您可以使用dplyr::group_split()

library(dplyr)
iris %>%
    group_by(Species) %>%
    group_split()

如果您尝试拆分和循环，请尝试拆分并lapply / vapply：

vapply(split(mtcars,mtcars$cyl),function(df) mean(df$mpg),double(1))

似乎真正的目标是从“ Class_Name”分组的总数据集中收集摘要数据，并且确实没有必要将其拆分为不同的数据框。对于base R和dplyr软件包，有几个很好的选择可以执行此摘要。

下面是使用split/sapply，tapply和group_by/summarize技术的示例。

df<-read.table(header=TRUE,text='Grade   Class_Dept   Class_Name   Class_Work
9       English      "English 1"    30
10      History      "Modern World" 50
11      Science      "AP Chem"      85
12      Math         "Calc BC"      45')

#Base R solution
#split into a list of dataframes by Class_name
dflist<-split(df,df$Class_Name)
#perform math operation on each dataframe
workmean<-sapply(dflist,function(x){ mean(x$Class_Work)})
workstdev<-sapply(dflist,function(x){ sd(x$Class_Work)})

workmean
#   AP Chem      Calc BC    English 1 Modern World 
#        85           45           30           50 

#tapply option:
tapply(df$Class_Work,df$Class_Name,mean)
#     AP Chem      Calc BC    English 1 Modern World 
#          85           45           30           50 

#dplyr solution
library(dplyr)
df %>% group_by(Class_Name) %>% summarize(mean=mean(Class_Work),stdev=sd(Class_Work))
# # A tibble: 4 x 3
#   Class_Name    mean stdev
#   <fct>        <dbl> <dbl>
# 1 AP Chem         85   NaN
# 2 Calc BC         45   NaN
# 3 English 1       30   NaN
# 4 Modern World    50   NaN

您可以data.table包装吗：

> dt <- iris
> setDT(dt)[,.(mean=mean(Petal.Width),std_dv=sd(Sepal.Length)),by=.(Species)]

     Species  mean    std_dv
1:     setosa 0.246 0.3524897
2: versicolor 1.326 0.5161711
3:  virginica 2.026 0.6358796