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2026-04-30 08:07:42 +00:00
parent 425f4ad827
commit 3a3027f171
100 changed files with 742 additions and 649 deletions
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reference/as.sir.md
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@@ -69,7 +69,7 @@ as.sir(x, ..., col_mo = NULL,
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"), host = NULL,
language = get_AMR_locale(), verbose = FALSE, info = interactive(),
parallel = FALSE, max_cores = -1, conserve_capped_values = NULL)
parallel = FALSE, conserve_capped_values = NULL)
sir_interpretation_history(clean = FALSE)
```
@@ -348,28 +348,16 @@ disk diffusion diameters:
- parallel:
A [logical](https://rdrr.io/r/base/logical.html) to indicate if
parallel computing must be used, defaults to `FALSE`. The `parallel`
package is part of base R and no additional packages are required. On
Unix/macOS with R \>= 4.0.0,
[`parallel::mclapply()`](https://rdrr.io/r/parallel/mclapply.html)
(fork-based) is used; on Windows and R \< 4.0.0,
[`parallel::parLapply()`](https://rdrr.io/r/parallel/clusterApply.html)
with a PSOCK cluster is used (requires the AMR package to be
installed, not just loaded via `devtools::load_all()`). Parallelism
distributes columns across cores; it is most beneficial when there are
many antibiotic columns and a large number of rows.
- max_cores:
Maximum number of cores to use if `parallel = TRUE`. Use a negative
value to subtract that number from the available number of cores, e.g.
a value of `-2` on an 8-core machine means that at most 6 cores will
be used. Defaults to `-1`. There will never be used more cores than
variables to analyse. The available number of cores are detected using
[`parallelly::availableCores()`](https://parallelly.futureverse.org/reference/availableCores.html)
if that package is installed, and base R's
[`parallel::detectCores()`](https://rdrr.io/r/parallel/detectCores.html)
otherwise.
parallel computing must be used, defaults to `FALSE`. Requires the
[`future.apply`](https://future.apply.futureverse.org/reference/future_lapply.html)
package. **A non-sequential
[`future::plan()`](https://future.futureverse.org/reference/plan.html)
must already be active before setting `parallel = TRUE`** — for
example, `future::plan(future::multisession)`. An error is thrown if
`parallel = TRUE` is used without a plan set by the user. Parallelism
distributes columns (and optionally row batches) across workers; it is
most beneficial when there are many antibiotic columns and a large
number of rows.
- clean:
@@ -425,7 +413,7 @@ The `as.sir()` function can work in four ways:
# for veterinary breakpoints, also set `host`:
your_data %>% mutate_if(is.mic, as.sir, host = "column_with_animal_species", guideline = "CLSI")
# fast processing with parallel computing:
# fast processing with parallel computing (requires future.apply):
as.sir(your_data, ..., parallel = TRUE)
- Operators like "\<=" will be considered according to the
@@ -458,7 +446,7 @@ The `as.sir()` function can work in four ways:
# for veterinary breakpoints, also set `host`:
your_data %>% mutate_if(is.disk, as.sir, host = "column_with_animal_species", guideline = "CLSI")
# fast processing with parallel computing:
# fast processing with parallel computing (requires future.apply):
as.sir(your_data, ..., parallel = TRUE)
4. For **interpreting a complete data set**, with automatic
@@ -679,29 +667,15 @@ sir_interpretation_history()
#> # A tibble: 4 × 18
#> datetime index method ab_given mo_given host_given input_given
#> <dttm> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 2026-04-25 14:25:30 1 MIC amoxicillin Escherich… human 8
#> 2 2026-04-25 14:25:30 1 MIC cipro Escherich… human 0.256
#> 3 2026-04-25 14:25:31 1 DISK tobra Escherich… human 16
#> 4 2026-04-25 14:25:31 1 DISK genta Escherich… human 18
#> 1 2026-04-30 08:03:38 1 MIC amoxicillin Escherich… human 8
#> 2 2026-04-30 08:03:38 1 MIC cipro Escherich… human 0.256
#> 3 2026-04-30 08:03:38 1 DISK tobra Escherich… human 16
#> 4 2026-04-30 08:03:39 1 DISK genta Escherich… human 18
#> # ℹ 11 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>,
#> # outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
#> # breakpoint_S_R <chr>, site <chr>
# \donttest{
# using parallel computing, which is available in base R:
as.sir(df_wide, parallel = TRUE, info = TRUE)
#> ℹ Run `sir_interpretation_history()` afterwards to retrieve a logbook with all
#> details of the breakpoint interpretations.
#>
#> Processing columns:
#>
#> DONE
#>
#> ℹ Run `sir_interpretation_history()` to retrieve a logbook with all details of
#> the breakpoint interpretations.
#> microorganism amoxicillin cipro tobra genta ERY
#> 1 Escherichia coli S I S S R
## Using dplyr -------------------------------------------------
if (require("dplyr")) {