R function inside C++ loop - c++

Question

I have a double loop in R. It works well, but the problem is that it runs slow with big data frames. So I would like to do the loop in C++ through the Rcpp package, but using an R function inside the loop. The R loop is:
> output2=list()
> for (j in r){
+ for (i in 1:nrow(DF)){
+ output2[[j]][i]=nrow(subset(DF,eval(parse(text=j))))
+ }
+ }
And the output is going to be a list.
An example of DF and r is:
> r
[1] "A==A[i] & B==B[i] " "A==A[i] & C==C[i] "
[3] "B==B[i] & C==C[i] " "A==A[i] & B==B[i] & C==C[i] "
> DF
A B C
1 11 22 88
2 11 22 47
3 2 30 21
4 3 30 21
My question is how I can put the expression in the C++ code. Another question is whether this way is better than make the entire code in C++.
I would be grateful if someone could help me with this issue.
Regards,

Answer 1

For loops aren't necessarily slow in R. It is calling a set of functions a very large number of times, which can be slow (an with more recent versions of R, even that isn't as slow as it was). However, for loops can often be completely avoided by using vectorised code which is many times faster.
In general using eval and parse is not needed and generally an indication that a suboptimal solution is used. In this case (without knowing the complete problem), I am not completely sure how to avoid that. However by writing the loops more efficient a speed gain of over a factor 20 can be gained without using Rcpp.
Generate data
r <- c("A==A[i] & B==B[i]", "A==A[i] & C==C[i] ", "B==B[i] & C==C[i] ",
"A==A[i] & B==B[i] & C==C[i] ")
DF <- read.table(textConnection(" A B C
1 11 22 88
2 11 22 47
3 2 30 21
4 3 30 21"))
DF <- DF[sample(nrow(DF), 1E3, replace=TRUE), ]
Measure time of initial implementation
> system.time({
+ output2=list()
+ for (j in r){
+ for (i in 1:nrow(DF)){
+ output2[[j]][i]=nrow(subset(DF,eval(parse(text=j))))
+ }
+ }
+ })
user system elapsed
1.120 0.007 1.127
Preallocate result; doesn't help much in this case
> system.time({
+ output2=vector(length(r), mode = "list")
+ names(output2) <- r
+ for (j in r){
+ output2[[i]] <- numeric(nrow(DF))
+ for (i in 1:nrow(DF)){
+ output2[[j]][i]=nrow(subset(DF,eval(parse(text=j))))
+ }
+ }
+ })
user system elapsed
1.116 0.000 1.116
subset is not needed as we only need the number of rows. subset ceates a completely new data.frame, which generates overhead
> system.time({
+ output2=vector(length(r), mode = "list")
+ names(output2) <- r
+ for (j in r){
+ output2[[i]] <- numeric(nrow(DF))
+ for (i in 1:nrow(DF)){
+ output2[[j]][i]=sum(eval(parse(text=j), envir = DF))
+ }
+ }
+ })
user system elapsed
0.622 0.003 0.626
Parsing r takes time and is repeated nrow(DF) times, remove form inner loop
> system.time({
+ output2=vector(length(r), mode = "list")
+ names(output2) <- r
+ for (j in r){
+ output2[[i]] <- numeric(nrow(DF))
+ expr <- parse(text=j)
+ for (i in 1:nrow(DF)){
+ output2[[j]][i]=sum(eval(expr, envir = DF))
+ }
+ }
+ })
user system elapsed
0.054 0.000 0.054
A more readable and even faster implementation using dplyr
> library(dplyr)
> system.time({
+ output3 <- DF %>% group_by(A,B) %>% mutate(a = n()) %>%
+ group_by(A,C) %>% mutate(b = n()) %>%
+ group_by(B,C) %>% mutate(c = n()) %>%
+ group_by(A,B,C) %>% mutate(d = n())
+ })
user system elapsed
0.010 0.000 0.009

Answer 2

I would have preferred to post this in comment as it doesn't fully answer the question but I don't have enough reputation to do so.
R is an interpreted language whereas C is a compiled one. Loops are slow in R but your expression output2[[j]][i]=nrow(subset(DF,eval(parse(text=j)))) represents at least 99% of the execution time. Therefore, it won't help to find a way to mix both languages. I advise you to keep both in R and find a way to speed up the process (maybe only one loop with a different expression ?) or find a way to translate your expression to a C one. I know that a lot of basic functions of R are coded in C (as you can see here), maybe it's already the case for nrow, subset and parse.
You can also use LAPACK/BLAS library to speed up some R functions:
LAPACK/BLAS handles matrix math in R. If that's all you need, you can find libraries that are much faster than the vanilla ones in R (you can use some of them in R too to improve performance!).
stated from this topic from stack overflow