This is a method for the tidyr replace_na() generic. It is translated to
data.table::fcoalesce().
Note that unlike tidyr::replace_na(), data.table::fcoalesce() cannot
replace NULL values in lists.
# S3 method for class 'dtplyr_step'
replace_na(data, replace = list())A lazy_dt().
If data is a data frame, replace takes a named list of
values, with one value for each column that has missing values to be
replaced. Each value in replace will be cast to the type of the column
in data that it being used as a replacement in.
If data is a vector, replace takes a single value. This single value
replaces all of the missing values in the vector. replace will be cast
to the type of data.
library(tidyr)
# Replace NAs in a data frame
dt <- lazy_dt(tibble(x = c(1, 2, NA), y = c("a", NA, "b")))
dt %>% replace_na(list(x = 0, y = "unknown"))
#> Source: local data table [3 x 2]
#> Call: copy(`_DT35`)[, `:=`(x = fcoalesce(x, 0), y = fcoalesce(y, "unknown"))]
#>
#> x y
#> <dbl> <chr>
#> 1 1 a
#> 2 2 unknown
#> 3 0 b
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Replace NAs using `dplyr::mutate()`
dt %>% dplyr::mutate(x = replace_na(x, 0))
#> Source: local data table [3 x 2]
#> Call: copy(`_DT35`)[, `:=`(x = fcoalesce(x, 0))]
#>
#> x y
#> <dbl> <chr>
#> 1 1 a
#> 2 2 NA
#> 3 0 b
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results