Hello All,
Season's greetings!
Am trying to replicate some SAS code in R. The SAS code uses if-then-do code
blocks. I've been trying to do likewise in R as that seems to be the most
reliable way to get the same result.
Below is some toy data and some code that does work. There are some things I
don't necessarily like about the code though. So I was hoping some people could
help make it better. One thing I don't like is that the within function
reverses the order of the computed columns such that test1:test5 becomes
test5:test1. I've used a mutate to overcome that but would prefer not to have
to do so.
Another, perhaps very small thing, is the need to calculate an ID variable
that becomes the basis for a grouping.
I did considerable Internet searching for R code that conditionally computes
blocks of code. I didn't find much though and so am wondering if my search
terms were not sufficient or if there is some other reason. It occurred to me
that maybe if-then-do code blocks like we often see in SAS as are frowned upon
and therefore not much implemented.
I'd be interested in seeing more R-compatible approaches if this is the case.
I've learned that it's a mistake to try and make R be like SAS. It's better to
let R be R. Trouble is I'm not always sure how to do that.
Thanks,
Paul
d1 <- data.frame(workshop=rep(1:2,4),
gender=rep(c("f","m"),each=4))
library(tibble)
library(plyr)
d2 <- d1 %>%
rownames_to_column("ID") %>%
mutate(test1 = NA, test2 = NA, test4 = NA, test5 = NA) %>%
ddply("ID",
within,
if (gender == "f" & workshop == 1) {
test1 <- 1
test1 <- 6 + test1
test2 <- 2 + test1
test4 <- 1
test5 <- 1
} else {
test1 <- test2 <- test4 <- test5 <- 0
})
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