ExperimenteR - 1 year ago 87
R Question

dplyr bug with data.table backend [in dplyr 0.4.3 or earlier]

As I was browsing through the answers here, I found this solution works exactly as expected with

`data.frame`
.

``````library(dplyr) # dplyr_0.4.3
library(data.table) # data.table_1.9.5
df <- structure(list(id = c(1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L),
a = c("AA",
"AB", "AA", "AB", "AB", "AB", "AB", "AA", "AA"), b = c(2L, 5L,
1L, 2L, 4L, 4L, 3L, 1L, 4L)), .Names = c("id", "a", "b"),
class = "data.frame", row.names = c(NA, -9L))

df %>%
group_by(id) %>%
mutate(relevance=+(a!='AA')) %>%
mutate(mean=cumsum(relevance * b) / cumsum(relevance))

Source: local data frame [9 x 5]
Groups: id [3]

id     a     b relevance  mean
(int) (chr) (int)     (int) (dbl)
1     1    AA     2         0   NaN
2     1    AB     5         1   5.0
3     1    AA     1         0   5.0
4     2    AB     2         1   2.0
5     2    AB     4         1   3.0
6     3    AB     4         1   4.0
7     3    AB     3         1   3.5
8     3    AA     1         0   3.5
9     3    AA     4         0   3.5
``````

However when run with
`data.table`
, it resulted in something beyond my comprehension.

``````setDT(df) %>%
group_by(id) %>%
mutate(relevance=+(a!='AA')) %>%
mutate(mean=cumsum(relevance * b) / cumsum(relevance))

Source: local data table [9 x 5]

id     a     b relevance     mean
(int) (chr) (int)     (int)    (dbl)
1     1    AA     2         0      NaN
2     1    AB     5         1 5.000000
3     1    AA     1         0 5.000000
4     2    AB     2         1 3.500000
5     2    AB     4         1 3.666667
6     3    AB     4         1 3.750000
7     3    AB     3         1 3.600000
8     3    AA     1         0 3.600000
9     3    AA     4         0 3.600000
``````

Is this an expected behaviour? If so, is there any guideline on when not to use
`data.table`
backend with
`dplyr`
?

The bug that causes grouping to be dropped after `mutate` on a data.table was resolved in 0.5.0.