{"id":2545,"date":"2023-07-21T19:20:33","date_gmt":"2023-07-21T19:20:33","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%ae%e4%b8%89%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90\/"},"modified":"2023-07-21T19:20:33","modified_gmt":"2023-07-21T19:20:33","slug":"r-%e3%81%ae%e4%b8%89%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%ae%e4%b8%89%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90\/","title":{"rendered":"R \u3067\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306f\u3001<\/strong> 3 \u3064\u306e\u56e0\u5b50\u306b\u5206\u6563\u3055\u308c\u305f 3 \u3064\u4ee5\u4e0a\u306e\u72ec\u7acb\u3057\u305f\u30b0\u30eb\u30fc\u30d7\u306e\u5e73\u5747\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001R \u3067 3 \u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b: R \u3067\u306e 3 \u5143\u914d\u7f6e\u5206\u6563\u5206\u6790<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u7814\u7a76\u8005\u304c\u30012 \u3064\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0\u304c\u5927\u5b66\u306e\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u306e\u9593\u3067\u30b8\u30e3\u30f3\u30d7\u306e\u9ad8\u3055\u306e\u5e73\u5747\u9ad8\u3055\u306e\u7570\u306a\u308b\u5411\u4e0a\u306b\u3064\u306a\u304c\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7814\u7a76\u8005\u306f\u3001\u6027\u5225\u3068\u90e8\u9580 (\u90e8\u9580 I \u307e\u305f\u306f II) \u3082\u30b8\u30e3\u30f3\u30d7\u306e\u9ad8\u3055\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u8003\u3048\u3066\u304a\u308a\u3001\u3053\u308c\u3089\u306e\u8981\u56e0\u306b\u95a2\u3059\u308b\u30c7\u30fc\u30bf\u3082\u53ce\u96c6\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5f7c\u306e\u76ee\u6a19\u306f\u3001\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3057\u3066\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u6027\u5225\u3001\u90e8\u9580\u304c\u30b8\u30e3\u30f3\u30d7\u306e\u9ad8\u3055\u306b\u3069\u306e\u3088\u3046\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3092\u5224\u65ad\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u3067\u3053\u306e 3 \u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u624b\u9806\u306b\u5f93\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001\u30c7\u30fc\u30bf\u3092\u4fdd\u6301\u3059\u308b\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u307e\u3057\u3087\u3046\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create dataset\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (program=rep(c(1, 2), each= <span style=\"color: #008000;\">20<\/span> ),\n                 gender=rep(c(' <span style=\"color: #ff0000;\">M<\/span> ', ' <span style=\"color: #ff0000;\">F<\/span> '), each= <span style=\"color: #008000;\">10<\/span> , times= <span style=\"color: #008000;\">2<\/span> ),\n                 division=rep(c(1, 2), each= <span style=\"color: #008000;\">5<\/span> , times= <span style=\"color: #008000;\">4<\/span> ),\n                 height=c(7, 7, 8, 8, 7, 6, 6, 5, 6, 5,\n                          5, 5, 4, 5, 4, 3, 3, 4, 3, 3,\n                          6, 6, 5, 4, 5, 4, 5, 4, 4, 3,\n                          2, 2, 1, 4, 4, 2, 1, 1, 2, 1)) \n\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>head(df)\n\n  program gender division height\n1 1 M 1 7\n2 1 M 1 7\n3 1 M 1 8\n4 1 M 1 8\n5 1 M 1 7\n6 1 M 2 6<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u8a18\u8ff0\u7d71\u8a08\u3092\u8868\u793a\u3059\u308b<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u524d\u306b\u3001 <a href=\"https:\/\/dplyr.tidyverse.org\/\" target=\"_blank\" rel=\"noopener\">dplyr<\/a>\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u6027\u5225\u3001\u90e8\u9580\u3054\u3068\u306b\u30b0\u30eb\u30fc\u30d7\u5316\u3055\u308c\u305f\u5e73\u5747\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5897\u52a0\u3092\u7c21\u5358\u306b\u8981\u7d04\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">library<\/span> (dplyr)\n\n<span style=\"color: #008080;\">#calculate mean jumping height increase grouped by program, gender, and division\n<\/span>df %&gt;%\n  group_by(program, gender, division) %&gt;%\n  summarize(mean_height = mean(height))\n\n# A tibble: 8 x 4\n# Groups: program, gender [4]\n  program gender division mean_height\n                 \n1 1 F 1 4.6\n2 1 F 2 3.2\n3 1 M 1 7.4\n4 1 M 2 5.6\n5 2 F 1 2.6\n6 2 F 2 1.4\n7 2 M 1 5.2\n8 2 M 2 4  \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0 1 \u3092\u4f7f\u7528\u3057\u305f\u30c7\u30a3\u30d3\u30b8\u30e7\u30f3 I \u5973\u5b50\u306e\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5e73\u5747\u5897\u52a0\u306f<strong>4.6 \u30a4\u30f3\u30c1<\/strong>\u3067\u3057\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0 1 \u3092\u4f7f\u7528\u3057\u305f\u30c7\u30a3\u30d3\u30b8\u30e7\u30f3 II \u5973\u5b50\u9078\u624b\u306e\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5e73\u5747\u5897\u52a0\u306f<strong>3.2 \u30a4\u30f3\u30c1<\/strong>\u3067\u3057\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0 1 \u3092\u4f7f\u7528\u3057\u305f\u30c7\u30a3\u30d3\u30b8\u30e7\u30f3 I \u7537\u5b50\u306e\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5e73\u5747\u5897\u52a0\u306f<strong>7.4 \u30a4\u30f3\u30c1<\/strong>\u3067\u3057\u305f\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u3005\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001 <strong>aov()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u4e09\u5143\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform three-way ANOVA<\/span>\nmodel &lt;- aov(height ~ program * gender * division, data=df)\n\n<span style=\"color: #008080;\">#view summary of three-way ANOVA\n<\/span>summary(model)\n\n                        Df Sum Sq Mean Sq F value Pr(&gt;F)    \nprogram 1 36.1 36.10 65.636 2.98e-09 ***\ngender 1 67.6 67.60 122.909 1.71e-12 ***\ndivision 1 19.6 19.60 35.636 1.19e-06 ***\nprogram:gender 1 0.0 0.00 0.000 1.000    \nprogram:division 1 0.4 0.40 0.727 0.400    \ngender:division 1 0.1 0.10 0.182 0.673    \nprogram:gender:division 1 0.1 0.10 0.182 0.673    \nResiduals 32 17.6 0.55                     \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>Pr(&gt;F)<\/strong>\u5217\u306b\u306f\u3001\u500b\u3005\u306e\u56e0\u5b50\u306e p \u5024\u3068\u56e0\u5b50\u9593\u306e\u4ea4\u4e92\u4f5c\u7528\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u30013 \u3064\u306e\u56e0\u5b50\u9593\u306e\u76f8\u4e92\u4f5c\u7528\u306f\u3069\u308c\u3082\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u306f\u306a\u304b\u3063\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001\u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u6027\u5225\u3001\u90e8\u9580\u306e 3 \u3064\u306e\u8981\u7d20\u306e\u305d\u308c\u305e\u308c\u304c\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u3042\u308b\u3053\u3068\u3082\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u3053\u3067\u3001\u518d\u3073 dplyr \u3092\u4f7f\u7528\u3057\u3066\u3001\u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u6027\u5225\u3001\u90e8\u9580\u306e\u5e73\u5747\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5897\u52a0\u3092\u500b\u5225\u306b\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">library<\/span> (dplyr)\n<\/span>\n#find mean jumping increase by program<\/span>\ndf %&gt;%\n  group_by(program) %&gt;%\n  summarize(mean_height = mean(height))\n\n# A tibble: 2 x 2\n  program mean_height\n           \n1 1 5.2\n2 2 3.3\n\n<span style=\"color: #008080;\">#find mean jumping increase by gender\n<\/span>df %&gt;%\n  group_by(gender) %&gt;%\n  summarize(mean_height = mean(height))\n\n# A tibble: 2 x 2\n  gender mean_height\n          \n1 F 2.95\n2M 5.55\n\n<span style=\"color: #008080;\">#find mean jumping increase by division\n<\/span>df %&gt;%\ngroup_by(division) %&gt;%\nsummarize(mean_height = mean(height))\n\n# A tibble: 2 x 2\n  division mean_height\n            \n1 1 4.95\n2 2 3.55<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0 1 \u3092\u4f7f\u7528\u3057\u305f\u500b\u4eba\u306e\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5e73\u5747\u5897\u52a0 ( <strong>5.2 \u30a4\u30f3\u30c1<\/strong>) \u306f\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0 2 \u3092\u4f7f\u7528\u3057\u305f\u500b\u4eba\u306e\u5e73\u5747\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5897\u52a0<strong>(3.3 \u30a4\u30f3\u30c1<\/strong>) \u3088\u308a\u3082\u5927\u304d\u304b\u3063\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7537\u6027\u306e\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5e73\u5747\u5897\u52a0 ( <strong>5.55 \u30a4\u30f3\u30c1<\/strong>) \u306f\u3001\u5973\u6027\u306e\u5e73\u5747\u5897\u52a0<strong>(2.95 \u30a4\u30f3\u30c1<\/strong>) \u3088\u308a\u3082\u5927\u304d\u304b\u3063\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30c7\u30a3\u30d3\u30b8\u30e7\u30f3 1 \u30d7\u30ec\u30fc\u30e4\u30fc\u306e\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5e73\u5747\u5897\u52a0 ( <strong>4.95 \u30a4\u30f3\u30c1<\/strong>) \u306f\u3001\u30c7\u30a3\u30d3\u30b8\u30e7\u30f3 2 \u30d7\u30ec\u30fc\u30e4\u30fc\u306e\u5e73\u5747\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5897\u52a0<strong>(3.55 \u30a4\u30f3\u30c1<\/strong>) \u3088\u308a\u3082\u5927\u304d\u304b\u3063\u305f\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7d50\u8ad6\u3068\u3057\u3066\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u6027\u5225\u3001\u90e8\u9580\u306f\u3059\u3079\u3066\u3001\u9078\u624b\u306e\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5411\u4e0a\u3092\u793a\u3059\u91cd\u8981\u306a\u6307\u6a19\u3067\u3042\u308b\u3068\u8a00\u3048\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001\u3053\u308c\u3089 3 \u3064\u306e\u8981\u7d20\u306e\u9593\u306b\u306f\u6709\u610f\u306a\u76f8\u4e92\u4f5c\u7528\u52b9\u679c\u306f\u306a\u3044\u3068\u8a00\u3048\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u4ed6\u306e ANOVA \u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a 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