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本篇內(nèi)容介紹了“R語(yǔ)言怎么實(shí)現(xiàn)散點(diǎn)圖組合頻率分布圖”的有關(guān)知識(shí)，在實(shí)際案例的操作過(guò)程中，不少人都會(huì)遇到這樣的困境，接下來(lái)就讓小編帶領(lǐng)大家學(xué)習(xí)一下如何處理這些情況吧！希望大家仔細(xì)閱讀，能夠?qū)W有所成！
部分?jǐn)?shù)據(jù)如下
做圖用到的是最后一列數(shù)據(jù)
df1<-read.csv("example1.csv",header=T)
library(ggplot2)
ggplot(df1) +
geom_histogram(aes(x = delay,y=..density..),
fill = '#dedede', colour = "black",
binwidth = 1) +
scale_y_continuous("Frequency", expand = c(0,0), limits = c(0,0.20)) +
scale_x_continuous("Delay from onset-to-isolation of infector (days)",
expand = c(0,0),
limits = c(0,27),
breaks = seq(0,27, by = 3)) +
theme_classic() +
theme(#aspect.ratio = 2,
legend.position = 'none')
這里新學(xué)到的知識(shí)點(diǎn)是theme()函數(shù)里的aspect.ratio參數(shù)，這個(gè)參數(shù)可以控制整幅圖占比，如果是0到1之間就是縱向的壓縮，如果是1到2之間就是縱向的壓縮，我們分別設(shè)置0.5和1.5看下效果
p0.5<-ggplot(df1) +
geom_histogram(aes(x = delay,y=..density..),
fill = '#dedede', colour = "black",
binwidth = 1) +
scale_y_continuous("Frequency", expand = c(0,0), limits = c(0,0.20)) +
scale_x_continuous("Delay from onset-to-isolation of infector (days)",
expand = c(0,0),
limits = c(0,27),
breaks = seq(0,27, by = 3)) +
theme_classic() +
theme(aspect.ratio = 0.5,
legend.position = 'none')
p0.5
p1.5<-ggplot(df1) +
geom_histogram(aes(x = delay,y=..density..),
fill = '#dedede', colour = "black",
binwidth = 1) +
scale_y_continuous("Frequency", expand = c(0,0), limits = c(0,0.20)) +
scale_x_continuous("Delay from onset-to-isolation of infector (days)",
expand = c(0,0),
limits = c(0,27),
breaks = seq(0,27, by = 3)) +
theme_classic() +
theme(aspect.ratio = 1.5,
legend.position = 'none')
cowplot::plot_grid(p0.5,p1.5,labels = c("p0.5","p1.5"))
散點(diǎn)圖的部分?jǐn)?shù)據(jù)如下
df2<-read.csv("example2.csv",header=T)
ggplot(df2) +
geom_smooth(method = lm, aes(x=delay, y = n), color = "black", alpha = 0.1, size = 0.7) +
geom_jitter(aes(x = delay, y = n, colour = cluster.risk), height = 0.3, width = 0.3) +
scale_y_continuous("Secondary Cases / Infector", breaks = 1:11) +
scale_x_continuous("Delay from onset-to-confirmation of infector (days)",
expand = c(0,0),
limits = c(0,27), breaks = seq(0,27, by = 3)) +
theme_classic() +
theme(aspect.ratio = 1, legend.position = c(0.85, 0.85), legend.title = element_blank()) #colours are modified custom in post
這里需要注意的是散點(diǎn)圖他用到的函數(shù)是geom_jitter()，而沒(méi)有用geom_point()，這兩個(gè)函數(shù)的區(qū)別是如果兩個(gè)點(diǎn)的坐標(biāo)是一樣的geom_jitter()函數(shù)也會(huì)將兩個(gè)點(diǎn)分開(kāi)，而geom_point()函數(shù)會(huì)將兩個(gè)點(diǎn)重疊的畫(huà)到一起
p1<-ggplot(df1) +
geom_histogram(aes(x = delay,y=..density..),
fill = '#dedede', colour = "black",
binwidth = 1) +
scale_y_continuous("Frequency", expand = c(0,0), limits = c(0,0.20)) +
scale_x_continuous("Delay from onset-to-isolation of infector (days)",
expand = c(0,0),
limits = c(0,27),
breaks = seq(0,27, by = 3)) +
theme_classic() +
theme(#aspect.ratio = 0.5,
legend.position = 'none')
p2<-ggplot(df2) +
geom_smooth(method = lm, aes(x=delay, y = n), color = "black", alpha = 0.1, size = 0.7) +
geom_jitter(aes(x = delay, y = n, colour = cluster.risk), height = 0.3, width = 0.3) +
scale_y_continuous("Secondary Cases / Infector", breaks = 1:11) +
scale_x_continuous("Delay from onset-to-confirmation of infector (days)",
expand = c(0,0),
limits = c(0,27), breaks = seq(0,27, by = 3)) +
theme_classic() +
theme( legend.position = c(0.85, 0.85),
legend.title = element_blank()) #colours are modified custom in post
library(aplot)
p2%>%
insert_top(p1,height = 0.3)
“R語(yǔ)言怎么實(shí)現(xiàn)散點(diǎn)圖組合頻率分布圖”的內(nèi)容就介紹到這里了，感謝大家的閱讀。如果想了解更多行業(yè)相關(guān)的知識(shí)可以關(guān)注億速云網(wǎng)站，小編將為大家輸出更多高質(zhì)量的實(shí)用文章！
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