{"id":469,"date":"2023-07-29T19:19:54","date_gmt":"2023-07-29T19:19:54","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d1%82%d0%b5%d0%bf%d0%bb%d0%be%d0%b2%d0%b0-%d0%ba%d0%b0%d1%80%d1%82%d0%b0-r-ggplot2\/"},"modified":"2023-07-29T19:19:54","modified_gmt":"2023-07-29T19:19:54","slug":"%d1%82%d0%b5%d0%bf%d0%bb%d0%be%d0%b2%d0%b0-%d0%ba%d0%b0%d1%80%d1%82%d0%b0-r-ggplot2","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d1%82%d0%b5%d0%bf%d0%bb%d0%be%d0%b2%d0%b0-%d0%ba%d0%b0%d1%80%d1%82%d0%b0-r-ggplot2\/","title":{"rendered":"\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443 \u0432 r \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e ggplot2"},"content":{"rendered":"

<\/p>\n

\n

\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443 \u0432 R \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e ggplot2.<\/span><\/p>\n

\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0442\u0435\u043f\u043b\u043e\u0432\u043e\u0457 \u043a\u0430\u0440\u0442\u0438 \u0432 R<\/span><\/strong><\/h3>\n

\u0429\u043e\u0431 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443, \u043c\u0438 \u0431\u0443\u0434\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0432\u0431\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 R mtcars<\/strong> .<\/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
 \u041d\u0430\u0440\u0430\u0437\u0456 mtcars<\/strong> \u043c\u0430\u0454 \u0448\u0438\u0440\u043e\u043a\u0438\u0439 \u0444\u043e\u0440\u043c\u0430\u0442, \u0430\u043b\u0435 \u043d\u0430\u043c \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0437\u043c\u0456\u0448\u0430\u0442\u0438 \u0439\u043e\u0433\u043e \u0437 \u0434\u043e\u0432\u0433\u0438\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u043e\u043c, \u0449\u043e\u0431 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443.<\/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
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0442\u0435\u043f\u043b\u043e\u0432\u043e\u0457 \u043a\u0430\u0440\u0442\u0438 \u0432 ggplot2:<\/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
 \u041d\u0430 \u0436\u0430\u043b\u044c, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f disp<\/em> \u043d\u0430\u0431\u0430\u0433\u0430\u0442\u043e \u0431\u0456\u043b\u044c\u0448\u0456, \u043d\u0456\u0436 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0441\u0456\u0445 \u0456\u043d\u0448\u0438\u0445 \u0437\u043c\u0456\u043d\u043d\u0438\u0445 \u0443 \u043a\u0430\u0434\u0440\u0456 \u0434\u0430\u043d\u0438\u0445, \u0432\u0430\u0436\u043a\u043e \u043f\u043e\u0431\u0430\u0447\u0438\u0442\u0438 \u043a\u043e\u043b\u0456\u0440\u043d\u0443 \u0432\u0430\u0440\u0456\u0430\u0446\u0456\u044e \u0456\u043d\u0448\u0438\u0445 \u0437\u043c\u0456\u043d\u043d\u0438\u0445.<\/span><\/p>\n
 \u041e\u0434\u0438\u043d \u0456\u0437 \u0441\u043f\u043e\u0441\u043e\u0431\u0456\u0432 \u0432\u0438\u0440\u0456\u0448\u0435\u043d\u043d\u044f \u0446\u0456\u0454\u0457 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0438 \u2014 \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u043c\u0430\u0441\u0448\u0442\u0430\u0431 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u043a\u043e\u0436\u043d\u043e\u0457 \u0437\u043c\u0456\u043d\u043d\u043e\u0457 \u0432\u0456\u0434 0 \u0434\u043e 1 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 rescale()<\/strong> \u0443 \u043f\u0430\u043a\u0435\u0442\u0456 scales() \u0456 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 ddply()<\/strong> \u0443 \u043f\u0430\u043a\u0435\u0442\u0456 plyr():<\/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
 \u041c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u043a\u043e\u043b\u044c\u043e\u0440\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u043e\u0457 \u043a\u0430\u0440\u0442\u0438, \u0437\u043c\u0456\u043d\u0438\u0432\u0448\u0438 \u043a\u043e\u043b\u044c\u043e\u0440\u0438, \u044f\u043a\u0456 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0442\u044c\u0441\u044f \u0432 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0456 scale_fill_gradient():<\/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
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0442\u0435\u043f\u043b\u043e\u0432\u0430 \u043a\u0430\u0440\u0442\u0430 \u043d\u0430\u0440\u0430\u0437\u0456 \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0430 \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0456\u043b\u044f. \u041d\u0430\u0442\u043e\u043c\u0456\u0441\u0442\u044c \u043c\u0438 \u043c\u043e\u0433\u043b\u0438 \u0431 \u0443\u043f\u043e\u0440\u044f\u0434\u043a\u0443\u0432\u0430\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u043e \u0434\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u043e\u0434\u043d\u0456\u0454\u0457 \u0437\u0456 \u0437\u043c\u0456\u043d\u043d\u0438\u0445, \u044f\u043a-\u043e\u0442 mpg,<\/em> \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0442\u0430\u043a\u043e\u0433\u043e \u043a\u043e\u0434\u0443:<\/span><\/p>\n
 #define car name as a new column, then order by mpg<\/em> descending\n<\/span>mtcars$car <- row.names(mtcars)\nmtcars$car <- with(mtcars, reorder(car, mpg))\n\n#melt mtcars into long format\n<\/span>melt_mtcars <- melt(mtcars)\n\n#rescale values for all variables in melted data frame\n<\/span>melt_mtcars <- ddply(melt_mtcars, .(variable), transform, rescale = rescale(value))\n\n#create heatmap using rescaled values\n<\/span>ggplot(melt_mtcars, aes(variable, char)) +\n  geom_tile(aes(fill = rescale), color = \"white\") +\n  scale_fill_gradient(low = \"white\", high = \"steelblue\")<\/strong><\/pre>\n
 \u0429\u043e\u0431 \u0432\u0456\u0434\u0441\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443 \u0437\u0430 \u0437\u0431\u0456\u043b\u044c\u0448\u0435\u043d\u043d\u044f\u043c mpg<\/em> , \u043f\u0440\u043e\u0441\u0442\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 -mpg<\/strong> \u0432 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0456 reorder():<\/span><\/p>\n
 #define car name as a new column, then order by mpg descending\n<\/span>mtcars$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
 \u041d\u0430\u0440\u0435\u0448\u0442\u0456, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043c\u0456\u0442\u043a\u0438 \u043e\u0441\u0435\u0439 x \u0456 y, \u0430 \u0442\u0430\u043a\u043e\u0436 \u043b\u0435\u0433\u0435\u043d\u0434\u0443, \u044f\u043a\u0449\u043e \u043d\u0430\u043c \u043d\u0435 \u043f\u043e\u0434\u043e\u0431\u0430\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0446\u0435 \u0432\u0438\u0433\u043b\u044f\u0434\u0430\u0454, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0438 labs() \u0456 theme():<\/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":"
\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443 \u0432 R \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e ggplot2. \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0442\u0435\u043f\u043b\u043e\u0432\u043e\u0457 \u043a\u0430\u0440\u0442\u0438 \u0432 R \u0429\u043e\u0431 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443, \u043c\u0438 \u0431\u0443\u0434\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0432\u0431\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 R mtcars . #view first six rows of mtcars head(mtcars) # mpg cyl disp hp drat wt qsec vs am gear carb #Mazda RX4 21.0 6 160 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"\n\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443 \u0432 R \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e ggplot2 - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0435\u043f\u043b\u043e\u0432\u0443 \u043a\u0430\u0440\u0442\u0443 \u0432 R \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e ggplot2.\" \/>\n<meta name=\"robots\" content=\"index, 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