improve as.mo()

.Rbuildignore

@@ -30,6 +30,7 @@
 ^vignettes/datasets\.Rmd$
 ^vignettes/EUCAST\.Rmd$
 ^vignettes/MDR\.Rmd$
+^vignettes/other_pkg.*\.Rmd$
 ^vignettes/PCA\.Rmd$
 ^vignettes/resistance_predict\.Rmd$
 ^vignettes/SPSS\.Rmd$

.github/workflows/website.yaml

@@ -65,7 +65,11 @@ jobs:
       - name: Set up R dependencies
         uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::pkgdown
+          # add extra packages for website articles:
+          extra-packages: |
+            any::pkgdown
+            any::tidymodels
+            any::data.table
 
       # Send updates to repo using GH Actions bot
       - name: Create website in separate branch

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.0.0.9018
+Version: 2.0.0.9019
-Date: 2023-05-17
+Date: 2023-05-24
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.0.0.9018
+# AMR 2.0.0.9019
 
 ## Changed
 * Added oxygen tolerance to over 25,000 bacteria in the `microorganisms` data set
@@ -12,6 +12,7 @@
 * Fixed some WHONET codes for microorganisms and consequently a couple of entries in `clinical_breakpoints`
 * Fixed a bug for `as.mo()` that led to coercion of `NA` values when using custom microorganism codes
 * Fixed usage of `icu_exclude` in `first_isolates()`
+* Improved `as.mo()` algorithm for searching on only species names
 
 
 # AMR 2.0.0

R/ab_property.R

@@ -69,7 +69,7 @@
 #' ab_atc_group2("AMX")
 #' ab_url("AMX")
 #'
-#' # smart lowercase tranformation
+#' # smart lowercase transformation
 #' ab_name(x = c("AMC", "PLB"))
 #' ab_name(x = c("AMC", "PLB"), tolower = TRUE)
 #'

R/av_property.R

@@ -61,7 +61,7 @@
 #' av_group("ACI")
 #' av_url("ACI")
 #'
-#' # smart lowercase tranformation
+#' # lowercase transformation
 #' av_name(x = c("ACI", "VALA"))
 #' av_name(x = c("ACI", "VALA"), tolower = TRUE)
 #'

R/first_isolate.R

@@ -191,13 +191,14 @@ first_isolate <- function(x = NULL,
   }
   meet_criteria(col_specimen, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
   if (is.logical(col_icu)) {
-    meet_criteria(col_icu, allow_class = "logical", has_length = c(1, nrow(x)), allow_NA = TRUE)
+    meet_criteria(col_icu, allow_class = "logical", has_length = c(1, nrow(x)), allow_NA = TRUE, allow_NULL = TRUE)
     x$newvar_is_icu <- col_icu
   } else if (!is.null(col_icu)) {
+    # add "logical" to the allowed classes here, since it may give an error in certain user input, and should then also say that logicals can be used too
     meet_criteria(col_icu, allow_class = c("character", "logical"), has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
     x$newvar_is_icu <- x[, col_icu, drop = TRUE]
   } else {
-    x$newvar_is_icu <- NA_real_
+    x$newvar_is_icu <- NA
   }
   # method
   method <- coerce_method(method)

R/mo.R

@@ -281,9 +281,16 @@ as.mo <- function(x,
       x_parts <- strsplit(gsub("-", " ", x_out, fixed = TRUE), " ", fixed = TRUE)[[1]]
 
       # do a pre-match on first character (and if it contains a space, first chars of first two terms)
-      if (length(x_parts) %in% c(2, 3)) {
+      if (length(x_parts) == 1) {
+        # for genus or species or subspecies
+        filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts, 1, 1) |
+                         AMR_env$MO_lookup$species_first == substr(x_parts, 1, 1) |
+                         AMR_env$MO_lookup$subspecies_first == substr(x_parts, 1, 1))
+      } else if (length(x_parts) %in% c(2, 3)) {
         # for genus + species + subspecies
-        filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) & (AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) | AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1)))
+        filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) &
+                         (AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) |
+                            AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1)))
       } else if (length(x_parts) > 3) {
         first_chars <- paste0("(^| )", "[", paste(substr(x_parts, 1, 1), collapse = ""), "]")
         filtr <- which(AMR_env$MO_lookup$full_first %like_case% first_chars)

R/sir.R

@@ -759,7 +759,7 @@ as_sir_method <- function(method_short,
   if (is.null(mo)) {
     stop_("No information was supplied about the microorganisms (missing argument `mo` and no column of class 'mo' found). See ?as.sir.\n\n",
       "To transform certain columns with e.g. mutate(), use `data %>% mutate(across(..., as.sir, mo = x))`, where x is your column with microorganisms.\n",
-      "To tranform all ", method_long, " in a data set, use `data %>% as.sir()` or `data %>% mutate_if(is.", method_short, ", as.sir)`.",
+      "To transform all ", method_long, " in a data set, use `data %>% as.sir()` or `data %>% mutate_if(is.", method_short, ", as.sir)`.",
       call = FALSE
     )
   }

_pkgdown.yml

@@ -70,9 +70,9 @@ home:
 navbar:
   title: "AMR (for R)"
   left:
-    - text: "Home"
+    # - text: "Home"
-      icon: "fa-home"
+    #   icon: "fa-home"
-      href: "index.html"
+    #   href: "index.html"
     - text: "How to"
       icon: "fa-question-circle"
       menu:
@@ -100,9 +100,9 @@ navbar:
       - text: "Work with WHONET Data"
         icon: "fa-globe-americas"
         href: "articles/WHONET.html"
-      - text: "Import Data From SPSS/SAS/Stata"
+      # - text: "Import Data From SPSS/SAS/Stata"
-        icon: "fa-file-upload"
+      #   icon: "fa-file-upload"
-        href: "articles/SPSS.html"
+      #   href: "articles/SPSS.html"
       - text: "Apply Eucast Rules"
         icon: "fa-exchange-alt"
         href: "articles/EUCAST.html"
@@ -115,16 +115,31 @@ navbar:
       - text: "Get Properties of an Antiviral Drug"
         icon: "fa-capsules"
         href: "reference/av_property.html" # reference instead of an article
+    - text: "With other pkgs"
+      icon: "fa-circles-overlap"
+      menu:
+      - text: "AMR & dplyr/tidyverse"
+        icon: "fa-circles-overlap"
+        href: "articles/other_pkg.html"
+      - text: "AMR & data.table"
+        icon: "fa-circles-overlap"
+        href: "articles/other_pkg.html"
+      - text: "AMR & tidymodels"
+        icon: "fa-circles-overlap"
+        href: "articles/other_pkg.html"
+      - text: "AMR & base R"
+        icon: "fa-circles-overlap"
+        href: "articles/other_pkg.html"
     - text: "Manual"
       icon: "fa-book-open"
       href: "reference/index.html"
     - text: "Authors"
       icon: "fa-users"
       href: "authors.html"
+  right:
     - text: "Changelog"
       icon: "far fa-newspaper"
       href: "news/index.html"
-  right:
     - text: "Source Code"
       icon: "fab fa-github"
       href: "https://github.com/msberends/AMR"

man/ab_property.Rd

@@ -119,7 +119,7 @@ ab_atc_group1("AMX")
 ab_atc_group2("AMX")
 ab_url("AMX")
 
-# smart lowercase tranformation
+# smart lowercase transformation
 ab_name(x = c("AMC", "PLB"))
 ab_name(x = c("AMC", "PLB"), tolower = TRUE)
 

man/av_property.Rd

@@ -92,7 +92,7 @@ av_tradenames("ACI")
 av_group("ACI")
 av_url("ACI")
 
-# smart lowercase tranformation
+# lowercase transformation
 av_name(x = c("ACI", "VALA"))
 av_name(x = c("ACI", "VALA"), tolower = TRUE)
 

vignettes/SPSS.Rmd.not

@@ -101,13 +101,13 @@ To work with R, probably the best option is to use [RStudio](https://www.rstudio
 
 To import a data file, just click *Import Dataset* in the Environment tab:
     
-![](https://github.com/msberends/AMR/raw/main/docs/import1.png)
+![](https://msberends.github.io/AMR/import1.png)
 
 If additional packages are needed, RStudio will ask you if they should be installed on beforehand.
 
 In the the window that opens, you can define all options (parameters) that should be used for import and you're ready to go:
 
-![](https://github.com/msberends/AMR/raw/main/docs/import2.png)
+![](https://msberends.github.io/AMR/import2.png)
 
 If you want named variables to be imported as factors so it resembles SPSS more, use `as_factor()`.
 

vignettes/other_pkg.Rmd

@@ -0,0 +1,25 @@
+---
+title: "Using AMR with other packages: AMR & dplyr/tidyverse"
+output: 
+  rmarkdown::html_vignette:
+    toc: true
+    toc_depth: 3
+vignette: >
+  %\VignetteIndexEntry{How to conduct AMR data analysis}
+  %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+editor_options: 
+  chunk_output_type: console
+---
+
+```{r setup, include = FALSE, results = 'markup'}
+knitr::opts_chunk$set(
+  warning = FALSE,
+  collapse = TRUE,
+  comment = "#>",
+  fig.width = 7.5,
+  fig.height = 5
+)
+```
+
+This page will be updated shortly, to give explicit examples of how to work ideally with the `AMR` package, for those who are used to working in `dplyr` or other tidyverse packages.