{
 "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 1.01.727\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": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plot\n",
    "\n",
    "A Scatter plot is used to display values for the typical relationship between independent and dependent variables. The first column of the dataset is the independent variable, whereas the following variables are the dependent ones. \n",
    "\n",
    "The colors vectors should have the same size as the number of dependent columns. \n",
    "\n",
    "More information: https://en.wikipedia.org/wiki/Scatter_plot"
   ]
  },
  {
   "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": {
      "image/png": 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JIMq2yttS1eTePBbQN1Tdbf7jUdPmxf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\nAwAAAOAJBHYAAAAAPIHADgAAAIAn/g8oMXz/O9pv5AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_library(\"dplyr\")\n",
    "\n",
    "grf <- plot_scatter(iris |> select(x = Sepal.Length, value = Sepal.Width, variable = Species), \n",
    "                    label_x = \"Sepal.Length\", label_y = \"Sepal.Width\", 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
}