Functional Programming Problem Using purr and R's data.table shift function

Mon, Jan 2, 2023 9:59 AM

D?nes, 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
mikelach at sas.upenn.edu

On Sat, Dec 31, 2022 at 9:22 AM D?nes T?th <toth.denes at 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:

?s 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
mikelach at sas.upenn.edu

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Functional Programming Problem Using purr and R's data.table shift function

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