簡介
漸變色柱形圖可以通過顏色深淺和柱形高矮表現(xiàn)更豐富的信息。
YlOrRd.png
開始作圖
水平柱狀圖只要在普通柱狀圖的基礎(chǔ)上 + coord_flip() 即可：
library(ggplot2)
testdata <- data.frame(feature = c("X474_PC.32.1p._PC.32.1p.",
"X446_DG.34.1._DG.16.0.18.1.",
"X548_PE.38.1._PE.38.1.",
"X580_PI.36.2._PI.18.1.18.1.",
"X472_PG.34.1._PG.16.0.18.1.",
"X628_PC.40.7p._PC.40.7p.",
"X498_PC.33.1p._PC.33.1p.",
"X438_ArachidylcarnitineAcCa.20.0._ArachidylcarnitineAcCa.20.0.",
"X639_PC.40.4._PC.18.2.22.2.",
"X479_PC.32.0._PC.16.0.16.0."),
importance = c(3.66, 3.08, 2.99, 2.91, 2.83, 2.77, 2.6, 2.59, 2.54, 2.51))
ggplot(testdata, aes(x = feature, y = importance, fill = feature)) +
geom_bar(stat="identity") +
coord_flip()
柱狀圖-1.png
我們發(fā)現(xiàn)懈息，柱子的排列順序居然和數(shù)據(jù)的順序不一致，為此咨跌，我們 需要將 “希望按照順序排列的軸” 強制轉(zhuǎn)換為 factor 類型沪么。轉(zhuǎn)換之后，柱狀圖的排列順序和數(shù)據(jù)順序一致了：
library(ggplot2)
testdata <- data.frame(feature = c("X474_PC.32.1p._PC.32.1p.",
"X446_DG.34.1._DG.16.0.18.1.",
"X548_PE.38.1._PE.38.1.",
"X580_PI.36.2._PI.18.1.18.1.",
"X472_PG.34.1._PG.16.0.18.1.",
"X628_PC.40.7p._PC.40.7p.",
"X498_PC.33.1p._PC.33.1p.",
"X438_ArachidylcarnitineAcCa.20.0._ArachidylcarnitineAcCa.20.0.",
"X639_PC.40.4._PC.18.2.22.2.",
"X479_PC.32.0._PC.16.0.16.0."),
importance = c(2.51, 2.54, 2.59, 2.6, 2.77, 2.83, 2.91, 2.99, 3.08, 3.66))
testdata[["feature"]] = factor(testdata[["feature"]], levels = as.character(testdata[["feature"]]))
ggplot(testdata, aes(x = feature, y = `importance`, fill = feature)) +
geom_bar(stat="identity") +
coord_flip()
調(diào)整順序后的柱狀圖.png
柱狀圖的默認(rèn)配色略顯浮夸锌半，不夠?qū)W術(shù)禽车，我們調(diào)整一下顏色，設(shè)置從上至下的漸變風(fēng)格刊殉。
在此之前殉摔，需要安裝調(diào)色板依賴包：
install.packages("RColorBrewer")
install.packages("remotes")
remotes::install_github("eprifti/momr")
library(ggplot2)
library(RColorBrewer)
library(momr)
testdata <- data.frame(feature = c("X474_PC.32.1p._PC.32.1p.",
"X446_DG.34.1._DG.16.0.18.1.",
"X548_PE.38.1._PE.38.1.",
"X580_PI.36.2._PI.18.1.18.1.",
"X472_PG.34.1._PG.16.0.18.1.",
"X628_PC.40.7p._PC.40.7p.",
"X498_PC.33.1p._PC.33.1p.",
"X438_ArachidylcarnitineAcCa.20.0._ArachidylcarnitineAcCa.20.0.",
"X639_PC.40.4._PC.18.2.22.2.",
"X479_PC.32.0._PC.16.0.16.0."),
importance = c(2.51, 2.54, 2.59, 2.6, 2.77, 2.83, 2.91, 2.99, 3.08, 3.66))
testdata[["feature"]] = factor(testdata[["feature"]], levels = as.character(testdata[["feature"]]))
cols<-brewer.pal(3, "YlOrRd")
pal<-colorRampPalette(cols)
mycolors<-pal(nrow(testdata))
ggplot(testdata, aes(x = feature, y = `importance`, fill = feature)) +
geom_bar(stat="identity") +
coord_flip() +
scale_fill_manual(values = mycolors)
漸變水平柱狀圖.png
去掉背景色和網(wǎng)格線：
library(ggplot2)
library(RColorBrewer)
library(momr)
testdata <- data.frame(feature = c("X474_PC.32.1p._PC.32.1p.",
"X446_DG.34.1._DG.16.0.18.1.",
"X548_PE.38.1._PE.38.1.",
"X580_PI.36.2._PI.18.1.18.1.",
"X472_PG.34.1._PG.16.0.18.1.",
"X628_PC.40.7p._PC.40.7p.",
"X498_PC.33.1p._PC.33.1p.",
"X438_ArachidylcarnitineAcCa.20.0._ArachidylcarnitineAcCa.20.0.",
"X639_PC.40.4._PC.18.2.22.2.",
"X479_PC.32.0._PC.16.0.16.0."),
importance = c(2.51, 2.54, 2.59, 2.6, 2.77, 2.83, 2.91, 2.99, 3.08, 3.66))
testdata[["feature"]] = factor(testdata[["feature"]], levels = as.character(testdata[["feature"]]))
cols<-brewer.pal(3, "YlOrRd")
pal<-colorRampPalette(cols)
mycolors<-pal(nrow(testdata))
ggplot(testdata, aes(x = feature, y = `importance`, fill = feature)) +
geom_bar(stat="identity") +
coord_flip() +
scale_fill_manual(values = mycolors) +
theme_minimal() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank())
去掉背景色和網(wǎng)格線.png
去掉 legend，去掉 x 軸 label：
library(ggplot2)
library(RColorBrewer)
library(momr)
testdata <- data.frame(feature = c("X474_PC.32.1p._PC.32.1p.",
"X446_DG.34.1._DG.16.0.18.1.",
"X548_PE.38.1._PE.38.1.",
"X580_PI.36.2._PI.18.1.18.1.",
"X472_PG.34.1._PG.16.0.18.1.",
"X628_PC.40.7p._PC.40.7p.",
"X498_PC.33.1p._PC.33.1p.",
"X438_ArachidylcarnitineAcCa.20.0._ArachidylcarnitineAcCa.20.0.",
"X639_PC.40.4._PC.18.2.22.2.",
"X479_PC.32.0._PC.16.0.16.0."),
importance = c(2.51, 2.54, 2.59, 2.6, 2.77, 2.83, 2.91, 2.99, 3.08, 3.66))
testdata[["feature"]] = factor(testdata[["feature"]], levels = as.character(testdata[["feature"]]))
cols<-brewer.pal(3, "YlOrRd")
pal<-colorRampPalette(cols)
mycolors<-pal(nrow(testdata))
ggplot(testdata, aes(x = feature, y = `importance`, fill = feature)) +
geom_bar(stat="identity") +
coord_flip() +
scale_fill_manual(values = mycolors) +
theme_minimal() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank()) +
theme(legend.position = 'none') +
xlab("")
去掉 legend.png
看看其他漸變色记焊，有沒有你中意的款：
顏色對比.png
歡迎留言钦勘、討論、點贊亚亲、轉(zhuǎn)發(fā)彻采，轉(zhuǎn)載請注明出處~
相關(guān)文章
[1] R 數(shù)據(jù)可視化：BoxPlot
[2] R 數(shù)據(jù)可視化：雙坐標(biāo)系柱線圖
[3] R 數(shù)據(jù)可視化：PCA 主成分分析圖
[4] R 數(shù)據(jù)可視化：環(huán)形柱狀圖
