Identify NA's in sequence row-wise

Question

I want to fill NA values in a sequence, which is row-wise, based on a condition. Please see example below.

ID | Observation 1 | Observation 2 | Observation 3 | Observation 4 | Observation 5
 A         NA              0               1             NA             NA

The condition is:

• all NA values before !NA values in the sequence should be left as NA;
• but all NAs after !NA values in the sequence should be tagged ("remove")

In the example above, NA value in Observation 1 should remain NA. However, the NA values in Observations 4 and 5 should be changed to "Remove".

Answer 1

You can define the function:

replace.na <- function(r,val) {
  i <- is.na(r)
  j <- which(i)
  k <- which(!i)
  r[j[j > k[length(k)]]] <- val
  r
}

Then, assuming that you have a data.frame like so:

r <- data.frame(ID=c('A','B'),obs1=c(NA,1),obs2=c(0,NA),obs3=c(1,2),obs4=c(NA,3),obs5=c(NA,NA))
##  ID obs1 obs2 obs3 obs4 obs5
##1  A   NA    0    1   NA   NA
##2  B    1   NA    2    3   NA

We can apply the function over the rows for all numeric columns of r:

r[,-1] <- t(apply(r[,-1],1,replace.na,999))    
##  ID obs1 obs2 obs3 obs4 obs5
##1  A   NA    0    1  999  999
##2  B    1   NA    2    3  999

This treats r[,-1] as a matrix and the output of apply fills a matrix, which by default is filled by columns. Therefore, we have to transpose the resulting matrix before replacing the columns back into r.

Another way to call replace.na is:

r[,-1] <- do.call(rbind,lapply(data.frame(t(r[,-1])),replace.na,999))

Here, we transpose the numeric columns of r first and make that a data.frame. This makes each row of r a column in the list of columns that is the resulting data frame. Then use lapply over these columns to apply replace.na and rbind the results.

---

If you want to flag all NA's after the first non-NA, then the function replace.na should be:

replace.na <- function(r,val) {
  i <- is.na(r)
  j <- which(i)
  k <- which(!i)
  r[j[j > k[1]]] <- val
  r
}

Applying it to the data:

r[,-1] <- do.call(rbind,lapply(data.frame(t(r[,-1])),replace.na,999))
##  ID obs1 obs2 obs3 obs4 obs5
##1  A   NA    0    1  999  999
##2  B    1  999    2    3  999