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

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 3.1.0.9011
+Version: 3.1.0.9012
 Date: 2026-10-30
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# 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.
 

index.Rmd

@@ -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`.
 
 ```{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(dplyr, warn.conflicts = FALSE)
 
-example_isolates %>%
-  mutate(bacteria = mo_fullname()) %>%
+example_isolates |>
+  mutate(bacteria = mo_fullname()) |>
   # filtering functions for microorganisms:
   filter(mo_is_gram_negative(),
-         mo_is_intrinsic_resistant(ab = "cefotax")) %>%
+         mo_is_intrinsic_resistant(ab = "cefotax")) |>
   # antimicrobial selectors:
   select(bacteria,
          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.
 
 ```{r}
-wisca(top_n_microorganisms(example_isolates, n = 10), # 10 most common species
-      antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
-      minimum = 10) # Recommended threshold: >=30
+example_isolates |>
+  top_n_microorganisms(n = 10) |> # 10 most common species
+  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:
 
 ```{r}
-wisca(top_n_microorganisms(example_isolates, n = 10),
-      antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
-      syndromic_group = "ward",
-      minimum = 10) # Recommended threshold: >=30
+example_isolates |>
+  top_n_microorganisms(n = 10) |>
+  wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
+        syndromic_group = "ward",
+        minimum = 10) # Recommended threshold: >=30
 ```
 
 **For AMR surveillance**, traditional antibiograms remain the right tool for tracking resistance per species over time:
 
 ```{r}
-antibiogram(example_isolates,
-            mo_transform = "gramstain",
-            antimicrobials = c("AMC", carbapenems(), "TZP"))
+example_isolates |>
+  antibiogram(mo_transform = "gramstain",
+              antimicrobials = c("AMC", carbapenems(), "TZP"))
 ```
 
 Combination antibiograms show the additional coverage gained by adding a second agent, stratified by species:
 
 ```{r}
-antibiogram(example_isolates,
-            mo_transform = "gramstain",
-            antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
+example_isolates |>
+  antibiogram(mo_transform = "gramstain",
+              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:
 
 ```{r}
-antibiogram(example_isolates,
-            antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
-            mo_transform = "gramstain",
-            ab_transform = "name",
-            language = "uk") # Ukrainian
+example_isolates |>
+  antibiogram(antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
+              mo_transform = "gramstain",
+              ab_transform = "name",
+              language = "uk") # Ukrainian
 ```
 
 ### 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:
 
 ```{r}
-example_isolates %>%
+example_isolates |>
   # group by ward:
-  group_by(ward) %>%
+  group_by(ward) |>
   # calculate AMR using resistance() for gentamicin and tobramycin
   # and get their 95% confidence intervals using sir_confidence_interval():
   summarise(across(c(GEN, TOB),
@@ -192,9 +194,9 @@ Or use [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_s
 library(AMR)
 library(dplyr)
 
-out <- example_isolates %>%
+out <- example_isolates |>
   # group by ward:
-  group_by(ward) %>%
+  group_by(ward) |>
   # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
   summarise(across(c(aminoglycosides(), polymyxins()),
             resistance))
@@ -203,12 +205,12 @@ out
 
 ```{r}
 # transform the antibiotic columns to names:
-out %>% set_ab_names()
+out |> set_ab_names()
 ```
 
 ```{r}
 # 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?

index.md

@@ -154,15 +154,15 @@ selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html),
 which work in base R, `dplyr` and `data.table`.
 
 ``` 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(dplyr, warn.conflicts = FALSE)
 
-example_isolates %>%
-  mutate(bacteria = mo_fullname()) %>%
+example_isolates |>
+  mutate(bacteria = mo_fullname()) |>
   # filtering functions for microorganisms:
   filter(mo_is_gram_negative(),
-         mo_is_intrinsic_resistant(ab = "cefotax")) %>%
+         mo_is_intrinsic_resistant(ab = "cefotax")) |>
   # antimicrobial selectors:
   select(bacteria,
          aminoglycosides(),
@@ -217,9 +217,10 @@ credible intervals via Bayesian simulation. See `vignette("WISCA")` for
 the full explanation.
 
 ``` r
-wisca(top_n_microorganisms(example_isolates, n = 10), # 10 most common species
-      antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
-      minimum = 10) # Recommended threshold: >=30
+example_isolates |>
+  top_n_microorganisms(n = 10) |> # 10 most common species
+  wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
+        minimum = 10) # Recommended threshold: >=30
 ```
 
 | 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:
 
 ``` r
-wisca(top_n_microorganisms(example_isolates, n = 10),
-      antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
-      syndromic_group = "ward",
-      minimum = 10) # Recommended threshold: >=30
+example_isolates |>
+  top_n_microorganisms(n = 10) |>
+  wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
+        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
 #> 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
@@ -252,9 +254,9 @@ wisca(top_n_microorganisms(example_isolates, n = 10),
 for tracking resistance per species over time:
 
 ``` r
-antibiogram(example_isolates,
-            mo_transform = "gramstain",
-            antimicrobials = c("AMC", carbapenems(), "TZP"))
+example_isolates |>
+  antibiogram(mo_transform = "gramstain",
+              antimicrobials = c("AMC", carbapenems(), "TZP"))
 #> 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:
 
 ``` r
-antibiogram(example_isolates,
-            mo_transform = "gramstain",
-            antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
+example_isolates |>
+  antibiogram(mo_transform = "gramstain",
+              antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
 ```
 
 | 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:
 
 ``` r
-antibiogram(example_isolates,
-            antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
-            mo_transform = "gramstain",
-            ab_transform = "name",
-            language = "uk") # Ukrainian
+example_isolates |>
+  antibiogram(antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
+              mo_transform = "gramstain",
+              ab_transform = "name",
+              language = "uk") # Ukrainian
 ```
 
 | Збудник       | Гентаміцин          | Тобраміцин         | Ципрофлоксацин     |
@@ -340,9 +342,9 @@ For a manual approach, you can use the `resistance` or
 `susceptibility()` function:
 
 ``` r
-example_isolates %>%
+example_isolates |>
   # group by ward:
-  group_by(ward) %>%
+  group_by(ward) |>
   # calculate AMR using resistance() for gentamicin and tobramycin
   # and get their 95% confidence intervals using sir_confidence_interval():
   summarise(across(c(GEN, TOB),
@@ -364,9 +366,9 @@ to select a series of antibiotic columns:
 library(AMR)
 library(dplyr)
 
-out <- example_isolates %>%
+out <- example_isolates |>
   # group by ward:
-  group_by(ward) %>%
+  group_by(ward) |>
   # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
   summarise(across(c(aminoglycosides(), polymyxins()),
             resistance))
@@ -390,7 +392,7 @@ out
 
 ``` r
 # transform the antibiotic columns to names:
-out %>% set_ab_names()
+out |> set_ab_names()
 #> # A tibble: 3 × 6
 #>   ward       gentamicin tobramycin amikacin kanamycin colistin
 #>   <chr>           <dbl>      <dbl>    <dbl>     <dbl>    <dbl>
@@ -401,7 +403,7 @@ out %>% set_ab_names()
 
 ``` r
 # transform the antibiotic column to ATC codes:
-out %>% set_ab_names(property = "atc")
+out |> set_ab_names(property = "atc")
 #> # A tibble: 3 × 6
 #>   ward       J01GB03 J01GB01 J01GB06 J01GB04 J01XB01
 #>   <chr>        <dbl>   <dbl>   <dbl>   <dbl>   <dbl>