[R] Is there a simple way to analyse all the data using dplyr?

[Eric Berger]

Hi,
I am not sure if the request is about a 'simple way' or requires
dplyr. Here's an approach without using dplyr that is just 2 lines
(not counting creating the data or outputting the result).

n <- 500
myDf <- data.frame( gender=sample(c("Man","Woman","Other"), n, replace = TRUE),
                    GPC_score=rnorm(n), scaleMeasures=runif(n))
aL   <- list(Man="Man",Woman="Woman",All=c("Man","Woman","Other"))
z    <- sapply( 1:length(aL), function(i) { x=myDf[ myDf$gender %in%
aL[[i]], ]; cor(x[,2],x[,3]) } )
names(z) <- names(aL)
z

HTH,
Eric


On Mon, Sep 21, 2020 at 3:13 PM Chris Evans <chrishold using psyctc.org> wrote:
>
> I am sure the answer is "yes" and I'm also sure the question may sound mad. Here's a reprex that I think captures what I'm doing
>
> n <- 500
> gender <- sample(c("Man","Woman","Other"), n, replace = TRUE)
> GPC_score <- rnorm(n)
> scaleMeasures <- runif(n)
> bind_cols(gender = gender,
> GPC_score = GPC_score,
> scaleMeasures = scaleMeasures) -> tibUse
>
> ### let's have the correlation between the two variables broken down by gender
> tibUse %>%
>   filter(gender != "Other") %>%
>   select(gender, GPC_score, scaleMeasures) %>%
>   na.omit() %>%
>   group_by(gender) %>%
>   summarise(cor = cor(cur_data())[1,2]) -> tmp1
>
> ### but I'd also like the correlation for the whole dataset, not by gender
> ### this is a kludge to achieve that which I am using partly because I cant'
> ### find the equivalent of cur_data() for an ungrouped tibble/df
> tibUse %>%
>   mutate(gender = "All") %>% # nasty kludge to get all the data!
>   select(gender, GPC_score, scaleMeasures) %>%
>   na.omit() %>%
>   group_by(gender) %>% # ditto!
>   summarise(cor = cor(cur_data())[1,2]) -> tmp2
>
> bind_rows(tmp1,
>   tmp2)
>
> ### gets me what I want:
> # A tibble: 3 x 2
> gender cor
> <chr> <dbl>
> 1 Man 0.0225
> 2 Woman 0.0685
> 3 All 0.0444
>
> In reality I have some functions that are more complex than cor()[2,1] (sorry about that particular kludge) that digest dataframes and I'd love to have a simpler way of doing this.
>
> So two questions:
> 1) I am sure there a term/function that works on an ungrouped tibble in dplyr as cur_data() does for a grouped tibble ... but I can't find it.
> 2) I suspect someone has automated a way to get the analysis of the complete data after the analyses of the groups within a single dplyr run ... it seems an obvious and common use case, but I can't find that either.
>
> Sorry, I'm over 99% sure I'm being stupid and missing the obvious here ... but that's the recurrent problem I have with my wetware and searchware doesn't seem to being fixing this!
>
> TIA,
>
> Chris
>
> --
> Small contribution in our coronavirus rigours:
> https://www.coresystemtrust.org.uk/home/free-options-to-replace-paper-core-forms-during-the-coronavirus-pandemic/
>
> Chris Evans <chris using psyctc.org> Visiting Professor, University of Sheffield <chris.evans using sheffield.ac.uk>
> I do some consultation work for the University of Roehampton <chris.evans using roehampton.ac.uk> and other places
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>    https://www.psyctc.org/psyctc/
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>
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>
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