r data.table lapply with multiple SDcols - r

Question

I am trying to use an lapply that takes two lists of columns into account (all are numeric type) in what would seem to be a simple line of code, but the output is not as expected.
This is my code :
x<-50
measure <- c("haz", "waz", "whz", "htcm", "wtkg", "bmi")
new_measure_1.5 <- paste(measure, "1.5", sep = "_")
new_temp_cols<-paste("temp", new_measure_1.5, sep = "_")
new_columns<-paste(new_measure_1.5, "1", sep="_")
newcols_1.5_months<-function(x, agedays, new_temp_cols, y){
ifelse(agedays==x, new_temp_cols, y)
}
DT[, (new_columns) := lapply(.SD, function(y) newcols_1.5_months(x, agedays, new_temp_cols, y)), .SDcols = new_columns ]
The above code results in new_columns (haz_1.5_1, waz_1.5_1, whz_1.5_1, wtkg_1.5_1, htcm_1.5_1, bmi_1.5_1) holding the names of the columns in the list new_temp_cols (temp_haz_1.5, temp_waz_1.5, temp_whz_1.5, temp_wtkg_1.5, temp_htcm_1.5, temp_bmi_1.5) as opposed to the values they hold in my data table. It seems that R is reading the list as a vector of strings rather than a vector of columns. Why is this?
I've tried using multiple .SDcols, but this doesn't work :
DT[, (new_columns) := lapply(.SD, function(y) newcols_1.5_months(x, agedays, new_temp_cols, y)), .SDcols = c(new_columns, new_temp_cols) ]
Is there a simple fix to this?
**** Editing to add a small subset of dummy data similar to my data table
measure<-c("haz", "waz")
new_measure_1.5 <- paste(measure, "1.5", sep = "_")
new_temp_cols<-paste("temp", new_measure_1.5, sep = "_")
new_columns<-paste(new_measure_1.5, "1", sep="_")
anthro <- data.table
(agedays = c(25,50,53,22,37,50,12,45,50,15,33,50),
temp_haz_1.5 = c(1.2,1.5,1.7,2.0,4.5,6.7,6.8,6.7,4.5,6.6,8.9,6.7),
temp_waz_1.5 = c(3.2,1.8,6.7,2.8,3.5,7.7,9.8,1.7,6.9,3.8,0.9,4.7),
haz_1.5_1 = c(1.2,2.5,4.7,7.0,4.7,6.8,6.3,2.7,5.5,8.6,3.9,6.7),
waz_1.5_1 =c(6.2,2.5,5.7,7.0,2.5,7.7,8.8,9.7,2.5,4.6,5.9,6.7))

Answer 1

If we have multiple columns, then use Map to loop over each of the corresponding sets of columns and apply the function.
library(data.table)
x <- 50
DT[, (new_columns) := Map(function(u, y)
newcols_1.5_months(u, DT[['agedays']], x, y),
.SD[, new_columns, with = FALSE],
.SD[, new_temp_cols, with = FALSE]) ]
Perhaps the function can be
newcols_1.5_months<- function(u, agedays, x, y){
ifelse(agedays==x, u, y)
}
data
DT <- data.table(agedays = c(25,50,53,22,37,50,12,45,50,15,33,50),
temp_haz_1.5 = c(1.2,1.5,1.7,2.0,4.5,6.7,6.8,6.7,4.5,6.6,8.9,6.7),
temp_waz_1.5 = c(3.2,1.8,6.7,2.8,3.5,7.7,9.8,1.7,6.9,3.8,0.9,4.7),
haz_1.5_1 = c(1.2,2.5,4.7,7.0,4.7,6.8,6.3,2.7,5.5,8.6,3.9,6.7),
waz_1.5_1 =c(6.2,2.5,5.7,7.0,2.5,7.7,8.8,9.7,2.5,4.6,5.9,6.7))