Dénes, thank you for the guidance - which is well-taken.
Your side note raises an interesting question: I find the piping %>%
operator readable. Is there any downside to it? Or is the side note meant
to tell me to drop the last: "%>% `[`"?
Thank you,
==
Michael Lachanski
PhD Student in Demography and Sociology
MA Candidate in Statistics
University of Pennsylvania
mikel...@sas.upenn.edu
On Sat, Dec 31, 2022 at 9:22 AM Dénes Tóth <toth.de...@kogentum.hu> wrote:
> 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
> >>
> >> [[alternative HTML version deleted]]
> >>
> >> ______________________________________________
> >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >>
> https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!IBzWLUs!VdfzdJ15GLScUok_hiqL3DvTJ20Ce8JMBkQ1NosBfyOvu68iuQkh9nsPZuUBbB9BtrsZBh86OjGyyj3lAB2g_xXCvB6t$
> >> PLEASE do read the posting guide
> >>
> https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!IBzWLUs!VdfzdJ15GLScUok_hiqL3DvTJ20Ce8JMBkQ1NosBfyOvu68iuQkh9nsPZuUBbB9BtrsZBh86OjGyyj3lAB2g_3rS2yQK$
> >> and provide commented, minimal, self-contained, reproducible code.
> > 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
> >
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> > PLEASE do read the posting guide
> >
> https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!IBzWLUs!VdfzdJ15GLScUok_hiqL3DvTJ20Ce8JMBkQ1NosBfyOvu68iuQkh9nsPZuUBbB9BtrsZBh86OjGyyj3lAB2g_3rS2yQK$
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
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