{ "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: RColorBrewer\n", "\n", "Loading required package: ggplot2\n", "\n" ] } ], "source": [ "load_library(\"RColorBrewer\")\n", "#color palette\n", "colors <- brewer.pal(4, 'Set1')\n", "\n", "load_library(\"ggplot2\")\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": [ "\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
exponentialuniformnormal
<dbl><dbl><dbl>
10.15989832.7055395.179180
22.37120593.4371704.297048
30.41201312.9869265.324625
40.15279773.0826604.794226
51.12496692.5478584.577240
62.03498813.3626974.941374
\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & exponential & uniform & normal\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & 0.1598983 & 2.705539 & 5.179180\\\\\n", "\t2 & 2.3712059 & 3.437170 & 4.297048\\\\\n", "\t3 & 0.4120131 & 2.986926 & 5.324625\\\\\n", "\t4 & 0.1527977 & 3.082660 & 4.794226\\\\\n", "\t5 & 1.1249669 & 2.547858 & 4.577240\\\\\n", "\t6 & 2.0349881 & 3.362697 & 4.941374\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "| | exponential <dbl> | uniform <dbl> | normal <dbl> |\n", "|---|---|---|---|\n", "| 1 | 0.1598983 | 2.705539 | 5.179180 |\n", "| 2 | 2.3712059 | 3.437170 | 4.297048 |\n", "| 3 | 0.4120131 | 2.986926 | 5.324625 |\n", "| 4 | 0.1527977 | 3.082660 | 4.794226 |\n", "| 5 | 1.1249669 | 2.547858 | 4.577240 |\n", "| 6 | 2.0349881 | 3.362697 | 4.941374 |\n", "\n" ], "text/plain": [ " exponential uniform normal \n", "1 0.1598983 2.705539 5.179180\n", "2 2.3712059 3.437170 4.297048\n", "3 0.4120131 2.986926 5.324625\n", "4 0.1527977 3.082660 4.794226\n", "5 1.1249669 2.547858 4.577240\n", "6 2.0349881 3.362697 4.941374" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# example4: 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": [ "## Density plot\n", "\n", "Computes and draws kernel density estimate, which is a smoothed version of the histogram. This is a useful alternative to the histogram for continuous data that comes from an underlying smooth distribution.\n", "\n", "More information: ?geom_density (R documentation)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using as id variables\n", "\n" ] }, { "data": { "image/png": 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Z/QaDRPhTS7ffv2oUOHZBeFIlGauHrV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\nNvJs27apqalbt26VXYgzYBEhAADOhwANSz3C2gkhfvjhB9mFOAO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gYUNarXbqsGHp6emTJk2SXYsETjMD7Wgd6PT09JdffvncuXODIgaF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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 300, "width": 480 } }, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=8, repr.plot.height=5)\n", "grf <- plot_density(example, colors=colors[1:3]) + font\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": [ "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", "Using as id variables\n", "\n", "Using as id variables\n", "\n" ] } ], "source": [ "load_library(\"dplyr\")\n", "grfe <- plot_density(example |> select(exponential), \n", " label_x = \"exponential\", color=colors[1]) + font \n", "grfu <- plot_density(example |> select(uniform), \n", " label_x = \"uniform\", color=colors[2]) + font \n", "grfn <- plot_density(example |> select(normal), \n", " label_x = \"normal\", color=colors[3]) + 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": 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jPPPJOfn49DtwDgNv3pT3/SaDTPblw33MGDY2XB4gUL70v7Zd/Bv//97xs3bhzX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\nmTNnHjt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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 240, "width": 900 } }, "output_type": "display_data" } ], "source": [ "load_library(\"gridExtra\") \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 }