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(v3.1.0.9012) Use the native R pipe throughout the examples on the website home page, and fix a typo

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dr. M.S. (Matthijs) Berends committed 2026-10-30 12:52:30 +01:00
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DESCRIPTION
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Package: AMR Package: AMR
Version: 3.1.0.9011 Version: 3.1.0.9012
Date: 2026-10-30 Date: 2026-10-30
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)
NEWS.md
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# AMR 3.1.0.9011 # AMR 3.1.0.9012
This version fixes a critical bug in WISCA that biased all coverage estimates of `wisca()` and `antibiogram(..., wisca = TRUE)` downwards since AMR 3.0.0. Users are strongly advised to re-run their WISCA analyses. This version fixes a critical bug in WISCA that biased all coverage estimates of `wisca()` and `antibiogram(..., wisca = TRUE)` downwards since AMR 3.0.0. Users are strongly advised to re-run their WISCA analyses.
index.Rmd
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@@ -70,15 +70,15 @@ With the help of contributors from all corners of the world, the `AMR` package i
One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html), which work in base R, `dplyr` and `data.table`. One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html), which work in base R, `dplyr` and `data.table`.
```{r} ```{r}
# AMR works great with dplyr, but it's not required or neccesary # AMR works great with dplyr, but it's not required or necessary
library(AMR) library(AMR)
library(dplyr, warn.conflicts = FALSE) library(dplyr, warn.conflicts = FALSE)
example_isolates %>% example_isolates |>
mutate(bacteria = mo_fullname()) %>% mutate(bacteria = mo_fullname()) |>
# filtering functions for microorganisms: # filtering functions for microorganisms:
filter(mo_is_gram_negative(), filter(mo_is_gram_negative(),
mo_is_intrinsic_resistant(ab = "cefotax")) %>% mo_is_intrinsic_resistant(ab = "cefotax")) |>
# antimicrobial selectors: # antimicrobial selectors:
select(bacteria, select(bacteria,
aminoglycosides(), aminoglycosides(),
@@ -94,44 +94,46 @@ The `AMR` package supports four types of antibiograms, with support for `r lengt
**For empirical therapy guidance (i.e., coverage estimates), use WISCA** (Weighted-Incidence Syndromic Combination Antibiogram). When a clinician starts empirical treatment, the causative pathogen is unknown. The relevant question is not *"what percentage of E. coli is susceptible?"* but *"what is the probability that this regimen will cover whatever pathogen is causing the infection?"*. WISCA answers that question directly, weighting susceptibility by pathogen incidence and providing credible intervals via Bayesian simulation. See `vignette("WISCA")` for the full explanation. **For empirical therapy guidance (i.e., coverage estimates), use WISCA** (Weighted-Incidence Syndromic Combination Antibiogram). When a clinician starts empirical treatment, the causative pathogen is unknown. The relevant question is not *"what percentage of E. coli is susceptible?"* but *"what is the probability that this regimen will cover whatever pathogen is causing the infection?"*. WISCA answers that question directly, weighting susceptibility by pathogen incidence and providing credible intervals via Bayesian simulation. See `vignette("WISCA")` for the full explanation.
```{r} ```{r}
wisca(top_n_microorganisms(example_isolates, n = 10), # 10 most common species example_isolates |>
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), top_n_microorganisms(n = 10) |> # 10 most common species
minimum = 10) # Recommended threshold: >=30 wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
minimum = 10) # Recommended threshold: >=30
``` ```
WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates: WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates:
```{r} ```{r}
wisca(top_n_microorganisms(example_isolates, n = 10), example_isolates |>
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), top_n_microorganisms(n = 10) |>
syndromic_group = "ward", wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
minimum = 10) # Recommended threshold: >=30 syndromic_group = "ward",
minimum = 10) # Recommended threshold: >=30
``` ```
**For AMR surveillance**, traditional antibiograms remain the right tool for tracking resistance per species over time: **For AMR surveillance**, traditional antibiograms remain the right tool for tracking resistance per species over time:
```{r} ```{r}
antibiogram(example_isolates, example_isolates |>
mo_transform = "gramstain", antibiogram(mo_transform = "gramstain",
antimicrobials = c("AMC", carbapenems(), "TZP")) antimicrobials = c("AMC", carbapenems(), "TZP"))
``` ```
Combination antibiograms show the additional coverage gained by adding a second agent, stratified by species: Combination antibiograms show the additional coverage gained by adding a second agent, stratified by species:
```{r} ```{r}
antibiogram(example_isolates, example_isolates |>
mo_transform = "gramstain", antibiogram(mo_transform = "gramstain",
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN")) antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
``` ```
Like many other functions in this package, `antibiogram()` and `wisca()` come with support for `r length(AMR:::LANGUAGES_SUPPORTED)` languages that are often detected automatically based on system language: Like many other functions in this package, `antibiogram()` and `wisca()` come with support for `r length(AMR:::LANGUAGES_SUPPORTED)` languages that are often detected automatically based on system language:
```{r} ```{r}
antibiogram(example_isolates, example_isolates |>
antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work antibiogram(antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
mo_transform = "gramstain", mo_transform = "gramstain",
ab_transform = "name", ab_transform = "name",
language = "uk") # Ukrainian language = "uk") # Ukrainian
``` ```
### Interpreting and plotting MIC and SIR values ### Interpreting and plotting MIC and SIR values
@@ -176,9 +178,9 @@ ggplot(data.frame(mic = some_mic_values,
For a manual approach, you can use the `resistance` or `susceptibility()` function: For a manual approach, you can use the `resistance` or `susceptibility()` function:
```{r} ```{r}
example_isolates %>% example_isolates |>
# group by ward: # group by ward:
group_by(ward) %>% group_by(ward) |>
# calculate AMR using resistance() for gentamicin and tobramycin # calculate AMR using resistance() for gentamicin and tobramycin
# and get their 95% confidence intervals using sir_confidence_interval(): # and get their 95% confidence intervals using sir_confidence_interval():
summarise(across(c(GEN, TOB), summarise(across(c(GEN, TOB),
@@ -192,9 +194,9 @@ Or use [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_s
library(AMR) library(AMR)
library(dplyr) library(dplyr)
out <- example_isolates %>% out <- example_isolates |>
# group by ward: # group by ward:
group_by(ward) %>% group_by(ward) |>
# calculate AMR using resistance(), over all aminoglycosides and polymyxins: # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()), summarise(across(c(aminoglycosides(), polymyxins()),
resistance)) resistance))
@@ -203,12 +205,12 @@ out
```{r} ```{r}
# transform the antibiotic columns to names: # transform the antibiotic columns to names:
out %>% set_ab_names() out |> set_ab_names()
``` ```
```{r} ```{r}
# transform the antibiotic column to ATC codes: # transform the antibiotic column to ATC codes:
out %>% set_ab_names(property = "atc") out |> set_ab_names(property = "atc")
``` ```
## What else can you do with this package? ## What else can you do with this package?
index.md
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@@ -154,15 +154,15 @@ selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html),
which work in base R, `dplyr` and `data.table`. which work in base R, `dplyr` and `data.table`.
``` r ``` r
# AMR works great with dplyr, but it's not required or neccesary # AMR works great with dplyr, but it's not required or necessary
library(AMR) library(AMR)
library(dplyr, warn.conflicts = FALSE) library(dplyr, warn.conflicts = FALSE)
example_isolates %>% example_isolates |>
mutate(bacteria = mo_fullname()) %>% mutate(bacteria = mo_fullname()) |>
# filtering functions for microorganisms: # filtering functions for microorganisms:
filter(mo_is_gram_negative(), filter(mo_is_gram_negative(),
mo_is_intrinsic_resistant(ab = "cefotax")) %>% mo_is_intrinsic_resistant(ab = "cefotax")) |>
# antimicrobial selectors: # antimicrobial selectors:
select(bacteria, select(bacteria,
aminoglycosides(), aminoglycosides(),
@@ -217,9 +217,10 @@ credible intervals via Bayesian simulation. See `vignette("WISCA")` for
the full explanation. the full explanation.
``` r ``` r
wisca(top_n_microorganisms(example_isolates, n = 10), # 10 most common species example_isolates |>
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), top_n_microorganisms(n = 10) |> # 10 most common species
minimum = 10) # Recommended threshold: >=30 wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
minimum = 10) # Recommended threshold: >=30
``` ```
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
@@ -230,10 +231,11 @@ WISCA supports stratification by any clinical variable, so you can
generate syndrome-specific or ward-specific coverage estimates: generate syndrome-specific or ward-specific coverage estimates:
``` r ``` r
wisca(top_n_microorganisms(example_isolates, n = 10), example_isolates |>
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), top_n_microorganisms(n = 10) |>
syndromic_group = "ward", wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
minimum = 10) # Recommended threshold: >=30 syndromic_group = "ward",
minimum = 10) # Recommended threshold: >=30
#> Warning: In 3 of 9 WISCA regimen(s) or group(s), the prior weight of 1 per pathogen accounts for more than #> Warning: In 3 of 9 WISCA regimen(s) or group(s), the prior weight of 1 per pathogen accounts for more than
#> 5% of the pathogen incidence (up to 6.4%), since there are few isolates relative to the number of #> 5% of the pathogen incidence (up to 6.4%), since there are few isolates relative to the number of
#> pathogens, i.e., rare pathogens or a small group. Because WISCA gives every pathogen a prior weight #> pathogens, i.e., rare pathogens or a small group. Because WISCA gives every pathogen a prior weight
@@ -252,9 +254,9 @@ wisca(top_n_microorganisms(example_isolates, n = 10),
for tracking resistance per species over time: for tracking resistance per species over time:
``` r ``` r
antibiogram(example_isolates, example_isolates |>
mo_transform = "gramstain", antibiogram(mo_transform = "gramstain",
antimicrobials = c("AMC", carbapenems(), "TZP")) antimicrobials = c("AMC", carbapenems(), "TZP"))
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
``` ```
@@ -267,9 +269,9 @@ Combination antibiograms show the additional coverage gained by adding a
second agent, stratified by species: second agent, stratified by species:
``` r ``` r
antibiogram(example_isolates, example_isolates |>
mo_transform = "gramstain", antibiogram(mo_transform = "gramstain",
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN")) antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
``` ```
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
@@ -282,11 +284,11 @@ come with support for 28 languages that are often detected automatically
based on system language: based on system language:
``` r ``` r
antibiogram(example_isolates, example_isolates |>
antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work antibiogram(antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
mo_transform = "gramstain", mo_transform = "gramstain",
ab_transform = "name", ab_transform = "name",
language = "uk") # Ukrainian language = "uk") # Ukrainian
``` ```
| Збудник | Гентаміцин | Тобраміцин | Ципрофлоксацин | | Збудник | Гентаміцин | Тобраміцин | Ципрофлоксацин |
@@ -340,9 +342,9 @@ For a manual approach, you can use the `resistance` or
`susceptibility()` function: `susceptibility()` function:
``` r ``` r
example_isolates %>% example_isolates |>
# group by ward: # group by ward:
group_by(ward) %>% group_by(ward) |>
# calculate AMR using resistance() for gentamicin and tobramycin # calculate AMR using resistance() for gentamicin and tobramycin
# and get their 95% confidence intervals using sir_confidence_interval(): # and get their 95% confidence intervals using sir_confidence_interval():
summarise(across(c(GEN, TOB), summarise(across(c(GEN, TOB),
@@ -364,9 +366,9 @@ to select a series of antibiotic columns:
library(AMR) library(AMR)
library(dplyr) library(dplyr)
out <- example_isolates %>% out <- example_isolates |>
# group by ward: # group by ward:
group_by(ward) %>% group_by(ward) |>
# calculate AMR using resistance(), over all aminoglycosides and polymyxins: # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()), summarise(across(c(aminoglycosides(), polymyxins()),
resistance)) resistance))
@@ -390,7 +392,7 @@ out
``` r ``` r
# transform the antibiotic columns to names: # transform the antibiotic columns to names:
out %>% set_ab_names() out |> set_ab_names()
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
#> ward gentamicin tobramycin amikacin kanamycin colistin #> ward gentamicin tobramycin amikacin kanamycin colistin
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
@@ -401,7 +403,7 @@ out %>% set_ab_names()
``` r ``` r
# transform the antibiotic column to ATC codes: # transform the antibiotic column to ATC codes:
out %>% set_ab_names(property = "atc") out |> set_ab_names(property = "atc")
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
#> ward J01GB03 J01GB01 J01GB06 J01GB04 J01XB01 #> ward J01GB03 J01GB01 J01GB06 J01GB04 J01XB01
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>