How to write a SAS macro in R? - r

Question

I want to test the SAS code my team has produced in R to compare the estimates that we get from each but being new to R am not having much luck. In SAS we have written 3 macros to produce three separate estimates (HFS010, HFS011, HFS012), an example of one given here;
%macro HFS010 (peninc_var, pengn_var, pentax_var, pentype_var, HFS010_x_var);
do i = 1 to dim(pentypex);
if &pentype_var = 1 and &pengn_var = 1 then &HFS010_x_var = &peninc_var;
else if &pentype_var = 1 and &pengn_var = 2 then &HFS010_x_var = &peninc_var + &pentax_var;
end;
%mend HFS010;
Basically the idea is that each macro produces an estimate for gross pension income (so where applicable adds tax deducted from pensions on to pension income value). There are three macros as we want separate estimates for cases where pentype = 1 (HFS010), pentype = 2 (HFS011) and pentype = 3 to 7 (HFS012) and the survey accepts up to 16 entries for pensions.
To attempt to produce an equivalent of the above code in R, I wrote the following;
for(i in 1:16) {
pens_data[[paste0("HFS010_",i)]] <- case_when(
pens_data[[paste0("pentype",i)]] == 1 & pens_data[[paste0("pengn",i)]] == 1 ~ pens_data[[paste0("peninc",i)]],
pens_data[[paste0("pentype",i)]] == 1 & pens_data[[paste0("pengn",i)]] == 2 ~ pens_data[[paste0("peninc",i)]] + pens_data[[paste0("pentax",i)]],
TRUE ~ 0)
This code does not produce errors but upon inspecting the estimates, there were some cases that should have estimates that were left blank.
Does anyone know of a way to write a macro in R? I thought of writing a function potentially for each of HFS010, HFS011, HFS012 but being new to R am not sure how to go about this.
If anyone has any suggestions as to why my R code isn't producing the correct estimates, or how they would write the equivalent of a SAS macro in R it would be greatly appreciated! I have tried to use defmacro but could not get this to work without errors.
Thanks so much!
Ashlee

Answer 1

There are many ways to write this in R. But first a copule of comments:
R works fine with vector, so we should as possible manipulate vectors. This is much faster and allows to avoid slow for loop with side effect.
In order to help other to give you answer please provide a reproducible example that cover both uses cases.
For example:
set.seed(1)
dx <- data.frame(
peninc_var=sample(c(1,3),5,TRUE),
pengn_var=sample(c(1,2),5,TRUE),
pentax_var=1:5)
Here an option in base R. I am creating the new variable HFS010_x_var using ifelse :
dx$HFS010_x_var <-
with(dx,{
## I am adding a last NO condition here to assign missing NA
ifelse(peninc_var==1 & pengn_var==1,peninc_var,
ifelse(peninc_var==1 & pengn_var==2,peninc_var + pentax_var,NA))
})
peninc_var pengn_var pentax_var HFS010_x_var
1: 1 2 1 2
2: 1 2 2 3
3: 3 2 3 NA
4: 3 2 4 NA
5: 1 1 5 1
Another option (more sugar syntax ) is to use data.table:
library(data.table)
setDT(dx)
dx[peninc_var==1 & pengn_var==1,HFS010_x_var := peninc_var]
dx[peninc_var==1 & pengn_var==2,HFS010_x_var := peninc_var+pentax_var]