d <- read.table(text=
'Name Month Rate1 Rate2
Aira 1 12 23
Aira 2 18 73
Aira 3 19 45
Ben 1 53 19
Ben 2 22 87
Ben 3 19 45
Cat 1 22 87
Cat 2 67 43
Cat 3 45 32', header=TRUE)
aggregate(d[, 3:4], list(d$Name), mean)
Group.1 Rate1 Rate2
1 Aira 16.33333 47.00000
2 Ben 31.33333 50.33333
3 Cat 44.66667 54.00000
# several summary columns with arbitrary names
library(dplyr)
mtcars %>%
group_by(cyl, gear) %>% # multiple group columns
summarise(sum_hp = sum(hp), mean_mpg = mean(mpg)) # multiple summary columns
# summarise all columns except grouping columns using "sum"
mtcars %>%
group_by(cyl) %>%
summarise(across(everything(), sum))
# summarise all columns except grouping columns using "sum" and "mean"
mtcars %>%
group_by(cyl) %>%
summarise(across(everything(), list(mean = mean, sum = sum)))
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