Edit2：dplyr的最新版本建议使用常规的summarise和across函数，如下所示：

library(dplyr)
mtcars %>% 
group_by(cyl, gear) %>%
summarise(across(everything(), mean))

您要查找的是dplyr中的?summarise_all或?summarise_each
编辑：完整代码：

library(dplyr)
mtcars %>% 
    group_by(cyl, gear) %>%
    summarise_all("mean")

# Source: local data frame [8 x 11]
# Groups: cyl [?]
# 
#     cyl  gear    mpg     disp       hp     drat       wt    qsec    vs    am     carb
#   <dbl> <dbl>  <dbl>    <dbl>    <dbl>    <dbl>    <dbl>   <dbl> <dbl> <dbl>    <dbl>
# 1     4     3 21.500 120.1000  97.0000 3.700000 2.465000 20.0100   1.0  0.00 1.000000
# 2     4     4 26.925 102.6250  76.0000 4.110000 2.378125 19.6125   1.0  0.75 1.500000
# 3     4     5 28.200 107.7000 102.0000 4.100000 1.826500 16.8000   0.5  1.00 2.000000
# 4     6     3 19.750 241.5000 107.5000 2.920000 3.337500 19.8300   1.0  0.00 1.000000
# 5     6     4 19.750 163.8000 116.5000 3.910000 3.093750 17.6700   0.5  0.50 4.000000
# 6     6     5 19.700 145.0000 175.0000 3.620000 2.770000 15.5000   0.0  1.00 6.000000
# 7     8     3 15.050 357.6167 194.1667 3.120833 4.104083 17.1425   0.0  0.00 3.083333
# 8     8     5 15.400 326.0000 299.5000 3.880000 3.370000 14.5500   0.0  1.00 6.000000

aggregate是在base中执行此操作的最简单方法：

aggregate(. ~ cyl + gear, data = mtcars, FUN = mean)
#   cyl gear    mpg     disp       hp     drat       wt    qsec  vs   am     carb
# 1   4    3 21.500 120.1000  97.0000 3.700000 2.465000 20.0100 1.0 0.00 1.000000
# 2   6    3 19.750 241.5000 107.5000 2.920000 3.337500 19.8300 1.0 0.00 1.000000
# 3   8    3 15.050 357.6167 194.1667 3.120833 4.104083 17.1425 0.0 0.00 3.083333
# 4   4    4 26.925 102.6250  76.0000 4.110000 2.378125 19.6125 1.0 0.75 1.500000
# 5   6    4 19.750 163.8000 116.5000 3.910000 3.093750 17.6700 0.5 0.50 4.000000
# 6   4    5 28.200 107.7000 102.0000 4.100000 1.826500 16.8000 0.5 1.00 2.000000
# 7   6    5 19.700 145.0000 175.0000 3.620000 2.770000 15.5000 0.0 1.00 6.000000
# 8   8    5 15.400 326.0000 299.5000 3.880000 3.370000 14.5500 0.0 1.00 6.000000

使用data.table.（然而你不能setDT(mtcars)因为绑定被锁定.复制它到一个不同的名字象dt并且尝试

library(data.table)
 mt_dt = as.data.table(mtcars)
 mt_dt[ , lapply(.SD, mean) , by=c("cyl", "gear")]

对于dplyr 1.1.0，您可以使用.by进行内联分组：

summarise(mtcars, across(everything(), mean), .by = c(cyl, gear))

#   cyl gear    mpg     disp       hp     drat       wt    qsec  vs   am     carb
# 1   6    4 19.750 163.8000 116.5000 3.910000 3.093750 17.6700 0.5 0.50 4.000000
# 2   4    4 26.925 102.6250  76.0000 4.110000 2.378125 19.6125 1.0 0.75 1.500000
# 3   6    3 19.750 241.5000 107.5000 2.920000 3.337500 19.8300 1.0 0.00 1.000000
# 4   8    3 15.050 357.6167 194.1667 3.120833 4.104083 17.1425 0.0 0.00 3.083333
# 5   4    3 21.500 120.1000  97.0000 3.700000 2.465000 20.0100 1.0 0.00 1.000000
# 6   4    5 28.200 107.7000 102.0000 4.100000 1.826500 16.8000 0.5 1.00 2.000000
# 7   8    5 15.400 326.0000 299.5000 3.880000 3.370000 14.5500 0.0 1.00 6.000000
# 8   6    5 19.700 145.0000 175.0000 3.620000 2.770000 15.5000 0.0 1.00 6.000000

您可以在dplyr::summarize中使用多个均值语句，如下所示：

library(dplyr)

mtcars %>% 
  group_by(cyl, gear) %>% 
  summarize(mean_hp = mean(hp), mean_wt = mean(wt))

# Source: local data frame [8 x 4]
# Groups: cyl [?]

#     cyl  gear  mean_hp  mean_wt
#   <dbl> <dbl>    <dbl>    <dbl>
# 1     4     3  97.0000 2.465000
# 2     4     4  76.0000 2.378125
# 3     4     5 102.0000 1.826500
# 4     6     3 107.5000 3.337500
# 5     6     4 116.5000 3.093750
# 6     6     5 175.0000 2.770000
# 7     8     3 194.1667 4.104083
# 8     8     5 299.5000 3.370000

为了完整起见，您可以使用包plyr并执行以下操作：

library(plyr)
ddply(mtcars,c('cyl','gear'), summarize,mean_hp=mean(hp))