学习笔记，仅供参考，有错必纠

准备

df <- data.frame(grade = c("A", "A", "A", "B", "B", "C"), 
                 a = c(1:6), 
                 b = c(6:1),
                 c = c(10:15))
> df
  grade a b  c
1     A 1 6 10
2     A 2 5 11
3     A 3 4 12
4     B 4 3 13
5     B 5 2 14
6     C 6 1 15

distinct()

Select only unique/distinct rows from a data frame. This is similar to ​​unique.data.frame()​​ but considerably faster.

library(dplyr)
df.0 <- distinct(df, grade, .keep_all = T)
> df.0
  grade a b  c
1     A 1 6 10
2     B 4 3 13
3     C 6 1 15

aggregate()

Splits the data into subsets, computes summary statistics for each, and returns the result in a convenient form.

df.1 <- aggregate(df[,-1], list(df$grade), FUN=sum)
> df.1
  Group.1 a  b  c
1       A 6 15 33
2       B 9  5 27
3       C 6  1 15