filtering within the summarise function of dplyr

Question

I am struggling a little with dplyr because I want to do two things at one and wonder if it is possible.

I want to calculate the mean of values and at the same time the mean for the values which have a specific value in an other column.

library(dplyr)
set.seed(1234)
df <- data.frame(id=rep(1:10, each=14),
                 tp=letters[1:14],
                 value_type=sample(LETTERS[1:3], 140, replace=TRUE),
                 values=runif(140))

df %>%
  group_by(id, tp) %>%
  summarise(
    all_mean=mean(values),
    A_mean=mean(values), # Only the values with value_type A
    value_count=sum(value_type == 'A')
  )

So the A_mean column should calculate the mean of values where value_count == 'A'.

I would normally do two separate commands and merge the results later, but I guess there is a more handy way and I just don't get it.

Thanks in advance.

Answer 1

We can try

 df %>%
     group_by(id, tp) %>%
     summarise(all_mean = mean(values), 
                A_mean = mean(values[value_type=="A"]),
                value_count=sum(value_type == 'A'))

Answer 2

You can do this with two summary steps:

df %>%
  group_by(id, tp, value_type) %>%
  summarise(A_mean = mean(values)) %>%
  summarise(all_mean = mean(A_mean),
            A_mean = sum(A_mean * (value_type == "A")),
            value_count = sum(value_type == "A"))

The first summary calculates the means per value_type and the second "sums" only the mean of value_type == "A"