R可视化：ggpubr的基本图形

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发布于 2024-06-11 15:17:00

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介绍

GGPlot2（通常缩写为ggplot2）是一个在R语言中广泛使用的绘图包，以其灵活和强大的数据可视化功能而闻名。它基于"The Grammar of Graphics"一书的概念，允许用户通过组合不同的视觉元素来创建自定义的图形。而ggpubr是ggplot2的一个扩展包，它进一步简化了图形的创建过程，特别是对于初学者来说，提供了一种更为直观和易于理解的绘图方式。

ggpubr包中包含了许多高级的绘图功能，其中stat_compare_means函数是一个特别有用的工具，它能够对不同的数据组进行假设检验分析，并且将检验结果直接可视化在图形上。这种功能对于科研人员和数据分析师来说非常有价值，因为它不仅提供了统计检验的结论，还通过图形的方式直观地展示了数据间的差异。

安装ggpubr包

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

准备数据

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

画图

不同类型的可视化图形

密度图density

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

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

箱线图boxplot

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

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

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

有序条形图 ordered bar plots

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

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

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

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

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

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

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

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

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

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

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

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

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

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

# 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

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

# 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

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)

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

r 语言

数据可视化

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

r 语言

数据可视化

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