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
Às 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
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