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R 数据可视化:水平渐变色柱状图

R 数据可视化:水平渐变色柱状图

作者: watermark | 来源:发表于2021-04-19 11:06 被阅读0次

    简介

    渐变色柱形图可以通过颜色深浅和柱形高矮表现更丰富的信息。

    YlOrRd.png

    开始作图

    水平柱状图只要在普通柱状图的基础上 + 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()
    
    柱状图-1.png

    我们发现,柱子的排列顺序居然和数据的顺序不一致,为此,我们 需要将 “希望按照顺序排列的轴” 强制转换为 factor 类型。转换之后,柱状图的排列顺序和数据顺序一致了:

    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()
    
    调整顺序后的柱状图.png

    柱状图的默认配色略显浮夸,不够学术,我们调整一下颜色,设置从上至下的渐变风格。

    在此之前,需要安装调色板依赖包:

    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)
    
    渐变水平柱状图.png

    去掉背景色和网格线:

    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())
    
    去掉背景色和网格线.png

    去掉 legend,去掉 x 轴 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

    看看其他渐变色,有没有你中意的款:

    颜色对比.png

    欢迎留言、讨论、点赞、转发,转载请注明出处~

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