加载R包 library(tidyverse) library(readxl) library(ggsignif) library(rstatix) library(ggpubr) li...
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)) 创建标准误差 gapminder%>%ggplot(aes(x=year,y=lifeExp))+stat_summary(fun=mean,geom="pointrange",fun.max=function(x)mean(x)+sd...
geom = "text", fun.data = ~ round(data.frame(mean = mean(.x), sd = sd(.x)), 2) ) 再比如 ggplot(mtcars, aes(cyl, mpg)) + stat_summary(aes( y=stage(mpg,after_stat = 25), ymax=after_stat(ymax+1), ymin=after_stat(ymin-1)), fun.data = "mean_cl_boot", colour = "...
smean.cl.normalcomputes 3 summary variables: the sample mean and lower and upper Gaussian confidence limits based on the t-distribution. smean.sdcomputes the mean and standard deviation. smean.sdlcomputes the mean plus or minus a constant times the standard deviation.smean.cl.bootis a very f...
常用的函数包括mean、median、sd等。你也可以自定义函数。 geom是一个几何对象,用于指定如何在图上表示汇总统计量。常用的几何对象包括geomPoint、GeomLine、GeomBar等。 ...表示其他可选参数,用于进一步定制图形。 以下是一个简单的示例,演示如何使用stat_summary()函数在散点图上添加均值线: R复制代码 # 加载所...
(x = group, y = value)) # 添加散点图 plot <- plot + geom_point() # 添加统计量和标签 plot <- plot + stat_summary(fun = "mean", geom = "text", aes(label = round(..y.., 2)), vjust = -0.5) # 美化图形 plot <- plot + labs(title = "Average Value by...
请参阅下面示例中的values_summary_stat。 例子 values_summary_stat #> [1] "mean" "median" "sd" "var" "sum" "prod" "min" #> [8] "max" summary_stat() #> Rolling Summary Statistic (qualitative) #> 8 possible values include: #> 'mean', 'median', 'sd', 'var', 'sum', 'prod...
stat_function(fun = dnorm, args = list(mean = 2, sd = .5)) # 画均值为2,标准差为0.5的正态分布密度图 f <- ggplot(data.frame(x = c(0, 10)), aes(x)) f + stat_function(fun = sin, color = "red") + # 绘制(0, 10)区间的正弦函数图形 ...
ggsummarystats生成的对象本质上是两个ggplot的列表;一个用于主图，称为main.plot，另一个用于表，称...
# 安装和加载必要的包 if (!require(ggplot2)) { install.packages("ggplot2") } library(ggplot2) # 创建示例数据 set.seed(123) data <- data.frame( group = rep(c("A", "B"), each = 50), value = c(rnorm(50, mean = 50, sd = 10), rnorm(50, mean = 60, sd = 10)) ) #...