Can dplyr summarise over several variables without listing each one?

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=dput(structure(list(sex = structure(c(1L, 1L, 2L, 2L), .Label = c("boy",
"girl"), class = "factor"), age = c(52L, 58L, 40L, 62L), bmi = c(25L,
23L, 30L, 26L), chol = c(187L, 220L, 190L, 204L)), .Names = c("sex",
"age", "bmi", "chol"), row.names = c(NA, -4L), class = "data.frame")))


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

41624

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最佳答案

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.