{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# R or Python? No, Julia.\n", "\n", "最近こんな感じの記事ばかり見かけます。RとPythonどっちが良いの？ってやつ。\n", "\n", "- https://news.mynavi.jp/article/20190619-845288/\n", "- https://news.mynavi.jp/article/20190619-845288/\n", "- https://www.messyer813.com/entry/2018/08/19/120959\n", "- https://uxmeetsdata.com/python-vs-r/\n", "\n", "どの言語いいかなんて一長一短だし，ほんと不毛だとおもいます。この不毛な争いを解決すべく，Juliaを使う人も多いはず（？）\n", "\n", "Juliaから他の言語を呼び出せることは前から知っていたのですが，ちゃんといじったことがなかったので，今回はトライしてみます。ちなみにRからもJuliaは動かせますし，Pythonからも動かせるらしいです。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RCall.jlを使う\n", "\n", "`RCall`はJuliaからRを呼び出すPkgです。PyCallってのもあるようです。私はｐｙてょｎ使えないので，気になる方は[こちら](https://github.com/JuliaPy/PyCall.jl)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# set up\n", "using Pkg\n", "Pkg.add(\"RCall\")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "R version 3.6.0 (2019-04-26) -- \"Planting of a Tree\"\n", "Copyright (C) 2019 The R Foundation for Statistical Computing\n", "Platform: x86_64-w64-mingw32/x64 (64-bit)\n", "\n", "R is free software and comes with ABSOLUTELY NO WARRANTY.\n", "You are welcome to redistribute it under certain conditions.\n", "Type 'license()' or 'licence()' for distribution details.\n", "\n", "R is a collaborative project with many contributors.\n", "Type 'contributors()' for more information and\n", "'citation()' on how to cite R or R packages in publications.\n", "\n", "Type 'demo()' for some demos, 'help()' for on-line help, or\n", "'help.start()' for an HTML browser interface to help.\n", "Type 'q()' to quit R.\n", "\n" ] } ], "source": [ "using RCall" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "基本的にはこれでOKですが，時々precompileでエラーを吐きます。エラーではPkg.buildしろって言われるのですが，`Pkg.Build`してもこんなエラーがでてくるときがあります。\n", "```{Julia}\n", "> LoadError: R cannot be found. Set the \"R_HOME\" environment variable to re-run Pkg.build(\"RCall\").\n", "```\n", "\n", "JuliaのENVにRのパスが通っていなかったりしますので，こんな設定が必要になるかもしれません。JuliaのENVにRのパスである`R_HOME`がないことを確認し，新しくRのパスを通してあげます。binまではいらないので，Rの各バージョンがあるディレクトリまでで十分です。\n", "```{Julia}\n", "ENV[\"R_HOME\"] # missing R ENV\n", "show(ENV)\n", "ENV[\"R_HOME\"]=\"C:\\\\hoge\\\\huga\\\\Documents\\\\R\\\\R-3.6.0\" # binまでは通さない。\n", "Pkg.build(\"RCall\") # rebuild\n", "```\n", "\n", "ちなみにENVにパスを通すのは毎回起動するたびにやらなきゃいけないみたいです。~/.julia/congig/startup.jlにパスを通すコードを書いておけば毎回起動時に通しておいてくれるので便利です。\n", "\n", "\n", "### RをJuliaから動かす\n", "\n", "そんなに難しくなく，基本は（1）juliaからRに変数を渡す，（2）Rで変数を操作する，（3）R変数をJuliaに渡す，というステップです。その逆も可能です。つまりR to JuliaもJulia to Rもできます。\n", "\n", "書き方は，[@bicycle1885](https://twitter.com/bicycle1885)さんがすでにまとめてくれています。\n", "\n", "http://bicycle1885.hatenablog.com/entry/2016/08/09/081525" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5-element Array{Int64,1}:\n", " 1\n", " 2\n", " 3\n", " 4\n", " 5" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rcopy(R\"1:5\") # get R eval" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RObject{RealSxp}\n", "[1] 4\n" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = 3.0\n", "R\"1 + $x\" # An inline R expression" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1×10 LinearAlgebra.Adjoint{Float64,Array{Float64,1}}:\n", " 0.383796 0.868393 0.231633 0.298366 … -1.35082 0.12759 -0.887251" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = 10\n", "@rput x \n", "R\"r = rnorm(x)\"\n", "@rget r\n", "r'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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}, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RObject{NilSxp}\n", "NULL\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rcopy(R\"1 + $x\")\n", "# multiple lines\n", "R\"\"\"\n", "n = 100 + 1\n", "r = rnorm(n)\n", "plot(c(1:n), r, type = \"l\")\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Juliaのloopの中にRを忍ばせることもできるので，モンテカルロシミュレーションなどをやるときに，**「Rのパッケージを使って分析したいけど，重たい処理はJuliaでやりたい」**ってときに便利かもしれません。" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The iteration time is 1 in R, and 1 in Julia.\n", "The iteration time is 2 in R, and 2 in Julia.\n", "The iteration time is 3 in R, and 3 in Julia.\n", "The iteration time is 4 in R, and 4 in Julia.\n", "The iteration time is 5 in R, and 5 in Julia.\n", "The iteration time is 6 in R, and 6 in Julia.\n", "The iteration time is 7 in R, and 7 in Julia.\n", "The iteration time is 8 in R, and 8 in Julia.\n", "The iteration time is 9 in R, and 9 in Julia.\n", "The iteration time is 10 in R, and 10 in Julia.\n" ] } ], "source": [ "for i in 1:10\n", " @rput i\n", " R\"\"\"\n", " cat(\"The iteration time is\",i,\"in R\")\n", " t = i\n", " \"\"\"\n", " @rget t\n", " println(\", and $t in Julia.\")\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RのデータをJuliaで扱う\n", "\n", "`reval`および`rcall`関数を使えば，Julia上でRのデータや関数を扱うことができます。\n", "たとえばデータフレームやtibbleも使えます。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RObject{VecSxp}\n", " 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\n", "7 4.6 3.4 1.4 0.3 setosa\n", "8 5.0 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 3.0 1.4 0.1 setosa\n", "14 4.3 3.0 1.1 0.1 setosa\n", "15 5.8 4.0 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 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 5.0 3.0 1.6 0.2 setosa\n", "27 5.0 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", "31 4.8 3.1 1.6 0.2 setosa\n", "32 5.4 3.4 1.5 0.4 setosa\n", "33 5.2 4.1 1.5 0.1 setosa\n", "34 5.5 4.2 1.4 0.2 setosa\n", "35 4.9 3.1 1.5 0.2 setosa\n", "36 5.0 3.2 1.2 0.2 setosa\n", "37 5.5 3.5 1.3 0.2 setosa\n", "38 4.9 3.6 1.4 0.1 setosa\n", "39 4.4 3.0 1.3 0.2 setosa\n", "40 5.1 3.4 1.5 0.2 setosa\n", "41 5.0 3.5 1.3 0.3 setosa\n", "42 4.5 2.3 1.3 0.3 setosa\n", "43 4.4 3.2 1.3 0.2 setosa\n", "44 5.0 3.5 1.6 0.6 setosa\n", "45 5.1 3.8 1.9 0.4 setosa\n", "46 4.8 3.0 1.4 0.3 setosa\n", "47 5.1 3.8 1.6 0.2 setosa\n", "48 4.6 3.2 1.4 0.2 setosa\n", "49 5.3 3.7 1.5 0.2 setosa\n", "50 5.0 3.3 1.4 0.2 setosa\n", "51 7.0 3.2 4.7 1.4 versicolor\n", "52 6.4 3.2 4.5 1.5 versicolor\n", "53 6.9 3.1 4.9 1.5 versicolor\n", "54 5.5 2.3 4.0 1.3 versicolor\n", "55 6.5 2.8 4.6 1.5 versicolor\n", "56 5.7 2.8 4.5 1.3 versicolor\n", "57 6.3 3.3 4.7 1.6 versicolor\n", "58 4.9 2.4 3.3 1.0 versicolor\n", "59 6.6 2.9 4.6 1.3 versicolor\n", "60 5.2 2.7 3.9 1.4 versicolor\n", "61 5.0 2.0 3.5 1.0 versicolor\n", "62 5.9 3.0 4.2 1.5 versicolor\n", "63 6.0 2.2 4.0 1.0 versicolor\n", "64 6.1 2.9 4.7 1.4 versicolor\n", "65 5.6 2.9 3.6 1.3 versicolor\n", "66 6.7 3.1 4.4 1.4 versicolor\n", "67 5.6 3.0 4.5 1.5 versicolor\n", "68 5.8 2.7 4.1 1.0 versicolor\n", "69 6.2 2.2 4.5 1.5 versicolor\n", "70 5.6 2.5 3.9 1.1 versicolor\n", "71 5.9 3.2 4.8 1.8 versicolor\n", "72 6.1 2.8 4.0 1.3 versicolor\n", "73 6.3 2.5 4.9 1.5 versicolor\n", "74 6.1 2.8 4.7 1.2 versicolor\n", "75 6.4 2.9 4.3 1.3 versicolor\n", "76 6.6 3.0 4.4 1.4 versicolor\n", "77 6.8 2.8 4.8 1.4 versicolor\n", "78 6.7 3.0 5.0 1.7 versicolor\n", "79 6.0 2.9 4.5 1.5 versicolor\n", "80 5.7 2.6 3.5 1.0 versicolor\n", "81 5.5 2.4 3.8 1.1 versicolor\n", "82 5.5 2.4 3.7 1.0 versicolor\n", "83 5.8 2.7 3.9 1.2 versicolor\n", "84 6.0 2.7 5.1 1.6 versicolor\n", "85 5.4 3.0 4.5 1.5 versicolor\n", "86 6.0 3.4 4.5 1.6 versicolor\n", "87 6.7 3.1 4.7 1.5 versicolor\n", "88 6.3 2.3 4.4 1.3 versicolor\n", "89 5.6 3.0 4.1 1.3 versicolor\n", "90 5.5 2.5 4.0 1.3 versicolor\n", "91 5.5 2.6 4.4 1.2 versicolor\n", "92 6.1 3.0 4.6 1.4 versicolor\n", "93 5.8 2.6 4.0 1.2 versicolor\n", "94 5.0 2.3 3.3 1.0 versicolor\n", "95 5.6 2.7 4.2 1.3 versicolor\n", "96 5.7 3.0 4.2 1.2 versicolor\n", "97 5.7 2.9 4.2 1.3 versicolor\n", "98 6.2 2.9 4.3 1.3 versicolor\n", "99 5.1 2.5 3.0 1.1 versicolor\n", "100 5.7 2.8 4.1 1.3 versicolor\n", "101 6.3 3.3 6.0 2.5 virginica\n", "102 5.8 2.7 5.1 1.9 virginica\n", "103 7.1 3.0 5.9 2.1 virginica\n", "104 6.3 2.9 5.6 1.8 virginica\n", "105 6.5 3.0 5.8 2.2 virginica\n", "106 7.6 3.0 6.6 2.1 virginica\n", "107 4.9 2.5 4.5 1.7 virginica\n", "108 7.3 2.9 6.3 1.8 virginica\n", "109 6.7 2.5 5.8 1.8 virginica\n", "110 7.2 3.6 6.1 2.5 virginica\n", "111 6.5 3.2 5.1 2.0 virginica\n", "112 6.4 2.7 5.3 1.9 virginica\n", "113 6.8 3.0 5.5 2.1 virginica\n", "114 5.7 2.5 5.0 2.0 virginica\n", "115 5.8 2.8 5.1 2.4 virginica\n", "116 6.4 3.2 5.3 2.3 virginica\n", "117 6.5 3.0 5.5 1.8 virginica\n", "118 7.7 3.8 6.7 2.2 virginica\n", "119 7.7 2.6 6.9 2.3 virginica\n", "120 6.0 2.2 5.0 1.5 virginica\n", "121 6.9 3.2 5.7 2.3 virginica\n", "122 5.6 2.8 4.9 2.0 virginica\n", "123 7.7 2.8 6.7 2.0 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 6.0 1.8 virginica\n", "127 6.2 2.8 4.8 1.8 virginica\n", "128 6.1 3.0 4.9 1.8 virginica\n", "129 6.4 2.8 5.6 2.1 virginica\n", "130 7.2 3.0 5.8 1.6 virginica\n", "131 7.4 2.8 6.1 1.9 virginica\n", "132 7.9 3.8 6.4 2.0 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 3.0 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 6.0 3.0 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 3.0 5.2 2.3 virginica\n", "147 6.3 2.5 5.0 1.9 virginica\n", "148 6.5 3.0 5.2 2.0 virginica\n", "149 6.2 3.4 5.4 2.3 virginica\n", "150 5.9 3.0 5.1 1.8 virginica\n" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iris = reval(\"iris\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RObject{VecSxp}\n", "# A tibble: 87 x 13\n", " name height mass hair_color skin_color eye_color birth_year gender\n", " \n", " 1 Luke~ 172 77 blond fair blue 19 male \n", " 2 C-3PO 167 75 gold yellow 112 \n", " 3 R2-D2 96 32 white, bl~ red 33 \n", " 4 Dart~ 202 136 none white yellow 41.9 male \n", " 5 Leia~ 150 49 brown light brown 19 female\n", " 6 Owen~ 178 120 brown, gr~ light blue 52 male \n", " 7 Beru~ 165 75 brown light blue 47 female\n", " 8 R5-D4 97 32 white, red red NA \n", " 9 Bigg~ 183 84 black light brown 24 male \n", "10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male \n", "# ... with 77 more rows, and 5 more variables: homeworld , species ,\n", "# films , vehicles , starships \n" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "starwars = reval(\"starwars\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "もちろん変数にもアクセスできる。" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RObject{CharSxp}\n", "\n" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "starwars[\"name\"][1]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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}, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RObject{IntSxp}\n", "[1] 55\n" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# using R function\n", "rcall(:plot, [1:100;], rand(100), type = \"l\", ylab = \"rand\")\n", "rcall(:sum, [1:10;])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "パッケージを使いたいときは，一応`@rlibrary`や`@rimport`といったマクロを使えますが，公式的にはトリプルクオーテーションで囲むのが良いとのこと。\n", "\n", "http://juliainterop.github.io/RCall.jl/stable/gettingstarted#@rlibrary-and-@rimport-macros-1" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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}, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RObject{VecSxp}\n" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# using R package\n", "R\"\"\"\n", "library(tidyverse)\n", "ggplot(tibble(x = seq(-4, 4, len = 101), prob = dnorm(seq(-4, 4, len = 101))), aes(x = x, y = prob)) +\n", "geom_line()\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "いままではR一択で分析していたのですが，ここまで簡単にJuliaからRが使えると，もはや言語の壁はありませんね。" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Julia 1.1.1", "language": "julia", "name": "julia-1.1" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 2 }