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R包：ggpubr傻瓜式绘图包

Posted on In , Valine:
Symbols count in article: 13k Reading time ≈ 12 mins.

介绍

ggpubr是我经常会用到的R包，它傻瓜式的画图方式对很多初次接触R绘图的人来讲是很友好的。该包有个stat_compare_means函数可以做组间假设检验分析。

安装

安装R包可参考如何安装R包。

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install.packages("ggpubr")
devtools::devtools::install_github("kassambara/ggpubr")
library(ggpubr)


plotdata <- data.frame(sex = factor(rep(c("F", "M"), each=200)),
                   weight = c(rnorm(200, 55), rnorm(200, 58)))

密度图density

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ggdensity(plotdata, 
          x = "weight",
          add = "mean", 
          rug = TRUE,    # x轴显示分布密度
          color = "sex", 
          fill = "sex",
          palette = c("#00AFBB", "#E7B800"))

柱状图histogram

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gghistogram(plotdata, 
            x = "weight",
            bins = 30,
            add = "mean", 
            rug = TRUE,
            color = "sex", 
            fill = "sex",
            palette = c("#00AFBB", "#E7B800"))

箱线图boxplot

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df <- ToothGrowth
head(df)
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
ggboxplot(df, 
          x = "dose", 
          y = "len",
          color = "dose", 
          palette =c("#00AFBB", "#E7B800", "#FC4E07"),
          add = "jitter", 
          shape = "dose")+
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 50)

小提琴图violin

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ggviolin(df, 
         x = "dose", 
         y = "len", 
         fill = "dose",
         palette = c("#00AFBB", "#E7B800", "#FC4E07"),
         add = "boxplot", 
         add.params = list(fill = "white"))+
  stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
  stat_compare_means(label.y = 50)

点图dotplot

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ggdotplot(ToothGrowth, 
          x = "dose", 
          y = "len",
          color = "dose", 
          palette = "jco", 
          binwidth = 1)

有序条形图 ordered bar plots

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data("mtcars")
dfm <- mtcars
dfm$cyl <- as.factor(dfm$cyl)
dfm$name <- rownames(dfm)
head(dfm[, c("name", "wt", "mpg", "cyl")])

ggbarplot(dfm, 
          x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in dscending order
          sort.by.groups = TRUE,      # Sort inside each group
          x.text.angle = 90)          # Rotate vertically x axis texts

偏差图Deviation graphs

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dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"), 
                      levels = c("low", "high"))
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])

ggbarplot(dfm, x = "name", y = "mpg_z",
          fill = "mpg_grp",           # change fill color by mpg_level
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in ascending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 90,          # Rotate vertically x axis texts
          ylab = "MPG z-score",
          rotate = FALSE,
          xlab = FALSE,
          legend.title = "MPG Group")

棒棒糖图 lollipop chart

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ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = TRUE,                                # Rotate vertically
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg),                       # Add mpg values as dot labels
           font.label = list(color = "white", size = 9, 
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr())                       # ggplot2 theme

偏差图Deviation graph

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ggdotchart(dfm, x = "name", y = "mpg_z",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           add.params = list(color = "lightgray", size = 2), # Change segment color and size
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg_z,1),                   # Add mpg values as dot labels
           font.label = list(color = "white", size = 9, 
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr())+                      # ggplot2 theme
  geom_hline(yintercept = 0, linetype = 2, color = "lightgray")

散点图scatterplot

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df <- datasets::iris
head(df)
ggscatter(df, 
          x = 'Sepal.Width', 
          y = 'Sepal.Length', 
          palette = 'jco', 
          shape = 'Species', 
          add = 'reg.line',
          color = 'Species', 
          conf.int = TRUE)

  • 添加回归线的系数

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    ggscatter(df, 
              x = 'Sepal.Width', 
              y = 'Sepal.Length', 
              palette = 'jco', 
              shape = 'Species', 
              add = 'reg.line',
              color = 'Species', 
              conf.int = TRUE)+
      stat_cor(aes(color=Species),method = "pearson", label.x = 3)

  • 添加聚类椭圆 concentration ellipses

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    data("mtcars")
    dfm <- mtcars
    dfm$cyl <- as.factor(dfm$cyl)
    dfm$name <- rownames(dfm)

    p1 <- ggscatter(dfm, 
              x = "wt", 
              y = "mpg",
              color = "cyl", 
              palette = "jco",
              shape = "cyl",
              ellipse = TRUE)
    p2 <- ggscatter(dfm, 
                    x = "wt", 
                    y = "mpg",
                    color = "cyl", 
                    palette = "jco",
                    shape = "cyl",
                    ellipse = TRUE,
                    ellipse.type = "convex")
    cowplot::plot_grid(p1, p2, align = "hv", nrow = 1)

  • 添加mean和stars

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    ggscatter(dfm, x = "wt", y = "mpg",
              color = "cyl", palette = "jco",
              shape = "cyl",
              ellipse = TRUE, 
              mean.point = TRUE,
              star.plot = TRUE)

  • 显示点标签

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    dfm$name <- rownames(dfm)
    p3 <- ggscatter(dfm, 
              x = "wt", 
              y = "mpg",
              color = "cyl", 
              palette = "jco",
              label = "name",
              repel = TRUE)
    p4 <- ggscatter(dfm, 
                    x = "wt", 
                    y = "mpg",
                    color = "cyl", 
                    palette = "jco",
                    label = "name",
                    repel = TRUE,
                    label.select = c("Toyota Corolla", "Merc 280", "Duster 360"))
    cowplot::plot_grid(p3, p4, align = "hv", nrow = 1)

气泡图bubble plot

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ggscatter(dfm, 
          x = "wt", 
          y = "mpg",
          color = "cyl",
          palette = "jco",
          size = "qsec", 
          alpha = 0.5)+
  scale_size(range = c(0.5, 15))    # Adjust the range of points size

连线图 lineplot

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p1 <- ggbarplot(ToothGrowth, 
          x = "dose", 
          y = "len", 
          add = "mean_se",
          color = "supp", 
          palette = "jco", 
          position = position_dodge(0.8))+
  stat_compare_means(aes(group = supp), label = "p.signif", label.y = 29)
p2 <- ggline(ToothGrowth, 
       x = "dose", 
       y = "len", 
       add = "mean_se",
       color = "supp", 
       palette = "jco")+
  stat_compare_means(aes(group = supp), label = "p.signif", 
                     label.y = c(16, 25, 29))
cowplot::plot_grid(p1, p2, ncol = 2, align = "hv")

添加边沿图 marginal plots

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library(ggExtra)
p <- ggscatter(iris, 
               x = "Sepal.Length", 
               y = "Sepal.Width",
               color = "Species", 
               palette = "jco",
               size = 3, 
               alpha = 0.6)
ggMarginal(p, type = "boxplot")

  • 第二种添加方式: 分别画出三个图，然后进行组合

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    sp <- ggscatter(iris, 
                    x = "Sepal.Length", 
                    y = "Sepal.Width",
                    color = "Species", 
                    palette = "jco",
                    size = 3, 
                    alpha = 0.6, 
                    ggtheme = theme_bw())             

    xplot <- ggboxplot(iris, 
                       x = "Species", 
                       y = "Sepal.Length", 
                       color = "Species", 
                       fill = "Species", 
                       palette = "jco",
                       alpha = 0.5, 
                       ggtheme = theme_bw())+ rotate()

    yplot <- ggboxplot(iris, 
                       x = "Species", 
                       y = "Sepal.Width",
                       color = "Species", 
                       fill = "Species", 
                       palette = "jco",
                       alpha = 0.5, 
                       ggtheme = theme_bw())


    sp <- sp + rremove("legend")
    yplot <- yplot + clean_theme() + rremove("legend")
    xplot <- xplot + clean_theme() + rremove("legend")
    cowplot::plot_grid(xplot, NULL, sp, yplot, ncol = 2, align = "hv", 
              rel_widths = c(2, 1), rel_heights = c(1, 2))

  • 上图主图和边沿图之间的space太大，第三种方法能克服这个缺点

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    library(cowplot) 
    # Main plot
    pmain <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species))+
      geom_point()+
      ggpubr::color_palette("jco")

    # Marginal densities along x axis
    xdens <- axis_canvas(pmain, axis = "x")+
      geom_density(data = iris, aes(x = Sepal.Length, fill = Species),
                   alpha = 0.7, size = 0.2)+
      ggpubr::fill_palette("jco")

    # Marginal densities along y axis
    # Need to set coord_flip = TRUE, if you plan to use coord_flip()
    ydens <- axis_canvas(pmain, axis = "y", coord_flip = TRUE)+
      geom_boxplot(data = iris, aes(x = Sepal.Width, fill = Species),
                   alpha = 0.7, size = 0.2)+
      coord_flip()+
      ggpubr::fill_palette("jco")

    p1 <- insert_xaxis_grob(pmain, xdens, grid::unit(.2, "null"), position = "top")
    p2 <- insert_yaxis_grob(p1, ydens, grid::unit(.2, "null"), position = "right")
    ggdraw(p2)

  • 第四种方法，通过grob设置

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    # Scatter plot colored by groups ("Species")
    #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
    sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width",
                    color = "Species", palette = "jco",
                    size = 3, alpha = 0.6)
    # Create box plots of x/y variables
    #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
    # Box plot of the x variable
    xbp <- ggboxplot(iris$Sepal.Length, width = 0.3, fill = "lightgray") +
      rotate() +
      theme_transparent()
    # Box plot of the y variable
    ybp <- ggboxplot(iris$Sepal.Width, width = 0.3, fill = "lightgray") +
      theme_transparent()
    # Create the external graphical objects
    # called a "grop" in Grid terminology
    xbp_grob <- ggplotGrob(xbp)
    ybp_grob <- ggplotGrob(ybp)
    # Place box plots inside the scatter plot
    #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
    xmin <- min(iris$Sepal.Length); xmax <- max(iris$Sepal.Length)
    ymin <- min(iris$Sepal.Width); ymax <- max(iris$Sepal.Width)
    yoffset <- (1/15)*ymax; xoffset <- (1/15)*xmax
    # Insert xbp_grob inside the scatter plot
    sp + annotation_custom(grob = xbp_grob, xmin = xmin, xmax = xmax, 
                           ymin = ymin-yoffset, ymax = ymin+yoffset) +
      # Insert ybp_grob inside the scatter plot
      annotation_custom(grob = ybp_grob,
                           xmin = xmin-xoffset, xmax = xmin+xoffset, 
                           ymin = ymin, ymax = ymax)

二维密度图 2d density

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sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width",
                color = "lightgray")
p1 <- sp + geom_density_2d()
# Gradient color
p2 <- sp + stat_density_2d(aes(fill = ..level..), geom = "polygon")
# Change gradient color: custom
p3 <- sp + stat_density_2d(aes(fill = ..level..), geom = "polygon")+
  gradient_fill(c("white", "steelblue"))
# Change the gradient color: RColorBrewer palette
p4 <- sp + stat_density_2d(aes(fill = ..level..), geom = "polygon") +
  gradient_fill("YlOrRd")

cowplot::plot_grid(p1, p2, p3, p4, ncol = 2, align = "hv")

混合图

混合表、字体和图

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# Density plot of "Sepal.Length"
#::::::::::::::::::::::::::::::::::::::
density.p <- ggdensity(iris, x = "Sepal.Length", 
                       fill = "Species", palette = "jco")
# Draw the summary table of Sepal.Length
#::::::::::::::::::::::::::::::::::::::
# Compute descriptive statistics by groups
stable <- desc_statby(iris, measure.var = "Sepal.Length",
                      grps = "Species")
stable <- stable[, c("Species", "length", "mean", "sd")]
# Summary table plot, medium orange theme
stable.p <- ggtexttable(stable, rows = NULL, 
                        theme = ttheme("mOrange"))
# Draw text
#::::::::::::::::::::::::::::::::::::::
text <- paste("iris data set gives the measurements in cm",
              "of the variables sepal length and width",
              "and petal length and width, respectively,",
              "for 50 flowers from each of 3 species of iris.",
             "The species are Iris setosa, versicolor, and virginica.", sep = " ")
text.p <- ggparagraph(text = text, face = "italic", size = 11, color = "black")
# Arrange the plots on the same page
ggarrange(density.p, stable.p, text.p, 
          ncol = 1, nrow = 3,
          heights = c(1, 0.5, 0.3))

  • 注释table在图上
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    density.p <- ggdensity(iris, x = "Sepal.Length", 
                           fill = "Species", palette = "jco")

    stable <- desc_statby(iris, measure.var = "Sepal.Length",
                          grps = "Species")
    stable <- stable[, c("Species", "length", "mean", "sd")]
    stable.p <- ggtexttable(stable, rows = NULL, 
                            theme = ttheme("mOrange"))
    density.p + annotation_custom(ggplotGrob(stable.p),
                                  xmin = 5.5, ymin = 0.7,
                                  xmax = 8)

systemic information

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sessionInfo()
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R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936  LC_CTYPE=Chinese (Simplified)_China.936   
[3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C                              
[5] LC_TIME=Chinese (Simplified)_China.936    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggpubr_0.4.0  ggplot2_3.3.2

loaded via a namespace (and not attached):
 [1] zip_2.0.4         Rcpp_1.0.3        cellranger_1.1.0  pillar_1.4.6      compiler_3.6.1    forcats_0.5.0    
 [7] tools_3.6.1       digest_0.6.27     lifecycle_0.2.0   tibble_3.0.4      gtable_0.3.0      pkgconfig_2.0.3  
[13] rlang_0.4.8       openxlsx_4.2.3    ggsci_2.9         rstudioapi_0.10   curl_4.3          haven_2.3.1      
[19] rio_0.5.16        withr_2.1.2       dplyr_1.0.2       generics_0.0.2    vctrs_0.3.4       hms_0.5.3        
[25] grid_3.6.1        tidyselect_1.1.0  glue_1.4.2        data.table_1.13.2 R6_2.4.1          rstatix_0.6.0    
[31] readxl_1.3.1      foreign_0.8-73    carData_3.0-4     farver_2.0.3      tidyr_1.0.0       purrr_0.3.3      
[37] car_3.0-10        magrittr_1.5      scales_1.1.0      backports_1.1.10  ellipsis_0.3.1    abind_1.4-5      
[43] colorspace_1.4-1  ggsignif_0.6.0    labeling_0.4.2    stringi_1.4.3     munsell_0.5.0     broom_0.7.2      
[49] crayon_1.3.4

Reference

  1. ggpubr: Publication Ready Plots

参考文章如引起任何侵权问题，可以与我联系，谢谢。


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