Replace NAs with specified values

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.

Usage

# S3 method for dtplyr_step
replace_na(data, replace = list())

Arguments

data

A lazy_dt().

replace

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.

Examples

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