Filling in NAs with last non-NA value

Problem

You want to replace NA’s in a vector or factor with the last non-NA value.

Solution

This code shows how to fill gaps in a vector. If you need to do this repeatedly, see the function below. The function also can fill in leading NA’s with the first good value and handle factors properly.

  1. # Sample data
  2. x <- c(NA,NA, "A","A", "B","B","B", NA,NA, "C", NA,NA,NA, "A","A","B", NA,NA)
  4. goodIdx <- !is.na(x)
  5. goodIdx
  6. #>  [1] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
  7. #> [15]  TRUE  TRUE FALSE FALSE
  9. # These are the non-NA values from x only
  10. # Add a leading NA for later use when we index into this vector
  11. goodVals <- c(NA, x[goodIdx])
  12. goodVals
  13. #>  [1] NA  "A" "A" "B" "B" "B" "C" "A" "A" "B"
  15. # Fill the indices of the output vector with the indices pulled from
  16. # these offsets of goodVals. Add 1 to avoid indexing to zero.
  17. fillIdx <- cumsum(goodIdx)+1
  18. fillIdx
  19. #>  [1]  1  1  2  3  4  5  6  6  6  7  7  7  7  8  9 10 10 10
  21. # The original vector with gaps filled
  22. goodVals[fillIdx]
  23. #>  [1] NA  NA  "A" "A" "B" "B" "B" "B" "B" "C" "C" "C" "C" "A" "A" "B" "B" "B"

A function for filling gaps

This function does the same as the code above. It can also fill leading NA’s with the first good value, and handle factors properly.

  1. fillNAgaps <- function(x, firstBack=FALSE) {
  2.     ## NA's in a vector or factor are replaced with last non-NA values
  3.     ## If firstBack is TRUE, it will fill in leading NA's with the first
  4.     ## non-NA value. If FALSE, it will not change leading NA's.
  6.     # If it's a factor, store the level labels and convert to integer
  7.     lvls <- NULL
  8.     if (is.factor(x)) {
  9.         lvls <- levels(x)
  10.         x    <- as.integer(x)
  11.     }
  13.     goodIdx <- !is.na(x)
  15.     # These are the non-NA values from x only
  16.     # Add a leading NA or take the first good value, depending on firstBack   
  17.     if (firstBack)   goodVals <- c(x[goodIdx][1], x[goodIdx])
  18.     else             goodVals <- c(NA,            x[goodIdx])
  20.     # Fill the indices of the output vector with the indices pulled from
  21.     # these offsets of goodVals. Add 1 to avoid indexing to zero.
  22.     fillIdx <- cumsum(goodIdx)+1
  24.     x <- goodVals[fillIdx]
  26.     # If it was originally a factor, convert it back
  27.     if (!is.null(lvls)) {
  28.         x <- factor(x, levels=seq_along(lvls), labels=lvls)
  29.     }
  31.     x
  32. }
  36. # Sample data
  37. x <- c(NA,NA, "A","A", "B","B","B", NA,NA, "C", NA,NA,NA, "A","A","B", NA,NA)
  38. x
  39. #>  [1] NA  NA  "A" "A" "B" "B" "B" NA  NA  "C" NA  NA  NA  "A" "A" "B" NA  NA
  41. fillNAgaps(x)
  42. #>  [1] NA  NA  "A" "A" "B" "B" "B" "B" "B" "C" "C" "C" "C" "A" "A" "B" "B" "B"
  44. # Fill the leading NA's with the first good value
  45. fillNAgaps(x, firstBack=TRUE)
  46. #>  [1] "A" "A" "A" "A" "B" "B" "B" "B" "B" "C" "C" "C" "C" "A" "A" "B" "B" "B"
  48. # It also works on factors
  49. y <- factor(x)
  50. y
  51. #>  [1] <NA> <NA> A    A    B    B    B    <NA> <NA> C    <NA> <NA> <NA> A    A    B    <NA>
  52. #> [18] <NA>
  53. #> Levels: A B C
  55. fillNAgaps(y)
  56. #>  [1] <NA> <NA> A    A    B    B    B    B    B    C    C    C    C    A    A    B    B   
  57. #> [18] B   
  58. #> Levels: A B C

Notes

This is adapted from na.locf() in the zoo library.