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(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>
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Package: AMR
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Package: AMR
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Version: 3.0.1.9078
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Version: 3.0.1.9079
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Date: 2026-06-27
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Date: 2026-06-27
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Title: Antimicrobial Resistance Data Analysis
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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2
NEWS.md
2
NEWS.md
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# AMR 3.0.1.9078
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# AMR 3.0.1.9079
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Planned as v3.1.0, end of June 2026.
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Planned as v3.1.0, end of June 2026.
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@@ -202,7 +202,7 @@
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#' # data.table --------------------------------------------------------------
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#' # data.table --------------------------------------------------------------
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#'
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#'
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#' # data.table is supported as well, just use it in the same way as with
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#' # data.table is supported as well, just use it in the same way as with
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#' # base R, but add `with = FALSE` if using a single AB selector.
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#' # base R, but add `with = FALSE` if using a single AMR selector.
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#'
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#'
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#' if (require("data.table")) {
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#' if (require("data.table")) {
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#' dt <- as.data.table(example_isolates)
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#' dt <- as.data.table(example_isolates)
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@@ -215,7 +215,7 @@
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#' dt[, carbapenems(), with = FALSE]
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#' dt[, carbapenems(), with = FALSE]
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#' }
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#' }
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#'
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#'
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#' # for multiple selections or AB selectors, `with = FALSE` is not needed:
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#' # for multiple selections or AMR selectors, `with = FALSE` is not needed:
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#' if (require("data.table")) {
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#' if (require("data.table")) {
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#' dt[, c("mo", aminoglycosides())]
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#' dt[, c("mo", aminoglycosides())]
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#' }
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#' }
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@@ -437,7 +437,7 @@ example_isolates[, amr_selector(oral_ddd > 1 & oral_units == "g")]
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# data.table --------------------------------------------------------------
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# data.table --------------------------------------------------------------
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# data.table is supported as well, just use it in the same way as with
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# data.table is supported as well, just use it in the same way as with
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# base R, but add `with = FALSE` if using a single AB selector.
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# base R, but add `with = FALSE` if using a single AMR selector.
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if (require("data.table")) {
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if (require("data.table")) {
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dt <- as.data.table(example_isolates)
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dt <- as.data.table(example_isolates)
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@@ -450,7 +450,7 @@ if (require("data.table")) {
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dt[, carbapenems(), with = FALSE]
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dt[, carbapenems(), with = FALSE]
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}
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}
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# for multiple selections or AB selectors, `with = FALSE` is not needed:
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# for multiple selections or AMR selectors, `with = FALSE` is not needed:
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if (require("data.table")) {
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if (require("data.table")) {
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dt[, c("mo", aminoglycosides())]
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dt[, c("mo", aminoglycosides())]
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}
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}
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@@ -36,21 +36,21 @@ scale_fill_mic(keep_operators = "edges", mic_range = NULL, ...)
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scale_x_sir(
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scale_x_sir(
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colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
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colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
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language = get_AMR_locale(),
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language = get_AMR_locale(),
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eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
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eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
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...
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...
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)
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)
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scale_colour_sir(
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scale_colour_sir(
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colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
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colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
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language = get_AMR_locale(),
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language = get_AMR_locale(),
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eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
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eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
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...
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...
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)
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)
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scale_fill_sir(
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scale_fill_sir(
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colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
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colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
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language = get_AMR_locale(),
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language = get_AMR_locale(),
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eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
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eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
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...
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...
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)
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)
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@@ -88,7 +88,7 @@ test_that("test-amr selectors.R", {
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expect_equal(nrow(example_isolates[any(carbapenems() != "R"), ]), 910, tolerance = 0.5)
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expect_equal(nrow(example_isolates[any(carbapenems() != "R"), ]), 910, tolerance = 0.5)
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expect_equal(nrow(example_isolates[carbapenems() != "R", ]), 704, tolerance = 0.5)
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expect_equal(nrow(example_isolates[carbapenems() != "R", ]), 704, tolerance = 0.5)
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# filter with multiple antibiotic selectors using c()
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# filter with multiple antimicrobial selectors using c()
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expect_equal(nrow(example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]), 26, tolerance = 0.5)
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expect_equal(nrow(example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]), 26, tolerance = 0.5)
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# filter + select in one go: get penicillins in carbapenems-resistant strains
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# filter + select in one go: get penicillins in carbapenems-resistant strains
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@@ -220,9 +220,9 @@ our_data_1st %>%
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count(mo_name(bacteria), sort = TRUE)
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count(mo_name(bacteria), sort = TRUE)
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```
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```
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## Select and filter with antibiotic selectors
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## Select and filter with antimicrobial selectors
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Using so-called antibiotic class selectors, you can select or filter columns based on the antibiotic class that your antibiotic results are in:
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Using so-called antimicrobial class selectors, you can select or filter columns based on the antimicrobial class that your antimicrobial results are in:
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```{r bug_drg 2a}
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```{r bug_drg 2a}
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our_data_1st %>%
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our_data_1st %>%
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@@ -234,7 +234,7 @@ our_data_1st %>%
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our_data_1st %>%
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our_data_1st %>%
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select(bacteria, where(is.sir))
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select(bacteria, where(is.sir))
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# filtering using AB selectors is also possible:
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# filtering using antimicrobial selectors is also possible:
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our_data_1st %>%
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our_data_1st %>%
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filter(any(aminoglycosides() == "R"))
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filter(any(aminoglycosides() == "R"))
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