data.table equivalent of tidyr::complete()

tidyr::complete() adds rows to a data.frame for combinations of column values that are missing from the data. Example:

library(dplyr)
library(tidyr)

df <- data.frame(person = c(1,2,2),
                 observation_id = c(1,1,2),
                 value = c(1,1,1))
df %>%
  tidyr::complete(person,
                  observation_id,
                  fill = list(value=0))

yields

# A tibble: 4 × 3
  person observation_id value
   <dbl>          <dbl> <dbl>
1      1              1     1
2      1              2     0
3      2              1     1
4      2              2     1

where the value of the combination person == 1 and observation_id == 2 that is missing in df has been filled in with a value of 0.

What would be the equivalent of this in data.table?

I reckon that the philosophy of data.table entails fewer specially-named functions for tasks than you'll find in the tidyverse, so some extra coding is required, like:

res = setDT(df)[
  CJ(person = person, observation_id = observation_id, unique=TRUE), 
  on=.(person, observation_id)
]

After this, you still have to manually handle the filling of values for missing levels. We can use setnafill to handle this efficiently & by-reference in recent versions of data.table:

setnafill(res, fill = 0, cols = 'value')

See @Jealie's answer regarding a feature that will sidestep this.

Certainly, it's crazy that the column names have to be entered three times here. But on the other hand, one can write a wrapper:

completeDT <- function(DT, cols, defs = NULL){
  mDT = do.call(CJ, c(DT[, ..cols], list(unique=TRUE)))
  res = DT[mDT, on=names(mDT)]
  if (length(defs)) 
    res[, names(defs) := Map(replace, .SD, lapply(.SD, is.na), defs), .SDcols=names(defs)]
  res[]
} 

completeDT(setDT(df), cols = c("person", "observation_id"), defs = c(value = 0))

   person observation_id value
1:      1              1     1
2:      1              2     0
3:      2              1     1
4:      2              2     1

As a quick way of avoiding typing the names three times for the first step, here's @thelatemail's idea:

vars <- c("person","observation_id")
df[do.call(CJ, c(mget(vars), unique=TRUE)), on=vars]

# or with magrittr...
c("person","observation_id") %>% df[do.call(CJ, c(mget(.), unique=TRUE)), on=.]

Update: now you don't need to enter names twice in CJ thanks to @MichaelChirico & @MattDowle for the improvement.

There might be a better answer out there, but this works:

dt[CJ(person=unique(dt$person), 
      observation_id=unique(dt$observation_id)),
   on=c('person','observation_id')]

Which gives:

   person observation_id value
1:      1              1     1
2:      2              1     1
3:      1              2    NA
4:      2              2     1

Now, if you would like to be able to fill with any value (and not NA), I would suggest to wait for the corresponding feature to be finished or contribute to it :)

It is worth noting that the completeDT function above doesn't carry many of the features that tidyr::complete does. In particular, empty factor levels are dropped - unlike tidyr::complete which keeps them. If you do want to keep empty factor the function can be edited as below. The make_vals function below could be made more sophisticated to handle other variable classes eg. full sequence for integers.

library(magrittr)
library(data.table)


dat <- data.frame(
  person = c(1,2,2),
  observation_id = factor(c(1,1,2), 1:3),
  value = c(1,1,1))

dat %>%
  tidyr::complete(
    person, observation_id, fill = list(value=0))
#> # A tibble: 6 x 3
#>   person observation_id value
#>    <dbl> <fct>          <dbl>
#> 1      1 1                  1
#> 2      1 2                  0
#> 3      1 3                  0
#> 4      2 1                  1
#> 5      2 2                  1
#> 6      2 3                  0

completeDT <- function(DT, cols, defs = NULL){
  
  make_vals <- function(col) {
    if(is.factor(col)) factor(levels(col))
    else unique(col)
  }
  
  mDT = do.call(CJ, c(lapply(DT[, ..cols], make_vals), list(unique=TRUE)))
  res = DT[mDT, on=names(mDT)]
  if (length(defs)) 
    res[, names(defs) := Map(replace, .SD, lapply(.SD, is.na), defs), .SDcols=names(defs)]
  res[]
} 

completeDT(DT = setDT(dat), cols = c("person", "observation_id"), defs = c(value = 0))
#>    person observation_id value
#> 1:      1              1     1
#> 2:      1              2     0
#> 3:      1              3     0
#> 4:      2              1     1
#> 5:      2              2     1
#> 6:      2              3     0

^{Created on 2021-03-08 by the reprex package (v0.3.0)}