Loop through data.table and create new columns basis some condition

Question

I have a data.table with quite a few columns. I need to loop through them and create new columns using some condition. Currently I am writing separate line of condition for

Answer 1

Your data should probably be in long format:

m = melt(DT, id=c("town","tc"))

Then just write your test once

m[, 
  is_outlier := +(abs(value-mean(value)) > 1.96*sd(value))
, by=.(town, tc, variable)]

I see no outliers in this data (according to the given definition of outlier):

m[, .N, by=is_outlier] # this is a handy alternative to table()

#    is_outlier   N
# 1:          0 160

How it works

• melt keeps the id columns and stacks all the rest into
  • variable (column names)
  • value (column contents)
• +x does the same thing as as.integer(x), coercing TRUE/FALSE to 1/0

---

If you really like your data in wide format, though:

vjs = setdiff(names(DT), c("town","tc"))
DT[, 
  paste0(vjs,".out") := lapply(.SD, function(x) +(abs(x-mean(x)) > 1.96*sd(x)))
, by=.(town, tc), .SDcols=vjs]