Replace NA with 0, only in numeric columns in data.table - r

Question

I have a data.table with columns of different data types. My goal is to select only numeric columns and replace NA values within these columns by 0.
I am aware that replacing na-values with zero goes like this:
DT[is.na(DT)] <- 0
To select only numeric columns, I found this solution, which works fine:
DT[, as.numeric(which(sapply(DT,is.numeric))), with = FALSE]
I can achieve what I want by assigning
DT2 <- DT[, as.numeric(which(sapply(DT,is.numeric))), with = FALSE]
and then do:
DT2[is.na(DT2)] <- 0
But of course I would like to have my original DT modified by reference. With the following, however:
DT[, as.numeric(which(sapply(DT,is.numeric))), with = FALSE]
[is.na(DT[, as.numeric(which(sapply(DT,is.numeric))), with = FALSE])]<- 0
I get
"Error in [.data.table([...] i is invalid type (matrix)"
What am I missing?
Any help is much appreciated!!

Answer 1

We can use set
for(j in seq_along(DT)){
set(DT, i = which(is.na(DT[[j]]) & is.numeric(DT[[j]])), j = j, value = 0)
}
Or create a index for numeric columns, loop through it and set the NA values to 0
ind <- which(sapply(DT, is.numeric))
for(j in ind){
set(DT, i = which(is.na(DT[[j]])), j = j, value = 0)
}
data
set.seed(24)
DT <- data.table(v1= c(NA, 1:4), v2 = c(NA, LETTERS[1:4]), v3=c(rnorm(4), NA))

Answer 2

I wanted to explore and possibly improve on the excellent answer given above by #akrun. Here's the data he used in his example:
library(data.table)
set.seed(24)
DT <- data.table(v1= c(NA, 1:4), v2 = c(NA, LETTERS[1:4]), v3=c(rnorm(4), NA))
DT
#> v1 v2 v3
#> 1: NA <NA> -0.5458808
#> 2: 1 A 0.5365853
#> 3: 2 B 0.4196231
#> 4: 3 C -0.5836272
#> 5: 4 D NA
And the two methods he suggested to use:
fun1 <- function(x){
for(j in seq_along(x)){
set(x, i = which(is.na(x[[j]]) & is.numeric(x[[j]])), j = j, value = 0)
}
}
fun2 <- function(x){
ind <- which(sapply(x, is.numeric))
for(j in ind){
set(x, i = which(is.na(x[[j]])), j = j, value = 0)
}
}
I think the first method above is really genius as it exploits the fact that NAs are typed.
First of all, even though .SD is not available in i argument, it is possible to pull the column name with get(), so I thought I could sub-assign data.table this way:
fun3 <- function(x){
nms <- names(x)[sapply(x, is.numeric)]
for(j in nms){
x[is.na(get(j)), (j):=0]
}
}
Generic case, of course would be to rely on .SD and .SDcols to work only on numeric columns
fun4 <- function(x){
nms <- names(x)[sapply(x, is.numeric)]
x[, (nms):=lapply(.SD, function(i) replace(i, is.na(i), 0)), .SDcols=nms]
}
But then I thought to myself "Hey, who says we can't go all the way to base R for this sort of operation. Here's simple lapply() with conditional statement, wrapped into setDT()
fun5 <- function(x){
setDT(
lapply(x, function(i){
if(is.numeric(i))
i[is.na(i)]<-0
i
})
)
}
Finally,we could use the same idea of conditional to limit the columns on which we apply the set()
fun6 <- function(x){
for(j in seq_along(x)){
if (is.numeric(x[[j]]) )
set(x, i = which(is.na(x[[j]])), j = j, value = 0)
}
}
Here are the benchmarks:
microbenchmark::microbenchmark(
for.set.2cond = fun1(copy(DT)),
for.set.ind = fun2(copy(DT)),
for.get = fun3(copy(DT)),
for.SDcol = fun4(copy(DT)),
for.list = fun5(copy(DT)),
for.set.if =fun6(copy(DT))
)
#> Unit: microseconds
#> expr min lq mean median uq max neval cld
#> for.set.2cond 59.812 67.599 131.6392 75.5620 114.6690 4561.597 100 a
#> for.set.ind 71.492 79.985 142.2814 87.0640 130.0650 4410.476 100 a
#> for.get 553.522 569.979 732.6097 581.3045 789.9365 7157.202 100 c
#> for.SDcol 376.919 391.784 527.5202 398.3310 629.9675 5935.491 100 b
#> for.list 69.722 81.932 137.2275 87.7720 123.6935 3906.149 100 a
#> for.set.if 52.380 58.397 116.1909 65.1215 72.5535 4570.445 100 a

Answer 3

You need tidyverse purrr function map_if along with ifelse to do the job in a single line of code.
library(tidyverse)
set.seed(24)
DT <- data.table(v1= sample(c(1:3,NA),20,replace = T), v2 = sample(c(LETTERS[1:3],NA),20,replace = T), v3=sample(c(1:3,NA),20,replace = T))
Below single line code takes a DT with numeric and non numeric columns and operates just on the numeric columns to replace the NAs to 0:
DT %>% map_if(is.numeric,~ifelse(is.na(.x),0,.x)) %>% as.data.table
So, tidyverse can be less verbose than data.table sometimes :-)