Summarizing multiple columns with dplyr?

Question

I'm struggling a bit with the dplyr-syntax. I have a data frame with different variables and one grouping variable. Now I want to calculate the mean for each column within each group, using dplyr in R. df <- data.frame( a = sample(1:5, n, replace = TRUE), b = sample(1:5, n, replace = TRUE), c = sample(1:5, n, replace = TRUE), d = sample(1:5, n, replace = TRUE), grp = sample(1:3, n, replace = TRUE) ) df %>% group_by(grp) %>% summarise(mean(a))This gives me the mean for column "a" for each group indicated by "grp".My question is: is it possible to get the means for each column within each group at once? Or do I have to repeat `df %>% group_by(grp) %>% summarise(mean(a))` for each column?What I would like to have is something like df %>% group_by(grp) %>% summarise(mean(a:d)) # "mean(a:d)" does not work

Answer 1

You can use `summarise_all()` to get the means of all columns at once--you can think of it like a data-frame-friendly `sapply()`. The code block below demonstrates how to use this function in your group-by case:

```

df %>%

group_by(grp) %>%

summarise_all(mean)

```

Hope this helps!

Answer 2

Thanks for the reproducible example!

library(dplyr)

n = 10

df <- data.frame( a = sample(1:5, n, replace = TRUE),

b = sample(1:5, n, replace = TRUE),

c = sample(1:5, n, replace = TRUE),

d = sample(1:5, n, replace = TRUE),

grp = sample(1:3, n, replace = TRUE) )

#you can include multiple means in summarize!

df_summary <- df%>% group_by(grp) %>%

summarise(mean(a),

mean(b),

mean(c),

mean(d))