一条命令轻松绘制CNS顶级配图-ggpubr

本文转载自“EasyChart”,己获授权。本平台编辑对内容进行测试、修改和补充。 Hadley Wickham创建的可视化包ggplot2可以流畅地进行优美的可视化,但是如果要通过ggplot2定制一套图形,尤其是...


Hadley Wickham创建的可视化包ggplot2可以流畅地进行优美的可视化,但是如果要通过ggplot2定制一套图形,尤其是适用于杂志期刊等出版物的图形,对于那些没有深入了解ggplot2的人来说就有点困难了,ggplot2的部分语法是很晦涩的。为此Alboukadel Kassambara创建了基于ggplot2的可视化包ggpubr用于绘制符合出版物要求的图形。

安装及加载ggpubr包

# 直接从CRAN安装
install.packages("ggpubr", repo="http://cran.us.r-project.org")

# GitHub上安装最新版本
install.packages("devtools", repo="http://cran.us.r-project.org")
library(devtools)
install_github("kassambara/ggpubr")

# 安装完之后直接加载就行:
library(ggpubr)

ggpubr可绘制图形

ggpubr可绘制大部分我们常用的图形,下面逐个介绍。

分布图(Distribution)

带有均值线和地毯线的密度图

#构建数据集
set.seed(123)
df <- data.frame( sex=factor(rep(c("f", "M"), each=200)), 
                  weight=c(rnorm(200, 55), rnorm(200, 58)))
# 预览数据格式
head(df) 
# 绘制密度图
ggdensity(df, x="weight", add = "mean", rug = TRUE, color = "sex", fill = "sex",
          palette = c("#00AFBB", "#E7B800"))

attachments-2017-12-CLprv1KJ5a433a334b351.png

图1. 密度图展示不同性别分组下体重的分布,X轴为体重,Y轴为自动累计的密度,X轴上添加地毯线进一步呈现样本的分布;按性别分别组标记轮廓线颜色,再按性别填充色展示各组的分布,使用palette自定义颜色,是不是很舒服。

带有均值线和边际地毯线的直方图

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

attachments-2017-12-vWxjlNAd5a433a4335030.png

图2. 带有均值线和边际地毯线的直方图,只是把密度比例还原为了原始数据counts值

箱线/小提琴图(barplot/violinplot)

箱线图+分组形状+统计

#加载数据集ToothGrowth
data("ToothGrowth")
df1 <- ToothGrowth
head(df1)
p <- ggboxplot(df1, x="dose", y="len", color = "dose", 
               palette = c("#00AFBB", "#E7B800", "#FC4E07"), 
               add = "jitter", shape="dose")#增加了jitter点,点shapedose映射
p

attachments-2017-12-HWn826or5a433a5324a6c.png

图3. 箱线图按组着色,同时样本点标记不同形状可以一步区分组或批次

箱线图+分组形状+统计

# 增加不同组间的p-value值,可以自定义需要标注的组间比较
my_comparisons <- list(c("0.5", "1"), c("1", "2"), c("0.5", "2"))
p+stat_compare_means(comparisons = my_comparisons)+ #不同组间的比较 
  stat_compare_means(label.y = 50)

attachments-2017-12-eI3Vlk3H5a433a650e8c1.png图4. stat_compare_means添加组间比较连线和统计P值

内有箱线图的小提琴图+星标记

ggviolin(df1, 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")+#label这里表示选择显著性标记(星号) 
  stat_compare_means(label.y = 50)

attachments-2017-12-y520NhI95a433a76677c7.png

图5. ggviolin绘制小提琴图, add = "boxplot"中间再添加箱线图,stat_compare_means中,设置lable="p.signif",即可添加星添加组间比较连线和统计P值按星分类。

条形/柱状图(barplot)

data("mtcars")
df2 <- mtcars
df2$cyl <- factor(df2$cyl)
df2$name <- rownames(df2) #添加一行name
head(df2[, c("name", "wt", "mpg", "cyl")])
ggbarplot(df2, x="name", y="mpg", fill = "cyl", color = "white", 
          palette = "npg", #杂志nature的配色 
          sort.val = "desc", #下降排序 
          sort.by.groups=FALSE, #不按组排序 
          x.text.angle=60)

attachments-2017-12-NubKT0465a433a8823b4a.png

图6. 柱状图展示不同车的速度,按cyl为分组信息进行填充颜色,颜色按nature配色方法(支持 ggsci包中的本色方案,如: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty"),按数值降序排列。

# 按组进行排序
ggbarplot(df2, x="name", y="mpg", fill = "cyl", color = "white", 
          palette = "aaas", #杂志Science的配色 
          sort.val = "asc", #上升排序,区别于desc,具体看图演示 
          sort.by.groups=TRUE,x.text.angle=60) #按组排序 x.text.angle=90

attachments-2017-12-qybZ4siM5a433a9663514.png

图7. 由上图中颜色改为Sciences配色方案(为什么感觉nature和sciences的配色方案没有文章里的看着舒服呢?),按组升序排布,且调整x轴标签60度角防止重叠。

偏差图

偏差图展示了与参考值之间的偏差

df2$mpg_z <- (df2$mpg-mean(df2$mpg))/sd(df2$mpg) # 相当于Zscore标准化,减均值,除标准差
df2$mpg_grp <- factor(ifelse(df2$mpg_z<0, "low", "high"), levels = c("low", "high"))
head(df2[, c("name", "wt", "mpg", "mpg_grp", "cyl")])
ggbarplot(df2, x="name", y="mpg_z", fill = "mpg_grp", color = "white", 
          palette = "jco", sort.val = "asc", sort.by.groups = FALSE, 
          x.text.angle=60, ylab = "MPG z-score", xlab = FALSE, legend.title="MPG Group")

attachments-2017-12-gxtZ2HQO5a43615a04892.png


图8. 基于Zscore的柱状图,就是原始值减均值,再除标准差。按jco杂志配色方案,升序排列,不按组排列。

坐标轴变换

ggbarplot(df2, x="name", y="mpg_z", fill = "mpg_grp", color = "white", 
          palette = "jco", sort.val = "desc", sort.by.groups = FALSE, 
          x.text.angle=90, ylab = "MPG z-score", xlab = FALSE, 
          legend.title="MPG Group", rotate=TRUE, ggtheme = theme_minimal()) # rotate设置x/y轴对换

attachments-2017-12-4JBjRCmB5a43611bac236.png

图9. rotate=TRUE翻转坐标轴,柱状图秒变条形图

棒棒糖图(Lollipop chart)

棒棒图可以代替条形图展示数据

ggdotchart(df2, x="name", y="mpg", color = "cyl", 
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), 
           sorting = "ascending", 
           add = "segments", ggtheme = theme_pubr())

attachments-2017-12-CFY45J3p5a433acd33336.png图10. 柱状图太多了单调,改用棒棒糖图添加多样性

可以自设置各种参数

ggdotchart(df2, x="name", y="mpg", color = "cyl", 
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), 
           sorting = "descending", add = "segments", rotate = TRUE, 
           group = "cyl", dot.size = 6, 
           label = round(df2$mpg), font.label = list(color="white", 
           size=9, vjust=0.5), ggtheme = theme_pubr())

attachments-2017-12-xMYCB9jJ5a433af7686ac.png

图11. 棒棒糖图简单调整,rotate = TRUE转换坐标轴, dot.size = 6调整糖的大小,label = round()添加糖心中的数值,font.label进一步设置字体样式

棒棒糖偏差图

ggdotchart(df2, x="name", y="mpg_z", color = "cyl", 
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), 
           sorting = "descending", add = "segment", 
           add.params = list(color="lightgray", size=2), 
           group = "cyl", dot.size = 6, label = round(df2$mpg_z, 1), 
           font.label = list(color="white", size=9, vjust=0.5), 
           ggtheme = theme_pubr())+ geom_line(yintercept=0, linetype=2, color="lightgray")

attachments-2017-12-ZSbLtzKL5a433b9c653db.png

图12. 同柱状图类似,用Z-score的值代替原始值绘图。

Cleveland点图

ggdotchart(df2, x="name", y="mpg", color = "cyl", 
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), 
           sorting = "descending", 
           rotate = TRUE, dot.size = 2, y.text.col=TRUE, 
           ggtheme = theme_pubr())+ theme_cleveland()

attachments-2017-12-NbjWzMoB5a433bc3e4bcf.png

图13. theme_cleveland()主题可设置为Cleveland点图样式

我测试的工作环境

sessionInfo()

R version 3.4.1 (2017-06-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS

Matrix products: default
BLAS: /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] bindrcpp_0.2     ggpubr_0.1.6.999 magrittr_1.5     ggplot2_2.2.1    devtools_1.13.4 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.14     bindr_0.1        munsell_0.4.3    colorspace_1.3-2 R6_2.2.2         rlang_0.1.4      httr_1.3.1      
 [8] plyr_1.8.4       dplyr_0.7.4      tools_3.4.1      grid_3.4.1       gtable_0.2.0     git2r_0.19.0     withr_2.1.0     
[15] lazyeval_0.2.1   digest_0.6.12    assertthat_0.2.0 tibble_1.3.4     ggsignif_0.4.0   ggsci_2.8        purrr_0.2.4     
[22] curl_3.0         memoise_1.1.0    glue_1.2.0       labeling_0.3     compiler_3.4.1   scales_0.5.0     pkgconfig_2.0.1


  • 发表于 2017-12-27 12:36
  • 阅读 ( 8952 )
  • 分类:其他组学

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