{"id":471,"date":"2023-07-29T19:19:54","date_gmt":"2023-07-29T19:19:54","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%92%e1%85%b5%e1%84%90%e1%85%b3%e1%84%86%e1%85%a2%e1%86%b8-r-ggplot2\/"},"modified":"2023-07-29T19:19:54","modified_gmt":"2023-07-29T19:19:54","slug":"%e1%84%92%e1%85%b5%e1%84%90%e1%85%b3%e1%84%86%e1%85%a2%e1%86%b8-r-ggplot2","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%92%e1%85%b5%e1%84%90%e1%85%b3%e1%84%86%e1%85%a2%e1%86%b8-r-ggplot2\/","title":{"rendered":"Ggplot2\ub97c \uc0ac\uc6a9\ud558\uc5ec r\uc5d0\uc11c \ud788\ud2b8 \ub9f5\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ggplot2\ub97c \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c \ud788\ud2b8 \ub9f5\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc608: R\uc5d0\uc11c \ud788\ud2b8\ub9f5 \uc0dd\uc131<\/span><\/strong><\/h3>\n

\ud788\ud2b8\ub9f5\uc744 \uc0dd\uc131\ud558\uae30 \uc704\ud574 \ub0b4\uc7a5\ub41c R \ub370\uc774\ud130\uc138\ud2b8 mtcars\ub97c<\/strong> \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/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
 \ud604\uc7ac mtcars<\/strong> \ub294 \uc640\uc774\ub4dc \ud615\uc2dd\uc774\uc9c0\ub9cc \ud788\ud2b8\ub9f5\uc744 \uc0dd\uc131\ud558\ub824\uba74 \uae34 \ud615\uc2dd\uc73c\ub85c \ud63c\ud569\ud574\uc57c \ud569\ub2c8\ub2e4.<\/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
 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec ggplot2\uc5d0\uc11c \ud788\ud2b8\ub9f5\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \uc548\ud0c0\uae5d\uac8c\ub3c4 disp<\/em> \uc758 \uac12\uc740 \ub370\uc774\ud130 \ud504\ub808\uc784 \ub0b4 \ub2e4\ub978 \ubaa8\ub4e0 \ubcc0\uc218\uc758 \uac12\ubcf4\ub2e4 \ud6e8\uc52c \ud06c\uae30 \ub54c\ubb38\uc5d0 \ub2e4\ub978 \ubcc0\uc218\uc758 \uc0c9\uc0c1 \ubcc0\ud654\ub97c \ud655\uc778\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 scales() \ud328\ud0a4\uc9c0\uc758 rescale()<\/strong> \ud568\uc218\uc640 plyr() \ud328\ud0a4\uc9c0\uc758 ddply()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac01 \ubcc0\uc218\uc758 \uac12\uc744 0\uc5d0\uc11c 1\ub85c \ub2e4\uc2dc \uc870\uc815\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/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
 scale_fill_gradient() \uc778\uc218\uc5d0 \uc0ac\uc6a9\ub41c \uc0c9\uc0c1\uc744 \ubcc0\uacbd\ud558\uc5ec \ud788\ud2b8\ub9f5 \uc0c9\uc0c1\uc744 \ubcc0\uacbd\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \ud604\uc7ac \ud788\ud2b8\ub9f5\uc740 \uc790\ub3d9\ucc28 \uc774\ub984\ubcc4\ub85c \ubd84\ub958\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \ub300\uc2e0 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec mpg<\/em> \uc640 \uac19\uc740 \ubcc0\uc218 \uc911 \ud558\ub098\uc758 \uac12\uc5d0 \ub530\ub77c \ud788\ud2b8\ub9f5\uc744 \uc815\ub82c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #define car name as a new column, then order by mpg<\/em> descending\n<\/span>mtcars$car <- row.names(mtcars)\nmtcars$car <- with(mtcars, reorder(car, mpg))\n\n#melt mtcars into long format\n<\/span>melt_mtcars <- melt(mtcars)\n\n#rescale values for all variables in melted data frame\n<\/span>melt_mtcars <- ddply(melt_mtcars, .(variable), transform, rescale = rescale(value))\n\n#create heatmap using rescaled values\n<\/span>ggplot(melt_mtcars, aes(variable, char)) +\n  geom_tile(aes(fill = rescale), color = \"white\") +\n  scale_fill_gradient(low = \"white\", high = \"steelblue\")<\/strong><\/pre>\n
 mpg \ub97c<\/em> \ub298\ub824 \ud788\ud2b8\ub9f5\uc744 \uc815\ub82c\ud558\ub824\uba74 reorder() \uc778\uc218\uc5d0 -mpg \ub97c<\/strong> \uc0ac\uc6a9\ud558\uba74 \ub429\ub2c8\ub2e4.<\/span><\/p>\n
 #define car name as a new column, then order by mpg descending\n<\/span>mtcars$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
 \ub9c8\uc9c0\ub9c9\uc73c\ub85c labs() \ubc0f theme() \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud45c\uc2dc\ub418\ub294 \ubc29\uc2dd\uc774 \ub9c8\uc74c\uc5d0 \ub4e4\uc9c0 \uc54a\uc73c\uba74 x \ubc0f y \ucd95 \ub808\uc774\ube14\uacfc \ubc94\ub840\ub97c \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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":"
\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ggplot2\ub97c \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c \ud788\ud2b8 \ub9f5\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc608: R\uc5d0\uc11c \ud788\ud2b8\ub9f5 \uc0dd\uc131 \ud788\ud2b8\ub9f5\uc744 \uc0dd\uc131\ud558\uae30 \uc704\ud574 \ub0b4\uc7a5\ub41c R \ub370\uc774\ud130\uc138\ud2b8 mtcars\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. #view first six rows of mtcars head(mtcars) # mpg cyl disp hp drat wt qsec vs am gear carb #Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 #Mazda […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-471","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nggplot2\ub97c \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c \uc5f4 \uc9c0\ub3c4\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95 - 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