{
 "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": "markdown",
   "metadata": {},
   "source": [
    "## Examples using data distribution\n",
    "The following examples use random variables so that different data distribution can be better viewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>exponential</th><th scope=col>uniform</th><th scope=col>normal</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0.09155231</td><td>2.612075</td><td>5.085059</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2.01816829</td><td>3.294267</td><td>4.457824</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>0.58176393</td><td>2.636868</td><td>4.982670</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>0.12511421</td><td>3.344929</td><td>6.220225</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>0.02524745</td><td>2.573040</td><td>5.234677</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>0.31813066</td><td>2.620384</td><td>3.070939</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & exponential & uniform & normal\\\\\n",
       "  & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 0.09155231 & 2.612075 & 5.085059\\\\\n",
       "\t2 & 2.01816829 & 3.294267 & 4.457824\\\\\n",
       "\t3 & 0.58176393 & 2.636868 & 4.982670\\\\\n",
       "\t4 & 0.12511421 & 3.344929 & 6.220225\\\\\n",
       "\t5 & 0.02524745 & 2.573040 & 5.234677\\\\\n",
       "\t6 & 0.31813066 & 2.620384 & 3.070939\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | exponential &lt;dbl&gt; | uniform &lt;dbl&gt; | normal &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | 0.09155231 | 2.612075 | 5.085059 |\n",
       "| 2 | 2.01816829 | 3.294267 | 4.457824 |\n",
       "| 3 | 0.58176393 | 2.636868 | 4.982670 |\n",
       "| 4 | 0.12511421 | 3.344929 | 6.220225 |\n",
       "| 5 | 0.02524745 | 2.573040 | 5.234677 |\n",
       "| 6 | 0.31813066 | 2.620384 | 3.070939 |\n",
       "\n"
      ],
      "text/plain": [
       "  exponential uniform  normal  \n",
       "1 0.09155231  2.612075 5.085059\n",
       "2 2.01816829  3.294267 4.457824\n",
       "3 0.58176393  2.636868 4.982670\n",
       "4 0.12511421  3.344929 6.220225\n",
       "5 0.02524745  2.573040 5.234677\n",
       "6 0.31813066  2.620384 3.070939"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example: dataset to be plotted  \n",
    "example <- data.frame(exponential = rexp(100000, rate = 1), \n",
    "                     uniform = runif(100000, min = 2.5, max = 3.5), \n",
    "                     normal = rnorm(100000, mean=5))\n",
    "head(example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram\n",
    "\n",
    "Visualize the distribution of a single continuous variable by dividing the x axis into bins and counting the number of observations in each bin. Histograms (geom_histogram()) display the counts with bars.\n",
    "More information: ?geom_histogram (R documentation)"
   ]
  },
  {
   "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",
      "Using  as id variables\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAHgCAIAAAD2dYQOAAAACXBIWXMAABJ0AAASdAHeZh94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\n9wAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_library(\"dplyr\")\n",
    "\n",
    "grf <- plot_hist(example |> select(exponential), \n",
    "                  label_x = \"exponential\", color=colors[1]) + font\n",
    "options(repr.plot.width=5, repr.plot.height=4)\n",
    "plot(grf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arrangement of graphs\n",
    "\n",
    "Function $grid.arrange$ is used to position previously computed charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using  as id variables\n",
      "\n",
      "Using  as id variables\n",
      "\n",
      "Using  as id variables\n",
      "\n"
     ]
    }
   ],
   "source": [
    "grfe <- plot_hist(example |> select(exponential), \n",
    "                  label_x = \"exponential\", color=colors[1]) + font\n",
    "grfu <- plot_hist(example |> select(uniform), \n",
    "                  label_x = \"uniform\", color=colors[1]) + font  \n",
    "grfn <- plot_hist(example |> select(normal), \n",
    "                  label_x = \"normal\", color=colors[1]) + font "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: gridExtra\n",
      "\n",
      "\n",
      "Attaching package: ‘gridExtra’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABwgAAAHgCAIAAAA+Nq4EAAAACXBIWXMAABJ0AAASdAHeZh94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\nAAAAn/P/AeTOtx4b3j3AAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_library(\"gridExtra\") \n",
    "\n",
    "options(repr.plot.width=15, repr.plot.height=4)\n",
    "grid.arrange(grfe, grfu, grfn, ncol=3)"
   ]
  },
  {
   "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
}