标签：lifeExp 线图 stat summary geom 分组 fun ggplot2 mean

ggplot2分组箱线图添加均值

• 一、不分组的简单箱线图加均值
• • stat_summary
  • • 分组箱线图

一、不分组的简单箱线图加均值

代码如下（示例）：

# Library
library(ggplot2)

# create data
names=c(rep("A", 20) , rep("B", 8) , rep("C", 30), rep("D", 80))
value=c( sample(2:5, 20 , replace=T) , sample(4:10, 8 , replace=T), sample(1:7, 30 , replace=T), sample(3:8, 80 , replace=T) )
data=data.frame(names,value)

# plot
p <- ggplot(data, aes(x=names, y=value, fill=names)) +
  geom_boxplot(alpha=0.7) +
  stat_summary(fun=mean, geom="point", shape=20, size=2.5, color="red", fill="red",alpha=0.7) +
  theme(legend.position="none") +
  scale_fill_brewer(palette="Accent")
p

stat_summary

用到了stat_summary函数算均值，具体介绍一下

代码如下（示例）：（图片略，直接跑可以跑出来）

library(tidyverse)
library(gapminder)
#----------------------
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) + 
  stat_summary(fun = "mean", geom = "point") + #对lifeExp求平均，用点点显示
  stat_summary(fun = "mean", geom = "line") #对lifeExp求平均，用线显示

#----------------------
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun = "median", geom = "bar")#对lifeExp求中位数，用bar显示

#----------------------
gapminder %>% 
  mutate(year = as.integer(year)) %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun = "mean", geom = "area",#对lifeExp求平均，用面积覆盖显示
               fill = "#EB5286",
               alpha = .5) +
  stat_summary(fun = "mean", geom = "point",#对lifeExp求平均，用点点显示
               color = "#6F213F") 

#----------------------

gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun = mean, #点是均值
               geom = "pointrange",#点和范围线
               fun.min = min,#对应范围是最大值和最小值
               fun.max = max)

#----------------------

gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(geom = "errorbar",#使用经典的误差线来显示最大值和最小值
               width = 1,#fun缺省
               fun.min = min,
               fun.max = max)

#----------------------
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun = mean,
               geom = "pointrange",
               fun.max = function(x) mean(x) + sd(x),
               fun.min = function(x) mean(x) - sd(x))

   #fun.data="mean_sd"在ggpubr包里
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun.data = "mean_sd",geom = "pointrange")
   #以上两种作用是一样的

gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun=mean,geom="bar")+
  stat_summary(fun.data = "mean_sd",geom = "errorbar",color="red")


#----------------------
    #标准差
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun = mean,
               geom = "pointrange",
               fun.max = function(x) mean(x) + sd(x) / sqrt(length(x)),
               fun.min = function(x) mean(x) - sd(x) / sqrt(length(x)))

#----------------------
library(Hmisc)
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun.data = "mean_cl_normal",#置信区间
               fun.args = list(conf.int = .99))#设置是99%质心区间

    #置信区间的误差线
gapminder %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  stat_summary(fun.data = "mean_cl_normal",
               geom = "errorbar",
               width = .4) +
  stat_summary(fun = "mean", geom = "point")

#----------------------
    #均值加置信区间
gapminder %>% 
  filter(year == 2007) %>% 
  ggplot(aes(x = continent, y = lifeExp)) +
  stat_summary(fun = "mean", geom = "bar", alpha = .7) +
  stat_summary(fun = "mean", geom = "point", 
               size = 1) +
  stat_summary(fun.data = "mean_cl_normal",
               geom = "errorbar",
               width = .2) 

#----------------------
    #用mean_cl_bool对mpg进行运算，返回均值，最大值，最小值3个向量组成的矩阵
#----------------------
    #position = position_dodge( )并排显示多个条形图
    #第一幅图的绘图命令中用到了参数position="dodge",第二幅图的绘图命令中用到的参数是position=position_dodge()。
    #这是因为position="dodge"是参数默认为0.9的position_dodge()的简写。当我们需要单独指定该参数的时候，必须输入完整的命令。
colors <-c("#E41A1C","#1E90FF","#FF8C00","#4DAF4A","#984EA3",
           "#40E0D0","#FFC0CB","#00BFFF","#FFDEAD","#90EE90",
           "#EE82EE","#00FFFF","#F0A3FF", "#0075DC", 
           "#993F00","#4C005C","#2BCE48","#FFCC99",
           "#808080","#94FFB5","#8F7C00","#9DCC00",
           "#C20088","#003380","#FFA405","#FFA8BB",
           "#426600","#FF0010","#5EF1F2","#00998F",
           "#740AFF","#990000","#FFFF00")
gapminder %>% 
  mutate(
    year = as.factor(year)
  ) %>%
  ggplot(aes(x = continent, y = lifeExp, fill = year)) +
  stat_summary(fun = "mean", geom = "bar", 
               alpha = .7, position = position_dodge(0.95)) +#组间的距离
  stat_summary(fun = "mean", geom = "point", 
               position = position_dodge(0.95),
               size = 1) +
  stat_summary(fun.data = "mean_cl_normal",
               geom = "errorbar",
               position = position_dodge(0.95),
               width = .2) +
  scale_fill_manual(values = colors)+ ###这个可以这样子改颜色！！！
  theme_minimal()+ #去掉黑背景还蛮好看
  scale_y_continuous(expand=c(0,5))+scale_x_discrete(expand=c(0.2,0))
    #离散型scale_y_discrete 或者scale_x_discrete就是用于更改坐标轴两边的留白的

参考： 通过ggplot2中stat_summary函数快速进行数据统计.

以及 stat_summary.

分组箱线图

所需数据格式（长数据）

p1 <- ggplot(baidata, aes(x=组别, y=值, fill=组别)) + 
  facet_wrap(~状态)+ labs (y="白细胞")+ #分面按照基线7天14天
  ggtitle("不同组别不同时间点白细胞变化") +
  theme(plot.title = element_text(hjust = 0.5,size = 10)) + 
  scale_fill_manual(values=c("lightgoldenrod1","lavender"))+
  geom_boxplot( color="azure4",outlier.colour="red",
                outlier.fill="red",outlier.size=1,outlier.alpha=0#notch=TRUE,notchwidth = 0.8
  )+ theme(text = element_text(family = "wqy-microhei"))

#outlier.alpha=0异常值透明度为0，就是不显示异常值

p1

p1+stat_summary(fun=mean,geom="point",color="red",alpha=0.5,size=1)

标签：lifeExp,线图,stat,summary,geom,分组,fun,ggplot2,mean
来源： https://blog.csdn.net/weixin_44607829/article/details/120447833

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。