Can dplyr summarise over several variables without listing each one?
dplyr, r
Solution
The `data.table` idiom is `lapply(.SD, mean)`, which is
DT <- data.table(df)
DT[, lapply(.SD, mean), by = sex]
# sex age bmi chol
# 1: boy 55 24 203.5
# 2: girl 51 28 197.0
I'm not sure of a `dplyr` idiom for the same thing, but you can do something like
dg <- group_by(df, sex)
# the names of the columns you want to summarize
cols <- names(dg)[-1]
# the dots component of your call to summarise
dots <- sapply(cols ,function(x) substitute(mean(x), list(x=as.name(x))))
do.call(summarise, c(list(.data=dg), dots))
# Source: local data frame [2 x 4]
# sex age bmi chol
# 1 boy 55 24 203.5
# 2 girl 51 28 197.0
Note that there is a github issue #178 to efficienctly implement the `plyr` idiom `colwise` in `dplyr`.
Problem
dplyr is amazingly fast, but I wonder if I'm missing something: is it possible summarise over several variables. For example: ``` library(dplyr) library(reshape2) df <- data.frame( sex = factor(rep(c("boy", "girl"), each = 2L)), age = c(52L, 58L, 40L, 62L), bmi = c(25L, 23L, 30L, 26L), chol = c(187L, 220L, 190L, 204L) ) df sex age bmi chol 1 boy 52 25 187 2 boy 58 23 220 3 girl 40 30 190 4 girl 62 26 204 dg=group_by(df,sex) ``` With this small dataframe, it's easy to write ``` summarise(dg,mean(age),mean(bmi),mean(chol)) ``` And I know that to get what I want, I could melt, get the means, and then dcast such as ``` dm=melt(df, id.var='sex') dmg=group_by(dm, sex, variable); x=summarise(dmg, means=mean(value)) dcast(x, sex~variable) ``` But what if I have >20 variables and a very large number of rows. Is there anything similar to .SD in data.table that would allow me to take the means of all variables in the grouped data frame? Or, is it possible to somehow use lapply on the grouped data frame? Thanks for any help