<\/p>\n
W tym samouczku wyja\u015bniono, jak utworzy\u0107 map\u0119 ciepln\u0105 w R za pomoc\u0105 ggplot2.<\/span><\/p>\n Aby utworzy\u0107 map\u0119 ciepln\u0105, u\u017cyjemy wbudowanego zestawu danych R mtcars<\/strong> .<\/span><\/p>\n Obecnie mtcars<\/strong> ma szeroki format, ale musimy go po\u0142\u0105czy\u0107 w d\u0142ugi format, aby utworzy\u0107 map\u0119 ciepln\u0105.<\/span><\/p>\n Mo\u017cemy u\u017cy\u0107 nast\u0119puj\u0105cego kodu, aby utworzy\u0107 map\u0119 ciepln\u0105 w ggplot2:<\/span><\/p>\n Niestety, poniewa\u017c warto\u015bci disp<\/em> s\u0105 znacznie wi\u0119ksze ni\u017c warto\u015bci wszystkich innych zmiennych w ramce danych, trudno jest dostrzec zmienno\u015b\u0107 kolor\u00f3w pozosta\u0142ych zmiennych.<\/span><\/p>\n Jednym ze sposob\u00f3w rozwi\u0105zania tego problemu jest przeskalowanie warto\u015bci ka\u017cdej zmiennej od 0 do 1 za pomoc\u0105 funkcji rescale()<\/strong> w pakiecie Scales() i funkcji ddply()<\/strong> w pakiecie plyr():<\/span><\/p>\n Mo\u017cemy r\u00f3wnie\u017c zmieni\u0107 kolory mapy cieplnej, zmieniaj\u0105c kolory u\u017cyte w argumencie Scale_fill_gradient():<\/span><\/p>\n Pami\u0119taj, \u017ce mapa cieplna jest obecnie podzielona na kategorie wed\u0142ug nazwy samochodu. Zamiast tego mogliby\u015bmy zam\u00f3wi\u0107 map\u0119 ciepln\u0105 wed\u0142ug warto\u015bci jednej ze zmiennych, np. mpg<\/em> , u\u017cywaj\u0105c nast\u0119puj\u0105cego kodu:<\/span><\/p>\n Aby posortowa\u0107 map\u0119 ciepln\u0105 poprzez zwi\u0119kszenie mpg<\/em> , po prostu u\u017cyj -mpg<\/strong> w argumencie reorder():<\/span><\/p>\n Na koniec mo\u017cemy usun\u0105\u0107 etykiety osi x i y oraz legend\u0119, je\u015bli nie podoba nam si\u0119 jej wygl\u0105d, u\u017cywaj\u0105c argument\u00f3w labs() i topic():<\/span><\/p>\n W tym samouczku wyja\u015bniono, jak utworzy\u0107 map\u0119 ciepln\u0105 w R za pomoc\u0105 ggplot2. Przyk\u0142ad: tworzenie mapy cieplnej w R Aby utworzy\u0107 map\u0119 ciepln\u0105, u\u017cyjemy wbudowanego zestawu danych 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 6 160 110 […]<\/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":[],"class_list":["post-471","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"\n Przyk\u0142ad: tworzenie mapy cieplnej w R<\/span><\/strong><\/h3>\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 #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$car <- rep(row.names(mtcars), 11)\n\n#view first six rows of melt_mtcars<\/em><\/span>\nhead(melt_mtcars)\n\n# variable value char\n#1 mpg 21.0 Mazda RX4\n#2 mpg 21.0 Mazda RX4 Wag\n#3 mpg 22.8 Datsun 710\n#4 mpg 21.4 Hornet 4 Drive\n#5 mpg 18.7 Hornet Sportabout\n#6 mpg 18.1 Valiant<\/strong><\/pre>\n library(ggplot2)\n\nggplot(melt_mtcars, aes(variable, char)) +\n geom_tile(aes(fill = value),<\/strong> color = \"white\") +\n scale_fill_gradient(low = \"white\",<\/strong> high = \"red\")<\/strong><\/pre>\n
#load libraries<\/span>\nlibrary(plyr)\nlibrary(scales)\n\n#rescale values for all variables in melted data frame<\/span>\nmelt_mtcars <- ddply(melt_mtcars, .(variable), transform, rescale = rescale(value))\n\n#create heatmap using rescaled values<\/span>\nggplot(melt_mtcars, aes(variable, char)) +\n geom_tile(aes(fill = rescale), color = \"white\") +\n scale_fill_gradient(low = \"white\", high = \"red\")\n<\/strong><\/pre>\n #create heatmap using blue color scale\n<\/span>ggplot(melt_mtcars, aes(variable, char)) +\n geom_tile(aes(fill = rescale), color = \"white\") +\n scale_fill_gradient(low = \"white\", high = \"steelblue\")<\/strong><\/pre>\n #define car name as a new column, then order by mpg<\/em> descending\n<\/span>mtcars$car <- row.names(mtcars)\nmtcars$car <- with(mtcars, reorder(car, mpg))\n\n#melt mtcars into long format\n<\/span>melt_mtcars <- melt(mtcars)\n\n#rescale values for all variables in melted data frame\n<\/span>melt_mtcars <- ddply(melt_mtcars, .(variable), transform, rescale = rescale(value))\n\n#create heatmap using rescaled values\n<\/span>ggplot(melt_mtcars, aes(variable, char)) +\n geom_tile(aes(fill = rescale), color = \"white\") +\n scale_fill_gradient(low = \"white\", high = \"steelblue\")<\/strong><\/pre>\n #define car name as a new column, then order by mpg descending\n<\/span>mtcars$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 #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":"