fix AMR vignette

.github/workflows/check.yaml

@@ -156,4 +156,4 @@ jobs:
         uses: actions/upload-artifact@v2
         with:
           name: artifacts-${{ matrix.config.os }}-r${{ matrix.config.r }}
-          path: ~/AMR.Rcheck
+          path: ${{ github.workspace }}/AMR.Rcheck

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 1.8.1.9043
+Version: 1.8.1.9045
 Date: 2022-08-28
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
@@ -20,6 +20,7 @@ Authors@R: c(
  person(family = "Knight",      c("Gwen"), role = "ctb", comment = c(ORCID = "0000-0002-7263-9896")),
  person(family = "Lenglet",     c("Annick"), role = "ctb", comment = c(ORCID = "0000-0003-2013-8405")),
  person(family = "Meijer",      c("Bart", "C."), role = "ctb"),
+ person(family = "Mykhailenko", c("Dmytro"), role = "ctb"),
  person(family = "Mymrikov",    c("Anton"), role = "ctb"),
  person(family = "Ny",          c("Sofia"), role = "ctb", comment = c(ORCID = "0000-0002-2017-1363")),
  person(family = "Schade",      c("Rogier", "P."), role = "ctb"),

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 1.8.1.9043
+# AMR 1.8.1.9045
 
 ### New
 * EUCAST 2022 and CLSI 2022 guidelines have been added for `as.rsi()`. EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations.

R/mo_matching_score.R

@@ -26,7 +26,7 @@
 #' Calculate the Matching Score for Microorganisms
 #'
 #' This algorithm is used by [as.mo()] and all the [`mo_*`][mo_property()] functions to determine the most probable match of taxonomic records based on user input.
-#' @author Dr Matthijs Berends
+#' @author Dr. Matthijs Berends
 #' @param x Any user input value(s)
 #' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
 #' @section Matching Score for Microorganisms:

data-raw/_pre_commit_hook.R

@@ -29,7 +29,7 @@
 library(dplyr, warn.conflicts = FALSE)
 devtools::load_all(quiet = TRUE)
 
-suppressMessages(set_AMR_locale("en"))
+suppressMessages(set_AMR_locale("English"))
 
 old_globalenv <- ls(envir = globalenv())
 
@@ -473,7 +473,7 @@ rm(list = current_globalenv[!current_globalenv %in% old_globalenv])
 rm(current_globalenv)
 
 devtools::load_all(quiet = TRUE)
-
+suppressMessages(set_AMR_locale("English"))
 
 # Update URLs -------------------------------------------------------------
 usethis::ui_info("Checking URLs for redirects")
@@ -500,3 +500,4 @@ invisible(capture.output(styler::style_dir(
 
 # Finished ----------------------------------------------------------------
 usethis::ui_done("All done")
+suppressMessages(reset_AMR_locale())

data-raw/translations.tsv

@@ -1,6 +1,6 @@
 pattern	regular_expr	case_sensitive	affect_ab_name	affect_mo_name	zh	da	nl	fr	de	el	it	ja	pl	pt	ru	es	sv	tr	uk
 language name English	FALSE	FALSE	FALSE	FALSE	Chinese	Danish	Dutch	French	German	Greek	Italian	Japanese	Polish	Portuguese	Russian	Spanish	Swedish	Turkish	Ukrainian
-language name	FALSE	FALSE	FALSE	FALSE	汉语	Dansk	Nederlands	Français	Deutsch	Ελληνικά	Italiano	日本語	Polski	Português	Русский	Español	Svenska	Türkçe	украї́нська
+language name	FALSE	FALSE	FALSE	FALSE	汉语	Dansk	Nederlands	Français	Deutsch	Ελληνικά	Italiano	日本語	Polski	Português	Русский	Español	Svenska	Türkçe	Українська
 Coagulase-negative Staphylococcus	TRUE	TRUE	FALSE	TRUE	凝固酶阴性葡萄球菌	Koagulase-negative stafylokokker	Coagulase-negatieve Staphylococcus	Staphylococcus à coagulase négative	Koagulase-negative Staphylococcus	Σταφυλόκοκκος με αρνητική πηκτικότητα	Staphylococcus negativo coagulasi	コアグラーゼ陰性ブドウ球菌	Staphylococcus koagulazoujemny	Staphylococcus coagulase negativo	Коагулазоотрицательный стафилококк	Staphylococcus coagulasa negativo	Koagulasnegativa stafylokocker	Koagülaz-negatif Stafilokok	Коагулазонегативний стафілокок
 Coagulase-positive Staphylococcus	TRUE	TRUE	FALSE	TRUE	凝固酶阳性葡萄球菌	Koagulase-positive stafylokokker	Coagulase-positieve Staphylococcus	Staphylococcus à coagulase positif	Koagulase-positive Staphylococcus	Σταφυλόκοκκος θετικός στην πήξη	Staphylococcus positivo coagulasi	コアグラーゼ陽性ブドウ球菌	Staphylococcus koagulazo-dodatni	Staphylococcus coagulase positivo	Коагулазоположительный стафилококк	Staphylococcus coagulasa positivo	Koagulaspositiva stafylokocker	Koagülaz-pozitif Stafilokok	Коагулазопозитивний стафілокок
 Beta-haemolytic Streptococcus	TRUE	TRUE	FALSE	TRUE	β-溶血性链球菌	Beta-haemolytiske streptokokker	Beta-hemolytische Streptococcus	Streptococcus Bêta-hémolytique	Beta-hämolytischer Streptococcus	Β-αιμολυτικός στρεπτόκοκκος	Streptococcus Beta-emolitico	ベータ溶血性レンサ球菌	Streptococcus beta-hemolityczny	Streptococcus Beta-hemolítico	Бета-гемолитический стрептококк	Streptococcus Beta-hemolítico	Beta-hemolytiska streptokocker	Beta-hemolitik Streptokok	Бета-гемолітичний стрептокок

man/mo_matching_score.Rd

@@ -54,5 +54,5 @@ mo_matching_score(
 )
 }
 \author{
-Dr Matthijs Berends
+Dr. Matthijs Berends
 }

vignettes/AMR.Rmd

@@ -569,7 +569,8 @@ plot(disk_values, mo = "E. coli", ab = "cipro")
 And when using the `ggplot2` package, but now choosing the latest implemented CLSI guideline (notice that the EUCAST-specific term "Susceptible, incr. exp." has changed to "Intermediate"):
 
 ```{r disk_plots_mo_ab, message = FALSE, warning = FALSE}
-autoplot(disk_values,
+autoplot(
+  disk_values,
   mo = "E. coli",
   ab = "cipro",
   guideline = "CLSI"
@@ -580,22 +581,22 @@ autoplot(disk_values,
 
 The next example uses the `example_isolates` data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practise AMR data analysis.
 
-We will compare the resistance to fosfomycin (column `FOS`) in hospital A and D. The input for the `fisher.test()` can be retrieved with a transformation like this:
+We will compare the resistance to amoxicillin/clavulanic acid (column `FOS`) between an ICU and other clinical wards. The input for the `fisher.test()` can be retrieved with a transformation like this:
 
 ```{r, results = 'markup'}
 # use package 'tidyr' to pivot data:
 library(tidyr)
 
 check_FOS <- example_isolates %>%
-  filter(ward %in% c("A", "D")) %>% # filter on only hospitals A and D
+  filter(ward %in% c("ICU", "Clinical")) %>% # filter on only these wards
-  select(ward, FOS) %>% # select the hospitals and fosfomycin
+  select(ward, AMC) %>% # select the wards and amoxi/clav
-  group_by(ward) %>% # group on the hospitals
+  group_by(ward) %>% # group on the wards
   count_df(combine_SI = TRUE) %>% # count all isolates per group (ward)
   pivot_wider(
-    names_from = ward, # transform output so A and D are columns
+    names_from = ward, # transform output so "ICU" and "Clinical" are columns
     values_from = value
   ) %>%
-  select(A, D) %>% # and only select these columns
+  select(ICU, Clinical) %>% # and only select these columns
   as.matrix() # transform to a good old matrix for fisher.test()
 
 check_FOS
@@ -608,4 +609,4 @@ We can apply the test now with:
 fisher.test(check_FOS)
 ```
 
-As can be seen, the p value is `r round(fisher.test(check_FOS)$p.value, 3)`, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.
+As can be seen, the p value is practically zero (`r format(fisher.test(check_FOS)$p.value, scientific = FALSE)`), which means that the amoxicillin/clavulanic acid resistance found in isolates between patients in ICUs and other clinical wards are really different.