Can I pass the grouped portions of a dataframe/tibble to a function in dplyr

Hello,

You can pass a grouped tibble to a function with grouped_modify but the 
function must return a data.frame (or similar).

## this will also do it
#sillyFun <- function(tib){
#? tibble(nrow = nrow(tib), ncol = ncol(tib))
#}


sillyFun <- function(tib){
 ? data.frame(nrow = nrow(tib), ncol = ncol(tib)))
}

tib %>%
 ? group_by(y) %>%
 ? group_modify(~ sillyFun(.))
## A tibble: 3 x 3
## Groups:?? y [3]
#????? y? nrow? ncol
#? <dbl> <int> <int>
#1???? 1??? 17???? 2
#2???? 2??? 21???? 2
#3???? 3??? 12???? 2


Hope this helps,

Rui Barradas

?s 09:43 de 05/07/2020, Chris Evans escreveu:

Apologies if this is a stupid question but searching keeps getting 
things I know and don't need.

What I want to do is to use the group-by() power of dplyr to run 
functions that expect a dataframe/tibble per group but I can't see how 
do it. Here is a reproducible example.

### create trivial tibble
n <- 50
x <- 1:n
y <- sample(1:3, n, replace = TRUE)
z <- rnorm(n)
tib <- as_tibble(cbind(x,y,z))

### create trivial function that expects a tibble/data frame
sillyFun <- function(tib){
return(list(nrow = nrow(tib),
ncol = ncol(tib)))
}

### works fine on the whole tibble
tib %>%
summarise(dim = list(sillyFun(.))) %>%
unnest_wider(dim)

That gives me:
# A tibble: 1 x 2
??? nrow? ncol
?? <int> <int>
1??? 50???? 3


### So I try the following hoping to apply the function to the grouped 
tibble
tib %>%
group_by(y) %>%
summarise(dim = list(sillyFun(.))) %>%
unnest_wider(dim)

### But that gives me:
# A tibble: 3 x 3
?????? y? nrow? ncol
?? <dbl> <int> <int>
1???? 1??? 50???? 3
2???? 2??? 50???? 3
3???? 3??? 50???? 3

Clearly "." is still passing the whole tibble, not the grouped 
subsets.? What I can't find is whether there is an alternative to "." 
that would pass just the grouped subset of the tibble.

I have bodged my way around this by writing a function that takes 
individual columns and reassembles them into a data frame that the 
actual functions I need to use require but that takes me back to a lot 
of clumsiness both selecting the variables to pass in the dplyr call 
to the function and putting the reassemble-to-data-frame bit in the 
function I call.? (The functions I really need are reliability 
explorations and can called on whole dataframes.)

I know I can do this using base R split and lapply but I feel sure it 
must be possible to do this within dplyr/tidyverse.? I'm slowly 
transferring most of my code to the tidyverse and hitting frustrations 
but also finding that it does really help me program more sensibly, 
handle relational data structures more easily, and write code that I 
seem better at reading when I come back to it after months on other 
things so I am slowly trying to move all my coding to tidyverse.? If I 
could see how to do this, it would help.

Very sorry if the answer should be blindingly obvious to me.? I'd also 
love to have pointers to guidance to the tidyverse written for people 
who aren't professional coders or statisticians and that go a bit 
beyond the obvious basics of tidyverse into issues like this.

TIA,

Chris