Drop columns per row based on a separate column value - r

Question

Given a dummy data frame that looks like this:
Data1<-rnorm(20, mean=20)
Data2<-rnorm(20, mean=21)
Data3<-rnorm(20, mean=22)
Data4<-rnorm(20, mean=19)
Data5<-rnorm(20, mean=20)
Data6<-rnorm(20, mean=23)
Data7<-rnorm(20, mean=21)
Data8<-rnorm(20, mean=25)
Index<-rnorm(20,mean=5)
DF<-data.frame(Data1,Data2,Data3,Data4,Data5,Data6,Data7,Data8,Index)
What I'd like to do is remove (make NA) certain columns per row based on the Index column. I took the long way and did this to give you an idea of what I'm trying to do:
DF[DF$Index>5.0,8]<-NA
DF[DF$Index>=4.5 & DF$Index<=5.0,7:8]<-NA
DF[DF$Index>=4.0 & DF$Index<=4.5,6:8]<-NA
DF[DF$Index>=3.5 & DF$Index<=4.0,5:8]<-NA
DF[DF$Index>=3.0 & DF$Index<=3.5,4:8]<-NA
DF[DF$Index>=2.5 & DF$Index<=3.0,3:8]<-NA
DF[DF$Index>=2.0 & DF$Index<=2.5,2:8]<-NA
DF[DF$Index<=2.0,1:8]<-NA
This works fine as is, but is not very adaptable. If the number of columns change, or I need to tweak the conditional statements, it's a pain to rewrite the entire code (the actual data set is much larger).
What I would like to do is be able to define a few variables, and then run some sort of loop or apply to do exactly what the lines of code above do.
As an example, in order to replicate my long code, something along the lines of this kind of logic:
NumCol<-8
Max<-5
Min<-2.0
if index > Max, then drop NumCol
if index >= (Max-0.5) & <=Max, than drop NumCol:(NumCol -1)
repeat until reach Min
I don't know if that's the most logical line of reasoning in R, and I'm pretty bad with Looping and apply, so I'm open to any line of thought that can replicate the above long lines of code with the ability to adjust the above variables.

Answer 1

If you don't mind changing your data.frame to a matrix, here is a solution that uses indexing by a matrix. The building of the two-column matrix of indices to drop is a nice review of the apply family of functions:
Seq <- seq(Min, Max, by = 0.5)
col.idx <- lapply(findInterval(DF$Index, Seq) + 1, seq, to = NumCol)
row.idx <- mapply(rep, seq_along(col.idx), sapply(col.idx, length))
drop.idx <- as.matrix(data.frame(unlist(row.idx), unlist(col.idx)))
M <- as.matrix(DF)
M[drop.idx] <- NA

Answer 2

Here is a memory efficient (but I can't claim elegant) data.table solution
It uses the very useful function findInterval to change you less than / greater than loop
#
library(data.table)
DT <- data.table(DF)
# create an index column which 1:8 represent your greater than less than
DT[,IND := findInterval(Index, c(-Inf, seq(2,5,by =0.5 ), Inf))]
# the columns you want to change
changing <- names(DT)[1:8]
setkey(DT, IND)
# loop through the indexes and alter by reference
for(.ind in DT[,unique(IND)]){
# the columns you want to change
.which <- tail(changing, .ind)
# create a call to `:=`(a = as(NA, class(a), b= as(NA, class(b))
pairlist <- mapply(sprintf, .which, .which, MoreArgs = list(fmt = '%s = as(NA,class(%s))'))
char_exp <- sprintf('`:=`( %s )',paste(pairlist, collapse = ','))
.e <- parse(text = char_exp)
DT[J(.ind), eval(.e)]
}