R code for if-then-do code blocks

this can be dome even faster, and I think more easily read, using only base R

d1 <- data.frame(workshop=rep(1:2,4),
                gender=rep(c("f","m"),each=4))

## needed by vector and rowbased, not needed by rmh
library(tibble)
library(plyr)
library(magrittr)

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
        })},
  rmh={
    data.frame(ID=rownames(d1),
               d1,
               test1=0,
               test2=0,
               test4=0,
               test5=0)
    myRowSubset <- d3$gender=="f" & d3$workshop==1
    test1 <- 1
    d3[myRowSubset, "test1"] <- test1 + 6
    d3[myRowSubset, "test2"] <- test1 + 6 + 2
    d3[myRowSubset, c("test4", "test5")] <- test1
  }
)

Unit: microseconds
     expr      min       lq      mean   median        uq        max neval cld
   vector 1281.994 1468.102  1669.266 1573.043  1750.354   3171.777   100  a
 rowbased 8131.230 8691.899 10894.700 9219.882 10435.642 133293.034   100   b
      rmh  925.571 1056.530  1167.568 1116.425  1221.457   1968.199   100  a
On Mon, Dec 17, 2018 at 12:15 PM Thierry Onkelinx via R-help
<r-help at r-project.org> wrote:

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
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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what the experiment died of. ~ Sir Ronald Aylmer Fisher
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ensure that a reasonable answer can be extracted from a given body of data.
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///////////////////////////////////////////////////////////////////////////////////////////

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Op ma 17 dec. 2018 om 16:30 schreef Paul Miller via R-help <
r-help at 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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