Hi Michael,
Note that you have to be very careful when using by-reference operations
in data.table (see `?data.table::set`), especially in a functional
programming approach. In your function, you avoid this problem by
calling `data.table(A)` which makes a copy of A even if it is already a
data.table. However, for large data.table-s, copying can be a very
expensive operation (esp. in terms of RAM usage), which can be totally
eliminated by using data.tables in the data.table-way (e.g., joining,
grouping, and aggregating in the same step by performing these
operations within `[`, see `?data.table`).
So instead of blindly functionalizing all your code, try to be
pragmatic. Functional programming is not about using pure functions in
*every* part of your code base, because it is unfeasible in 99.9% of
real-world problems. Even Haskell has `IO` and `do`; the point is that
the imperative and functional parts of the code are clearly separated
and imperative components are (tried to be) as top-level as possible.
So when using data.table, a good strategy is to use pure functions for
performing within-data.table operations, e.g., `DT[, lapply(.SD, mean),
.SDcols = is.numeric]`, and when these operations alter `DT` by
reference, invoke the chains of these operations in "pure" wrappers -
e.g., calling `A <- copy(A)` on the top and then modifying `A` directly.
Cheers,
Denes
Side note: You do not need to use `DT[ , A:= shift(A, fill = NA, type =
"lag", n = 1)] %>% `[`(return(DT))`. `[.data.table` returns the result
(the modified DT) invisibly. If you want to let auto-print work, you can
just use `DT[ , A:= shift(A, fill = NA, type = "lag", n = 1)][]`.
Note that this also means you usually you do not need to use magrittr's
or base-R pipe when transforming data.table-s. You can do this instead:
```
DT[
## filter rows where 'x' column equals "a"
x == "a"
][
## calculate the mean of `z` for each gender and assign it to `y`
, y := mean(z), by = "gender"
][
## do whatever you want
...
]
```
On 12/31/22 13:39, Rui Barradas wrote:
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
______________________________________________
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http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
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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.
