can I switch the grouping variable in a single dplyr statement?

Question

Here's a simple example to illustrate the issue:

library(data.table)
dt = data.table(a = c(1,1,2,2), b = 1:2)

dt[, c := cumsum(a), by = b][, d := cumsum(a), by = c]
#   a b c d
#1: 1 1 1 1
#2: 1 2 1 2
#3: 2 1 3 2
#4: 2 2 3 4

Attempting to do the same in dplyr I fail because the first group_by is persistent and the grouping is by both b and c:

df = data.frame(a = c(1,1,2,2), b = 1:2)

df %.% group_by(b) %.% mutate(c = cumsum(a)) %.%
       group_by(c) %.% mutate(d = cumsum(a))
#  a b c d
#1 1 1 1 1
#2 1 2 1 1
#3 2 1 3 2
#4 2 2 3 2

Is this a bug or a feature? If it's a feature, then how would one replicate the data.table solution in a single statement?

Answer 1

Try this:

> df %>% group_by(b) %>% mutate(c = cumsum(a)) %>%
+        group_by(c) %>% mutate(d = cumsum(a))
Source: local data frame [4 x 4]
Groups: c

  a b c d
1 1 1 1 1
2 1 2 1 2
3 2 1 3 2
4 2 2 3 4

Update

With newer version of dplyr use %>% rather than %.% and ungroup is no longer needed (as per David Arenburg's comment).