将空白读取为R数据表中的missing（NA）

Question

I am trying to create a function which can find missing location and impute the missing in a data table. Now this function uses is.na() extensively to find out the missing location and also to replace it with imputation value. It is working fine for all type of variable until input is character type column and have blank cells as missing, because is.na() is not able to identify it as missing hence it skips these cells for imputation.

Example:

    library(data.table)
    t<-data.table(x=c('an','ax','','az'),y=c('bn','','bz','bx'))
          x  y
      1: an bn
      2: ax      
      3:    bz 
      4: az bx
      is.na(t[,x])
      [1] FALSE FALSE FALSE FALSE

where it should be

      [1] FALSE FALSE TRUE FALSE

Any help is highly appreciated.

Thanks.

Answer 1

You can use the fast nzchar like this :

is.na(x) | !nzchar(x) 

For example :

x <- c(NA,'','a')
is.na(x) | !nzchar(x) 
## [1]  TRUE  TRUE FALSE

apply this to OP example:

I wrap this in a function with ifelse :

tt <- data.table(x=c('an','ax','','az'),y=c('bn','','bz','bx'))
tt[, lapply(.SD,
            function(x)
              ifelse(is.na(x) | !nzchar(x),'some value',x)) ]

           x          y
1:         an         bn
2:         ax some value
3: some value         bz
4:         az         bx

Answer 2

Another solution using conditional assignment (using i ):

DT <- data.table(x = c('an','ax','','az',NA),
                 y = c(NA,'bn','','bz','bx'))
DT[x %in% c(NA, ""), x := 'some value']
DT[y %in% c(NA, ""), y := 'some value']

Result:

            x          y
1:         an some value
2:         ax         bn
3: some value some value
4:         az         bz
5: some value         bx