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.onkel...@inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
///////////////////////////////////////////////////////////////////////////////////////////
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than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
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ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////
<https://www.inbo.be>
Op ma 17 dec. 2018 om 16:30 schreef Paul Miller via R-help <
r-help@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
> })
>
> ______________________________________________
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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