Alternative to a nested for do loop - r

Question

I have a data frame which name is df, of 200+ variables with 300,000+ observations (200+ columns, 300000+ rows)
The end goal of my R code is to find the outlier of each column and replace them with a certain value, say, NA. If the value is already NA, skip and proceed to the next loop
for (j in 1:ncol(df)){
outnumtext <- paste0('out_value <- boxplot.stats(df$',colnames(df[j]),')$out')
eval(parse(text=outnumtext))
for (k in 1:nrow(df)){
replacetext <- paste0('
if ((df[',k,',',j,'] %in% out_value) & !(is.na(df[',k,',',j,']))) {
df[',k,',',j,'] <- NA
} else if (is.na(df[',k,',',j,'])) {
next
} else {
next
}')
eval(parse(text=replacetext))
}
}
I discovered that using the for loop in r and looping through each and every one of the rows in every column, considerably slows down the running. Are there any alternatives to this?
Thank you very much in advance!
Edit P/S: The real code is not just replacing outliers with NA, but has several ways of dealing based on several conditions (where if & if else conditions will be executed accordingly). However my goal is to get a possible alternative in reducing the running time, thus I tried simplifying my original code as much as possible to get to the main point

Answer 1

You don't want to use loops for this. You could try dplyr::mutate_all().
It will still be slow over 300K+ rows, but should be better than the loop.
library(dplyr)
df <- df %>%
mutate_all(funs(ifelse(. %in% boxplot.stats(.)$out, NA, .)))
Example:
exdata <- structure(list(x = c(200, 6, 8, 2, 7, 1, 4, 9, 3, 5, 1000),
y = c(300, 1, 18, 3, 2, 16, 14, 9, 11, 6, 100)),
row.names = c(NA, -11L),
class = "data.frame")
exdata
x y
1 200 300
2 6 1
3 8 18
4 2 3
5 7 2
6 1 16
7 4 14
8 9 9
9 3 11
10 5 6
11 1000 100
data1 %>%
mutate_all(funs(ifelse(. %in% boxplot.stats(.)$out, NA, .)))
x y
1 NA NA
2 6 1
3 8 18
4 2 3
5 7 2
6 1 16
7 4 14
8 9 9
9 3 11
10 5 6
11 NA NA