Group by multiple columns in dplyr, using string vector input
Just so as to write the code in full, here's an update on Hadley's answer with the new syntax:
library(dplyr)
df <- data.frame(
asihckhdoydk = sample(LETTERS[1:3], 100, replace=TRUE),
a30mvxigxkgh = sample(LETTERS[1:3], 100, replace=TRUE),
value = rnorm(100)
)
# Columns you want to group by
grp_cols <- names(df)[-3]
# Convert character vector to list of symbols
dots <- lapply(grp_cols, as.symbol)
# Perform frequency counts
df %>%
group_by_(.dots=dots) %>%
summarise(n = n())
output:
Source: local data frame [9 x 3]
Groups: asihckhdoydk
asihckhdoydk a30mvxigxkgh n
1 A A 10
2 A B 10
3 A C 13
4 B A 14
5 B B 10
6 B C 12
7 C A 9
8 C B 12
9 C C 10
Since this question was posted, dplyr added scoped versions of group_by
(documentation here). This lets you use the same functions you would use with select
, like so:
data = data.frame(
asihckhdoydkhxiydfgfTgdsx = sample(LETTERS[1:3], 100, replace=TRUE),
a30mvxigxkghc5cdsvxvyv0ja = sample(LETTERS[1:3], 100, replace=TRUE),
value = rnorm(100)
)
# get the columns we want to average within
columns = names(data)[-3]
library(dplyr)
df1 <- data %>%
group_by_at(vars(one_of(columns))) %>%
summarize(Value = mean(value))
#compare plyr for reference
df2 <- plyr::ddply(data, columns, plyr::summarize, value=mean(value))
table(df1 == df2, useNA = 'ifany')
## TRUE
## 27
The output from your example question is as expected (see comparison to plyr above and output below):
# A tibble: 9 x 3
# Groups: asihckhdoydkhxiydfgfTgdsx [?]
asihckhdoydkhxiydfgfTgdsx a30mvxigxkghc5cdsvxvyv0ja Value
<fctr> <fctr> <dbl>
1 A A 0.04095002
2 A B 0.24943935
3 A C -0.25783892
4 B A 0.15161805
5 B B 0.27189974
6 B C 0.20858897
7 C A 0.19502221
8 C B 0.56837548
9 C C -0.22682998
Note that since dplyr::summarize
only strips off one layer of grouping at a time, you've still got some grouping going on in the resultant tibble (which can sometime catch people by suprise later down the line). If you want to be absolutely safe from unexpected grouping behavior, you can always add %>% ungroup
to your pipeline after you summarize.