纯纯的ggplot2画好看的柱状图，统计、分面

柱状图是最常见的统计作图,当然Excel和Prism都可以画,还有一些shiny可以交互画图,但是用R的话,也可以有很好看的效果,本文用Rmarkdown做下效果,要有统计结果,要有统计标识,还有个各个样本的数值.

比如有这样一个表:

如果是在excel上,我们其实可以用代码直接复制过来

data <- read.table(pipe(“pbpaste”), # 读取剪切板中的数据 sep=", # 指定分隔符 header = TRUE

当然我们也可以直接用代码导入进来,最好是csv格式的,这个格式稳定,当然也可以直接用File的Import Dataset

data <- read.csv("~/Desktop/data.csv")

library(ggplot2) #画图
library(ggpubr) ### 加载了这个包就不用再次统计均数和标准差了,统计也方便
library(ggsignif)  ### 统计,当然用ggpubr的话会更简单,但是标识线的颜色改不了
ggplot(data,
       aes(x=Group,y=Value,color=Group,fill=Group))+
    geom_bar(stat="summary",fun=mean,position="dodge")+ #柱状图
    stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.5,position = position_dodge(0.9))+ ##'mean_sd' 自动计算均数+标准差，添加误差棒,当然也可以计算mean+se,mean_ci等，跟ggpubr一模一样，width可以设置误差棒的宽度，而0.9是误差棒的位置
    facet_grid(~Gene,scales = 'free')+ #分面
    theme_minimal(base_size = 13)+ #主题和字体大小
    scale_color_manual(values = c('steelblue','firebrick'))+
    scale_fill_manual(values = c('steelblue','firebrick'))+
    geom_signif(comparisons = list(c("Control","Treat")),test = 't.test')+
    labs(x=NULL,y='Relative gene expression')

如果不想显示具体的P值，还可以自动标星号， geom_signif里面加一句map_signif_level=T

ggplot(data,
       aes(x=Group,y=Value,color=Group,fill=Group))+
    geom_bar(stat="summary",fun=mean,position="dodge")+
    stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.5,position = position_dodge(0.9))+
    facet_grid(~Gene,scales = 'free')+
    theme_minimal(base_size = 13)+
    scale_color_manual(values = c('steelblue','firebrick'))+
    scale_fill_manual(values = c('steelblue','firebrick'))+
    geom_signif(comparisons = list(c("Control","Treat")),map_signif_level=T,test = 't.test')+
    labs(x=NULL,y='Relative gene expression')+
    geom_dotplot(stackdir = "center", binaxis = "y", 
                 fill = "gray", 
                 dotsize = 0.9,position = position_dodge(0.9))

## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.

比如你不想要Group的标签,也想换个prism主题假装一下grahpad作图,也可以的

library(ggprism)
ggplot(data,
       aes(x=Group,y=Value,color=Group,fill=Group))+
    geom_bar(stat="summary",fun=mean,position="dodge")+
    stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.5,position = position_dodge(0.9))+
    facet_wrap(~Gene,scales = 'free')+
    theme_prism(base_size = 12)+
    scale_color_manual(values = c('steelblue','brown'))+
    scale_fill_manual(values = c('steelblue','brown'))+
    geom_signif(comparisons = list(c("Control","Treat")),map_signif_level=T,test = 't.test')+
    labs(x=NULL,y='Relative gene expression')+
    geom_dotplot(stackdir = "center", binaxis = "y", 
                 fill = "gray", 
                 dotsize = 0.9,position = position_dodge(0.9))+
     theme(legend.position ="none")

## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.

## install.packages("ggbreak") #需要安装的只要一条指令
library(ggbreak)
ggplot(data,
       aes(x=Group,y=Value,color=Group,fill=Group))+
    geom_bar(stat="summary",fun=mean,position="dodge")+
    stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.5,position = position_dodge(0.9))+
    facet_grid(~Gene,scales = 'free')+
    theme_minimal(base_size = 13)+
    scale_color_manual(values = c('steelblue','firebrick'))+
    scale_fill_manual(values = c('steelblue','firebrick'))+
    geom_signif(comparisons = list(c("Control","Treat")),map_signif_level=T,test = 't.test')+
    labs(x=NULL,y='Relative gene expression')+
   geom_dotplot(stackdir = "center", binaxis = "y", 
                 fill = "gray", 
                 dotsize = 0.9,position = position_dodge(0.9))+
  scale_y_break(c(1.5, 10) , scales='free')

## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.

ggplot(data,
       aes(x=Group,y=Value,color=Group,fill=Group))+
    geom_bar(stat="summary",fun=mean,position="dodge")+
    stat_summary(fun.data = 'mean_sd', geom = "errorbar", width = 0.5,position = position_dodge(0.9))+
    facet_grid(~Gene,scales = 'free')+
    theme_minimal(base_size = 13)+
    scale_color_manual(values = c('steelblue','firebrick'))+
    scale_fill_manual(values = c('steelblue','firebrick'))+
    geom_signif(comparisons = list(c("Control","Treat")),map_signif_level=T,test = 't.test')+
    labs(x=NULL,y='Relative gene expression')+
  scale_y_break(c(1.5, 10) , scales='free')