Às 06:50 de 31/12/2022, Michael Lachanski escreveu:
Hello,
I am trying to make a habit of "functionalizing" all of my code as
recommended by Hadley Wickham. I have found it surprisingly difficult to do
so because several intermediate features from data.table break or give
unexpected results using purrr and its data.table adaptation, tidytable.
Here is the a minimal working example of what has stumped me most recently:
===
library(data.table); library(tidytable)
minimal_failing_function <- function(A){
DT <- data.table(A)
DT[ , A:= shift(A, fill = NA, type = "lag", n = 1)] %>% `[`
return(DT)}
# works
minimal_failing_function(c(1,2))
# fails
tidytable::pmap_dfr(.l = list(c(1,2)),
.f = minimal_failing_function)
===
These should ideally give the same output, but do not. This also fails
using purrr::pmap_dfr rather than tidytable. I am using R 4.2.2 and I am on
Mac OS Ventura 13.1.
Thank you for any help you can provide or general guidance.
==
Michael Lachanski
PhD Student in Demography and Sociology
MA Candidate in Statistics
University of Pennsylvania
mikel...@sas.upenn.edu
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Hello,
Use map_dfr instead of pmap_dfr.
library(data.table)
library(tidytable)
minimal_failing_function <- function(A) {
DT <- data.table(A)
DT[ , A:= shift(A, fill = NA, type = "lag", n = 1)] %>% `[`
return(DT)
}
# works
tidytable::map_dfr(.x = list(c(1,2)),
.f = minimal_failing_function)
#> # A tidytable: 2 × 1
#> A
#> <dbl>
#> 1 NA
#> 2 1
Hope this helps,
Rui Barradas
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
