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Migrate parallel computing in as.sir() from parallel:: to future/future.apply (#280)

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* Migrate parallel computing in as.sir() from parallel:: to future/future.apply

Replace parallel::mclapply() and parallel::parLapply() with
future.apply::future_lapply(), enabling transparent support for any
future backend (multisession, multicore, mirai_multisession, cluster)
on all platforms including Windows.

When parallel = TRUE the function now: (1) respects an active
future::plan() set by the user without overriding it on exit, or
(2) sets a temporary multisession plan with parallelly::availableCores()
and tears it down on exit. The max_cores argument controls worker count
only when no user plan is active.

future and future.apply are added to Suggests in DESCRIPTION.

https://claude.ai/code/session_01M1Jvf2Miu6JL4TQrEh1wS8

* Require user plan() for parallel=TRUE; fix as_wt_nwt false-positive warnings

- parallel = TRUE now errors with a cli-styled message if no non-sequential
  future::plan() is active; users must call e.g. future::plan(future::multisession)
  before using parallel = TRUE (breaking change)
- Removed auto-setup/teardown of multisession plan inside as.sir(), which was
  slow and caused version-mismatch issues with load_all() workflows
- Added as_wt_nwt to the exclusion list in as_sir_method() to suppress
  false-positive "no longer used" warnings during parallel runs
- Fixed pieces_per_col row-batch calculation to use n_workers (total available
  workers from the active plan) instead of n_cores (workers clipped to n_cols),
  so row-batch mode activates correctly when n_cols < n_workers
- Updated @param parallel and @param max_cores roxygen docs; regenerated man/as.sir.Rd
- Updated sequential-mode hint to instruct users to set plan() first

https://claude.ai/code/session_01M1Jvf2Miu6JL4TQrEh1wS8

* fix parallel

* fix parallel

* unit tests

* unit tedts

---------

Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Matthijs Berends
2026-04-30 08:57:19 +01:00
committed by GitHub
parent 3f1b20c304
commit 23beebc6c3
11 changed files with 277 additions and 294 deletions
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118
index.md
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@@ -26,12 +26,9 @@
<div style="display: flex; font-size: 0.8em;">
<p style="text-align:left; width: 50%;">
<small><a href="https://amr-for-r.org/">amr-for-r.org</a></small>
</p>
<p style="text-align:right; width: 50%;">
<small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">doi.org/10.18637/jss.v104.i03</a></small>
</p>
@@ -174,24 +171,26 @@ example_isolates %>%
#> ℹ Using column mo as input for `mo_fullname()`
#> ℹ Using column mo as input for `mo_is_gram_negative()`
#> ℹ Using column mo as input for `mo_is_intrinsic_resistant()`
#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant
#> Phenotypes' v1.2 (2023). This note will be shown once per session.
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> ℹ Determining intrinsic resistance based on 'EUCAST Expected
#> Resistant Phenotypes' v1.2 (2023). This note will be shown
#> once per session.
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
#> # A tibble: 35 × 7
#> bacteria GEN TOB AMK KAN IPM MEM
#> <chr> <sir> <sir> <sir> <sir> <sir> <sir>
#> 1 Pseudomonas aeruginosa I S NA R S NA
#> 2 Pseudomonas aeruginosa I S NA R S NA
#> 3 Pseudomonas aeruginosa I S NA R S NA
#> 4 Pseudomonas aeruginosa S S S R NA S
#> 5 Pseudomonas aeruginosa S S S R S S
#> 6 Pseudomonas aeruginosa S S S R S S
#> 7 Stenotrophomonas maltophilia R R R R R R
#> 8 Pseudomonas aeruginosa S S S R NA S
#> 9 Pseudomonas aeruginosa S S S R NA S
#> 10 Pseudomonas aeruginosa S S S R S S
#> bacteria GEN TOB AMK KAN IPM MEM
#> <chr> <sir> <sir> <sir> <sir> <sir> <sir>
#> 1 Pseudomonas aer… I S NA R S NA
#> 2 Pseudomonas aer… I S NA R S NA
#> 3 Pseudomonas aer… I S NA R S NA
#> 4 Pseudomonas aer… S S S R NA S
#> 5 Pseudomonas aer… S S S R S S
#> 6 Pseudomonas aer… S S S R S S
#> 7 Stenotrophomona… R R R R R R
#> 8 Pseudomonas aer… S S S R NA S
#> 9 Pseudomonas aer… S S S R NA S
#> 10 Pseudomonas aer… S S S R S S
#> # ℹ 25 more rows
```
@@ -215,23 +214,24 @@ output format automatically (such as markdown, LaTeX, HTML, etc.).
``` r
antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems()))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
```
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:---|:---|:---|:---|:---|:---|:---|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
In combination antibiograms, it is clear that combined antimicrobials
yield higher empiric coverage:
@@ -242,10 +242,10 @@ antibiogram(example_isolates,
mo_transform = "gramstain")
```
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|:---|
| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) |
| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) |
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:--------------|:------------------------|:-------------------------------------|:-------------------------------------|
| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) |
| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) |
Like many other functions in this package, `antibiogram()` comes with
support for 28 languages that are often detected automatically based on
@@ -318,16 +318,18 @@ example_isolates %>%
summarise(across(c(GEN, TOB),
list(total_R = resistance,
conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
#> category susceptible. Set the `guideline` argument or the `AMR_guideline`
#> option to either "CLSI" or "EUCAST", see `?AMR-options`.
#> ℹ `resistance()` assumes the EUCAST guideline and thus
#> considers the 'I' category susceptible. Set the `guideline`
#> argument or the `AMR_guideline` option to either "CLSI" or
#> "EUCAST", see `?AMR-options`.
#> ℹ This message will be shown once per session.
#> # A tibble: 3 × 5
#> ward GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int
#> <chr> <dbl> <chr> <dbl> <chr>
#> 1 Clinical 0.229 0.205-0.254 0.315 0.284-0.347
#> 2 ICU 0.290 0.253-0.33 0.400 0.353-0.449
#> 3 Outpatient 0.2 0.131-0.285 0.368 0.254-0.493
#> ward GEN_total_R GEN_conf_int TOB_total_R
#> <chr> <dbl> <chr> <dbl>
#> 1 Clinical 0.229 0.205-0.254 0.315
#> 2 ICU 0.290 0.253-0.33 0.400
#> 3 Outpatient 0.2 0.131-0.285 0.368
#> # ℹ 1 more variable: TOB_conf_int <chr>
```
Or use [antimicrobial
@@ -344,15 +346,16 @@ out <- example_isolates %>%
# calculate AMR using resistance(), over all aminoglycosides and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()),
resistance))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `polymyxins()` using column COL (colistin)
#> Warning: There was 1 warning in `summarise()`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()),
#> resistance)`.
#> ℹ In group 3: `ward = "Outpatient"`.
#> Caused by warning:
#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient"
#> (whilst `minimum = 30`).
#> ! Introducing NA: only 23 results available for KAN in group:
#> ward = "Outpatient" (whilst `minimum = 30`).
out
#> # A tibble: 3 × 6
#> ward GEN TOB AMK KAN COL
@@ -366,11 +369,12 @@ out
# transform the antibiotic columns to names:
out %>% set_ab_names()
#> # A tibble: 3 × 6
#> ward gentamicin tobramycin amikacin kanamycin colistin
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Clinical 0.229 0.315 0.626 1 0.780
#> 2 ICU 0.290 0.400 0.662 1 0.857
#> 3 Outpatient 0.2 0.368 0.605 NA 0.889
#> ward gentamicin tobramycin amikacin kanamycin
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Clinical 0.229 0.315 0.626 1
#> 2 ICU 0.290 0.400 0.662 1
#> 3 Outpatient 0.2 0.368 0.605 NA
#> # ℹ 1 more variable: colistin <dbl>
```
``` r