group_by() into fill() not working as expected - r

Question

I'm trying to do a Last Observation Carried Forward operation on some poorly formatted data using dplyr and tidyr. It isn't working as I'd expect.
library(dplyr)
library(tidyr)
df <- data.frame(id=c(1,1,2,2,3,3),
email=c('bob#email.com', NA, 'joe#email.com', NA, NA, NA))
df2 <- df %>% group_by(id) %>% fill(email)
This results in:
Source: local data frame [6 x 2]
Groups: id [3]
id email
(dbl) (fctr)
1 1 bob#email.com
2 1 bob#email.com
3 2 joe#email.com
4 2 joe#email.com
5 3 joe#email.com
6 3 joe#email.com
I expect it to be:
Source: local data frame [6 x 2]
Groups: id [3]
id email
(dbl) (fctr)
1 1 bob#email.com
2 1 bob#email.com
3 2 joe#email.com
4 2 joe#email.com
5 3 NA
6 3 NA
The reason I expect it to be the latter is because of group_by's documentation saying, "The group_by function takes an existing tbl and converts it into a grouped tbl where operations are performed "by group"." The group in this case is determined by the id variable, and the following operation is fill(email). However, it's pretty clearly NOT doing that.
And before anybody asks, it makes no difference if the fields are both character instead of numeric or factor.
UPDATE
#aosmith pointed out this open issue on Github. I'm going to say that there won't be a proper solution to this problem until that issue is resolved. Everything else would just be a workaround. So, if somebody makes a successful PR addressing that issue and posts it here, I'd be happy to mark it as the solution.

Answer 1

Looks like this has been fixed in the development version of tidyr. You now get the expected result per id using fill from tidyr_0.3.1.9000.
df %>% group_by(id) %>% fill(email)
Source: local data frame [6 x 2]
Groups: id [3]
id email
(dbl) (fctr)
1 1 bob#email.com
2 1 bob#email.com
3 2 joe#email.com
4 2 joe#email.com
5 3 NA
6 3 NA

Answer 2

Luckily you can still use zoo::na.locf for this:
df %>%
group_by(id) %>%
mutate(email = zoo::na.locf(email, na.rm = FALSE))
# Source: local data frame [6 x 2]
# Groups: id [3]
#
# id email
# (dbl) (fctr)
# 1 1 bob#email.com
# 2 1 bob#email.com
# 3 2 joe#email.com
# 4 2 joe#email.com
# 5 3 NA
# 6 3 NA

Answer 3

Another option is to use do from dplyr:
df3 <- df %>% group_by(id) %>% do(fill(.,email))

Answer 4

Two questions, does it has be duplicated and do you have to use dplyr and tidyr?
Maybe this could be a solution?
(
bar <- data.frame(id=c(1,1,2,2,3,3),
email=c('bob#email.com', NA, 'joe#email.com', NA, NA, NA))
)
#> id email
#> 1 bob#email.com
#> 1 <NA>
#> 2 joe#email.com
#> 2 <NA>
#> 3 <NA>
#> 3 <NA>
(
foo <- bar[!duplicated(bar$id),]
)
#> id email
#> 1 bob#email.com
#> 2 joe#email.com
#> 3 <NA>

Answer 5

This is kind of ugly, but it is another option that uses dplyr and works with your sample data
df %>%
group_by(id) %>%
mutate(email = email[ !is.na(email) ][1])

Answer 6

I have come across this issue quite a few times, I do worry about using this..
df2 <- df %>% group_by(id) %>% fill(email)
on large data sets as I have had mixed results and found the following work around. The split function used with map_df ensures you apply whatever you are doing to the a specific df for each id and map_df then re binds all the individual df like magic. It has also proved handy in lots of other circumstances. Somewhat obsolete now this issue has been fixed but still a useful alternative that avoids group_by().
df %>% split(.$id) %>% map_df(function(x){ x %>% fill(email)})