dplyr: access current group variable - r

Question

After using data.table for quite some time I now thought it's time to try dplyr. It's fun, but I wasn't able to figure out how to access
the current grouping variable
returning multiple values per group
The following example shows is working fine with data.table. How would you write this with dplyr
library(data.table)
foo <- matrix(c(1, 2, 3, 4), ncol = 2)
dt <- data.table(a = c(1, 1, 2), b = c(4, 5, 6))
# data.table (expected)
dt[, .(c = foo[, a]), by = a]
a c
1: 1 1
2: 1 2
3: 2 3
4: 2 4
# dplyr (?)
library(dplyr)
dt %>%
group_by(a) %>%
summarize(c = foo[a])

Answer 1

We can use do from dplyr. (No other packages used). The do is very handy for expanding rows. We only need to wrap with data.frame.
dt %>%
group_by(a) %>%
do(data.frame(c = foo[, unique(.$a)]))
# a c
# <dbl> <dbl>
#1 1 1
#2 1 2
#3 2 3
#4 2 4
Or instead of unique we can subset by the 1st observation
dt %>%
group_by(a) %>%
do(data.frame(c = foo[, .$a[1]]))
# a c
# <dbl> <dbl>
#1 1 1
#2 1 2
#3 2 3
#4 2 4
Or with dplyr >= 1.0.0 (EDIT: Based on #Todd West comments)
dt %>%
reframe(c = foo[, cur_group()$a], .by = 'a')
a c
1 1 1
2 1 2
3 2 3
4 2 4
This can be also done without using any packages
stack(lapply(split(dt$a, dt$a), function(x) foo[,unique(x)]))[2:1]
# ind values
#1 1 1
#2 1 2
#3 2 3
#4 2 4

Answer 2

You can still access the group variable but it is like a normal vector with one unique value for each group, so if you put unique around it, it will work. And at same time, dplyr does not seem to expand rows like data.table automatically, you will need the unnest from tidyr package:
library(dplyr); library(tidyr)
dt %>%
group_by(a) %>%
summarize(c = list(foo[,unique(a)])) %>%
unnest()
# Source: local data frame [4 x 2]
# a c
# <dbl> <dbl>
# 1 1 1
# 2 1 2
# 3 2 3
# 4 2 4
Or we can use first to speed up, since we've already know the group variable vector is the same for every group:
dt %>%
group_by(a) %>%
summarize(c = list(foo[,first(a)])) %>%
unnest()
# Source: local data frame [4 x 2]
# a c
# <dbl> <dbl>
# 1 1 1
# 2 1 2
# 3 2 3
# 4 2 4

Answer 3

To access a grouping variable in a grouped operation (map, walk, mutate), we can refer to .y which is exposed automatically within the evaluation context.
Example
> iris %>% group_by(Species) %>% group_walk(~{ print(.y) })
# A tibble: 1 x 1
Species
<fct>
1 setosa
# A tibble: 1 x 1
Species
<fct>
1 versicolor
# A tibble: 1 x 1
Species
<fct>
1 virginica
This is also documented with more details in https://dplyr.tidyverse.org/reference/group_map.html
The key, a tibble with exactly one row and columns for each grouping variable, exposed as .y.
Regarding the other proposed solutions: Afaik do is not recommended any longer, and the other solution with unqiue is imho clumsy (as it requires another reference to the dataframe in question).