{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting up R in Jupyter Notebook\n", "- [This is by far the easiest install that I have done of this kind](https://www.continuum.io/blog/developer/jupyter-and-conda-r)\n", "- just open up your terminal and run the following commands; \n", " - `conda install -c r r-essentials`\n", " - `conda create -n my-r-env -c r r-essentials`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\"Hello World!\"" ], "text/latex": [ "\"Hello World!\"" ], "text/markdown": [ "\"Hello World!\"" ], "text/plain": [ "[1] \"Hello World!\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Let's run a quick test\n", "e<-'Hello World!'\n", "e" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Attaching package: 'dplyr'\n", "\n", "The following objects are masked from 'package:stats':\n", "\n", " filter, lag\n", "\n", "The following objects are masked from 'package:base':\n", "\n", " intersect, setdiff, setequal, union\n", "\n" ] } ], "source": [ "library(dplyr)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies
15.1 3.5 1.4 0.2 setosa
24.9 3 1.4 0.2 setosa
34.7 3.2 1.3 0.2 setosa
44.6 3.1 1.5 0.2 setosa
55 3.6 1.4 0.2 setosa
65.4 3.9 1.7 0.4 setosa
74.6 3.4 1.4 0.3 setosa
85 3.4 1.5 0.2 setosa
94.4 2.9 1.4 0.2 setosa
104.9 3.1 1.5 0.1 setosa
115.4 3.7 1.5 0.2 setosa
124.8 3.4 1.6 0.2 setosa
134.8 3 1.4 0.1 setosa
144.3 3 1.1 0.1 setosa
155.8 4 1.2 0.2 setosa
165.7 4.4 1.5 0.4 setosa
175.4 3.9 1.3 0.4 setosa
185.1 3.5 1.4 0.3 setosa
195.7 3.8 1.7 0.3 setosa
205.1 3.8 1.5 0.3 setosa
215.4 3.4 1.7 0.2 setosa
225.1 3.7 1.5 0.4 setosa
234.6 3.6 1 0.2 setosa
245.1 3.3 1.7 0.5 setosa
254.8 3.4 1.9 0.2 setosa
265 3 1.6 0.2 setosa
275 3.4 1.6 0.4 setosa
285.2 3.5 1.5 0.2 setosa
295.2 3.4 1.4 0.2 setosa
304.7 3.2 1.6 0.2 setosa
..................
1216.9 3.2 5.7 2.3 virginica
1225.6 2.8 4.9 2 virginica
1237.7 2.8 6.7 2 virginica
1246.3 2.7 4.9 1.8 virginica
1256.7 3.3 5.7 2.1 virginica
1267.2 3.2 6 1.8 virginica
1276.2 2.8 4.8 1.8 virginica
1286.1 3 4.9 1.8 virginica
1296.4 2.8 5.6 2.1 virginica
1307.2 3 5.8 1.6 virginica
1317.4 2.8 6.1 1.9 virginica
1327.9 3.8 6.4 2 virginica
1336.4 2.8 5.6 2.2 virginica
1346.3 2.8 5.1 1.5 virginica
1356.1 2.6 5.6 1.4 virginica
1367.7 3 6.1 2.3 virginica
1376.3 3.4 5.6 2.4 virginica
1386.4 3.1 5.5 1.8 virginica
1396 3 4.8 1.8 virginica
1406.9 3.1 5.4 2.1 virginica
1416.7 3.1 5.6 2.4 virginica
1426.9 3.1 5.1 2.3 virginica
1435.8 2.7 5.1 1.9 virginica
1446.8 3.2 5.9 2.3 virginica
1456.7 3.3 5.7 2.5 virginica
1466.7 3 5.2 2.3 virginica
1476.3 2.5 5 1.9 virginica
1486.5 3 5.2 2 virginica
1496.2 3.4 5.4 2.3 virginica
1505.9 3 5.1 1.8 virginica
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n", "\\hline\n", "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n", "\t2 & 4.9 & 3 & 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 & 3.6 & 1.4 & 0.2 & setosa\\\\\n", "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n", "\t7 & 4.6 & 3.4 & 1.4 & 0.3 & setosa\\\\\n", "\t8 & 5 & 3.4 & 1.5 & 0.2 & setosa\\\\\n", "\t9 & 4.4 & 2.9 & 1.4 & 0.2 & setosa\\\\\n", "\t10 & 4.9 & 3.1 & 1.5 & 0.1 & setosa\\\\\n", "\t11 & 5.4 & 3.7 & 1.5 & 0.2 & setosa\\\\\n", "\t12 & 4.8 & 3.4 & 1.6 & 0.2 & setosa\\\\\n", "\t13 & 4.8 & 3 & 1.4 & 0.1 & setosa\\\\\n", "\t14 & 4.3 & 3 & 1.1 & 0.1 & setosa\\\\\n", "\t15 & 5.8 & 4 & 1.2 & 0.2 & setosa\\\\\n", "\t16 & 5.7 & 4.4 & 1.5 & 0.4 & setosa\\\\\n", "\t17 & 5.4 & 3.9 & 1.3 & 0.4 & setosa\\\\\n", "\t18 & 5.1 & 3.5 & 1.4 & 0.3 & setosa\\\\\n", "\t19 & 5.7 & 3.8 & 1.7 & 0.3 & setosa\\\\\n", "\t20 & 5.1 & 3.8 & 1.5 & 0.3 & setosa\\\\\n", "\t21 & 5.4 & 3.4 & 1.7 & 0.2 & setosa\\\\\n", "\t22 & 5.1 & 3.7 & 1.5 & 0.4 & setosa\\\\\n", "\t23 & 4.6 & 3.6 & 1 & 0.2 & setosa\\\\\n", "\t24 & 5.1 & 3.3 & 1.7 & 0.5 & setosa\\\\\n", "\t25 & 4.8 & 3.4 & 1.9 & 0.2 & setosa\\\\\n", "\t26 & 5 & 3 & 1.6 & 0.2 & setosa\\\\\n", "\t27 & 5 & 3.4 & 1.6 & 0.4 & setosa\\\\\n", "\t28 & 5.2 & 3.5 & 1.5 & 0.2 & setosa\\\\\n", "\t29 & 5.2 & 3.4 & 1.4 & 0.2 & setosa\\\\\n", "\t30 & 4.7 & 3.2 & 1.6 & 0.2 & setosa\\\\\n", "\t... & ... & ... & ... & ... & ...\\\\\n", "\t121 & 6.9 & 3.2 & 5.7 & 2.3 & virginica\\\\\n", "\t122 & 5.6 & 2.8 & 4.9 & 2 & virginica\\\\\n", "\t123 & 7.7 & 2.8 & 6.7 & 2 & virginica\\\\\n", "\t124 & 6.3 & 2.7 & 4.9 & 1.8 & virginica\\\\\n", "\t125 & 6.7 & 3.3 & 5.7 & 2.1 & virginica\\\\\n", "\t126 & 7.2 & 3.2 & 6 & 1.8 & virginica\\\\\n", "\t127 & 6.2 & 2.8 & 4.8 & 1.8 & virginica\\\\\n", "\t128 & 6.1 & 3 & 4.9 & 1.8 & virginica\\\\\n", "\t129 & 6.4 & 2.8 & 5.6 & 2.1 & virginica\\\\\n", "\t130 & 7.2 & 3 & 5.8 & 1.6 & virginica\\\\\n", "\t131 & 7.4 & 2.8 & 6.1 & 1.9 & virginica\\\\\n", "\t132 & 7.9 & 3.8 & 6.4 & 2 & virginica\\\\\n", "\t133 & 6.4 & 2.8 & 5.6 & 2.2 & virginica\\\\\n", "\t134 & 6.3 & 2.8 & 5.1 & 1.5 & virginica\\\\\n", "\t135 & 6.1 & 2.6 & 5.6 & 1.4 & virginica\\\\\n", "\t136 & 7.7 & 3 & 6.1 & 2.3 & virginica\\\\\n", "\t137 & 6.3 & 3.4 & 5.6 & 2.4 & virginica\\\\\n", "\t138 & 6.4 & 3.1 & 5.5 & 1.8 & virginica\\\\\n", "\t139 & 6 & 3 & 4.8 & 1.8 & virginica\\\\\n", "\t140 & 6.9 & 3.1 & 5.4 & 2.1 & virginica\\\\\n", "\t141 & 6.7 & 3.1 & 5.6 & 2.4 & virginica\\\\\n", "\t142 & 6.9 & 3.1 & 5.1 & 2.3 & virginica\\\\\n", "\t143 & 5.8 & 2.7 & 5.1 & 1.9 & virginica\\\\\n", "\t144 & 6.8 & 3.2 & 5.9 & 2.3 & virginica\\\\\n", "\t145 & 6.7 & 3.3 & 5.7 & 2.5 & virginica\\\\\n", "\t146 & 6.7 & 3 & 5.2 & 2.3 & virginica\\\\\n", "\t147 & 6.3 & 2.5 & 5 & 1.9 & virginica\\\\\n", "\t148 & 6.5 & 3 & 5.2 & 2 & virginica\\\\\n", "\t149 & 6.2 & 3.4 & 5.4 & 2.3 & virginica\\\\\n", "\t150 & 5.9 & 3 & 5.1 & 1.8 & virginica\\\\\n", "\\end{tabular}\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 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 3.6 1.4 0.2 setosa\n", "6 5.4 3.9 1.7 0.4 setosa\n", "7 4.6 3.4 1.4 0.3 setosa\n", "8 5 3.4 1.5 0.2 setosa\n", "9 4.4 2.9 1.4 0.2 setosa\n", "10 4.9 3.1 1.5 0.1 setosa\n", "11 5.4 3.7 1.5 0.2 setosa\n", "12 4.8 3.4 1.6 0.2 setosa\n", "13 4.8 3 1.4 0.1 setosa\n", "14 4.3 3 1.1 0.1 setosa\n", "15 5.8 4 1.2 0.2 setosa\n", "16 5.7 4.4 1.5 0.4 setosa\n", "17 5.4 3.9 1.3 0.4 setosa\n", "18 5.1 3.5 1.4 0.3 setosa\n", "19 5.7 3.8 1.7 0.3 setosa\n", "20 5.1 3.8 1.5 0.3 setosa\n", "21 5.4 3.4 1.7 0.2 setosa\n", "22 5.1 3.7 1.5 0.4 setosa\n", "23 4.6 3.6 1 0.2 setosa\n", "24 5.1 3.3 1.7 0.5 setosa\n", "25 4.8 3.4 1.9 0.2 setosa\n", "26 5 3 1.6 0.2 setosa\n", "27 5 3.4 1.6 0.4 setosa\n", "28 5.2 3.5 1.5 0.2 setosa\n", "29 5.2 3.4 1.4 0.2 setosa\n", "30 4.7 3.2 1.6 0.2 setosa\n", "... ... ... ... ... ...\n", "121 6.9 3.2 5.7 2.3 virginica\n", "122 5.6 2.8 4.9 2 virginica\n", "123 7.7 2.8 6.7 2 virginica\n", "124 6.3 2.7 4.9 1.8 virginica\n", "125 6.7 3.3 5.7 2.1 virginica\n", "126 7.2 3.2 6 1.8 virginica\n", "127 6.2 2.8 4.8 1.8 virginica\n", "128 6.1 3 4.9 1.8 virginica\n", "129 6.4 2.8 5.6 2.1 virginica\n", "130 7.2 3 5.8 1.6 virginica\n", "131 7.4 2.8 6.1 1.9 virginica\n", "132 7.9 3.8 6.4 2 virginica\n", "133 6.4 2.8 5.6 2.2 virginica\n", "134 6.3 2.8 5.1 1.5 virginica\n", "135 6.1 2.6 5.6 1.4 virginica\n", "136 7.7 3 6.1 2.3 virginica\n", "137 6.3 3.4 5.6 2.4 virginica\n", "138 6.4 3.1 5.5 1.8 virginica\n", "139 6 3 4.8 1.8 virginica\n", "140 6.9 3.1 5.4 2.1 virginica\n", "141 6.7 3.1 5.6 2.4 virginica\n", "142 6.9 3.1 5.1 2.3 virginica\n", "143 5.8 2.7 5.1 1.9 virginica\n", "144 6.8 3.2 5.9 2.3 virginica\n", "145 6.7 3.3 5.7 2.5 virginica\n", "146 6.7 3 5.2 2.3 virginica\n", "147 6.3 2.5 5 1.9 virginica\n", "148 6.5 3 5.2 2 virginica\n", "149 6.2 3.4 5.4 2.3 virginica\n", "150 5.9 3 5.1 1.8 virginica" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iris" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
SpeciesSepal.Width.Avg
1versicolor2.77
2virginica2.974
3setosa3.428
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & Species & Sepal.Width.Avg\\\\\n", "\\hline\n", "\t1 & versicolor & 2.77 \\\\\n", "\t2 & virginica & 2.974 \\\\\n", "\t3 & setosa & 3.428 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ " Species Sepal.Width.Avg\n", "1 versicolor 2.770\n", "2 virginica 2.974\n", "3 setosa 3.428" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iris %>%\n", "group_by(Species) %>%\n", "summarise(Sepal.Width.Avg = mean(Sepal.Width)) %>%\n", "arrange(Sepal.Width.Avg)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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x9/UrcaZ9YGYE6XXTanm9V4kzRhJF79HaRzPM7HRJ/FIgXpHI/zMdFnsUhBOsfj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\nLJIIgEUSAbBIIgAWSQTAIokAWCQRgP8HRTiKB6NlQDgAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "library(ggplot2)\n", "ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + geom_point(size=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "3.3.1" } }, "nbformat": 4, "nbformat_minor": 0 }