[R] R code for if-then-do code blocks

[Thierry Onkelinx]

Dear Paul,

R's power is that is works vectorised. Unlike SAS which is rowbased. Using
R in a SAS way will lead to very slow code.

Your examples can be written vectorised

d1 %>%
  rownames_to_column("ID") %>%
  mutate(
    test1 = ifelse(gender == "f" & workshop == 1, 7, 0),
    test2 = ifelse(gender == "f" & workshop == 1, test1 + 2, 0),
    test4 = ifelse(gender == "f" & workshop == 1, 1, 0),
    test5 = test4
  )

Here is a speed comparison.

library(microbenchmark)
microbenchmark(
  vector = {d1 %>%
    rownames_to_column("ID") %>%
    mutate(
      test1 = ifelse(gender == "f" & workshop == 1, 7, 0),
      test2 = ifelse(gender == "f" & workshop == 1, test1 + 2, 0),
      test4 = ifelse(gender == "f" & workshop == 1, 1, 0),
      test5 = test4
    ) },
  rowbased = {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
        })}
)


Best regards,

Thierry

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
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Op ma 17 dec. 2018 om 16:30 schreef Paul Miller via R-help <
r-help using r-project.org>:

> 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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