Fill missing values by a single value

Replace all missing values in the specified columns by the same value.

fill_by_value(x, ..., value = 0)

Arguments

x	A data frame.
...	The unquoted column names of the variables that should be filled.
value	The value to replace the missing values by.

Value

x with the altered columns.

Examples

library(dplyr) # for the pipe operator
x <- seq(as.Date('2016-01-01'), by = 'day', length.out = 366)
x <- x[sample(1:366, 200)] %>% sort
x_df <- data_frame(x  = x,
                   y1 = runif(200, 10, 20) %>% round,
                   y2 = runif(200, 1, 50) %>% round,
                   y3 = runif(200, 20, 40) %>% round,
                   y4 = sample(letters[1:5], 200, replace = TRUE))
x_padded <- x_df %>% pad
#> pad applied on the interval: day
x_padded %>% fill_by_value(y1)
#> # A tibble: 365 x 5
#>    x             y1    y2    y3 y4   
#>    <date>     <dbl> <dbl> <dbl> <chr>
#>  1 2016-01-01    19     4    22 d    
#>  2 2016-01-02     0    NA    NA NA   
#>  3 2016-01-03     0    NA    NA NA   
#>  4 2016-01-04     0    NA    NA NA   
#>  5 2016-01-05     0    NA    NA NA   
#>  6 2016-01-06    13    50    25 d    
#>  7 2016-01-07    19    22    25 d    
#>  8 2016-01-08     0    NA    NA NA   
#>  9 2016-01-09    18    29    21 b    
#> 10 2016-01-10    15    41    24 c    
#> # … with 355 more rows
x_df %>% pad %>% fill_by_value(y1, y2, value = 42)
#> pad applied on the interval: day
#> # A tibble: 365 x 5
#>    x             y1    y2    y3 y4   
#>    <date>     <dbl> <dbl> <dbl> <chr>
#>  1 2016-01-01    19     4    22 d    
#>  2 2016-01-02    42    42    NA NA   
#>  3 2016-01-03    42    42    NA NA   
#>  4 2016-01-04    42    42    NA NA   
#>  5 2016-01-05    42    42    NA NA   
#>  6 2016-01-06    13    50    25 d    
#>  7 2016-01-07    19    22    25 d    
#>  8 2016-01-08    42    42    NA NA   
#>  9 2016-01-09    18    29    21 b    
#> 10 2016-01-10    15    41    24 c    
#> # … with 355 more rows