{ "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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 3
xsincosine
<dbl><dbl><dbl>
10.000.00000006.000000
20.250.24740405.968912
30.500.47942555.877583
40.750.68163885.731689
51.000.84147105.540302
61.250.94898465.315322
\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & x & sin & cosine\\\\\n", " & & & \\\\\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 <dbl> | sin <dbl> | cosine <dbl> |\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": [ "## Series plot\n", "\n", "A series plot is a type of chart that displays information as a series of data points connected by straight line segments. \n", "\n", "It is similar to a scatter plot except that their x-axis value orders the measurement points.\n", "\n", "More information: https://en.wikipedia.org/wiki/Line_chart" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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MrkstpmaUsCOlFDs/aZlSmmXtw3JvSZPDhA2pJgjRQK8aVpg/zcW48SPniy6p2N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\nKhq++jvbuKph6Y4TXi52M4cHSl0guhAzdrAyY6N9gj3br6DX6Q2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O9uoof9djRTUlNc2mV0VRGD/I79FxYaZNbQBw6Xxc7d6dk7DmQN5b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\nL+uuj7RMFoL3p0OAY8LDtXPmZJkSy5pMpv4DKZY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oebdchfu16jxo3MS3FrSkYkXDti2MVks2HgAEMrwo7CIAABu1SURBVDR2APD8mPDw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\nDAXqOb6zODmECI7j1q5dq9PpPvzww7yfifNDiAjkdiK0Gju32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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "grf <- plot_series(serie, colors=colors[1:2]) + 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 }