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 = "red", linewidth =...
ggplot(.,aes(x=weight,y=species.coverage,fill=weight))+#geom_boxplot(outlier.size=1)+stat_summary(fun="mean",size=2,geom="bar",position=position_dodge(0.75))+## 绘制bar,数值来源于计算后的均值stat_summary(fun.data="mean_cl_boot",geom="errorbar",width=.15,position=position_dodge(0.75...
g + stat_summary(fun.data = "mean_cl_boot", color = "red", size = 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 g + stat_summary(fun.y = "median", color = "red", size = 2, geom = "point") # 计算各组中位数 g + stat_summary(fun.y = "...
此外，您正在使用mean_cl_boot，它使用bootstrap方法来计算标准误差，每次可能略有不同。
因此每个误差条都有一个唯一的标准误差。此外，您正在使用mean_cl_boot，它使用bootstrap方法来计算标准...
I noticed thatstat_summary_bindoes not have a summary function set by default, raising a PlotnineError 'No summary function'. This seems to run contrary to its docstring stating that "mean_cl_boot" is the default. When using stat_summary the stated behaviourisused. ...
stat_summary允许我们通过不同的可视化显示任何类型的数据统计信息。无论我们是要可视化点还是线或面,请接着往下看在此示例中,我们将两个参数传递给stat_summary函数。首先,我们告诉stat_summary fun.y = mean我们想要计算变量lifeExp的平均值。使用参数geom = "bar"我们告诉stat_summary将平均值显示为条形图...
(x=year, y=auth_num, col=journal, fill=journal)) + stat_summary...(fun.data="mean_cl_boot", geom="ribbon", #width=.2, alpha=I(.5)) + stat_summary...image.png 根据上图确实可以看出学术期刊的作者数量确实是有增加的趋势的这里新学到的知识点是使用stat_summary()函数添加置信区间,之前...
与默认使用position_dodge的geom_boxplot相反，geom_point或geom_errorbar使用position="identity"。因此，...
# 读取数据# Load datatop_genus <- read.delim("data/top_genus.txt",header = TRUE,row.names =1,sep ="\t",stringsAsFactors = FALSE,check.names = FALSE) # 设置 Genus 因子级别# Setting Genus Factor Levelstop_genus$Genus <- factor(top_genus$Genus,levels = c("Bacillus","Cronobacter","Un...