Using nested apply functions instead of nested for loops

Here are four ways to do what the inner instruction does.

First, a dataset example.

set.seed(5345)    # Make the results reproducible
df.i <- matrix(1:400, ncol = 40)
is.na(df.i) <- sample(400, 50)

Now, the comment by @Dave2e: just one for loop, vectorize the inner most one.

df.i2 <- df.i3 <- df.i1 <- df.i    # Work with copies

for (j in 1:ncol(df.i1)) {
  df.i1[,j] <- ifelse(is.na(df.i1[, j]), df.i1[, 39], df.i1[, j])
}

Then, vectorized, no loops at all.

df.i2 <- ifelse(is.na(df.i), df.i[, 39], df.i)

Another vectorized, by @Gregor in the comment, much better since ifelse is known to be relatively slow.

df.i3[is.na(df.i3)] <- df.i3[row(df.i3)[is.na(df.i3)], 39]

And your solution, as posted in the question.

for (j in 1:ncol(df.i)) {
  for (i in 1:nrow(df.i)) {
    df.i[i,j] <- ifelse(is.na(df.i[i,j]), df.i[i,39], df.i[i,j])
  }
}

Compare the results.

identical(df.i, df.i1)
#[1] TRUE

identical(df.i, df.i2)
#[1] TRUE

identical(df.i, df.i3)
#[1] TRUE

Benchmarks.

After the comment by @Gregor I have decided to benchmark the 4 solutions. As expected each optimization gives a significant seep up and his fully vectorized solution is the fastest.

f <- function(df.i){
  for (j in 1:ncol(df.i)) {
    for (i in 1:nrow(df.i)) {
      df.i[i,j] <- ifelse(is.na(df.i[i,j]), df.i[i,39], df.i[i,j])
    }
  }
  df.i
}

f1 <- function(df.i1){
  for (j in 1:ncol(df.i1)) {
    df.i1[,j] <- ifelse(is.na(df.i1[, j]), df.i1[, 39], df.i1[, j])
  }
  df.i1
}

f2 <- function(df.i2){
  df.i2 <- ifelse(is.na(df.i2), df.i2[, 39], df.i2)
  df.i2
}

f3 <- function(df.i3){
  df.i3[is.na(df.i3)] <- df.i3[row(df.i3)[is.na(df.i3)], 39]
  df.i3
}

microbenchmark::microbenchmark(
  two_loops = f(df.i),
  one_loop = f1(df.i1),
  ifelse = f2(df.i2),
  vectorized = f3(df.i3)
)
#Unit: microseconds
#      expr      min        lq       mean    median       uq      max neval
# two_loops 1125.017 1143.4995 1226.93089 1152.5665 1190.599 5209.431   100
#  one_loop  492.945  500.7045  518.73060  504.9435  516.638  678.951   100
#    ifelse   42.269   45.7770   50.55519   48.4140   50.470  198.533   100
#vectorized   12.626   14.5520   16.21975   15.6380   17.663   27.525   100