n",
    "\n",
    "More information: https://en.wikipedia.org/wiki/Pie_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nAABAEgQ7AAAASfwv3LJBnmNfhLYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grf <- plot_pieplot(data, colors=colors[1:3]) + font\n",
    "plot(grf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}