{"id":471,"date":"2023-07-29T19:19:54","date_gmt":"2023-07-29T19:19:54","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%92%e3%83%bc%e3%83%88%e3%83%9e%e3%83%83%e3%83%95%e3%82%9a-r-ggplot2\/"},"modified":"2023-07-29T19:19:54","modified_gmt":"2023-07-29T19:19:54","slug":"%e3%83%92%e3%83%bc%e3%83%88%e3%83%9e%e3%83%83%e3%83%95%e3%82%9a-r-ggplot2","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%92%e3%83%bc%e3%83%88%e3%83%9e%e3%83%83%e3%83%95%e3%82%9a-r-ggplot2\/","title":{"rendered":"Ggplot2 \u3092\u4f7f\u7528\u3057\u3066 r \u3067\u30d2\u30fc\u30c8 \u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001ggplot2 \u3092\u4f7f\u7528\u3057\u3066 R \u3067\u30d2\u30fc\u30c8 \u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u4f8b: R \u3067\u306e\u30d2\u30fc\u30c8 \u30de\u30c3\u30d7\u306e\u4f5c\u6210<\/span><\/strong><\/h3>\n

\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u7d44\u307f\u8fbc\u307f\u306e R \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8mtcars<\/strong>\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #view first six rows of mtcars\n<\/em><\/span>head(mtcars)\n\n# mpg cyl disp hp drat wt qsec vs am gear carb\n#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\n#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\n#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\n#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\n#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\n#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1\n<\/strong><\/pre>\n
\u73fe\u5728\u3001 mtcars \u306f<\/strong>\u30ef\u30a4\u30c9\u5f62\u5f0f\u3067\u3059\u304c\u3001\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u3053\u308c\u3092\u30ed\u30f3\u30b0\u5f62\u5f0f\u306b\u30d6\u30ec\u30f3\u30c9\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
 #load reshape2<\/em> package to use melt() function<\/span>\nlibrary(reshape2)\n\n#melt mtcars into long format<\/span>\nmelt_mtcars <- melt(mtcars)\n\n#add column for car name<\/span>\nmelt_mtcars$car <- rep(row.names(mtcars), 11)\n\n#view first six rows of melt_mtcars<\/em><\/span>\nhead(melt_mtcars)\n\n# variable value char\n#1 mpg 21.0 Mazda RX4\n#2 mpg 21.0 Mazda RX4 Wag\n#3 mpg 22.8 Datsun 710\n#4 mpg 21.4 Hornet 4 Drive\n#5 mpg 18.7 Hornet Sportabout\n#6 mpg 18.1 Valiant<\/strong><\/pre>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001ggplot2 \u3067\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 library(ggplot2)\n\nggplot(melt_mtcars, aes(variable, char)) +\n  geom_tile(aes(fill = value),<\/strong> color = \"white\") +\n  scale_fill_gradient(low = \"white\",<\/strong> high = \"red\")<\/strong><\/pre>\n
\u6b8b\u5ff5\u306a\u304c\u3089\u3001 disp<\/em>\u306e\u5024\u306f\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u5185\u306e\u4ed6\u306e\u3059\u3079\u3066\u306e\u5909\u6570\u306e\u5024\u3088\u308a\u3082\u306f\u308b\u304b\u306b\u5927\u304d\u3044\u305f\u3081\u3001\u4ed6\u306e\u5909\u6570\u306e\u8272\u306e\u5909\u5316\u3092\u898b\u308b\u306e\u306f\u56f0\u96e3\u3067\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001scales() \u30d1\u30c3\u30b1\u30fc\u30b8\u306erescale()<\/strong>\u95a2\u6570\u3068 plyr() \u30d1\u30c3\u30b1\u30fc\u30b8\u306eddply()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u5909\u6570\u306e\u5024\u3092 0 \u304b\u3089 1 \u306b\u518d\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n
 #load libraries<\/span>\nlibrary(plyr)\nlibrary(scales)\n\n#rescale values for all variables in melted data frame<\/span>\nmelt_mtcars <- ddply(melt_mtcars, .(variable), transform, rescale = rescale(value))\n\n#create heatmap using rescaled values<\/span>\nggplot(melt_mtcars, aes(variable, char)) +\n  geom_tile(aes(fill = rescale), color = \"white\") +\n  scale_fill_gradient(low = \"white\", high = \"red\")\n<\/strong><\/pre>\n
\u307e\u305f\u3001scale_fill_gradient() \u5f15\u6570\u3067\u4f7f\u7528\u3055\u308c\u308b\u8272\u3092\u5909\u66f4\u3059\u308b\u3053\u3068\u3067\u3001\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u306e\u8272\u3092\u5909\u66f4\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #create heatmap using blue color scale\n<\/span>ggplot(melt_mtcars, aes(variable, char)) +\n  geom_tile(aes(fill = rescale), color = \"white\") +\n  scale_fill_gradient(low = \"white\", high = \"steelblue\")<\/strong><\/pre>\n
\u73fe\u5728\u3001\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u306f\u8eca\u540d\u3054\u3068\u306b\u5206\u985e\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u4ee3\u308f\u308a\u306b\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001 mpg<\/em>\u306a\u3069\u306e\u5909\u6570\u306e 1 \u3064\u306e\u5024\u306b\u5f93\u3063\u3066\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3092\u4e26\u3079\u66ff\u3048\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #define car name as a new column, then order by mpg<\/em> descending\n<\/span>mtcars$car <- row.names(mtcars)\nmtcars$car <- with(mtcars, reorder(car, mpg))\n\n#melt mtcars into long format\n<\/span>melt_mtcars <- melt(mtcars)\n\n#rescale values for all variables in melted data frame\n<\/span>melt_mtcars <- ddply(melt_mtcars, .(variable), transform, rescale = rescale(value))\n\n#create heatmap using rescaled values\n<\/span>ggplot(melt_mtcars, aes(variable, char)) +\n  geom_tile(aes(fill = rescale), color = \"white\") +\n  scale_fill_gradient(low = \"white\", high = \"steelblue\")<\/strong><\/pre>\n
 mpg<\/em>\u3092\u5897\u3084\u3057\u3066\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3092\u4e26\u3079\u66ff\u3048\u308b\u306b\u306f\u3001\u5358\u306b reorder() \u5f15\u6570\u3067-mpg<\/strong>\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #define car name as a new column, then order by mpg descending\n<\/span>mtcars$car <- row.names(mtcars)\nmtcars$car <- with(mtcars, reorder(car, -mpg<\/span> ))\n\n#melt mtcars into long format\n<\/span>melt_mtcars <- melt(mtcars)\n\n#rescale values for all variables in melted data frame\n<\/span>melt_mtcars <- ddply(melt_mtcars, .(variable), transform, rescale = rescale(value))\n\n#create heatmap using rescaled values\n<\/span>ggplot(melt_mtcars, aes(variable, char)) +\n  geom_tile(aes(fill = rescale), color = \"white\") +\n  scale_fill_gradient(low = \"white\", high = \"steelblue\")<\/strong><\/pre>\n
\u6700\u5f8c\u306b\u3001\u898b\u305f\u76ee\u304c\u6c17\u306b\u5165\u3089\u306a\u3044\u5834\u5408\u306f\u3001\u5f15\u6570 labs() \u3068 theme() \u3092\u4f7f\u7528\u3057\u3066\u3001x \u8ef8\u3068 y \u8ef8\u306e\u30e9\u30d9\u30eb\u3068\u51e1\u4f8b\u3092\u524a\u9664\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #create heatmap with no axis labels or legend\n<\/span>ggplot(melt_mtcars, aes(variable, char)) +\n  geom_tile(aes(fill = rescale), color = \"white\") +\n  scale_fill_gradient(low = \"white\", high = \"steelblue\") +\n  labs(x = \"\", y = \"\") +\n  theme(legend.position = \"none\")<\/span><\/strong><\/pre>\n","protected":false},"excerpt":{"rendered":"
\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001ggplot2 \u3092\u4f7f\u7528\u3057\u3066 R \u3067\u30d2\u30fc\u30c8 \u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u4f8b: R \u3067\u306e\u30d2\u30fc\u30c8 \u30de\u30c3\u30d7\u306e\u4f5c\u6210 \u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u7d44\u307f\u8fbc\u307f\u306e R \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8mtcars\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-471","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nggplot2 \u3092\u4f7f\u7528\u3057\u3066 R \u3067\u30d2\u30fc\u30c8 \u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5 - Statology<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001ggplot2 \u3092\u4f7f\u7528\u3057\u3066 R \u3067\u30d2\u30fc\u30c8 \u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/\u30d2\u30fc\u30c8\u30de\u30c3\u30d5\u309a-r-ggplot2\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ggplot2 \u3092\u4f7f\u7528\u3057\u3066 R \u3067\u30d2\u30fc\u30c8 \u30de\u30c3\u30d7\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5 - 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