Group by multiple columns in dplyr, using string vector input

I'm trying to transfer my understanding of plyr into dplyr, but I can't figure out how to group by multiple columns.

# make data with weird column names that can't be hard coded
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]

# plyr - works
ddply(data, columns, summarize, value=mean(value))

# dplyr - raises error
data %.%
  group_by(columns) %.%
  summarise(Value = mean(value))
#> Error in eval(expr, envir, enclos) : index out of bounds

What am I missing to translate the plyr example into a dplyr-esque syntax?

Edit 2017: Dplyr has been updated, so a simpler solution is available. See the currently selected answer.

Solution

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.