Add missing rows to data.table according to multiple keyed columns

A couple of possibilities are here - https://github.com/Rdatatable/data.table/pull/814

CJ.dt = function(...) {
  rows = do.call(CJ, lapply(list(...), function(x) if(is.data.frame(x)) seq_len(nrow(x)) else seq_along(x)));
  do.call(data.table, Map(function(x, y) x[y], list(...), rows))
}

setkey(mydata, name, job, sex, from)

mydata[CJ.dt(unique(data.table(name, job, sex)), unique(from))]
#     name     job    sex from      score
# 1: chris  doctor   male  NYT  0.7383247
# 2: chris  doctor   male   BG         NA
# 3: chris  doctor   male TIME         NA
# 4: chris  doctor   male USAT         NA
# 5: chris  lawyer female  NYT         NA
# 6: chris  lawyer female   BG -0.8204684
# 7: chris  lawyer female TIME         NA
# 8: chris  lawyer female USAT         NA
# 9: chris  lawyer   male  NYT  0.4874291
#10: chris  lawyer   male   BG         NA
#11: chris  lawyer   male TIME         NA
#12: chris  lawyer   male USAT         NA
#13:  john teacher   male  NYT -0.6264538
#14:  john teacher   male   BG -0.8356286
#15:  john teacher   male TIME  1.5952808
#16:  john teacher   male USAT  0.1836433
#17:  mary  police female  NYT         NA
#18:  mary  police female   BG         NA
#19:  mary  police female TIME         NA
#20:  mary  police female USAT  0.3295078

The dev version of tidyr now has an elegant way to do this because the expand() function now supports nesting and crossing:

library(dplyr)

mydata <- data_frame(
  name = c("john","john","john","john","mary","chris","chris","chris"),
  job = c("teacher","teacher","teacher","teacher","police","lawyer","lawyer","doctor"),
  sex = c("male","male","male","male","female","female","male","male"),
  from = c("NYT","USAT","BG","TIME","USAT","BG","NYT","NYT"),
  score = rnorm(8)
)

mydata %>% 
  expand(c(name, job, sex), from) %>% 
  left_join(mydata)

#> Joining by: c("name", "job", "sex", "from")
#> Source: local data frame [20 x 5]
#> 
#>     name     job    sex from      score
#> 1  chris  doctor   male   BG         NA
#> 2  chris  doctor   male  NYT  0.5448206
#> 3  chris  doctor   male TIME         NA
#> 4  chris  doctor   male USAT         NA
#> 5  chris  lawyer female   BG  1.2015173
#> 6  chris  lawyer female  NYT         NA
#> 7  chris  lawyer female TIME         NA
#> 8  chris  lawyer female USAT         NA
#> 9  chris  lawyer   male   BG         NA
#> 10 chris  lawyer   male  NYT -1.0930237
#> 11 chris  lawyer   male TIME         NA
#> 12 chris  lawyer   male USAT         NA
#> 13  john teacher   male   BG  1.1345461
#> 14  john teacher   male  NYT  1.3032946
#> 15  john teacher   male TIME  2.4901830
#> 16  john teacher   male USAT -1.6449096
#> 17  mary  police female   BG         NA
#> 18  mary  police female  NYT         NA
#> 19  mary  police female TIME         NA
#> 20  mary  police female USAT -0.2443080