简介
Hadley Wickham撰写的ggplot2[1]是好用的软件包，是可视化工具的必备包。但是，需要知道ggplot2一定的理论与原理，对新手来说，入门门槛还是比较高的。
而ggpubr软件包提供了一些易于使用的功能，基于ggplot2编写，语法十分简单的的图标。对于一些刚了解R语言，而想用R做可发表的图表的人来说，这真的太好用了！
下面对该文章ggpubr: Publication Ready Plots[2]进行讲解。
安装
- 从CRAN安装：
install.packages("ggpubr")
- 从GitHub安装最新版本：
if(!require(devtools))install.packages("devtools") devtools :: install_github("kassambara / ggpubr")
可视化函数汇总
| 图形 | 命令 |
| 密度图 | ggdensity() |
| 箱型图 | ggboxplot() |
| 柱状图 | gghistogram() |
| 小提琴图 | ggdotchart() |
| 条形图 | ggdotchart() |
| 棒棒图,克利夫兰图 | ggdotchart() |
密度图
library(ggpubr) # 构建数据集 set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4)
- 密度图(
ggdensity)与平均线(add = "mean")；按性别("sex")进行颜色填充;加入边际地毯（rug = TRUE）并使用自定义面板(palette = c("#00AFBB", "#E7B800"))。
ggdensity(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800"))
下面是绘制柱状图(gghistogram)，其他参数和上面类似。
gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800"))
箱型图
# Load data data("ToothGrowth") df <- ToothGrowth head(df, 4)
按剂量("dose")进行颜色填充；添加抖动点并按剂量("dose")更改形状。
p <- ggboxplot(df, x = "dose", y = "len", color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"), add = "jitter", shape = "dose") p
还可以比较不同组均值之间的关系（stat_compare_means(label.y = 50)），并添加p值(stat_compare_means(label.y = 50) )。
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") ) p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value stat_compare_means(label.y = 50) # Add global p-value
小提琴图
下图是小提琴图(ggviolin)与箱型图的结合(add = "boxplot")，按剂量("dose")进行颜色填充；增加白色填充(add.params = list(fill = "white"))的箱型图，并加入各组比较的p值。
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) # Add global the p-value
条形图
数据集
这里用mtcars数据集中进行绘制。
data("mtcars") dfm <- mtcars dfm$cyl <- as.factor(dfm$cyl) dfm$name <- rownames(dfm) head(dfm[, c("name", "wt", "mpg", "cyl")])
有序的条形图
条形图(ggbarplot)中可以利用sort.val = "desc"把数据从大到小排序并且不在组内进行排序(sort.by.groups = FALSE)，而是所有数据排序；旋转x轴标签(x.text.angle = 90)。值得一提的是，这里使用了jco杂志的颜色版式（palette = "jco"）。
ggbarplot(dfm, x = "name", y = "mpg", fill = "cyl", # change fill color by cyl color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "desc", # Sort the value in dscending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90 # Rotate vertically x axis texts )
如果按照组内排序的话(sort.by.groups = TRUE) ，就是下面这样。
ggbarplot(dfm, x = "name", y = "mpg", fill = "cyl", # change fill color by cyl color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "asc", # Sort the value in dscending order sort.by.groups = TRUE, # Sort inside each group x.text.angle = 90 # Rotate vertically x axis texts )
偏差图
偏差图显示了定量值与参考值的偏差。在下面的R代码中，我们将绘制来自mtcars数据集的mpgz-score变化（标准化的一种）。
# Calculate the z-score of the mpg data dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg) dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"), levels = c("low", "high")) head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
根据上面的数据，创建一个有序的箱型图，按升序对值排序（sort.val = "asc"）。这里和前面箱型图不同的是，使用刚建的mpg_grp变量作为填充参数，而该参数是因子（含两个水平，levels = c("low", "high")）。
ggbarplot(dfm, x = "name", y = "mpg_z", fill = "mpg_grp", # change fill color by mpg_level color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "asc", # Sort the value in ascending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90, # Rotate vertically x axis texts ylab = "MPG z-score", xlab = FALSE, legend.title = "MPG Group" )
旋转x，y轴(rotate = TRUE)并进行降序排序（sort.val = “desc”），如下图所示，该图非常美观，可读性很强。
ggbarplot(dfm, x = "name", y = "mpg_z", fill = "mpg_grp", # change fill color by mpg_level color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "desc", # Sort the value in descending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90, # Rotate vertically x axis texts ylab = "MPG z-score", legend.title = "MPG Group", rotate = TRUE, ggtheme = theme_minimal() )
散点图
棒糖图
棒糖图是条形图的另一种选择，最终图像像棒棒糖一样。
ggdotchart(dfm, x = "name", y = "mpg", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "ascending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots ggtheme = theme_pubr() # ggplot2 theme )
ggdotchart(dfm, x = "name", y = "mpg", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots rotate = TRUE, # Rotate vertically group = "cyl", # Order by groups dot.size = 6, # Large dot size label = round(dfm$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
还是使用上面的数据集，构建棒棒糖系列的偏差图。改变线段的颜色和大小: add.params = list(color = “lightgray”, size = 2)
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")
克利夫兰散点图
在前面的基础上，只要加入以下代码即可得到克利夫兰散点图(theme_cleveland())。
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 rotate = TRUE, # Rotate vertically dot.size = 2, # Large dot size y.text.col = TRUE, # Color y text by groups ggtheme = theme_pubr() # ggplot2 theme )+ theme_cleveland() # Add dashed grids
参考资料
[1]
ggplot2: https://ggplot2.tidyverse.org/
[2]
ggpubr: Publication Ready Plots: http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/
