Mean per group in a data.frame

Or use group_by & summarise_at from the dplyr package:

library(dplyr)

d %>%
  group_by(Name) %>%
  summarise_at(vars(-Month), funs(mean(., na.rm=TRUE)))

# A tibble: 3 x 3
  Name  Rate1 Rate2
  <fct> <dbl> <dbl>
1 Aira   16.3  47.0
2 Ben    31.3  50.3
3 Cat    44.7  54.0

See ?summarise_at for the many ways to specify the variables to act on. Here, vars(-Month) says all variables except Month.

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In more recent versions of tidyverse/dplyr, using summarise(across(...)) is preferred to summarise_at:

d %>% 
  group_by(Name) %>%
  summarise(across(-Month, mean, na.rm = TRUE))

This type of operation is exactly what aggregate was designed for:

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

Here we aggregate columns 3 and 4 of data.frame d, grouping by d$Name, and applying the mean function.

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Or, using a formula interface:

aggregate(. ~ Name, d[-2], mean)