Replace NA with previous and next rows mean in R

Question

How could I Replace a NA with mean of its previous and next rows in a fast manner?

  name grade
1    A    56
2    B    NA
3    C    70
4    D    96


        

Answer 1

Assume you have a data.frame df like this:

> df
  name grade
1    A    56
2    B    NA
3    C    70
4    D    96
5    E    NA
6    F    95

Then you can use the following:

> ind <- which(is.na(df$grade))
> df$grade[ind] <- sapply(ind, function(i) with(df, mean(c(grade[i-1], grade[i+1]))))
> df
  name grade
1    A    56
2    B    63
3    C    70
4    D    96
5    E  95.5
6    F    95

Answer 2

Or you may try na.approx from package zoo: "Missing values (NAs) are replaced by linear interpolation"

library(zoo)
x <- c(56, NA, 70, 96)
na.approx(x)
# [1] 56 63 70 96

This also works if you have more than one consecutive NA:

vals <- c(1, NA, NA, 7, NA, 10)
na.approx(vals) 
# [1]  1.0  3.0  5.0  7.0  8.5 10.0

na.approx is based on the base function approx, which may be used instead:

vals <- c(1, NA, NA, 7, NA, 10)
xout <- seq_along(vals)
x <- xout[!is.na(vals)]
y <- vals[!is.na(vals)]

approx(x = x, y = y, xout = xout)$y
# [1]  1.0  3.0  5.0  7.0  8.5 10.0

Answer 3

An alternative solution, using the median instead of mean, is represented by the na.roughfix function of the randomForest package. As described in the documentation, it works with a data frame or numeric matrix. Specifically, for numeric variables, NAs are replaced with column medians. For factor variables, NAs are replaced with the most frequent levels (breaking ties at random). If object contains no NAs, it is returned unaltered.

Using the same examples as @Henrik,

library(randomForest)
x <- c(56, NA, 70, 96) 
na.roughfix(x)

#[1] 56 70 70 96

or with a larger matrix:

y <- matrix(1:50, nrow = 10)
y[sample(1:length(y), 4, replace = FALSE)] <- NA
y
#      [,1] [,2] [,3] [,4] [,5]
# [1,]    1   11   21   31   41
# [2,]    2   12   22   32   42
# [3,]    3   NA   23   33   NA
# [4,]    4   14   24   34   44
# [5,]    5   15   25   35   45
# [6,]    6   16   NA   36   46
# [7,]    7   17   27   37   47
# [8,]    8   18   28   38   48
# [9,]    9   19   29   39   49
# [10,]   10  20   NA   40   50

na.roughfix(y)
#      [,1] [,2] [,3] [,4] [,5]
# [1,]    1   11 21.0   31   41
# [2,]    2   12 22.0   32   42
# [3,]    3   16 23.0   33   46
# [4,]    4   14 24.0   34   44
# [5,]    5   15 25.0   35   45
# [6,]    6   16 24.5   36   46
# [7,]    7   17 27.0   37   47
# [8,]    8   18 28.0   38   48
# [9,]    9   19 29.0   39   49
#[10,]   10   20 24.5   40   50