{
 "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": "markdown",
   "metadata": {},
   "source": [
    "#### Synthetic time series"
   ]
  },
  {
   "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>x</th><th scope=col>sin</th><th scope=col>cosine</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>0.00</td><td>0.0000000</td><td>6.000000</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>0.25</td><td>0.2474040</td><td>5.968912</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>0.50</td><td>0.4794255</td><td>5.877583</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>0.75</td><td>0.6816388</td><td>5.731689</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1.00</td><td>0.8414710</td><td>5.540302</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1.25</td><td>0.9489846</td><td>5.315322</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & x & sin & cosine\\\\\n",
       "  & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 0.00 & 0.0000000 & 6.000000\\\\\n",
       "\t2 & 0.25 & 0.2474040 & 5.968912\\\\\n",
       "\t3 & 0.50 & 0.4794255 & 5.877583\\\\\n",
       "\t4 & 0.75 & 0.6816388 & 5.731689\\\\\n",
       "\t5 & 1.00 & 0.8414710 & 5.540302\\\\\n",
       "\t6 & 1.25 & 0.9489846 & 5.315322\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | x &lt;dbl&gt; | sin &lt;dbl&gt; | cosine &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | 0.00 | 0.0000000 | 6.000000 |\n",
       "| 2 | 0.25 | 0.2474040 | 5.968912 |\n",
       "| 3 | 0.50 | 0.4794255 | 5.877583 |\n",
       "| 4 | 0.75 | 0.6816388 | 5.731689 |\n",
       "| 5 | 1.00 | 0.8414710 | 5.540302 |\n",
       "| 6 | 1.25 | 0.9489846 | 5.315322 |\n",
       "\n"
      ],
      "text/plain": [
       "  x    sin       cosine  \n",
       "1 0.00 0.0000000 6.000000\n",
       "2 0.25 0.2474040 5.968912\n",
       "3 0.50 0.4794255 5.877583\n",
       "4 0.75 0.6816388 5.731689\n",
       "5 1.00 0.8414710 5.540302\n",
       "6 1.25 0.9489846 5.315322"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- seq(0, 10, 0.25)\n",
    "serie <- data.frame(x, sin=sin(x), cosine=cos(x)+5)\n",
    "head(serie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot points\n",
    "\n",
    "A point plot is similiar to series plot without drawing lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OGMwemyUwDAIbEVCwAAoAiKHQAAgCIo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\nCYIdAACAJAh2AAAAkvg/H2lNpe1mvXIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The function returns a preset graphic that can be enhanced. \n",
    "grf <- plot_points(serie, colors=colors[1:2])\n",
    "# Increasing the font size of the graphics\n",
    "grf <- grf + font\n",
    "# Actual plot\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
}