1
0
mirror of https://github.com/msberends/AMR.git synced 2026-06-29 17:36:20 +02:00

(v3.0.1.9079) docs: rename remaining 'antibiotic selectors'/'AB selectors' to 'antimicrobial selectors'/'AMR selectors'

* docs: rename remaining 'antibiotic selectors'/'AB selectors' to 'antimicrobial selectors'/'AMR selectors'

Five leftover occurrences of the old terminology updated in the AMR
vignette (section heading + code comment), the amr_selectors test file,
and two roxygen example comments in amr_selectors.R. The auto-generated
man/antimicrobial_selectors.Rd will update on the next devtools::document() run.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0169LucQ7SHDLHdTnDs1ENhd

* fix

---------

Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Matthijs Berends
2026-06-27 14:31:58 +02:00
committed by GitHub
parent 12cabca29d
commit 03be4b87fc
10 changed files with 13 additions and 13 deletions

View File

@@ -1,5 +1,5 @@
Package: AMR Package: AMR
Version: 3.0.1.9078 Version: 3.0.1.9079
Date: 2026-06-27 Date: 2026-06-27
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)

View File

@@ -1,4 +1,4 @@
# AMR 3.0.1.9078 # AMR 3.0.1.9079
Planned as v3.1.0, end of June 2026. Planned as v3.1.0, end of June 2026.

View File

@@ -202,7 +202,7 @@
#' # data.table -------------------------------------------------------------- #' # data.table --------------------------------------------------------------
#' #'
#' # data.table is supported as well, just use it in the same way as with #' # data.table is supported as well, just use it in the same way as with
#' # base R, but add `with = FALSE` if using a single AB selector. #' # base R, but add `with = FALSE` if using a single AMR selector.
#' #'
#' if (require("data.table")) { #' if (require("data.table")) {
#' dt <- as.data.table(example_isolates) #' dt <- as.data.table(example_isolates)
@@ -215,7 +215,7 @@
#' dt[, carbapenems(), with = FALSE] #' dt[, carbapenems(), with = FALSE]
#' } #' }
#' #'
#' # for multiple selections or AB selectors, `with = FALSE` is not needed: #' # for multiple selections or AMR selectors, `with = FALSE` is not needed:
#' if (require("data.table")) { #' if (require("data.table")) {
#' dt[, c("mo", aminoglycosides())] #' dt[, c("mo", aminoglycosides())]
#' } #' }

Binary file not shown.

Binary file not shown.

Binary file not shown.

View File

@@ -437,7 +437,7 @@ example_isolates[, amr_selector(oral_ddd > 1 & oral_units == "g")]
# data.table -------------------------------------------------------------- # data.table --------------------------------------------------------------
# data.table is supported as well, just use it in the same way as with # data.table is supported as well, just use it in the same way as with
# base R, but add `with = FALSE` if using a single AB selector. # base R, but add `with = FALSE` if using a single AMR selector.
if (require("data.table")) { if (require("data.table")) {
dt <- as.data.table(example_isolates) dt <- as.data.table(example_isolates)
@@ -450,7 +450,7 @@ if (require("data.table")) {
dt[, carbapenems(), with = FALSE] dt[, carbapenems(), with = FALSE]
} }
# for multiple selections or AB selectors, `with = FALSE` is not needed: # for multiple selections or AMR selectors, `with = FALSE` is not needed:
if (require("data.table")) { if (require("data.table")) {
dt[, c("mo", aminoglycosides())] dt[, c("mo", aminoglycosides())]
} }

View File

@@ -36,21 +36,21 @@ scale_fill_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_x_sir( scale_x_sir(
colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"), colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
language = get_AMR_locale(), language = get_AMR_locale(),
eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST", eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
... ...
) )
scale_colour_sir( scale_colour_sir(
colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"), colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
language = get_AMR_locale(), language = get_AMR_locale(),
eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST", eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
... ...
) )
scale_fill_sir( scale_fill_sir(
colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"), colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
language = get_AMR_locale(), language = get_AMR_locale(),
eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST", eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
... ...
) )

View File

@@ -88,7 +88,7 @@ test_that("test-amr selectors.R", {
expect_equal(nrow(example_isolates[any(carbapenems() != "R"), ]), 910, tolerance = 0.5) expect_equal(nrow(example_isolates[any(carbapenems() != "R"), ]), 910, tolerance = 0.5)
expect_equal(nrow(example_isolates[carbapenems() != "R", ]), 704, tolerance = 0.5) expect_equal(nrow(example_isolates[carbapenems() != "R", ]), 704, tolerance = 0.5)
# filter with multiple antibiotic selectors using c() # filter with multiple antimicrobial selectors using c()
expect_equal(nrow(example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]), 26, tolerance = 0.5) expect_equal(nrow(example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]), 26, tolerance = 0.5)
# filter + select in one go: get penicillins in carbapenems-resistant strains # filter + select in one go: get penicillins in carbapenems-resistant strains

View File

@@ -220,9 +220,9 @@ our_data_1st %>%
count(mo_name(bacteria), sort = TRUE) count(mo_name(bacteria), sort = TRUE)
``` ```
## Select and filter with antibiotic selectors ## Select and filter with antimicrobial selectors
Using so-called antibiotic class selectors, you can select or filter columns based on the antibiotic class that your antibiotic results are in: Using so-called antimicrobial class selectors, you can select or filter columns based on the antimicrobial class that your antimicrobial results are in:
```{r bug_drg 2a} ```{r bug_drg 2a}
our_data_1st %>% our_data_1st %>%
@@ -234,7 +234,7 @@ our_data_1st %>%
our_data_1st %>% our_data_1st %>%
select(bacteria, where(is.sir)) select(bacteria, where(is.sir))
# filtering using AB selectors is also possible: # filtering using antimicrobial selectors is also possible:
our_data_1st %>% our_data_1st %>%
filter(any(aminoglycosides() == "R")) filter(any(aminoglycosides() == "R"))