Fill missing values by the most prevalent nonmissing value

For each specified column in x replace the missing values by the most prevalent nonmissing value.

fill_by_prevalent(x, ...)

Arguments

x	A data frame.
...	The unquoted column names of the variables that should be filled.

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 = rep(letters[1:3], c(80, 70, 50)) %>% sample,
                  y2 = rep(letters[2:5], c(60, 80, 40, 20)) %>% sample)
x_df %>% pad %>% fill_by_prevalent(y1, y2)
#> pad applied on the interval: day
#> # A tibble: 366 x 3
#>    x          y1    y2   
#>    <date>     <chr> <chr>
#>  1 2016-01-01 a     c    
#>  2 2016-01-02 a     c    
#>  3 2016-01-03 a     c    
#>  4 2016-01-04 a     c    
#>  5 2016-01-05 a     e    
#>  6 2016-01-06 a     c    
#>  7 2016-01-07 b     b    
#>  8 2016-01-08 a     c    
#>  9 2016-01-09 a     c    
#> 10 2016-01-10 c     c    
#> # … with 356 more rows