Fastest way to replace NAs in a large data.table

Question

I have a large data.table, with many missing values scattered throughout its ~200k rows and 200 columns. I would like to re code those NA values to zeros as efficiently as

Answer 1

My understanding is that the secret to fast operations in R is to utilise vector (or arrays, which are vectors under the hood.)

In this solution I make use of a data.matrix which is an array but behave a bit like a data.frame. Because it is an array, you can use a very simple vector substitution to replace the NAs:

A little helper function to remove the NAs. The essence is a single line of code. I only do this to measure execution time.

remove_na <- function(x){
  dm <- data.matrix(x)
  dm[is.na(dm)] <- 0
  data.table(dm)
}

A little helper function to create a data.table of a given size.

create_dt <- function(nrow=5, ncol=5, propNA = 0.5){
  v <- runif(nrow * ncol)
  v[sample(seq_len(nrow*ncol), propNA * nrow*ncol)] <- NA
  data.table(matrix(v, ncol=ncol))
}

Demonstration on a tiny sample:

library(data.table)
set.seed(1)
dt <- create_dt(5, 5, 0.5)

dt
            V1        V2        V3        V4        V5
[1,]        NA 0.8983897        NA 0.4976992 0.9347052
[2,] 0.3721239 0.9446753        NA 0.7176185 0.2121425
[3,] 0.5728534        NA 0.6870228 0.9919061        NA
[4,]        NA        NA        NA        NA 0.1255551
[5,] 0.2016819        NA 0.7698414        NA        NA

remove_na(dt)
            V1        V2        V3        V4        V5
[1,] 0.0000000 0.8983897 0.0000000 0.4976992 0.9347052
[2,] 0.3721239 0.9446753 0.0000000 0.7176185 0.2121425
[3,] 0.5728534 0.0000000 0.6870228 0.9919061 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.0000000 0.1255551
[5,] 0.2016819 0.0000000 0.7698414 0.0000000 0.0000000