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new, automated website

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dr. M.S. (Matthijs) Berends 2022-08-21 16:37:20 +02:00
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commit 952d16de33
315 changed files with 839 additions and 34495 deletions
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1
.Rbuildignore
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@ -11,7 +11,6 @@
^codecov\.yml$
^cran-comments\.md$
^CRAN-RELEASE$
^developer-guideline.md$
^\.github$
^doc$
^docs$

4
.github/workflows/check.yaml vendored
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@ -99,9 +99,9 @@ jobs:
if: runner.os == 'Linux'
# update the below with sysreqs::sysreqs("DESCRIPTION") and check the "DEB" entries (for Ubuntu).
# we don't want to depend on the sysreqs pkg here, as it requires quite a recent R version
# as of May 2021: https://sysreqs.r-hub.io/pkg/AMR,R,cleaner,curl,dplyr,ggplot2,ggtext,knitr,microbenchmark,pillar,readxl,rmarkdown,rstudioapi,rvest,skimr,tidyr,tinytest,xml2,backports,crayon,rlang,vctrs,evaluate,highr,markdown,stringr,yaml,xfun,cli,ellipsis,fansi,lifecycle,utf8,glue,mime,magrittr,stringi,generics,R6,tibble,tidyselect,pkgconfig,purrr,digest,gtable,isoband,MASS,mgcv,scales,withr,nlme,Matrix,farver,labeling,munsell,RColorBrewer,viridisLite,lattice,colorspace,gridtext,Rcpp,RCurl,png,jpeg,bitops,cellranger,progress,rematch,hms,prettyunits,htmltools,jsonlite,tinytex,base64enc,httr,selectr,openssl,askpass,sys,repr,cpp11
# as of May 2021: https://sysreqs.r-hub.io/pkg/AMR,R,cleaner,curl,dplyr,ggplot2,knitr,microbenchmark,pillar,readxl,rmarkdown,rstudioapi,rvest,skimr,tidyr,tinytest,xml2,backports,crayon,rlang,vctrs,evaluate,highr,markdown,stringr,yaml,xfun,cli,ellipsis,fansi,lifecycle,utf8,glue,mime,magrittr,stringi,generics,R6,tibble,tidyselect,pkgconfig,purrr,digest,gtable,isoband,MASS,mgcv,scales,withr,nlme,Matrix,farver,labeling,munsell,RColorBrewer,viridisLite,lattice,colorspace,gridtext,Rcpp,RCurl,png,jpeg,bitops,cellranger,progress,rematch,hms,prettyunits,htmltools,jsonlite,tinytex,base64enc,httr,selectr,openssl,askpass,sys,repr,cpp11
run: |
sudo apt install -y libssl-dev libxml2-dev libicu-dev libcurl4-openssl-dev libpng-dev
sudo apt install -y libssl-dev libxml2-dev libcurl4-openssl-dev
- name: Restore cached R packages
# this step will add the step 'Post Restore cached R packages' on a succesful run

3
.github/workflows/website.yaml vendored
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@ -29,7 +29,7 @@
on:
push:
# only on main
branches: 'main - REMOVE THIS LATER'
branches: 'main'
name: Update website
@ -57,5 +57,4 @@ jobs:
run: |
git config user.name "github-actions"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
# Rscript -e 'pkgdown::build_favicons()'
Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE, clean = TRUE, install = TRUE, branch = "gh-pages")'

6
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 1.8.1.9018
Date: 2022-08-20
Version: 1.8.1.9019
Date: 2022-08-21
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
@ -12,6 +12,7 @@ Authors@R: c(
person("Dennis", "Souverein", role = c("aut", "ctb"), comment = c(ORCID = "0000-0003-0455-0336")),
person(c("Erwin", "E.", "A."), "Hassing", role = c("aut", "ctb")),
person("Casper", "Albers", role = "ths", comment = c(ORCID = "0000-0002-9213-6743")),
person("Peter", "Dutey-Magni", role = "ctb", comment = c(ORCID = "0000-0002-8942-9836")),
person("Judith", "Fonville", role = "ctb"),
person("Alex", "Friedrich", role = "ths", comment = c(ORCID = "0000-0003-4881-038X")),
person("Corinna", "Glasner", role = "ths", comment = c(ORCID = "0000-0003-1241-1328")),
@ -33,7 +34,6 @@ Enhances:
Suggests:
curl,
dplyr,
ggtext,
knitr,
progress,
readxl,

8
NEWS.md
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@ -1,4 +1,4 @@
# AMR 1.8.1.9018
# AMR 1.8.1.9019
### 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.
@ -13,6 +13,12 @@
* Using any `random_*()` function (such as `random_mic()`) is now possible by directly calling the package without loading it first: `AMR::random_mic(10)`
* Added *Toxoplasma gondii* (`P_TXPL_GOND`) to the `microorganisms` data set, together with its genus, family, and order
* Changed value in column `prevalence` of the `microorganisms` data set from 3 to 2 for these genera: *Acholeplasma*, *Alistipes*, *Alloprevotella*, *Bergeyella*, *Borrelia*, *Brachyspira*, *Butyricimonas*, *Cetobacterium*, *Chlamydia*, *Chlamydophila*, *Deinococcus*, *Dysgonomonas*, *Elizabethkingia*, *Empedobacter*, *Haloarcula*, *Halobacterium*, *Halococcus*, *Myroides*, *Odoribacter*, *Ornithobacterium*, *Parabacteroides*, *Pedobacter*, *Phocaeicola*, *Porphyromonas*, *Riemerella*, *Sphingobacterium*, *Streptobacillus*, *Tenacibaculum*, *Terrimonas*, *Victivallis*, *Wautersiella*, *Weeksella*
* Fix for using the form `df[carbapenems() == "R", ]` using the latest `vctrs` package
* Fix for using `info = FALSE` in `mdro()`
### Other
* New website to make use of the new Bootstrap 5 and pkgdown v2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions
* Added Peter Dutey-Magni and Anton Mymrikov as contributors, to thank them for their valuable input
# `AMR` 1.8.1

2
R/ab.R
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@ -26,7 +26,6 @@
#' Transform Input to an Antibiotic ID
#'
#' Use this function to determine the antibiotic code of one or more antibiotics. The data set [antibiotics] will be searched for abbreviations, official names and synonyms (brand names).
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [character] vector to determine to antibiotic ID
#' @param flag_multiple_results a [logical] to indicate whether a note should be printed to the console that probably more than one antibiotic code or name can be retrieved from a single input value.
#' @param info a [logical] to indicate whether a progress bar should be printed, defaults to `TRUE` only in interactive mode
@ -55,7 +54,6 @@
#' * [antibiotics] for the [data.frame] that is being used to determine ATCs
#' * [ab_from_text()] for a function to retrieve antimicrobial drugs from clinical text (from health care records)
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @export
#' @examples
#' # these examples all return "ERY", the ID of erythromycin:

2
R/ab_from_text.R
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@ -26,7 +26,6 @@
#' Retrieve Antimicrobial Drug Names and Doses from Clinical Text
#'
#' Use this function on e.g. clinical texts from health care records. It returns a [list] with all antimicrobial drugs, doses and forms of administration found in the texts.
#' @inheritSection lifecycle Stable Lifecycle
#' @param text text to analyse
#' @param type type of property to search for, either `"drug"`, `"dose"` or `"administration"`, see *Examples*
#' @param collapse a [character] to pass on to `paste(, collapse = ...)` to only return one [character] per element of `text`, see *Examples*
@ -53,7 +52,6 @@
#' `df %>% mutate(abx = ab_from_text(clinical_text, collapse = "|"))`
#' @export
#' @return A [list], or a [character] if `collapse` is not `NULL`
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # mind the bad spelling of amoxicillin in this line,
#' # straight from a true health care record:

11
R/ab_property.R
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@ -26,7 +26,6 @@
#' Get Properties of an Antibiotic
#'
#' Use these functions to return a specific property of an antibiotic from the [antibiotics] data set. All input values will be evaluated internally with [as.ab()].
#' @inheritSection lifecycle Stable Lifecycle
#' @param x any (vector of) text that can be coerced to a valid antibiotic code with [as.ab()]
#' @param tolower a [logical] to indicate whether the first [character] of every output should be transformed to a lower case [character]. This will lead to e.g. "polymyxin B" and not "polymyxin b".
#' @param property one of the column names of one of the [antibiotics] data set: `vector_or(colnames(antibiotics), sort = FALSE)`.
@ -54,7 +53,6 @@
#' @export
#' @seealso [antibiotics]
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # all properties:
#' ab_name("AMX") # "Amoxicillin"
@ -101,15 +99,18 @@
#' \donttest{
#' if (require("dplyr")) {
#' example_isolates %>%
#' set_ab_names()
#' set_ab_names() %>%
#' head()
#'
#' # this does the same:
#' example_isolates %>%
#' rename_with(set_ab_names)
#' rename_with(set_ab_names)%>%
#' head()
#'
#' # set_ab_names() works with any AB property:
#' example_isolates %>%
#' set_ab_names(property = "atc")
#' set_ab_names(property = "atc")%>%
#' head()
#'
#' example_isolates %>%
#' set_ab_names(where(is.rsi)) %>%

65
R/ab_selectors.R
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@ -26,7 +26,6 @@
#' Antibiotic Selectors
#'
#' These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group, without the need to define the columns or antibiotic abbreviations. In short, if you have a column name that resembles an antimicrobial agent, it will be picked up by any of these functions that matches its pharmaceutical class: "cefazolin", "CZO" and "J01DB04" will all be picked up by [cephalosporins()].
#' @inheritSection lifecycle Stable Lifecycle
#' @param ab_class an antimicrobial class or a part of it, such as `"carba"` and `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
#' @param filter an [expression] to be evaluated in the [antibiotics] data set, such as `name %like% "trim"`
#' @param only_rsi_columns a [logical] to indicate whether only columns of class `<rsi>` must be selected (defaults to `FALSE`), see [as.rsi()]
@ -46,103 +45,105 @@
#' @return (internally) a [character] vector of column names, with additional class `"ab_selector"`
#' @export
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
#' df <- example_isolates[ , c("hospital_id", "mo",
#' "AMP", "AMC", "TZP", "CXM", "CRO", "GEN",
#' "TOB", "COL", "IPM", "MEM", "TEC", "VAN")]
#'
#' # base R ------------------------------------------------------------------
#'
#' # select columns 'IPM' (imipenem) and 'MEM' (meropenem)
#' example_isolates[, carbapenems()]
#' df[, carbapenems()]
#'
#' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
#' example_isolates[, c("mo", aminoglycosides())]
#' df[, c("mo", aminoglycosides())]
#'
#' # select only antibiotic columns with DDDs for oral treatment
#' example_isolates[, administrable_per_os()]
#' df[, administrable_per_os()]
#'
#' # filter using any() or all()
#' example_isolates[any(carbapenems() == "R"), ]
#' subset(example_isolates, any(carbapenems() == "R"))
#' df[any(carbapenems() == "R"), ]
#' subset(df, any(carbapenems() == "R"))
#'
#' # filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
#' example_isolates[any(carbapenems()), ]
#' example_isolates[all(carbapenems()), ]
#' df[any(carbapenems()), ]
#' df[all(carbapenems()), ]
#'
#' # filter with multiple antibiotic selectors using c()
#' example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
#' df[all(c(carbapenems(), aminoglycosides()) == "R"), ]
#'
#' # filter + select in one go: get penicillins in carbapenems-resistant strains
#' example_isolates[any(carbapenems() == "R"), penicillins()]
#' df[any(carbapenems() == "R"), penicillins()]
#'
#' # You can combine selectors with '&' to be more specific. For example,
#' # penicillins() would select benzylpenicillin ('peni G') and
#' # administrable_per_os() would select erythromycin. Yet, when combined these
#' # drugs are both omitted since benzylpenicillin is not administrable per os
#' # and erythromycin is not a penicillin:
#' example_isolates[, penicillins() & administrable_per_os()]
#' df[, penicillins() & administrable_per_os()]
#'
#' # ab_selector() applies a filter in the `antibiotics` data set and is thus very
#' # flexible. For instance, to select antibiotic columns with an oral DDD of at
#' # least 1 gram:
#' example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
#' df[, ab_selector(oral_ddd > 1 & oral_units == "g")]
#'
#' # dplyr -------------------------------------------------------------------
#' \donttest{
#' if (require("dplyr")) {
#'
#' # get AMR for all aminoglycosides e.g., per hospital:
#' example_isolates %>%
#' df %>%
#' group_by(hospital_id) %>%
#' summarise(across(aminoglycosides(), resistance))
#'
#' # You can combine selectors with '&' to be more specific:
#' example_isolates %>%
#' df %>%
#' select(penicillins() & administrable_per_os())
#'
#' # get AMR for only drugs that matter - no intrinsic resistance:
#' example_isolates %>%
#' df %>%
#' filter(mo_genus() %in% c("Escherichia", "Klebsiella")) %>%
#' group_by(hospital_id) %>%
#' summarise(across(not_intrinsic_resistant(), resistance))
#'
#' # get susceptibility for antibiotics whose name contains "trim":
#' example_isolates %>%
#' df %>%
#' filter(first_isolate()) %>%
#' group_by(hospital_id) %>%
#' summarise(across(ab_selector(name %like% "trim"), susceptibility))
#'
#' # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
#' example_isolates %>%
#' df %>%
#' select(carbapenems())
#'
#' # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
#' example_isolates %>%
#' df %>%
#' select(mo, aminoglycosides())
#'
#' # any() and all() work in dplyr's filter() too:
#' example_isolates %>%
#' df %>%
#' filter(any(aminoglycosides() == "R"),
#' all(cephalosporins_2nd() == "R"))
#'
#' # also works with c():
#' example_isolates %>%
#' df %>%
#' filter(any(c(carbapenems(), aminoglycosides()) == "R"))
#'
#' # not setting any/all will automatically apply all():
#' example_isolates %>%
#' df %>%
#' filter(aminoglycosides() == "R")
#' #> i Assuming a filter on all 4 aminoglycosides.
#'
#' # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
#' example_isolates %>%
#' df %>%
#' select(mo, ab_class("mycobact"))
#'
#' # get bug/drug combinations for only macrolides in Gram-positives:
#' example_isolates %>%
#' # get bug/drug combinations for only glycopeptides in Gram-positives:
#' df %>%
#' filter(mo_is_gram_positive()) %>%
#' select(mo, macrolides()) %>%
#' select(mo, glycopeptides()) %>%
#' bug_drug_combinations() %>%
#' format()
#'
@ -151,10 +152,12 @@
#' select(penicillins()) # only the 'J01CA01' column will be selected
#'
#'
#' # with dplyr 1.0.0 and higher (that adds 'across()'), this is all equal:
#' example_isolates[carbapenems() == "R", ]
#' example_isolates %>% filter(carbapenems() == "R")
#' example_isolates %>% filter(across(carbapenems(), ~.x == "R"))
#' # with recent versions of dplyr this is all equal:
#' x <- df[carbapenems() == "R", ]
#' y <- df %>% filter(carbapenems() == "R")
#' z <- df %>% filter(if_all(carbapenems(), ~.x == "R"))
#' identical(x, y)
#' identical(y, z)
#' }
#' }
ab_class <- function(ab_class,

23
R/age.R
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@ -25,8 +25,7 @@
#' Age in Years of Individuals
#'
#' Calculates age in years based on a reference date, which is the sytem date at default.
#' @inheritSection lifecycle Stable Lifecycle
#' Calculates age in years based on a reference date, which is the system date at default.
#' @param x date(s), [character] (vectors) will be coerced with [as.POSIXlt()]
#' @param reference reference date(s) (defaults to today), [character] (vectors) will be coerced with [as.POSIXlt()]
#' @param exact a [logical] to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of [year-to-date](https://en.wikipedia.org/wiki/Year-to-date) (YTD) of `x` by the number of days in the year of `reference` (either 365 or 366).
@ -37,16 +36,20 @@
#' This function vectorises over both `x` and `reference`, meaning that either can have a length of 1 while the other argument has a larger length.
#' @return An [integer] (no decimals) if `exact = FALSE`, a [double] (with decimals) otherwise
#' @seealso To split ages into groups, use the [age_groups()] function.
#' @inheritSection AMR Read more on Our Website!
#' @export
#' @examples
#' # 10 random birth dates
#' df <- data.frame(birth_date = Sys.Date() - runif(10) * 25000)
#' # 10 random pre-Y2K birth dates
#' df <- data.frame(birth_date = as.Date("2000-01-01") - runif(10) * 25000)
#'
#' # add ages
#' df$age <- age(df$birth_date)
#'
#' # add exact ages
#' df$age_exact <- age(df$birth_date, exact = TRUE)
#'
#' # add age at millenium switch
#' df$age_at_y2k <- age(df$birth_date, "2000-01-01")
#'
#' df
age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
meet_criteria(x, allow_class = c("character", "Date", "POSIXt"))
@ -115,7 +118,6 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
#' Split Ages into Age Groups
#'
#' Split ages into age groups defined by the `split` argument. This allows for easier demographic (antimicrobial resistance) analysis.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x age, e.g. calculated with [age()]
#' @param split_at values to split `x` at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See *Details*.
#' @param na.rm a [logical] to indicate whether missing values should be removed
@ -131,7 +133,7 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
#' @return Ordered [factor]
#' @seealso To determine ages, based on one or more reference dates, use the [age()] function.
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)
#'
@ -150,7 +152,7 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
#' age_groups(ages, split_at = "fives")
#'
#' # split specifically for children
#' age_groups(ages, c(1, 2, 4, 6, 13, 17))
#' age_groups(ages, c(1, 2, 4, 6, 13, 18))
#' age_groups(ages, "children")
#'
#' \donttest{
@ -161,7 +163,10 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
#' filter(mo == as.mo("E. coli")) %>%
#' group_by(age_group = age_groups(age)) %>%
#' select(age_group, CIP) %>%
#' ggplot_rsi(x = "age_group", minimum = 0)
#' ggplot_rsi(x = "age_group",
#' minimum = 0,
#' x.title = "Age Group",
#' title = "Ciprofloxacin resistance per age group")
#' }
#' }
age_groups <- function(x, split_at = c(12, 25, 55, 75), na.rm = FALSE) {

2
R/atc_online.R
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@ -26,7 +26,6 @@
#' Get ATC Properties from WHOCC Website
#'
#' Gets data from the WHOCC website to determine properties of an Anatomical Therapeutic Chemical (ATC) (e.g. an antibiotic), such as the name, defined daily dose (DDD) or standard unit.
#' @inheritSection lifecycle Stable Lifecycle
#' @param atc_code a [character] (vector) with ATC code(s) of antibiotics, will be coerced with [as.ab()] and [ab_atc()] internally if not a valid ATC code
#' @param property property of an ATC code. Valid values are `"ATC"`, `"Name"`, `"DDD"`, `"U"` (`"unit"`), `"Adm.R"`, `"Note"` and `groups`. For this last option, all hierarchical groups of an ATC code will be returned, see *Examples*.
#' @param administration type of administration when using `property = "Adm.R"`, see *Details*
@ -61,7 +60,6 @@
#' **N.B. This function requires an internet connection and only works if the following packages are installed: `curl`, `rvest`, `xml2`.**
#' @export
#' @rdname atc_online
#' @inheritSection AMR Read more on Our Website!
#' @source <https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/>
#' @examples
#' \donttest{

2
R/availability.R
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@ -26,12 +26,10 @@
#' Check Availability of Columns
#'
#' Easy check for data availability of all columns in a data set. This makes it easy to get an idea of which antimicrobial combinations can be used for calculation with e.g. [susceptibility()] and [resistance()].
#' @inheritSection lifecycle Stable Lifecycle
#' @param tbl a [data.frame] or [list]
#' @param width number of characters to present the visual availability, defaults to filling the width of the console
#' @details The function returns a [data.frame] with columns `"resistant"` and `"visual_resistance"`. The values in that columns are calculated with [resistance()].
#' @return [data.frame] with column names of `tbl` as row names
#' @inheritSection AMR Read more on Our Website!
#' @export
#' @examples
#' availability(example_isolates)

4
R/bug_drug_combinations.R
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@ -26,7 +26,6 @@
#' Determine Bug-Drug Combinations
#'
#' Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use [format()] on the result to prettify it to a publishable/printable format, see *Examples*.
#' @inheritSection lifecycle Stable Lifecycle
#' @inheritParams eucast_rules
#' @param combine_IR a [logical] to indicate whether values R and I should be summed
#' @param add_ab_group a [logical] to indicate where the group of the antimicrobials must be included as a first column
@ -41,11 +40,10 @@
#' @rdname bug_drug_combinations
#' @return The function [bug_drug_combinations()] returns a [data.frame] with columns "mo", "ab", "S", "I", "R" and "total".
#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' \donttest{
#' x <- bug_drug_combinations(example_isolates)
#' x
#' head(x)
#' format(x, translate_ab = "name (atc)")
#'
#' # Use FUN to change to transformation of microorganism codes

11
R/catalogue_of_life.R
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@ -59,7 +59,6 @@ format_included_data_number <- function(data) {
#' The Catalogue of Life (<http://www.catalogueoflife.org>) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.9 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation.
#'
#' The syntax used to transform the original data to a cleansed \R format, can be found here: <https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R>.
#' @inheritSection AMR Read more on Our Website!
#' @name catalogue_of_life
#' @rdname catalogue_of_life
#' @seealso Data set [microorganisms] for the actual data. \cr
@ -71,28 +70,19 @@ format_included_data_number <- function(data) {
#'
#' # Get a note when a species was renamed
#' mo_shortname("Chlamydophila psittaci")
#' # Note: 'Chlamydophila psittaci' (Everett et al., 1999) was renamed back to
#' # 'Chlamydia psittaci' (Page, 1968)
#' #> [1] "C. psittaci"
#'
#' # Get any property from the entire taxonomic tree for all included species
#' mo_class("E. coli")
#' #> [1] "Gammaproteobacteria"
#'
#' mo_family("E. coli")
#' #> [1] "Enterobacteriaceae"
#'
#' mo_gramstain("E. coli") # based on kingdom and phylum, see ?mo_gramstain
#' #> [1] "Gram-negative"
#'
#' mo_ref("E. coli")
#' #> [1] "Castellani et al., 1919"
#'
#' # Do not get mistaken - this package is about microorganisms
#' mo_kingdom("C. elegans")
#' #> [1] "Fungi" # Fungi?!
#' mo_name("C. elegans")
#' #> [1] "Cladosporium elegans" # Because a microorganism was found
NULL
#' Version info of included Catalogue of Life
@ -102,7 +92,6 @@ NULL
#' @details For LPSN, see [microorganisms].
#' @return a [list], which prints in pretty format
#' @inheritSection catalogue_of_life Catalogue of Life
#' @inheritSection AMR Read more on Our Website!
#' @export
catalogue_of_life_version <- function() {

6
R/count.R
View File

@ -28,7 +28,6 @@
#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in `summarise()` from the `dplyr` package and also support grouped variables, see *Examples*.
#'
#' [count_resistant()] should be used to count resistant isolates, [count_susceptible()] should be used to count susceptible isolates.
#' @inheritSection lifecycle Stable Lifecycle
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with [as.rsi()] if needed.
#' @inheritParams proportion
#' @inheritSection as.rsi Interpretation of R and S/I
@ -45,11 +44,11 @@
#' @rdname count
#' @name count
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' ?example_isolates
#' # run ?example_isolates for more info.
#'
#' # base R ------------------------------------------------------------
#' count_resistant(example_isolates$AMX) # counts "R"
#' count_susceptible(example_isolates$AMX) # counts "S" and "I"
#' count_all(example_isolates$AMX) # counts "S", "I" and "R"
@ -72,6 +71,7 @@
#' count_susceptible(example_isolates$AMX)
#' susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX)
#'
#' # dplyr -------------------------------------------------------------
#' \donttest{
#' if (require("dplyr")) {
#' example_isolates %>%

49
R/custom_eucast_rules.R
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@ -26,82 +26,67 @@
#' Define Custom EUCAST Rules
#'
#' Define custom EUCAST rules for your organisation or specific analysis and use the output of this function in [eucast_rules()].
#' @inheritSection lifecycle Stable Lifecycle
#' @param ... rules in [formula][`~`()] notation, see *Examples*
#' @details
#' Some organisations have their own adoption of EUCAST rules. This function can be used to define custom EUCAST rules to be used in the [eucast_rules()] function.
#'
#' @section How it works:
#'
#' ### Basics
#'
#' If you are familiar with the [`case_when()`][dplyr::case_when()] function of the `dplyr` package, you will recognise the input method to set your own rules. Rules must be set using what \R considers to be the 'formula notation'. The rule itself is written *before* the tilde (`~`) and the consequence of the rule is written *after* the tilde:
#'
#' ```
#' ```{r}
#' x <- custom_eucast_rules(TZP == "S" ~ aminopenicillins == "S",
#' TZP == "R" ~ aminopenicillins == "R")
#' ```
#'
#' These are two custom EUCAST rules: if TZP (piperacillin/tazobactam) is "S", all aminopenicillins (ampicillin and amoxicillin) must be made "S", and if TZP is "R", aminopenicillins must be made "R". These rules can also be printed to the console, so it is immediately clear how they work:
#'
#' ```
#' ```{r}
#' x
#' #> A set of custom EUCAST rules:
#' #>
#' #> 1. If TZP is S then set to S:
#' #> amoxicillin (AMX), ampicillin (AMP)
#' #>
#' #> 2. If TZP is R then set to R:
#' #> amoxicillin (AMX), ampicillin (AMP)
#' ```
#'
#' The rules (the part *before* the tilde, in above example `TZP == "S"` and `TZP == "R"`) must be evaluable in your data set: it should be able to run as a filter in your data set without errors. This means for the above example that the column `TZP` must exist. We will create a sample data set and test the rules set:
#'
#' ```
#' df <- data.frame(mo = c("E. coli", "K. pneumoniae"),
#' TZP = "R",
#' amox = "",
#' AMP = "")
#' ```{r}
#' df <- data.frame(mo = c("Escherichia coli", "Klebsiella pneumoniae"),
#' TZP = as.rsi("R"),
#' ampi = as.rsi("S"),
#' cipro = as.rsi("S"))
#' df
#' #> mo TZP amox AMP
#' #> 1 E. coli R
#' #> 2 K. pneumoniae R
#'
#' eucast_rules(df, rules = "custom", custom_rules = x)
#' #> mo TZP amox AMP
#' #> 1 E. coli R R R
#' #> 2 K. pneumoniae R R R
#' eucast_rules(df, rules = "custom", custom_rules = x, info = FALSE)
#' ```
#'
#' ### Using taxonomic properties in rules
#'
#' There is one exception in variables used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), quote = "\u0096", sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
#' There is one exception in variables used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
#'
#' ```
#' ```{r}
#' y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
#' TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R")
#'
#' eucast_rules(df, rules = "custom", custom_rules = y)
#' #> mo TZP amox AMP
#' #> 1 E. coli R
#' #> 2 K. pneumoniae R R R
#' eucast_rules(df, rules = "custom", custom_rules = y, info = FALSE)
#' ```
#'
#' ### Usage of antibiotic group names
#'
#' It is possible to define antibiotic groups instead of single antibiotics for the rule consequence, the part *after* the tilde. In above examples, the antibiotic group `aminopenicillins` is used to include ampicillin and amoxicillin. The following groups are allowed (case-insensitive). Within parentheses are the agents that will be matched when running the rule.
#'
#' `r paste0(" * ", sapply(DEFINED_AB_GROUPS, function(x) paste0("\u0096", tolower(gsub("^AB_", "", x)), "\u0096\\cr(", vector_and(ab_name(eval(parse(text = x), envir = asNamespace("AMR")), language = NULL, tolower = TRUE), quotes = FALSE), ")"), USE.NAMES = FALSE), "\n", collapse = "")`
#' `r paste0(" * ", sapply(DEFINED_AB_GROUPS, function(x) paste0("\"", tolower(gsub("^AB_", "", x)), "\"\\cr(", vector_and(ab_name(eval(parse(text = x), envir = asNamespace("AMR")), language = NULL, tolower = TRUE), quotes = FALSE), ")"), USE.NAMES = FALSE), "\n", collapse = "")`
#' @returns A [list] containing the custom rules
#' @inheritSection AMR Read more on Our Website!
#' @export
#' @examples
#' x <- custom_eucast_rules(AMC == "R" & genus == "Klebsiella" ~ aminopenicillins == "R",
#' AMC == "I" & genus == "Klebsiella" ~ aminopenicillins == "I")
#' x
#'
#' # run the custom rule set (verbose = TRUE will return a logbook instead of the data set):
#' eucast_rules(example_isolates,
#' rules = "custom",
#' custom_rules = x,
#' info = FALSE)
#' info = FALSE,
#' verbose = TRUE)
#'
#' # combine rule sets
#' x2 <- c(x,

46
R/data.R
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@ -72,8 +72,10 @@
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: <https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm>
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection WHOCC WHOCC
#' @inheritSection AMR Read more on Our Website!
#' @seealso [microorganisms], [intrinsic_resistant]
#' @examples
#' head(antibiotics)
#' head(antivirals)
"antibiotics"
#' @rdname antibiotics
@ -136,8 +138,9 @@
#'
#' * Retrieved from the `r SNOMED_VERSION$title`, OID `r SNOMED_VERSION$current_oid`, version `r SNOMED_VERSION$current_version`; url: <`r SNOMED_VERSION$url`>
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @seealso [as.mo()], [mo_property()], [microorganisms.codes], [intrinsic_resistant]
#' @examples
#' head(microorganisms)
"microorganisms"
#' Data Set with Previously Accepted Taxonomic Names
@ -153,8 +156,9 @@
#'
#' Parte, A.C. (2018). LPSN - List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @seealso [as.mo()] [mo_property()] [microorganisms]
#' @examples
#' head(microorganisms.old)
"microorganisms.old"
#' Data Set with `r format(nrow(microorganisms.codes), big.mark = ",")` Common Microorganism Codes
@ -165,8 +169,9 @@
#' - `mo`\cr ID of the microorganism in the [microorganisms] data set
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection catalogue_of_life Catalogue of Life
#' @inheritSection AMR Read more on Our Website!
#' @seealso [as.mo()] [microorganisms]
#' @examples
#' head(microorganisms.codes)
"microorganisms.codes"
#' Data Set with `r format(nrow(example_isolates), big.mark = ",")` Example Isolates
@ -184,7 +189,8 @@
#' - `mo`\cr ID of microorganism created with [as.mo()], see also [microorganisms]
#' - `PEN:RIF`\cr `r sum(vapply(FUN.VALUE = logical(1), example_isolates, is.rsi))` different antibiotics with class [`rsi`] (see [as.rsi()]); these column names occur in the [antibiotics] data set and can be translated with [ab_name()]
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' head(example_isolates)
"example_isolates"
#' Data Set with Unclean Data
@ -197,7 +203,8 @@
#' - `bacteria`\cr info about microorganism that can be transformed with [as.mo()], see also [microorganisms]
#' - `AMX:GEN`\cr 4 different antibiotics that have to be transformed with [as.rsi()]
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' head(example_isolates_unclean)
"example_isolates_unclean"
#' Data Set with `r format(nrow(WHONET), big.mark = ",")` Isolates - WHONET Example
@ -231,7 +238,8 @@
#' - `Date of data entry`\cr [Date] this data was entered in WHONET
#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.rsi))` different antibiotics. You can lookup the abbreviations in the [antibiotics] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using [as.rsi()].
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' head(WHONET)
"WHONET"
#' Data Set for R/SI Interpretation
@ -250,16 +258,11 @@
#' - `breakpoint_R`\cr Highest MIC value or lowest number of millimetres that leads to "R"
#' - `uti`\cr A [logical] value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
#' @details
#' Overview of the data set:
#'
#' ```{r}
#' head(rsi_translation)
#' ```
#'
#' The repository of this `AMR` package contains a file comprising this exact data set: <https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt>. This file **allows for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. The file is updated automatically and the `mo` and `ab` columns have been transformed to contain the full official names instead of codes.
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @seealso [intrinsic_resistant]
#' @examples
#' head(rsi_translation)
"rsi_translation"
#' Data Set with Bacterial Intrinsic Resistance
@ -272,18 +275,8 @@
#'
#' This data set is based on `r format_eucast_version_nr(3.3)`.
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' \donttest{
#' if (require("dplyr")) {
#' intrinsic_resistant %>%
#' mutate(mo = mo_name(mo),
#' ab = ab_name(mo))
#' filter(ab == "Vancomycin" & mo %like% "Enterococcus") %>%
#' pull(mo)
#' #> [1] "Enterococcus casseliflavus" "Enterococcus gallinarum"
#' }
#' }
#' head(intrinsic_resistant)
"intrinsic_resistant"
#' Data Set with Treatment Dosages as Defined by EUCAST
@ -301,5 +294,6 @@
#' - `eucast_version`\cr Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply
#' @details `r format_eucast_version_nr(11.0)` are based on the dosages in this data set.
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' head(dosage)
"dosage"

2
R/deprecated.R
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@ -26,8 +26,6 @@
#' Deprecated Functions
#'
#' These functions are so-called '[Deprecated]'. **They will be removed in a future release.** Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
#' @inheritSection lifecycle Retired Lifecycle
#' @inheritSection AMR Read more on Our Website!
#' @keywords internal
#' @name AMR-deprecated
# @export

21
R/disk.R
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@ -26,7 +26,6 @@
#' Transform Input to Disk Diffusion Diameters
#'
#' This transforms a vector to a new class [`disk`], which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 6 and 50.
#' @inheritSection lifecycle Stable Lifecycle
#' @rdname as.disk
#' @param x vector
#' @param na.rm a [logical] indicating whether missing values should be removed
@ -35,18 +34,22 @@
#' @aliases disk
#' @export
#' @seealso [as.rsi()]
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' \donttest{
#' # transform existing disk zones to the `disk` class
#' df <- data.frame(microorganism = "E. coli",
#' # transform existing disk zones to the `disk` class (using base R)
#' df <- data.frame(microorganism = "Escherichia coli",
#' AMP = 20,
#' CIP = 14,
#' GEN = 18,
#' TOB = 16)
#' df[, 2:5] <- lapply(df[, 2:5], as.disk)
#' # same with dplyr:
#' # df %>% mutate(across(AMP:TOB, as.disk))
#' str(df)
#'
#' #' \donttest{
#' # transforming is easier with dplyr:
#' if (require("dplyr")) {
#' df %>% mutate(across(AMP:TOB, as.disk))
#' }
#' }
#'
#' # interpret disk values, see ?as.rsi
#' as.rsi(x = as.disk(18),
@ -54,8 +57,8 @@
#' ab = "ampicillin", # and `ab` with as.ab()
#' guideline = "EUCAST")
#'
#' as.rsi(df)
#' }
#' # interpret whole data set, pretend to be all from urinary tract infections:
#' as.rsi(df, uti = TRUE)
as.disk <- function(x, na.rm = FALSE) {
meet_criteria(x, allow_class = c("disk", "character", "numeric", "integer"), allow_NA = TRUE)
meet_criteria(na.rm, allow_class = "logical", has_length = 1)

37
R/episode.R
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@ -26,7 +26,6 @@
#' Determine (New) Episodes for Patients
#'
#' These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument `episode_days`. This can be used to determine clinical episodes for any epidemiological analysis. The [get_episode()] function returns the index number of the episode per group, while the [is_new_episode()] function returns values `TRUE`/`FALSE` to indicate whether an item in a vector is the start of a new episode.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x vector of dates (class `Date` or `POSIXt`), will be sorted internally to determine episodes
#' @param episode_days required episode length in days, can also be less than a day or `Inf`, see *Details*
#' @param ... ignored, only in place to allow future extensions
@ -42,16 +41,16 @@
#' @seealso [first_isolate()]
#' @rdname get_episode
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
#' # See ?example_isolates
#' df <- example_isolates[sample(seq_len(2000), size = 200), ]
#'
#' get_episode(example_isolates$date, episode_days = 60) # indices
#' is_new_episode(example_isolates$date, episode_days = 60) # TRUE/FALSE
#' get_episode(df$date, episode_days = 60) # indices
#' is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#'
#' # filter on results from the third 60-day episode only, using base R
#' example_isolates[which(get_episode(example_isolates$date, 60) == 3), ]
#' df[which(get_episode(df$date, 60) == 3), ]
#'
#' # the functions also work for less than a day, e.g. to include one per hour:
#' get_episode(c(Sys.time(),
@ -62,24 +61,24 @@
#' if (require("dplyr")) {
#' # is_new_episode() can also be used in dplyr verbs to determine patient
#' # episodes based on any (combination of) grouping variables:
#' example_isolates %>%
#' df %>%
#' mutate(condition = sample(x = c("A", "B", "C"),
#' size = 2000,
#' replace = TRUE)) %>%
#' group_by(condition) %>%
#' mutate(new_episode = is_new_episode(date, 365))
#' mutate(new_episode = is_new_episode(date, 365)) %>%
#' select(patient_id, date, condition, new_episode)
#'
#' example_isolates %>%
#' df %>%
#' group_by(hospital_id, patient_id) %>%
#' transmute(date,
#' patient_id,
#' new_index = get_episode(date, 60),
#' new_logical = is_new_episode(date, 60))
#'
#'
#' example_isolates %>%
#' df %>%
#' group_by(hospital_id) %>%
#' summarise(patients = n_distinct(patient_id),
#' summarise(n_patients = n_distinct(patient_id),
#' n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
#' n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),
#' n_episodes_30 = sum(is_new_episode(date, episode_days = 30)))
@ -87,21 +86,23 @@
#'
#' # grouping on patients and microorganisms leads to the same
#' # results as first_isolate() when using 'episode-based':
#' x <- example_isolates %>%
#' x <- df %>%
#' filter_first_isolate(include_unknown = TRUE,
#' method = "episode-based")
#'
#' y <- example_isolates %>%
#' y <- df %>%
#' group_by(patient_id, mo) %>%
#' filter(is_new_episode(date, 365))
#' filter(is_new_episode(date, 365)) %>%
#' ungroup()
#'
#' identical(x$patient_id, y$patient_id)
#' identical(x, y)
#'
#' # but is_new_episode() has a lot more flexibility than first_isolate(),
#' # since you can now group on anything that seems relevant:
#' example_isolates %>%
#' df %>%
#' group_by(patient_id, mo, hospital_id, ward_icu) %>%
#' mutate(flag_episode = is_new_episode(date, 365))
#' mutate(flag_episode = is_new_episode(date, 365)) %>%
#' select(group_vars(.), flag_episode)
#' }
#' }
get_episode <- function(x, episode_days, ...) {

29
R/eucast_rules.R
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@ -52,7 +52,6 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
#' Apply rules for clinical breakpoints and intrinsic resistance as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, <https://eucast.org>), see *Source*. Use [eucast_dosage()] to get a [data.frame] with advised dosages of a certain bug-drug combination, which is based on the [dosage] data set.
#'
#' To improve the interpretation of the antibiogram before EUCAST rules are applied, some non-EUCAST rules can applied at default, see *Details*.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x data with antibiotic columns, such as `amox`, `AMX` and `AMC`
#' @param info a [logical] to indicate whether progress should be printed to the console, defaults to only print while in interactive sessions
#' @param rules a [character] vector that specifies which rules should be applied. Must be one or more of `"breakpoints"`, `"expert"`, `"other"`, `"custom"`, `"all"`, and defaults to `c("breakpoints", "expert")`. The default value can be set to another value, e.g. using `options(AMR_eucastrules = "all")`. If using `"custom"`, be sure to fill in argument `custom_rules` too. Custom rules can be created with [custom_eucast_rules()].
@ -76,11 +75,11 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
#'
#' Custom rules can be created using [custom_eucast_rules()], e.g.:
#'
#' ```
#' ```{r}
#' x <- custom_eucast_rules(AMC == "R" & genus == "Klebsiella" ~ aminopenicillins == "R",
#' AMC == "I" & genus == "Klebsiella" ~ aminopenicillins == "I")
#'
#' eucast_rules(example_isolates, rules = "custom", custom_rules = x)
#' eucast_rules(example_isolates, rules = "custom", custom_rules = x, info = FALSE)
#' ```
#'
#'
@ -113,8 +112,9 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_9.0_Breakpoint_Tables.xlsx)
#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 10.0, 2020. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_10.0_Breakpoint_Tables.xlsx)
#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 11.0, 2021. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_11.0_Breakpoint_Tables.xlsx)
#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 12.0, 2022. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_12.0_Breakpoint_Tables.xlsx)
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' \donttest{
#' a <- data.frame(mo = c("Staphylococcus aureus",
@ -131,33 +131,26 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
#' FOX = "S", # Cefoxitin
#' stringsAsFactors = FALSE)
#'
#' a
#' # mo VAN AMX COL CAZ CXM PEN FOX
#' # 1 Staphylococcus aureus - - - - - S S
#' # 2 Enterococcus faecalis - - - - - S S
#' # 3 Escherichia coli - - - - - S S
#' # 4 Klebsiella pneumoniae - - - - - S S
#' # 5 Pseudomonas aeruginosa - - - - - S S
#' head(a)
#'
#'
#' # apply EUCAST rules: some results wil be changed
#' b <- eucast_rules(a)
#'
#' b
#' # mo VAN AMX COL CAZ CXM PEN FOX
#' # 1 Staphylococcus aureus - S R R S S S
#' # 2 Enterococcus faecalis - - R R R S R
#' # 3 Escherichia coli R - - - - R S
#' # 4 Klebsiella pneumoniae R R - - - R S
#' # 5 Pseudomonas aeruginosa R R - - R R R
#' head(b)
#'
#'
#' # do not apply EUCAST rules, but rather get a data.frame
#' # containing all details about the transformations:
#' c <- eucast_rules(a, verbose = TRUE)
#' head(c)
#' }
#'
#' # Dosage guidelines:
#'
#' eucast_dosage(c("tobra", "genta", "cipro"), "iv")
#'
#' eucast_dosage(c("tobra", "genta", "cipro"), "iv", version_breakpoints = 10)
eucast_rules <- function(x,
col_mo = NULL,
info = interactive(),

14
R/first_isolate.R
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@ -26,7 +26,6 @@
#' Determine First Isolates
#'
#' Determine first isolates of all microorganisms of every patient per episode and (if needed) per specimen type. These functions support all four methods as summarised by Hindler *et al.* in 2007 (\doi{10.1086/511864}). To determine patient episodes not necessarily based on microorganisms, use [is_new_episode()] that also supports grouping with the `dplyr` package.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [data.frame] containing isolates. Can be left blank for automatic determination, see *Examples*.
#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column with a date class
#' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
@ -126,7 +125,6 @@
#' - **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#'
#' - Hindler JF and Stelling J (2007). **Analysis and Presentation of Cumulative Antibiograms: A New Consensus Guideline from the Clinical and Laboratory Standards Institute.** Clinical Infectious Diseases, 44(6), 867-873. \doi{10.1086/511864}
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
@ -134,7 +132,7 @@
#' example_isolates[first_isolate(), ]
#' \donttest{
#' # get all first Gram-negatives
#' example_isolates[which(first_isolate() & mo_is_gram_negative()), ]
#' example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ]
#'
#' if (require("dplyr")) {
#' # filter on first isolates using dplyr:
@ -143,12 +141,13 @@
#'
#' # short-hand version:
#' example_isolates %>%
#' filter_first_isolate()
#' filter_first_isolate(info = FALSE)
#'
#' # grouped determination of first isolates (also prints group names):
#' # flag the first isolates per group:
#' example_isolates %>%
#' group_by(hospital_id) %>%
#' mutate(first = first_isolate())
#' mutate(first = first_isolate()) %>%
#' select(hospital_id, date, patient_id, mo, first)
#'
#' # now let's see if first isolates matter:
#' A <- example_isolates %>%
@ -163,6 +162,9 @@
#' resistance = resistance(GEN)) # gentamicin resistance
#'
#' # Have a look at A and B.
#' A
#' B
#'
#' # B is more reliable because every isolate is counted only once.
#' # Gentamicin resistance in hospital D appears to be 4.2% higher than
#' # when you (erroneously) would have used all isolates for analysis.

7
R/g.test.R
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@ -26,7 +26,6 @@
#' *G*-test for Count Data
#'
#' [g.test()] performs chi-squared contingency table tests and goodness-of-fit tests, just like [chisq.test()] but is more reliable (1). A *G*-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a ***G*-test of goodness-of-fit**), or to see whether the proportions of one variable are different for different values of the other variable (called a ***G*-test of independence**).
#' @inheritSection lifecycle Questioning Lifecycle
#' @inherit stats::chisq.test params return
#' @details If `x` is a [matrix] with one row or column, or if `x` is a vector and `y` is not given, then a *goodness-of-fit test* is performed (`x` is treated as a one-dimensional contingency table). The entries of `x` must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in `p`, or are all equal if `p` is not given.
#'
@ -76,7 +75,6 @@
#' - The possibility to simulate p values with `simulate.p.value` was removed
#' @export
#' @importFrom stats pchisq complete.cases
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # = EXAMPLE 1 =
#' # Shivrain et al. (2006) crossed clearfield rice (which are resistant
@ -88,8 +86,7 @@
#' # ratio.
#'
#' x <- c(772, 1611, 737)
#' G <- g.test(x, p = c(1, 2, 1) / 4)
#' # G$p.value = 0.12574.
#' g.test(x, p = c(1, 2, 1) / 4)
#'
#' # There is no significant difference from a 1:2:1 ratio.
#' # Meaning: resistance controlled by a single gene with two co-dominant
@ -105,11 +102,9 @@
#'
#' x <- c(1752, 1895)
#' g.test(x)
#' # p = 0.01787343
#'
#' # There is a significant difference from a 1:1 ratio.
#' # Meaning: there are significantly more left-billed birds.
#'
g.test <- function(x,
y = NULL,
# correct = TRUE,

28
R/ggplot_pca.R
View File

@ -26,7 +26,6 @@
#' PCA Biplot with `ggplot2`
#'
#' Produces a `ggplot2` variant of a so-called [biplot](https://en.wikipedia.org/wiki/Biplot) for PCA (principal component analysis), but is more flexible and more appealing than the base \R [biplot()] function.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x an object returned by [pca()], [prcomp()] or [princomp()]
#' @inheritParams stats::biplot.prcomp
#' @param labels an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the [pca()] function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see [pca()].
@ -64,23 +63,28 @@
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
#'
#' # See ?pca for more info about Principal Component Analysis (PCA).
#' \donttest{
#' if (require("dplyr")) {
#' pca_model <- example_isolates %>%
#' filter(mo_genus(mo) == "Staphylococcus") %>%
#' group_by(species = mo_shortname(mo)) %>%
#' summarise_if (is.rsi, resistance) %>%
#' pca(FLC, AMC, CXM, GEN, TOB, TMP, SXT, CIP, TEC, TCY, ERY)
#' # calculate the resistance per group first
#' resistance_data <- example_isolates %>%
#' group_by(order = mo_order(mo), # group on anything, like order
#' genus = mo_genus(mo)) %>% # and genus as we do here;
#' filter(n() >= 30) %>% # filter on only 30 results per group
#' summarise_if(is.rsi, resistance) # then get resistance of all drugs
#'
#' # old (base R)
#' biplot(pca_model)
#' # now conduct PCA for certain antimicrobial agents
#' pca_result <- resistance_data %>%
#' pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
#'
#' # new
#' ggplot_pca(pca_model)
#' summary(pca_result)
#'
#' # old base R plotting method:
#' biplot(pca_result)
#' # new ggplot2 plotting method using this package:
#' ggplot_pca(pca_result)
#'
#' if (require("ggplot2")) {
#' ggplot_pca(pca_model) +
#' ggplot_pca(pca_result) +
#' scale_colour_viridis_d() +
#' labs(title = "Title here")
#' }

2
R/ggplot_rsi.R
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@ -26,7 +26,6 @@
#' AMR Plots with `ggplot2`
#'
#' Use these functions to create bar plots for AMR data analysis. All functions rely on [ggplot2][ggplot2::ggplot()] functions.
#' @inheritSection lifecycle Stable Lifecycle
#' @param data a [data.frame] with column(s) of class [`rsi`] (see [as.rsi()])
#' @param position position adjustment of bars, either `"fill"`, `"stack"` or `"dodge"`
#' @param x variable to show on x axis, either `"antibiotic"` (default) or `"interpretation"` or a grouping variable
@ -65,7 +64,6 @@
#' [ggplot_rsi()] is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (`%>%`). See *Examples*.
#' @rdname ggplot_rsi
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' \donttest{
#' if (require("ggplot2") & require("dplyr")) {

2
R/guess_ab_col.R
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@ -26,7 +26,6 @@
#' Guess Antibiotic Column
#'
#' This tries to find a column name in a data set based on information from the [antibiotics] data set. Also supports WHONET abbreviations.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [data.frame]
#' @param search_string a text to search `x` for, will be checked with [as.ab()] if this value is not a column in `x`
#' @param verbose a [logical] to indicate whether additional info should be printed
@ -34,7 +33,6 @@
#' @details You can look for an antibiotic (trade) name or abbreviation and it will search `x` and the [antibiotics] data set for any column containing a name or code of that antibiotic. **Longer columns names take precedence over shorter column names.**
#' @return A column name of `x`, or `NULL` when no result is found.
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' df <- data.frame(amox = "S",
#' tetr = "R")

14
R/italicise_taxonomy.R
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@ -25,8 +25,7 @@
#' Italicise Taxonomic Families, Genera, Species, Subspecies
#'
#' According to the binomial nomenclature, the lowest four taxonomic levels (family, genus, species, subspecies) should be printed in italic. This function finds taxonomic names within strings and makes them italic.
#' @inheritSection lifecycle Stable Lifecycle
#' According to the binomial nomenclature, the lowest four taxonomic levels (family, genus, species, subspecies) should be printed in italics. This function finds taxonomic names within strings and makes them italic.
#' @param string a [character] (vector)
#' @param type type of conversion of the taxonomic names, either "markdown" or "ansi", see *Details*
#' @details
@ -35,23 +34,12 @@
#' The taxonomic names can be italicised using markdown (the default) by adding `*` before and after the taxonomic names, or using ANSI colours by adding `\033[3m` before and `\033[23m` after the taxonomic names. If multiple ANSI colours are not available, no conversion will occur.
#'
#' This function also supports abbreviation of the genus if it is followed by a species, such as "E. coli" and "K. pneumoniae ozaenae".
#' @inheritSection AMR Read more on Our Website!
#' @export
#' @examples
#' italicise_taxonomy("An overview of Staphylococcus aureus isolates")
#' italicise_taxonomy("An overview of S. aureus isolates")
#'
#' cat(italicise_taxonomy("An overview of S. aureus isolates", type = "ansi"))
#'
#' # since ggplot2 supports no markdown (yet), use
#' # italicise_taxonomy() and the `ggtext` package for titles:
#' \donttest{
#' if (require("ggplot2") && require("ggtext")) {
#' autoplot(example_isolates$AMC,
#' title = italicise_taxonomy("Amoxi/clav in E. coli")) +
#' theme(plot.title = ggtext::element_markdown())
#' }
#' }
italicise_taxonomy <- function(string, type = c("markdown", "ansi")) {
if (missing(type)) {
type <- "markdown"

2
R/join_microorganisms.R
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@ -26,7 +26,6 @@
#' Join [microorganisms] to a Data Set
#'
#' Join the data set [microorganisms] easily to an existing data set or to a [character] vector.
#' @inheritSection lifecycle Stable Lifecycle
#' @rdname join
#' @name join
#' @aliases join inner_join
@ -37,7 +36,6 @@
#' @details **Note:** As opposed to the `join()` functions of `dplyr`, [character] vectors are supported and at default existing columns will get a suffix `"2"` and the newly joined columns will not get a suffix.
#'
#' If the `dplyr` package is installed, their join functions will be used. Otherwise, the much slower [merge()] and [interaction()] functions from base \R will be used.
#' @inheritSection AMR Read more on Our Website!
#' @return a [data.frame]
#' @export
#' @examples

4
R/key_antimicrobials.R
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@ -26,7 +26,6 @@
#' (Key) Antimicrobials for First Weighted Isolates
#'
#' These functions can be used to determine first weighted isolates by considering the phenotype for isolate selection (see [first_isolate()]). Using a phenotype-based method to determine first isolates is more reliable than methods that disregard phenotypes.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank to determine automatically
#' @param y,z [character] vectors to compare
#' @inheritParams first_isolate
@ -82,7 +81,6 @@
#' @rdname key_antimicrobials
#' @export
#' @seealso [first_isolate()]
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
@ -110,7 +108,7 @@
#' first_weighted = first_isolate(col_keyantimicrobials = "keyab")
#' )
#'
#' # Check the difference, in this data set it results in more isolates:
#' # Check the difference in this data set, 'weighted' results in more isolates:
#' sum(my_patients$first_regular, na.rm = TRUE)
#' sum(my_patients$first_weighted, na.rm = TRUE)
#' }

5
R/kurtosis.R
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@ -26,14 +26,15 @@
#' Kurtosis of the Sample
#'
#' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. A normal distribution has a kurtosis of 3 and a excess kurtosis of 0.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a vector of values, a [matrix] or a [data.frame]
#' @param na.rm a [logical] to indicate whether `NA` values should be stripped before the computation proceeds
#' @param excess a [logical] to indicate whether the *excess kurtosis* should be returned, defined as the kurtosis minus 3.
#' @seealso [skewness()]
#' @rdname kurtosis
#' @inheritSection AMR Read more on Our Website!
#' @export
#' @examples
#' kurtosis(rnorm(10000))
#' kurtosis(rnorm(10000), excess = TRUE)
kurtosis <- function(x, na.rm = FALSE, excess = FALSE) {
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
meet_criteria(excess, allow_class = "logical", has_length = 1)

54
R/lifecycle.R
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@ -1,54 +0,0 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
###############
# NOTE TO SELF: could also have done this with the 'lifecycle' package, but why add a package dependency for such an easy job??
###############
#' Lifecycles of Functions in the `AMR` Package
#' @name lifecycle
#' @rdname lifecycle
#' @description Functions in this `AMR` package are categorised using [the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle](https://lifecycle.r-lib.org/articles/stages.html).
#'
#' \if{html}{\figure{lifecycle_tidyverse.svg}{options: height="200" style=margin-bottom:"5"} \cr}
#' This page contains a section for every lifecycle (with text borrowed from the aforementioned Tidyverse website), so they can be used in the manual pages of the functions.
#' @section Experimental Lifecycle:
#' \if{html}{\figure{lifecycle_experimental.svg}{options: style=margin-bottom:"5"} \cr}
#' The [lifecycle][AMR::lifecycle] of this function is **experimental**. An experimental function is in early stages of development. The unlying code might be changing frequently. Experimental functions might be removed without deprecation, so you are generally best off waiting until a function is more mature before you use it in production code. Experimental functions are only available in development versions of this `AMR` package and will thus not be included in releases that are submitted to CRAN, since such functions have not yet matured enough.
#' @section Maturing Lifecycle:
#' \if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:"5"} \cr}
#' The [lifecycle][AMR::lifecycle] of this function is **maturing**. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome [to suggest changes at our repository](https://github.com/msberends/AMR/issues) or [write us an email (see section 'Contact Us')][AMR::AMR].
#' @section Stable Lifecycle:
#' \if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:"5"} \cr}
#' The [lifecycle][AMR::lifecycle] of this function is **stable**. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
#'
#' If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
#' @section Retired Lifecycle:
#' \if{html}{\figure{lifecycle_retired.svg}{options: style=margin-bottom:"5"} \cr}
#' The [lifecycle][AMR::lifecycle] of this function is **retired**. A retired function is no longer under active development, and (if appropiate) a better alternative is available. No new arguments will be added, and only the most critical bugs will be fixed. In a future version, this function will be removed.
#' @section Questioning Lifecycle:
#' \if{html}{\figure{lifecycle_questioning.svg}{options: style=margin-bottom:"5"} \cr}
#' The [lifecycle][AMR::lifecycle] of this function is **questioning**. This function might be no longer be optimal approach, or is it questionable whether this function should be in this `AMR` package at all.
NULL

9
R/like.R
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@ -26,7 +26,6 @@
#' Vectorised Pattern Matching with Keyboard Shortcut
#'
#' Convenient wrapper around [grepl()] to match a pattern: `x %like% pattern`. It always returns a [`logical`] vector and is always case-insensitive (use `x %like_case% pattern` for case-sensitive matching). Also, `pattern` can be as long as `x` to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [character] vector where matches are sought, or an object which can be coerced by [as.character()] to a [character] vector.
#' @param pattern a [character] vector containing regular expressions (or a [character] string for `fixed = TRUE`) to be matched in the given [character] vector. Coerced by [as.character()] to a [character] string if possible.
#' @param ignore.case if `FALSE`, the pattern matching is *case sensitive* and if `TRUE`, case is ignored during matching.
@ -44,27 +43,21 @@
#' Using RStudio? The `%like%`/`%unlike%` functions can also be directly inserted in your code from the Addins menu and can have its own keyboard shortcut like `Shift+Ctrl+L` or `Shift+Cmd+L` (see menu `Tools` > `Modify Keyboard Shortcuts...`). If you keep pressing your shortcut, the inserted text will be iterated over `%like%` -> `%unlike%` -> `%like_case%` -> `%unlike_case%`.
#' @source Idea from the [`like` function from the `data.table` package](https://github.com/Rdatatable/data.table/blob/ec1259af1bf13fc0c96a1d3f9e84d55d8106a9a4/R/like.R), although altered as explained in *Details*.
#' @seealso [grepl()]
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' a <- "This is a test"
#' b <- "TEST"
#' a %like% b
#' #> TRUE
#' b %like% a
#' #> FALSE
#'
#' # also supports multiple patterns
#' a <- c("Test case", "Something different", "Yet another thing")
#' b <- c( "case", "diff", "yet")
#' a %like% b
#' #> TRUE TRUE TRUE
#' a %unlike% b
#' #> FALSE FALSE FALSE
#'
#' a[1] %like% b
#' #> TRUE FALSE FALSE
#' a %like% b[1]
#' #> TRUE FALSE FALSE
#'
#' # get isolates whose name start with 'Ent' or 'ent'
#' example_isolates[which(mo_name(example_isolates$mo) %like% "^ent"), ]

19
R/mdro.R
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@ -26,7 +26,6 @@
#' Determine Multidrug-Resistant Organisms (MDRO)
#'
#' Determine which isolates are multidrug-resistant organisms (MDRO) according to international, national and custom guidelines.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank for automatic determination.
#' @param guideline a specific guideline to follow, see sections *Supported international / national guidelines* and *Using Custom Guidelines* below. When left empty, the publication by Magiorakos *et al.* (see below) will be followed.
#' @param ... in case of [custom_mdro_guideline()]: a set of rules, see section *Using Custom Guidelines* below. Otherwise: column name of an antibiotic, see section *Antibiotics* below.
@ -137,15 +136,17 @@
#' @rdname mdro
#' @aliases MDR XDR PDR BRMO 3MRGN 4MRGN
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @source
#' See the supported guidelines above for the [list] of publications used for this function.
#' @examples
#' mdro(example_isolates, guideline = "EUCAST")
#' out <- mdro(example_isolates, guideline = "EUCAST")
#' str(out)
#' table(out)
#'
#' mdro(example_isolates,
#' out <- mdro(example_isolates,
#' guideline = custom_mdro_guideline(AMX == "R" ~ "Custom MDRO 1",
#' VAN == "R" ~ "Custom MDRO 2"))
#' table(out)
#'
#' \donttest{
#' if (require("dplyr")) {
@ -155,10 +156,10 @@
#'
#' # no need to define `x` when used inside dplyr verbs:
#' example_isolates %>%
#' mutate(MDRO = mdro(),
#' EUCAST = eucast_exceptional_phenotypes(),
#' BRMO = brmo(),
#' MRGN = mrgn())
#' mutate(MDRO = mdro()) %>%
#' pull(MDRO) %>%
#' table()
#'
#' }
#' }
mdro <- function(x = NULL,
@ -191,7 +192,9 @@ mdro <- function(x = NULL,
info.bak <- info
# don't thrown info's more than once per call
if (isTRUE(info)) {
info <- message_not_thrown_before("mdro")
}
if (interactive() & verbose == TRUE & info == TRUE) {
txt <- paste0("WARNING: In Verbose mode, the mdro() function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.",

16
R/mic.R
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@ -43,7 +43,6 @@ valid_mic_levels <- c(c(t(vapply(FUN.VALUE = character(9), ops,
#' Transform Input to Minimum Inhibitory Concentrations (MIC)
#'
#' This transforms vectors to a new class [`mic`], which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.
#' @inheritSection lifecycle Stable Lifecycle
#' @rdname as.mic
#' @param x a [character] or [numeric] vector
#' @param na.rm a [logical] indicating whether missing values should be removed
@ -95,32 +94,35 @@ valid_mic_levels <- c(c(t(vapply(FUN.VALUE = character(9), ops,
#' @aliases mic
#' @export
#' @seealso [as.rsi()]
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
#' mic_data
#' is.mic(mic_data)
#'
#' # this can also coerce combined MIC/RSI values:
#' as.mic("<=0.002; S") # will return <=0.002
#' as.mic("<=0.002; S")
#'
#' # mathematical processing treats MICs as [numeric] values
#' # mathematical processing treats MICs as numeric values
#' fivenum(mic_data)
#' quantile(mic_data)
#' all(mic_data < 512)
#'
#' # interpret MIC values
#' as.rsi(x = as.mic(2),
#' mo = as.mo("S. pneumoniae"),
#' mo = as.mo("Streptococcus pneumoniae"),
#' ab = "AMX",
#' guideline = "EUCAST")
#' as.rsi(x = as.mic(4),
#' mo = as.mo("S. pneumoniae"),
#' as.rsi(x = as.mic(c(0.01, 2, 4, 8)),
#' mo = as.mo("Streptococcus pneumoniae"),
#' ab = "AMX",
#' guideline = "EUCAST")
#'
#' # plot MIC values, see ?plot
#' plot(mic_data)
#' plot(mic_data, mo = "E. coli", ab = "cipro")
#' autoplot(mic_data, mo = "E. coli", ab = "cipro")
#' autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "nl") # Dutch
#' autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "uk") # Ukrainian
as.mic <- function(x, na.rm = FALSE) {
meet_criteria(x, allow_class = c("mic", "character", "numeric", "integer", "factor"), allow_NA = TRUE)
meet_criteria(na.rm, allow_class = "logical", has_length = 1)

2
R/mo.R
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@ -26,7 +26,6 @@
#' Transform Input to a Microorganism Code
#'
#' Use this function to determine a valid microorganism code ([`mo`]). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see *Source*). The input can be almost anything: a full name (like `"Staphylococcus aureus"`), an abbreviated name (such as `"S. aureus"`), an abbreviation known in the field (such as `"MRSA"`), or just a genus. See *Examples*.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [character] vector or a [data.frame] with one or two columns
#' @param Becker a [logical] to indicate whether staphylococci should be categorised into coagulase-negative staphylococci ("CoNS") and coagulase-positive staphylococci ("CoPS") instead of their own species, according to Karsten Becker *et al.* (1,2,3).
#'
@ -116,7 +115,6 @@
#'
#' The [`mo_*`][mo_property()] functions (such as [mo_genus()], [mo_gramstain()]) to get properties based on the returned code.
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' \donttest{
#' # These examples all return "B_STPHY_AURS", the ID of S. aureus:

2
R/mo_matching_score.R
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@ -26,7 +26,6 @@
#' 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.
#' @inheritSection lifecycle Stable Lifecycle
#' @author Dr Matthijs Berends
#' @param x Any user input value(s)
#' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
@ -53,7 +52,6 @@
#' Since `AMR` version 1.8.1, common microorganism abbreviations are ignored in determining the matching score. These abbreviations are currently: `r vector_and(pkg_env$mo_field_abbreviations, quotes = FALSE)`.
#' @export
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' as.mo("E. coli")
#' mo_uncertainties()

111
R/mo_property.R
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@ -26,7 +26,6 @@
#' Get Properties of a Microorganism
#'
#' Use these functions to return a specific property of a microorganism based on the latest accepted taxonomy. All input values will be evaluated internally with [as.mo()], which makes it possible to use microbial abbreviations, codes and names as input. See *Examples*.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x any [character] (vector) that can be coerced to a valid microorganism code with [as.mo()]. Can be left blank for auto-guessing the column containing microorganism codes if used in a data set, see *Examples*.
#' @param property one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, quotes = TRUE)`, or must be `"shortname"`
#' @param language language of the returned text, defaults to system language (see [get_AMR_locale()]) and can be overwritten by setting the option `AMR_locale`, e.g. `options(AMR_locale = "de")`, see [translate]. Also used to translate text like "no growth". Use `language = NULL` or `language = ""` to prevent translation.
@ -67,93 +66,91 @@
#' @export
#' @seealso Data set [microorganisms]
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # taxonomic tree -----------------------------------------------------------
#' mo_kingdom("E. coli") # "Bacteria"
#' mo_phylum("E. coli") # "Proteobacteria"
#' mo_class("E. coli") # "Gammaproteobacteria"
#' mo_order("E. coli") # "Enterobacterales"
#' mo_family("E. coli") # "Enterobacteriaceae"
#' mo_genus("E. coli") # "Escherichia"
#' mo_species("E. coli") # "coli"
#' mo_subspecies("E. coli") # ""
#' mo_kingdom("Klebsiella pneumoniae")
#' mo_phylum("Klebsiella pneumoniae")
#' mo_class("Klebsiella pneumoniae")
#' mo_order("Klebsiella pneumoniae")
#' mo_family("Klebsiella pneumoniae")
#' mo_genus("Klebsiella pneumoniae")
#' mo_species("Klebsiella pneumoniae")
#' mo_subspecies("Klebsiella pneumoniae")
#'
#' # colloquial properties ----------------------------------------------------
#' mo_name("E. coli") # "Escherichia coli"
#' mo_fullname("E. coli") # "Escherichia coli" - same as mo_name()
#' mo_shortname("E. coli") # "E. coli"
#' mo_name("Klebsiella pneumoniae")
#' mo_fullname("Klebsiella pneumoniae")
#' mo_shortname("Klebsiella pneumoniae")
#'
#' # other properties ---------------------------------------------------------
#' mo_gramstain("E. coli") # "Gram-negative"
#' mo_snomed("E. coli") # 112283007, 116395006, ... (SNOMED codes)
#' mo_type("E. coli") # "Bacteria" (equal to kingdom, but may be translated)
#' mo_rank("E. coli") # "species"
#' mo_url("E. coli") # get the direct url to the online database entry
#' mo_synonyms("E. coli") # get previously accepted taxonomic names
#' mo_gramstain("Klebsiella pneumoniae")
#' mo_snomed("Klebsiella pneumoniae")
#' mo_type("Klebsiella pneumoniae")
#' mo_rank("Klebsiella pneumoniae")
#' mo_url("Klebsiella pneumoniae")
#' mo_synonyms("Klebsiella pneumoniae")
#'
#' # scientific reference -----------------------------------------------------
#' mo_ref("E. coli") # "Castellani et al., 1919"
#' mo_authors("E. coli") # "Castellani et al."
#' mo_year("E. coli") # 1919
#' mo_lpsn("E. coli") # 776057 (LPSN record ID)
#' mo_ref("Klebsiella pneumoniae")
#' mo_authors("Klebsiella pneumoniae")
#' mo_year("Klebsiella pneumoniae")
#' mo_lpsn("Klebsiella pneumoniae")
#'
#' # abbreviations known in the field -----------------------------------------
#' mo_genus("MRSA") # "Staphylococcus"
#' mo_species("MRSA") # "aureus"
#' mo_shortname("VISA") # "S. aureus"
#' mo_gramstain("VISA") # "Gram-positive"
#' mo_genus("MRSA")
#' mo_species("MRSA")
#' mo_shortname("VISA")
#' mo_gramstain("VISA")
#'
#' mo_genus("EHEC") # "Escherichia"
#' mo_species("EHEC") # "coli"
#' mo_genus("EHEC")
#' mo_species("EHEC")
#'
#' # known subspecies ---------------------------------------------------------
#' mo_name("doylei") # "Campylobacter jejuni doylei"
#' mo_genus("doylei") # "Campylobacter"
#' mo_species("doylei") # "jejuni"
#' mo_subspecies("doylei") # "doylei"
#' mo_name("doylei")
#' mo_genus("doylei")
#' mo_species("doylei")
#' mo_subspecies("doylei")
#'
#' mo_fullname("K. pneu rh") # "Klebsiella pneumoniae rhinoscleromatis"
#' mo_shortname("K. pneu rh") # "K. pneumoniae"
#' mo_fullname("K. pneu rh")
#' mo_shortname("K. pneu rh")
#'
#' \donttest{
#' # Becker classification, see ?as.mo ----------------------------------------
#' mo_fullname("S. epi") # "Staphylococcus epidermidis"
#' mo_fullname("S. epi", Becker = TRUE) # "Coagulase-negative Staphylococcus (CoNS)"
#' mo_shortname("S. epi") # "S. epidermidis"
#' mo_shortname("S. epi", Becker = TRUE) # "CoNS"
#' mo_fullname("S. epi")
#' mo_fullname("S. epi", Becker = TRUE)
#' mo_shortname("S. epi")
#' mo_shortname("S. epi", Becker = TRUE)
#'
#' # Lancefield classification, see ?as.mo ------------------------------------
#' mo_fullname("S. pyo") # "Streptococcus pyogenes"
#' mo_fullname("S. pyo", Lancefield = TRUE) # "Streptococcus group A"
#' mo_shortname("S. pyo") # "S. pyogenes"
#' mo_shortname("S. pyo", Lancefield = TRUE) # "GAS" (='Group A Streptococci')
#' mo_fullname("S. pyo")
#' mo_fullname("S. pyo", Lancefield = TRUE)
#' mo_shortname("S. pyo")
#' mo_shortname("S. pyo", Lancefield = TRUE)
#'
#'
#' # language support --------------------------------------------------------
#' mo_gramstain("E. coli", language = "de") # "Gramnegativ"
#' mo_gramstain("E. coli", language = "nl") # "Gram-negatief"
#' mo_gramstain("E. coli", language = "es") # "Gram negativo"
#' mo_gramstain("Klebsiella pneumoniae", language = "de")
#' mo_gramstain("Klebsiella pneumoniae", language = "nl")
#' mo_gramstain("Klebsiella pneumoniae", language = "es")
#'
#' # mo_type is equal to mo_kingdom, but mo_kingdom will remain official
#' mo_kingdom("E. coli") # "Bacteria" on a German system
#' mo_type("E. coli") # "Bakterien" on a German system
#' mo_type("E. coli") # "Bacteria" on an English system
#' mo_kingdom("Klebsiella pneumoniae")
#' mo_type("Klebsiella pneumoniae")
#' mo_type("Klebsiella pneumoniae")
#'
#' mo_fullname("S. pyogenes",
#' Lancefield = TRUE,
#' language = "de") # "Streptococcus Gruppe A"
#' language = "de")
#' mo_fullname("S. pyogenes",
#' Lancefield = TRUE,
#' language = "nl") # "Streptococcus groep A"
#' language = "nl")
#'
#'
#' # other --------------------------------------------------------------------
#'
#' mo_is_yeast(c("Candida", "E. coli")) # TRUE, FALSE
#' mo_is_yeast(c("Candida", "Trichophyton", "Klebsiella"))
#'
#' # gram stains and intrinsic resistance can also be used as a filter in dplyr verbs
#' \donttest{
#' # gram stains and intrinsic resistance can be used as a filter in dplyr verbs
#' if (require("dplyr")) {
#' example_isolates %>%
#' filter(mo_is_gram_positive())
@ -164,11 +161,11 @@
#'
#'
#' # get a list with the complete taxonomy (from kingdom to subspecies)
#' mo_taxonomy("E. coli")
#' mo_taxonomy("Klebsiella pneumoniae")
#'
#' # get a list with the taxonomy, the authors, Gram-stain,
#' # SNOMED codes, and URL to the online database
#' mo_info("E. coli")
#' }
#' mo_info("Klebsiella pneumoniae")
#' }
mo_name <- function(x, language = get_AMR_locale(), ...) {
if (missing(x)) {

2
R/mo_source.R
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@ -28,7 +28,6 @@
#' @description These functions can be used to predefine your own reference to be used in [as.mo()] and consequently all [`mo_*`][mo_property()] functions (such as [mo_genus()] and [mo_gramstain()]).
#'
#' This is **the fastest way** to have your organisation (or analysis) specific codes picked up and translated by this package, since you don't have to bother about it again after setting it up once.
#' @inheritSection lifecycle Stable Lifecycle
#' @param path location of your reference file, this can be any text file (comma-, tab- or pipe-separated) or an Excel file (see *Details*). Can also be `""`, `NULL` or `FALSE` to delete the reference file.
#' @param destination destination of the compressed data file, default to the user's home directory.
#' @rdname mo_source
@ -121,7 +120,6 @@
#'
#' If the original file (in the previous case an Excel file) is moved or deleted, the `mo_source.rds` file will be removed upon the next use of [as.mo()] or any [`mo_*`][mo_property()] function.
#' @export
#' @inheritSection AMR Read more on Our Website!
set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_source.rds")) {
meet_criteria(path, allow_class = "character", has_length = 1, allow_NULL = TRUE)
meet_criteria(destination, allow_class = "character", has_length = 1)

14
R/pca.R
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@ -26,7 +26,6 @@
#' Principal Component Analysis (for AMR)
#'
#' Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [data.frame] containing [numeric] columns
#' @param ... columns of `x` to be selected for PCA, can be unquoted since it supports quasiquotation.
#' @inheritParams stats::prcomp
@ -36,7 +35,6 @@
#' @return An object of classes [pca] and [prcomp]
#' @importFrom stats prcomp
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
@ -47,6 +45,7 @@
#' resistance_data <- example_isolates %>%
#' group_by(order = mo_order(mo), # group on anything, like order
#' genus = mo_genus(mo)) %>% # and genus as we do here;
#' filter(n() >= 30) %>% # filter on only 30 results per group
#' summarise_if(is.rsi, resistance) # then get resistance of all drugs
#'
#' # now conduct PCA for certain antimicrobial agents
@ -55,8 +54,17 @@
#'
#' pca_result
#' summary(pca_result)
#'
#' # old base R plotting method:
#' biplot(pca_result)
#' ggplot_pca(pca_result) # a new and convenient plot function
#' # new ggplot2 plotting method using this package:
#' ggplot_pca(pca_result)
#'
#' if (require("ggplot2")) {
#' ggplot_pca(pca_result) +
#' scale_colour_viridis_d() +
#' labs(title = "Title here")
#' }
#' }
#' }
pca <- function(x,

3
R/plot.R
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@ -26,8 +26,7 @@
#' Plotting for Classes `rsi`, `mic` and `disk`
#'
#' Functions to plot classes `rsi`, `mic` and `disk`, with support for base \R and `ggplot2`.
#' @inheritSection lifecycle Stable Lifecycle
#' @inheritSection AMR Read more on Our Website!
#' @param x,object values created with [as.mic()], [as.disk()] or [as.rsi()] (or their `random_*` variants, such as [random_mic()])
#' @param mo any (vector of) text that can be coerced to a valid microorganism code with [as.mo()]
#' @param ab any (vector of) text that can be coerced to a valid antimicrobial code with [as.ab()]

9
R/proportion.R
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@ -28,7 +28,6 @@
#' @description These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in `summarise()` from the `dplyr` package and also support grouped variables, see *Examples*.
#'
#' [resistance()] should be used to calculate resistance, [susceptibility()] should be used to calculate susceptibility.\cr
#' @inheritSection lifecycle Stable Lifecycle
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with [as.rsi()] if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See *Examples*.
#' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than `minimum` will return `NA` with a warning. The default number of `30` isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see *Source*.
#' @param as_percent a [logical] to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of `0.123456` will then be returned as `"12.3%"`.
@ -88,11 +87,11 @@
#' @aliases portion
#' @name proportion
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' ?example_isolates
#' # run ?example_isolates for more info.
#'
#' # base R ------------------------------------------------------------
#' resistance(example_isolates$AMX) # determines %R
#' susceptibility(example_isolates$AMX) # determines %S+I
#'
@ -103,6 +102,7 @@
#' proportion_IR(example_isolates$AMX)
#' proportion_R(example_isolates$AMX)
#'
#' # dplyr -------------------------------------------------------------
#' \donttest{
#' if (require("dplyr")) {
#' example_isolates %>%
@ -157,10 +157,11 @@
#' proportion_df(translate = FALSE)
#'
#' # It also supports grouping variables
#' # (use rsi_df to also include the count)
#' example_isolates %>%
#' select(hospital_id, AMX, CIP) %>%
#' group_by(hospital_id) %>%
#' proportion_df(translate = FALSE)
#' rsi_df(translate = FALSE)
#' }
#' }
resistance <- function(...,

20
R/random.R
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@ -26,7 +26,6 @@
#' Random MIC Values/Disk Zones/RSI Generation
#'
#' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial agent, the generated results will reflect reality as much as possible.
#' @inheritSection lifecycle Stable Lifecycle
#' @param size desired size of the returned vector. If used in a [data.frame] call or `dplyr` verb, will get the current (group) size if left blank.
#' @param mo any [character] that can be coerced to a valid microorganism code with [as.mo()]
#' @param ab any [character] that can be coerced to a valid antimicrobial agent code with [as.ab()]
@ -39,21 +38,20 @@
#' @name random
#' @rdname random
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' random_mic(100)
#' random_disk(100)
#' random_rsi(100)
#' random_mic(25)
#' random_disk(25)
#' random_rsi(25)
#'
#' \donttest{
#' # make the random generation more realistic by setting a bug and/or drug:
#' random_mic(100, "Klebsiella pneumoniae") # range 0.0625-64
#' random_mic(100, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
#' random_mic(100, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
#' random_mic(25, "Klebsiella pneumoniae") # range 0.0625-64
#' random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
#' random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
#'
#' random_disk(100, "Klebsiella pneumoniae") # range 8-50
#' random_disk(100, "Klebsiella pneumoniae", "ampicillin") # range 11-17
#' random_disk(100, "Streptococcus pneumoniae", "ampicillin") # range 12-27
#' random_disk(25, "Klebsiella pneumoniae") # range 8-50
#' random_disk(25, "Klebsiella pneumoniae", "ampicillin") # range 11-17
#' random_disk(25, "Streptococcus pneumoniae", "ampicillin") # range 12-27
#' }
random_mic <- function(size = NULL, mo = NULL, ab = NULL, ...) {
meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)

20
R/resistance_predict.R
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@ -26,7 +26,6 @@
#' Predict Antimicrobial Resistance
#'
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns `se_min` and `se_max`. See *Examples* for a real live example.
#' @inheritSection lifecycle Stable Lifecycle
#' @param object model data to be plotted
#' @param col_ab column name of `x` containing antimicrobial interpretations (`"R"`, `"I"` and `"S"`)
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
@ -64,7 +63,6 @@
#' @rdname resistance_predict
#' @export
#' @importFrom stats predict glm lm
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' x <- resistance_predict(example_isolates,
#' col_ab = "AMX",
@ -99,24 +97,8 @@
#' model = "binomial",
#' info = FALSE,
#' minimum = 15)
#'
#' head(data)
#' autoplot(data)
#'
#' ggplot(data,
#' aes(x = year)) +
#' geom_col(aes(y = value),
#' fill = "grey75") +
#' geom_errorbar(aes(ymin = se_min,
#' ymax = se_max),
#' colour = "grey50") +
#' scale_y_continuous(limits = c(0, 1),
#' breaks = seq(0, 1, 0.1),
#' labels = paste0(seq(0, 100, 10), "%")) +
#' labs(title = expression(paste("Forecast of Amoxicillin Resistance in ",
#' italic("E. coli"))),
#' y = "%R",
#' x = "Year") +
#' theme_minimal(base_size = 13)
#' }
#' }
resistance_predict <- function(x,

12
R/rsi.R
View File

@ -26,7 +26,6 @@
#' Interpret MIC and Disk Values, or Clean Raw R/SI Data
#'
#' Interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing R/SI values. This transforms the input to a new class [`rsi`], which is an ordered [factor] with levels `S < I < R`.
#' @inheritSection lifecycle Stable Lifecycle
#' @rdname as.rsi
#' @param x vector of values (for class [`mic`]: MIC values in mg/L, for class [`disk`]: a disk diffusion radius in millimetres)
#' @param mo any (vector of) text that can be coerced to valid microorganism codes with [as.mo()], can be left empty to determine it automatically
@ -98,15 +97,10 @@
#' @export
#' @seealso [as.mic()], [as.disk()], [as.mo()]
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' example_isolates
#' summary(example_isolates) # see all R/SI results at a glance
#' \donttest{
#' if (require("skimr")) {
#' # class <rsi> supported in skim() too:
#' skim(example_isolates)
#' }
#' }
#'
#' # For INTERPRETING disk diffusion and MIC values -----------------------
#'
#' # a whole data set, even with combined MIC values and disk zones
@ -796,7 +790,7 @@ exec_as.rsi <- function(method,
lookup_lancefield[i],
lookup_other[i]))
if (any(get_record$uti == TRUE, na.rm = TRUE) && message_not_thrown_before("as.rsi", "msg3", ab)) {
if (any(get_record$uti == TRUE, na.rm = TRUE) && !any(uti == TRUE, na.rm = TRUE) && message_not_thrown_before("as.rsi", "msg3", ab)) {
warning_("in `as.rsi()`: interpretation of ", font_bold(ab_name(ab, tolower = TRUE)), " is only available for (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use argument `uti` to set which isolates are from urine. See ?as.rsi.")
rise_warning <- TRUE
}

4
R/skewness.R
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@ -28,13 +28,13 @@
#' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
#'
#' When negative ('left-skewed'): the left tail is longer; the mass of the distribution is concentrated on the right of a histogram. When positive ('right-skewed'): the right tail is longer; the mass of the distribution is concentrated on the left of a histogram. A normal distribution has a skewness of 0.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a vector of values, a [matrix] or a [data.frame]
#' @param na.rm a [logical] value indicating whether `NA` values should be stripped before the computation proceeds
#' @seealso [kurtosis()]
#' @rdname skewness
#' @inheritSection AMR Read more on Our Website!
#' @export
#' @examples
#' skewness(runif(1000))
skewness <- function(x, na.rm = FALSE) {
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
UseMethod("skewness")

4
R/translate.R
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@ -26,12 +26,11 @@
#' Translate Strings from the AMR Package
#'
#' For language-dependent output of AMR functions, like [mo_name()], [mo_gramstain()], [mo_type()] and [ab_name()].
#' @inheritSection lifecycle Stable Lifecycle
#' @param x text to translate
#' @param lang language to choose. Use one of these supported language names or ISO-639-1 codes: `r paste0('"', sapply(LANGUAGES_SUPPORTED_NAMES, function(x) x[[1]]), '" ("' , LANGUAGES_SUPPORTED, '")', collapse = ", ")`.
#' @details The currently `r length(LANGUAGES_SUPPORTED)` supported languages are `r vector_and(sapply(LANGUAGES_SUPPORTED_NAMES, function(x) x[[1]]), quotes = FALSE, sort = FALSE)`. All these languages have translations available for all antimicrobial agents and colloquial microorganism names.
#'
#' Please read about adding or updating a language in [our developer guideline](https://github.com/msberends/AMR/blob/main/developer-guideline.md).
#' Please read about adding or updating a language in [our Wiki](https://github.com/msberends/AMR/wiki/).
#'
#' ## Changing the Default Language
#' The system language will be used at default (as returned by `Sys.getenv("LANG")` or, if `LANG` is not set, [Sys.getlocale("LC_COLLATE")]), if that language is supported. But the language to be used can be overwritten in two ways and will be checked in this order:
@ -42,7 +41,6 @@
#' 2. Setting the system variable `LANGUAGE` or `LANG`, e.g. by adding `LANGUAGE="de_DE.utf8"` to your `.Renviron` file in your home directory.
#'
#' Thus, if the R option `AMR_locale` is set, the system variables `LANGUAGE` and `LANG` will be ignored.
#' @inheritSection AMR Read more on Our Website!
#' @rdname translate
#' @name translate
#' @export

14
R/vctrs.R
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@ -28,8 +28,9 @@
# They are to convert AMR-specific classes to bare characters and integers.
# All of them will be exported using s3_register() in R/zzz.R when loading the package.
# S3: ab_selector
# see https://github.com/tidyverse/dplyr/issues/5955 why this is required
# S3: ab_selector
vec_ptype2.character.ab_selector <- function(x, y, ...) {
x
}
@ -40,6 +41,17 @@ vec_cast.character.ab_selector <- function(x, to, ...) {
unclass(x)
}
# S3: ab_selector_any_all
vec_ptype2.logical.ab_selector_any_all <- function(x, y, ...) {
x
}
vec_ptype2.ab_selector_any_all.logical <- function(x, y, ...) {
y
}
vec_cast.logical.ab_selector_any_all <- function(x, to, ...) {
unclass(x)
}
# S3: ab
vec_ptype2.character.ab <- function(x, y, ...) {
x

2
R/whocc.R
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@ -35,7 +35,7 @@
#' The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
#'
#' **NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package.** See <https://www.whocc.no/copyright_disclaimer/.>
#' @inheritSection AMR Read more on Our Website!
#' @name WHOCC
#' @rdname WHOCC
#' @examples

8
R/zzz.R
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@ -87,6 +87,9 @@ if (utf8_supported && !is_latex) {
s3_register("vctrs::vec_ptype2", "ab_selector.character")
s3_register("vctrs::vec_ptype2", "character.ab_selector")
s3_register("vctrs::vec_cast", "character.ab_selector")
s3_register("vctrs::vec_ptype2", "ab_selector_any_all.logical")
s3_register("vctrs::vec_ptype2", "logical.ab_selector_any_all")
s3_register("vctrs::vec_cast", "logical.ab_selector_any_all")
s3_register("vctrs::vec_ptype2", "disk.integer")
s3_register("vctrs::vec_ptype2", "integer.disk")
s3_register("vctrs::vec_cast", "integer.disk")
@ -106,11 +109,6 @@ if (utf8_supported && !is_latex) {
assign(x = "MO.old_lookup", value = create_MO.old_lookup(), envir = asNamespace("AMR"))
# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
assign(x = "INTRINSIC_R", value = create_intr_resistance(), envir = asNamespace("AMR"))
# for building the website, only print first 5 rows of a data set
# if (Sys.getenv("IN_PKGDOWN") != "" && !interactive()) {
# ...
# }
}
# Helper functions --------------------------------------------------------

4
README.md
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@ -8,9 +8,7 @@
<img src="https://msberends.github.io/AMR/AMR_intro.svg" align="center" height="300px" />
The latest built **source package** (`AMR_latest.tar.gz`) can be found in folder [/data-raw/](https://github.com/msberends/AMR/tree/main/data-raw).
`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. It is currently being used in over 150 countries.
`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. It is currently being used in over 175 countries.
After installing this package, R knows ~71,000 distinct microbial species and all ~570 antibiotic, antimycotic, and antiviral drugs by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. Antimicrobial names and group names are available in Danish, Dutch, English, French, German, Italian, Portuguese and Spanish.

43
_pkgdown.yml
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@ -26,14 +26,39 @@
title: "AMR (for R)"
url: "https://msberends.github.io/AMR/"
development:
mode: "release" # improves indexing by search engines
version_tooltip: "Latest development version"
template:
bootstrap: 5
bootswatch: "flatly"
assets: "pkgdown/logos" # use logos in this folder
bslib:
code_font: {google: "Fira Code"}
body-text-align: "justify"
# the green "success" colour of this bootstrap theme should be the same as the green in our logo
success: "#128f76"
link-color: "#128f76"
navbar-padding-y: "0.5rem"
opengraph:
twitter:
creator: "@msberends"
site: "@univgroningen"
card: summary_large_image
news:
one_page: true
cran_dates: true
footer:
structure:
left: [devtext]
right: [logo]
components:
devtext: '<code>AMR</code> (for R). Developed at the <a target="_blank" href="https://www.rug.nl">University of Groningen</a> in collaboration with non-profit organisations<br><a target="_blank" href="https://www.certe.nl">Certe Medical Diagnostics and Advice Foundation</a> and <a target="_blank" href="https://www.umcg.nl">University Medical Center Groningen</a>.'
logo: '<a target="_blank" href="https://www.rug.nl"><img src="https://github.com/msberends/AMR/raw/main/pkgdown/logos/logo_rug.png" style="max-width: 200px;"></a>'
home:
sidebar:
structure: [toc, links, authors, citation]
navbar:
title: "AMR (for R)"
left:
@ -161,7 +186,6 @@ reference:
- "`catalogue_of_life`"
- "`catalogue_of_life_version`"
- "`WHOCC`"
- "`lifecycle`"
- "`example_isolates_unclean`"
- "`rsi_translation`"
- "`WHONET`"
@ -200,14 +224,3 @@ reference:
contents:
- "`AMR-deprecated`"
template:
bootstrap: 3
opengraph:
twitter:
creator: "@msberends"
site: "@univgroningen"
card: summary_large_image
assets: "pkgdown/logos" # use logos in this folder
params:
noindex: false
bootswatch: "flatly"

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# Developer Guideline
Welcome to the Developer Guideline of the `AMR` R package. This guideline explains about repository workflows and updates of package elements.
## Copyright
This R package and of its components are licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/main/LICENSE). In a nutshell, this means that this package:
- May be used for commercial purposes
- May be used for private purposes
- May **not** be used for patent purposes
- May be modified, although:
- Modifications **must** be released under the same license when distributing the package
- Changes made to the code **must** be documented
- May be distributed, although:
- Source code **must** be made available when the package is distributed
- A copy of the license and copyright notice **must** be included with the package.
- Comes with a LIMITATION of liability
- Comes with NO warranty
## General Intended Git(Hub) Workflow
All updates to the reposo should be done using `git commit`, preferably with the following predefined pre-commit Git hook.
This repository provides automated semantic versioning and R documentation updates by using a pre-commit Git hook. When using `git commit`, a script will be run to increase the version number, update the date and R documentation. To set this up, run this command when working locally in the repository:
```bash
git config --local core.hooksPath ".github/prehooks"
```
Now, when using `git commit`:
```bash
git commit -am "test commit"
# Running pre-commit hook...
# >> Updating R documentation...
# >> done.
# >>
# >> Updating semantic versioning and date...
# >> - latest tag is 'v1.8.1', with 26 previous commits
# >> - testpkg pkg version set to 1.8.1.9027
# >> - updated DESCRIPTION
# >> - updated NEWS.md
# >>
# [main 300b93e] test commit
# 3 files changed, 3 insertions(+), 4 deletions(-)
```
### Website Generation
### Commiting Changes
## Updating the AMR Package
### Update EUCAST/CLSI Guidelines
### Update the Microbial Taxonomy
### Update the Antimicrobial Agents
### Add or Update a Language for Translation
For the most ideal workflow, please fork this repository and make the changes in your own forked repository. Afterwards, please create a Pull Request. If you are unfamiliar with these terms, no problem at all! Then please send us the edited files by email or any way you prefer.
The repository file [`data-raw/translations.tsv`](https://github.com/msberends/AMR/blob/main/developer-guideline.md) contains all translations. This file will be read by all functions where a translated output can be desired, like all `mo_*` functions (such as `mo_name()`, `mo_gramstain()`, `mo_type()`, etc.) and ``ab_*` functions (such as `ab_name()`, `ab_group()`, etc.).
1. To **add** a translation, edit `data-raw/translations.tsv` (you can copy the contains to MS Excel for convenience and paste the contents back later), add a column where the new column name is a [ISO 639-1 language code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) (such as `en` for English, `de` for German and `es` for Spanish) and put in the new column all translated text from the first column.\
\
To **update** a translation, open `data-raw/translations.tsv` and save it with the language updates.
2. Set the current working directory to the AMR package root folder (either by opening the AMR package as RStudio project, or by setting the working directory with `setwd()`).
3. Run `source("data-raw/_language_update.R)"` to update the internal package data. If you have the `roxygen2` package installed, this script automatically updates the package documentation as well.
Many thanks for your contribution!

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<h1 data-toc-skip>How to apply EUCAST rules</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/EUCAST.Rmd" class="external-link"><code>vignettes/EUCAST.Rmd</code></a></small>
<div class="hidden name"><code>EUCAST.Rmd</code></div>
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<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
<p>What are EUCAST rules? The European Committee on Antimicrobial
Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">on
their website</a>:</p>
<blockquote>
<p><em>EUCAST expert rules are a tabulated collection of expert
knowledge on intrinsic resistances, exceptional resistance phenotypes
and interpretive rules that may be applied to antimicrobial
susceptibility testing in order to reduce errors and make appropriate
recommendations for reporting particular resistances.</em></p>
</blockquote>
<p>In Europe, a lot of medical microbiological laboratories already
apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown
<em>et al.</em>, 2015</a>). Our package features their latest insights
on intrinsic resistance and unusual phenotypes (v3.3, 2021).</p>
<p>Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function we use for this
purpose can also apply additional rules, like forcing
<help title="ATC: J01CA01">ampicillin</help> = R in isolates when
<help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
</div>
<div class="section level2">
<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
</h2>
<p>These rules can be used to discard impossible bug-drug combinations
in your data. For example, <em>Klebsiella</em> produces beta-lactamase
that prevents ampicillin (or amoxicillin) from working against it. In
other words, practically every strain of <em>Klebsiella</em> is
resistant to ampicillin.</p>
<p>Sometimes, laboratory data can still contain such strains with
ampicillin being susceptible to ampicillin. This could be because an
antibiogram is available before an identification is available, and the
antibiogram is then not re-interpreted based on the identification
(namely, <em>Klebsiella</em>). EUCAST expert rules solve this, that can
be applied using <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<span class="st">"Escherichia"</span><span class="op">)</span>,
ampicillin <span class="op">=</span> <span class="st">"S"</span><span class="op">)</span>
<span class="va">oops</span>
<span class="co"># mo ampicillin</span>
<span class="co"># 1 Klebsiella S</span>
<span class="co"># 2 Escherichia S</span>
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
<span class="co"># mo ampicillin</span>
<span class="co"># 1 Klebsiella R</span>
<span class="co"># 2 Escherichia S</span></code></pre></div>
<p>A more convenient function is
<code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline,
but allows to check for one or more specific microorganisms or
antibiotics:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>, <span class="st">"Escherichia"</span><span class="op">)</span>,
<span class="st">"ampicillin"</span><span class="op">)</span>
<span class="co"># [1] TRUE FALSE</span>
<span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span><span class="op">)</span>
<span class="co"># [1] TRUE FALSE</span></code></pre></div>
<p>EUCAST rules can not only be used for correction, they can also be
used for filling in known resistance and susceptibility based on results
of other antimicrobials drugs. This process is called <em>interpretive
reading</em>, is basically a form of imputation, and is part of the
<code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function as well:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span>,
<span class="st">"Enterococcus faecalis"</span>,
<span class="st">"Escherichia coli"</span>,
<span class="st">"Klebsiella pneumoniae"</span>,
<span class="st">"Pseudomonas aeruginosa"</span><span class="op">)</span>,
VAN <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Vancomycin</span>
AMX <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Amoxicillin</span>
COL <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Colistin</span>
CAZ <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Ceftazidime</span>
CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span>
PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span>
FOX <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Cefoxitin</span>
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="left">mo</th>
<th align="center">VAN</th>
<th align="center">AMX</th>
<th align="center">COL</th>
<th align="center">CAZ</th>
<th align="center">CXM</th>
<th align="center">PEN</th>
<th align="center">FOX</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">Staphylococcus aureus</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="left">Enterococcus faecalis</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="odd">
<td align="left">Escherichia coli</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="left">Klebsiella pneumoniae</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="odd">
<td align="left">Pseudomonas aeruginosa</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="left">mo</th>
<th align="center">VAN</th>
<th align="center">AMX</th>
<th align="center">COL</th>
<th align="center">CAZ</th>
<th align="center">CXM</th>
<th align="center">PEN</th>
<th align="center">FOX</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">Staphylococcus aureus</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="left">Enterococcus faecalis</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="left">Escherichia coli</td>
<td align="center">R</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">R</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="left">Klebsiella pneumoniae</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">R</td>
<td align="center">S</td>
</tr>
<tr class="odd">
<td align="left">Pseudomonas aeruginosa</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
</tr>
</tbody>
</table>
</div>
</div>
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<h1 data-toc-skip>How to determine multi-drug resistance
(MDR)</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/MDR.Rmd" class="external-link"><code>vignettes/MDR.Rmd</code></a></small>
<div class="hidden name"><code>MDR.Rmd</code></div>
</div>
<p>With the function <code><a href="../reference/mdro.html">mdro()</a></code>, you can determine which
micro-organisms are multi-drug resistant organisms (MDRO).</p>
<div class="section level3">
<h3 id="type-of-input">Type of input<a class="anchor" aria-label="anchor" href="#type-of-input"></a>
</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function takes a data set as input, such as a
regular <code>data.frame</code>. It tries to automatically determine the
right columns for info about your isolates, such as the name of the
species and all columns with results of antimicrobial agents. See the
help page for more info about how to set the right settings for your
data with the command <code><a href="../reference/mdro.html">?mdro</a></code>.</p>
<p>For WHONET data (and most other data), all settings are automatically
set correctly.</p>
</div>
<div class="section level3">
<h3 id="guidelines">Guidelines<a class="anchor" aria-label="anchor" href="#guidelines"></a>
</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function support multiple guidelines. You can
select a guideline with the <code>guideline</code> parameter. Currently
supported guidelines are (case-insensitive):</p>
<ul>
<li>
<p><code>guideline = "CMI2012"</code> (default)</p>
<p>Magiorakos AP, Srinivasan A <em>et al.</em> “Multidrug-resistant,
extensively drug-resistant and pandrug-resistant bacteria: an
international expert proposal for interim standard definitions for
acquired resistance.” Clinical Microbiology and Infection (2012) (<a href="https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "EUCAST3.2"</code> (or simply
<code>guideline = "EUCAST"</code>)</p>
<p>The European international guideline - EUCAST Expert Rules Version
3.2 “Intrinsic Resistance and Unusual Phenotypes” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "EUCAST3.1"</code></p>
<p>The European international guideline - EUCAST Expert Rules Version
3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "TB"</code></p>
<p>The international guideline for multi-drug resistant tuberculosis -
World Health Organization “Companion handbook to the WHO guidelines for
the programmatic management of drug-resistant tuberculosis” (<a href="https://www.who.int/tb/publications/pmdt_companionhandbook/en/" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "MRGN"</code></p>
<p>The German national guideline - Mueller <em>et al.</em> (2015)
Antimicrobial Resistance and Infection Control 4:7. DOI:
10.1186/s13756-015-0047-6</p>
</li>
<li>
<p><code>guideline = "BRMO"</code></p>
<p>The Dutch national guideline - Rijksinstituut voor Volksgezondheid en
Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen)
(ZKH)” (<a href="https://www.rivm.nl/wip-richtlijn-brmo-bijzonder-resistente-micro-organismen-zkh" class="external-link">link</a>)</p>
</li>
</ul>
<p>Please suggest your own (country-specific) guidelines by letting us
know: <a href="https://github.com/msberends/AMR/issues/new" class="external-link uri">https://github.com/msberends/AMR/issues/new</a>.</p>
<div class="section level4">
<h4 id="custom-guidelines">Custom Guidelines<a class="anchor" aria-label="anchor" href="#custom-guidelines"></a>
</h4>
<p>You can also use your own custom guideline. Custom guidelines can be
set with the <code><a href="../reference/mdro.html">custom_mdro_guideline()</a></code> function. This is of
great importance if you have custom rules to determine MDROs in your
hospital, e.g., rules that are dependent on ward, state of contact
isolation or other variables in your data.</p>
<p>If you are familiar with <code><a href="https://dplyr.tidyverse.org/reference/case_when.html" class="external-link">case_when()</a></code> of the
<code>dplyr</code> package, you will recognise the input method to set
your own rules. Rules must be set using what R considers to be the
‘formula notation’:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">custom</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">custom_mdro_guideline</a></span><span class="op">(</span><span class="va">CIP</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type A"</span>,
<span class="va">ERY</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type B"</span><span class="op">)</span></code></pre></div>
<p>If a row/an isolate matches the first rule, the value after the first
<code>~</code> (in this case <em>‘Elderly Type A’</em>) will be set as
MDRO value. Otherwise, the second rule will be tried and so on. The
maximum number of rules is unlimited.</p>
<p>You can print the rules set in the console for an overview. Colours
will help reading it if your console supports colours.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">custom</span>
<span class="co"># A set of custom MDRO rules:</span>
<span class="co"># 1. If CIP is "R" and age is higher than 60 then: Elderly Type A</span>
<span class="co"># 2. If ERY is "R" and age is higher than 60 then: Elderly Type B</span>
<span class="co"># 3. Otherwise: Negative</span>
<span class="co"># </span>
<span class="co"># Unmatched rows will return NA.</span>
<span class="co"># Results will be of class &lt;factor&gt;, with ordered levels: Negative &lt; Elderly Type A &lt; Elderly Type B</span></code></pre></div>
<p>The outcome of the function can be used for the
<code>guideline</code> argument in the <code><a href="../reference/mdro.html">mdro()</a></code> function:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">example_isolates</span>, guideline <span class="op">=</span> <span class="va">custom</span><span class="op">)</span>
<span class="co"># Determining MDROs based on custom rules, resulting in factor levels:</span>
<span class="co"># Negative &lt; Elderly Type A &lt; Elderly Type B.</span>
<span class="co"># - Custom MDRO rule 1: `CIP == "R" &amp; age &gt; 60` (198 rows matched)</span>
<span class="co"># - Custom MDRO rule 2: `ERY == "R" &amp; age &gt; 60` (732 rows matched)</span>
<span class="fu"><a href="https://rdrr.io/r/base/table.html" class="external-link">table</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># x</span>
<span class="co"># Negative Elderly Type A Elderly Type B </span>
<span class="co"># 1070 198 732</span></code></pre></div>
<p>The rules set (the <code>custom</code> object in this case) could be
exported to a shared file location using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> if you
collaborate with multiple users. The custom rules set could then be
imported using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS()</a></code>.</p>
</div>
</div>
<div class="section level3">
<h3 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered
<code>factor</code> for predefined guidelines. For example, the output
of the default guideline by Magiorakos <em>et al.</em> returns a
<code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in
that order.</p>
<p>The next example uses the <code>example_isolates</code> data set.
This is a data set included with this package and contains full
antibiograms of 2,000 microbial isolates. It reflects reality and can be
used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline
on this data set, we get:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># to support pipes: %&gt;%</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span>
<span class="co"># ℹ Using column 'mo' as input for `col_mo`.</span>
<span class="co"># Auto-guessing columns suitable for analysis... OK.</span>
<span class="co"># ℹ Reliability would be improved if these antimicrobial results would be</span>
<span class="co"># available too: ampicillin/sulbactam (SAM), aztreonam (ATM), cefotetan</span>
<span class="co"># (CTT), ceftaroline (CPT), daptomycin (DAP), doripenem (DOR), ertapenem</span>
<span class="co"># (ETP), fusidic acid (FUS), gentamicin-high (GEH), levofloxacin (LVX),</span>
<span class="co"># minocycline (MNO), netilmicin (NET), polymyxin B (PLB),</span>
<span class="co"># quinupristin/dalfopristin (QDA), streptomycin-high (STH), telavancin (TLV)</span>
<span class="co"># and ticarcillin/clavulanic acid (TCC)</span>
<span class="co"># Table 1 - Staphylococcus aureus... OK.</span>
<span class="co"># Table 2 - Enterococcus spp.... OK.</span>
<span class="co"># Table 3 - Enterobacteriaceae... OK.</span>
<span class="co"># Table 4 - Pseudomonas aeruginosa... OK.</span>
<span class="co"># Table 5 - Acinetobacter spp.... OK.</span>
<span class="co"># Warning: in `mdro()`: NA introduced for isolates where the available percentage of</span>
<span class="co"># antimicrobial classes was below 50% (set with `pct_required_classes`)</span></code></pre></div>
<p>Only results with ‘R’ are considered as resistance. Use
<code>combine_SI = FALSE</code> to also consider ‘I’ as resistance.</p>
<p>Determining multidrug-resistant organisms (MDRO), according to:
Guideline: Multidrug-resistant, extensively drug-resistant and
pandrug-resistant bacteria: an international expert proposal for interim
standard definitions for acquired resistance. Author(s): Magiorakos AP,
Srinivasan A, Carey RB, …, Vatopoulos A, Weber JT, Monnet DL Source:
Clinical Microbiology and Infection 18:3, 2012; doi:
10.1111/j.1469-0691.2011.03570.x</p>
<p>(16 isolates had no test results)</p>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 2,000<br>
Levels: 4: Negative &lt; Multi-drug-resistant (MDR) &lt; Extensively
drug-resistant …<br>
Available: 1,729 (86.45%, NA: 271 = 13.55%)<br>
Unique: 2</p>
<table style="width:100%;" class="table">
<colgroup>
<col width="4%">
<col width="38%">
<col width="9%">
<col width="12%">
<col width="16%">
<col width="19%">
</colgroup>
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">Negative</td>
<td align="right">1601</td>
<td align="right">92.6%</td>
<td align="right">1601</td>
<td align="right">92.6%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Multi-drug-resistant (MDR)</td>
<td align="right">128</td>
<td align="right">7.4%</td>
<td align="right">1729</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
<p>For another example, I will create a data set to determine multi-drug
resistant TB:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># random_rsi() is a helper function to generate</span>
<span class="co"># a random vector with values S, I and R</span>
<span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>rifampicin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
isoniazid <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
gatifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
ethambutol <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
pyrazinamide <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
moxifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
kanamycin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>Because all column names are automatically verified for valid drug
names or codes, this would have worked exactly the same way:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
INH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
GAT <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
ETH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
PZA <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
MFX <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
KAN <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>The data set now looks like this:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span>
<span class="co"># 1 R I R R I S</span>
<span class="co"># 2 I I S S R S</span>
<span class="co"># 3 I S R R S R</span>
<span class="co"># 4 R I S R R I</span>
<span class="co"># 5 R I I I R I</span>
<span class="co"># 6 R I R S R I</span>
<span class="co"># kanamycin</span>
<span class="co"># 1 R</span>
<span class="co"># 2 R</span>
<span class="co"># 3 S</span>
<span class="co"># 4 I</span>
<span class="co"># 5 I</span>
<span class="co"># 6 R</span></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set. You can
use:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></code></pre></div>
<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># ℹ No column found as input for `col_mo`, assuming all rows contain</span>
<span class="co"># Mycobacterium tuberculosis.</span>
<span class="co"># Auto-guessing columns suitable for analysis... OK.</span>
<span class="co"># ℹ Reliability would be improved if these antimicrobial results would be</span>
<span class="co"># available too: capreomycin (CAP), rifabutin (RIB) and rifapentine (RFP)</span>
<span class="co"># </span>
<span class="co"># Only results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.</span>
<span class="co"># </span>
<span class="co"># Determining multidrug-resistant organisms (MDRO), according to:</span>
<span class="co"># Guideline: Companion handbook to the WHO guidelines for the programmatic</span>
<span class="co"># management of drug-resistant tuberculosis</span>
<span class="co"># Author(s): WHO (World Health Organization)</span>
<span class="co"># Version: WHO/HTM/TB/2014.11, 2014</span>
<span class="co"># Source: https://www.who.int/publications/i/item/9789241548809</span></code></pre></div>
<p>Create a frequency table of the results:</p>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 5,000<br>
Levels: 5: Negative &lt; Mono-resistant &lt; Poly-resistant &lt;
Multi-drug-resistant &lt;…<br>
Available: 5,000 (100%, NA: 0 = 0%)<br>
Unique: 5</p>
<table style="width:100%;" class="table">
<colgroup>
<col width="4%">
<col width="38%">
<col width="9%">
<col width="12%">
<col width="16%">
<col width="19%">
</colgroup>
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3218</td>
<td align="right">64.36%</td>
<td align="right">3218</td>
<td align="right">64.36%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">979</td>
<td align="right">19.58%</td>
<td align="right">4197</td>
<td align="right">83.94%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">458</td>
<td align="right">9.16%</td>
<td align="right">4655</td>
<td align="right">93.10%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">237</td>
<td align="right">4.74%</td>
<td align="right">4892</td>
<td align="right">97.84%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">108</td>
<td align="right">2.16%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>
</tbody>
</table>
</div>
</div>
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<h1 data-toc-skip>How to conduct principal component analysis
(PCA) for AMR</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
<div class="hidden name"><code>PCA.Rmd</code></div>
</div>
<p><strong>NOTE: This page will be updated soon, as the pca() function
is currently being developed.</strong></p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
</div>
<div class="section level2">
<h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
</h2>
<p>For PCA, we need to transform our AMR data first. This is what the
<code>example_isolates</code> data set in this package looks like:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="co"># Rows: 2,000</span>
<span class="co"># Columns: 49</span>
<span class="co"># $ date <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…</span>
<span class="co"># $ hospital_id <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span> D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B, …</span>
<span class="co"># $ ward_icu <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span> FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TR…</span>
<span class="co"># $ ward_clinical <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FA…</span>
<span class="co"># $ ward_outpatient <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…</span>
<span class="co"># $ age <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71…</span>
<span class="co"># $ gender <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M",…</span>
<span class="co"># $ patient_id <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A77334", "A77334", "067927", "067927", "067927", "067…</span>
<span class="co"># $ mo <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPH…</span>
<span class="co"># $ PEN <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, …</span>
<span class="co"># $ OXA <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ FLC <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, …</span>
<span class="co"># $ AMX <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, …</span>
<span class="co"># $ AMC <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, …</span>
<span class="co"># $ AMP <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, …</span>
<span class="co"># $ TZP <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ CZO <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ FEP <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ CXM <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R,…</span>
<span class="co"># $ FOX <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ CTX <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
<span class="co"># $ CAZ <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S…</span>
<span class="co"># $ CRO <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
<span class="co"># $ GEN <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ TOB <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA…</span>
<span class="co"># $ AMK <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ KAN <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ TMP <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R…</span>
<span class="co"># $ SXT <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, …</span>
<span class="co"># $ NIT <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ FOS <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ LNZ <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span>
<span class="co"># $ CIP <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, …</span>
<span class="co"># $ MFX <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ VAN <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S…</span>
<span class="co"># $ TEC <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span>
<span class="co"># $ TCY <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S…</span>
<span class="co"># $ TGC <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, …</span>
<span class="co"># $ DOX <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, …</span>
<span class="co"># $ ERY <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, …</span>
<span class="co"># $ CLI <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, …</span>
<span class="co"># $ AZM <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, …</span>
<span class="co"># $ IPM <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
<span class="co"># $ MEM <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ MTR <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ CHL <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ COL <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R…</span>
<span class="co"># $ MUP <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ RIF <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span></code></pre></div>
<p>Now to transform this to a data set with only resistance percentages
per taxonomic order and genus:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span>
genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># and genus as we do here</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># then get resistance of all drugs</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,
<span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span><span class="op">)</span> <span class="co"># and select only relevant columns</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<span class="co"># <span style="color: #949494;"># A tibble: 6 × 10</span></span>
<span class="co"># <span style="color: #949494;"># Groups: order [5]</span></span>
<span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span>
<span class="co"># <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
<span class="co"># <span style="color: #BCBCBC;">1</span> (unknown order) (unknown ge… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span>
<span class="co"># <span style="color: #BCBCBC;">2</span> Actinomycetales Schaalia <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span>
<span class="co"># <span style="color: #BCBCBC;">3</span> Bacteroidales Bacteroides <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span>
<span class="co"># <span style="color: #BCBCBC;">4</span> Campylobacterales Campylobact… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span>
<span class="co"># <span style="color: #BCBCBC;">5</span> Caryophanales Gemella <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span>
<span class="co"># <span style="color: #BCBCBC;">6</span> Caryophanales Listeria <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
</h2>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows
that contain numeric values in all selected variables, so we now only
need to do:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<span class="co"># ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP"</span>
<span class="co"># and "TOB". Total observations available: 7.</span></code></pre></div>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
function:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
<span class="co"># Groups (n=4, named as 'order'):</span>
<span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span>
<span class="co"># Importance of components:</span>
<span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span>
<span class="co"># Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 5.674e-17</span>
<span class="co"># Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span>
<span class="co"># Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></code></pre></div>
<pre><code><span class="co"># Groups (n=4, named as 'order'):</span>
<span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></code></pre>
<p>Good news. The first two components explain a total of 93.3% of the
variance (see the PC1 and PC2 values of the <em>Proportion of
Variance</em>. We can create a so-called biplot with the base R
<code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance
per drug explain the difference per microorganism.</p>
</div>
<div class="section level2">
<h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
</h2>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
<p>But we can’t see the explanation of the points. Perhaps this works
better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that
automatically adds the right labels and even groups:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
<p>You can also print an ellipse per group, and edit the appearance:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
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<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
<h4 data-toc-skip class="author">Dr. Matthijs
Berends</h4>
<h4 data-toc-skip class="date">11 mei 2022</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/SPSS.Rmd" class="external-link"><code>vignettes/SPSS.Rmd</code></a></small>
<div class="hidden name"><code>SPSS.Rmd</code></div>
</div>
<div class="section level2">
<h2 id="spss-sas-stata">SPSS / SAS / Stata<a class="anchor" aria-label="anchor" href="#spss-sas-stata"></a>
</h2>
<p>SPSS (Statistical Package for the Social Sciences) is probably the
most well-known software package for statistical analysis. SPSS is
easier to learn than R, because in SPSS you only have to click a menu to
run parts of your analysis. Because of its user-friendliness, it is
taught at universities and particularly useful for students who are new
to statistics. From my experience, I would guess that pretty much all
(bio)medical students know it at the time they graduate. SAS and Stata
are comparable statistical packages popular in big industries.</p>
</div>
<div class="section level2">
<h2 id="compared-to-r">Compared to R<a class="anchor" aria-label="anchor" href="#compared-to-r"></a>
</h2>
<p>As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come
with major downsides when comparing it with R:</p>
<ul>
<li>
<p><strong>R is highly modular.</strong></p>
<p>The <a href="https://cran.r-project.org/" class="external-link">official R network
(CRAN)</a> features more than 16,000 packages at the time of writing,
our <code>AMR</code> package being one of them. All these packages were
peer-reviewed before publication. Aside from this official channel,
there are also developers who choose not to submit to CRAN, but rather
keep it on their own public repository, like GitHub. So there may even
be a lot more than 14,000 packages out there.</p>
<p>Bottom line is, you can really extend it yourself or ask somebody to
do this for you. Take for example our <code>AMR</code> package. Among
other things, it adds reliable reference data to R to help you with the
data cleaning and analysis. SPSS, SAS and Stata will never know what a
valid MIC value is or what the Gram stain of <em>E. coli</em> is. Or
that all species of <em>Klebiella</em> are resistant to amoxicillin and
that Floxapen<sup>®</sup> is a trade name of flucloxacillin. These facts
and properties are often needed to clean existing data, which would be
very inconvenient in a software package without reliable reference data.
See below for a demonstration.</p>
</li>
<li>
<p><strong>R is extremely flexible.</strong></p>
<p>Because you write the syntax yourself, you can do anything you want.
The flexibility in transforming, arranging, grouping and summarising
data, or drawing plots, is endless - with SPSS, SAS or Stata you are
bound to their algorithms and format styles. They may be a bit flexible,
but you can probably never create that very specific publication-ready
plot without using other (paid) software. If you sometimes write
syntaxes in SPSS to run a complete analysis or to ‘automate’ some of
your work, you could do this a lot less time in R. You will notice that
writing syntaxes in R is a lot more nifty and clever than in SPSS.
Still, as working with any statistical package, you will have to have
knowledge about what you are doing (statistically) and what you are
willing to accomplish.</p>
</li>
<li>
<p><strong>R can be easily automated.</strong></p>
<p>Over the last years, <a href="https://rmarkdown.rstudio.com/" class="external-link">R
Markdown</a> has really made an interesting development. With R
Markdown, you can very easily produce reports, whether the format has to
be Word, PowerPoint, a website, a PDF document or just the raw data to
Excel. It even allows the use of a reference file containing the layout
style (e.g. fonts and colours) of your organisation. I use this a lot to
generate weekly and monthly reports automatically. Just write the code
once and enjoy the automatically updated reports at any interval you
like.</p>
<p>For an even more professional environment, you could create <a href="https://shiny.rstudio.com/" class="external-link">Shiny apps</a>: live manipulation of
data using a custom made website. The webdesign knowledge needed
(JavaScript, CSS, HTML) is almost <em>zero</em>.</p>
</li>
<li>
<p><strong>R has a huge community.</strong></p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com" class="external-link">StackOverflow.com</a>, the largest
online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes" class="external-link">447,735
R-related questions</a> have already been asked on this platform (that
covers questions and answers for any programming language). In my own
experience, most questions are answered within a couple of
minutes.</p>
</li>
<li>
<p><strong>R understands any data type, including
SPSS/SAS/Stata.</strong></p>
<p>And that’s not vice versa I’m afraid. You can import data from any
source into R. For example from SPSS, SAS and Stata (<a href="https://haven.tidyverse.org/" class="external-link">link</a>), from Minitab, Epi Info
and EpiData (<a href="https://cran.r-project.org/package=foreign" class="external-link">link</a>), from Excel
(<a href="https://readxl.tidyverse.org/" class="external-link">link</a>), from flat files like
CSV, TXT or TSV (<a href="https://readr.tidyverse.org/" class="external-link">link</a>), or
directly from databases and datawarehouses from anywhere on the world
(<a href="https://dbplyr.tidyverse.org/" class="external-link">link</a>). You can even scrape
websites to download tables that are live on the internet (<a href="https://github.com/hadley/rvest" class="external-link">link</a>) or get the results of
an API call and transform it into data in only one command (<a href="https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread" class="external-link">link</a>).</p>
<p>And the best part - you can export from R to most data formats as
well. So you can import an SPSS file, do your analysis neatly in R and
export the resulting tables to Excel files for sharing.</p>
</li>
<li>
<p><strong>R is completely free and open-source.</strong></p>
<p>No strings attached. It was created and is being maintained by
volunteers who believe that (data) science should be open and publicly
available to everybody. SPSS, SAS and Stata are quite expensive. IBM
SPSS Staticstics only comes with subscriptions nowadays, varying <a href="https://www.ibm.com/products/spss-statistics/pricing" class="external-link">between USD
1,300 and USD 8,500</a> per user <em>per year</em>. SAS Analytics Pro
costs <a href="https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html" class="external-link">around
USD 10,000</a> per computer. Stata also has a business model with
subscription fees, varying <a href="https://www.stata.com/order/new/bus/single-user-licenses/dl/" class="external-link">between
USD 600 and USD 2,800</a> per computer per year, but lower prices come
with a limitation of the number of variables you can work with. And
still they do not offer the above benefits of R.</p>
<p>If you are working at a midsized or small company, you can save it
tens of thousands of dollars by using R instead of e.g. SPSS - gaining
even more functions and flexibility. And all R enthousiasts can do as
much PR as they want (like I do here), because nobody is officially
associated with or affiliated by R. It is really free.</p>
</li>
<li>
<p><strong>R is (nowadays) the preferred analysis software in
academic papers.</strong></p>
<p>At present, R is among the world most powerful statistical languages,
and it is generally very popular in science (Bollmann <em>et al.</em>,
2017). For all the above reasons, the number of references to R as an
analysis method in academic papers <a href="https://r4stats.com/2014/08/20/r-passes-spss-in-scholarly-use-stata-growing-rapidly/" class="external-link">is
rising continuously</a> and has even surpassed SPSS for academic use
(Muenchen, 2014).</p>
<p>I believe that the thing with SPSS is, that it has always had a great
user interface which is very easy to learn and use. Back when they
developed it, they had very little competition, let alone from R. R
didn’t even had a professional user interface until the last decade
(called RStudio, see below). How people used R between the nineties and
2010 is almost completely incomparable to how R is being used now. The
language itself <a href="https://www.tidyverse.org/packages/" class="external-link">has been
restyled completely</a> by volunteers who are dedicated professionals in
the field of data science. SPSS was great when there was nothing else
that could compete. But now in 2022, I don’t see any reason why SPSS
would be of any better use than R.</p>
</li>
</ul>
<p>To demonstrate the first point:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># not all values are valid MIC values:</span>
<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span><span class="op">(</span><span class="fl">0.125</span><span class="op">)</span>
<span class="co"># Class &lt;mic&gt;</span>
<span class="co"># [1] 0.125</span>
<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span><span class="op">(</span><span class="st">"testvalue"</span><span class="op">)</span>
<span class="co"># Class &lt;mic&gt;</span>
<span class="co"># [1] &lt;NA&gt;</span>
<span class="co"># the Gram stain is available for all bacteria:</span>
<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="st">"E. coli"</span><span class="op">)</span>
<span class="co"># [1] "Gram-negative"</span>
<span class="co"># Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST:</span>
<span class="va">klebsiella_test</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="st">"klebsiella"</span>,
amox <span class="op">=</span> <span class="st">"S"</span>,
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
<span class="va">klebsiella_test</span> <span class="co"># (our original data)</span>
<span class="co"># mo amox</span>
<span class="co"># 1 klebsiella S</span>
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">klebsiella_test</span>, info <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="co"># (the edited data by EUCAST rules)</span>
<span class="co"># mo amox</span>
<span class="co"># 1 klebsiella R</span>
<span class="co"># hundreds of trade names can be translated to a name, trade name or an ATC code:</span>
<span class="fu"><a href="../reference/ab_property.html">ab_name</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
<span class="co"># [1] "Flucloxacillin"</span>
<span class="fu"><a href="../reference/ab_property.html">ab_tradenames</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
<span class="co"># [1] "floxacillin" "floxapen" "floxapen sodium salt"</span>
<span class="co"># [4] "fluclox" "flucloxacilina" "flucloxacillin" </span>
<span class="co"># [7] "flucloxacilline" "flucloxacillinum" "fluorochloroxacillin"</span>
<span class="fu"><a href="../reference/ab_property.html">ab_atc</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
<span class="co"># [1] "J01CF05"</span></code></pre></div>
</div>
<div class="section level2">
<h2 id="import-data-from-spsssasstata">Import data from SPSS/SAS/Stata<a class="anchor" aria-label="anchor" href="#import-data-from-spsssasstata"></a>
</h2>
<div class="section level3">
<h3 id="rstudio">RStudio<a class="anchor" aria-label="anchor" href="#rstudio"></a>
</h3>
<p>To work with R, probably the best option is to use <a href="https://www.rstudio.com/products/rstudio/" class="external-link">RStudio</a>. It is an
open-source and free desktop environment which not only allows you to
run R code, but also supports project management, version management,
package management and convenient import menus to work with other data
sources. You can also install <a href="https://www.rstudio.com/products/rstudio/" class="external-link">RStudio Server</a> on a
private or corporate server, which brings nothing less than the complete
RStudio software to you as a website (at home or at work).</p>
<p>To import a data file, just click <em>Import Dataset</em> in the
Environment tab:</p>
<p><img src="https://github.com/msberends/AMR/raw/main/docs/import1.png"></p>
<p>If additional packages are needed, RStudio will ask you if they
should be installed on beforehand.</p>
<p>In the the window that opens, you can define all options (parameters)
that should be used for import and you’re ready to go:</p>
<p><img src="https://github.com/msberends/AMR/raw/main/docs/import2.png"></p>
<p>If you want named variables to be imported as factors so it resembles
SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.html" class="external-link">as_factor()</a></code>.</p>
<p>The difference is this:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">SPSS_data</span>
<span class="co"># # A tibble: 4,203 x 4</span>
<span class="co"># v001 sex status statusage</span>
<span class="co"># &lt;dbl&gt; &lt;dbl+lbl&gt; &lt;dbl+lbl&gt; &lt;dbl&gt;</span>
<span class="co"># 1 10002 1 1 76.6</span>
<span class="co"># 2 10004 0 1 59.1</span>
<span class="co"># 3 10005 1 1 54.5</span>
<span class="co"># 4 10006 1 1 54.1</span>
<span class="co"># 5 10007 1 1 57.7</span>
<span class="co"># 6 10008 1 1 62.8</span>
<span class="co"># 7 10010 0 1 63.7</span>
<span class="co"># 8 10011 1 1 73.1</span>
<span class="co"># 9 10017 1 1 56.7</span>
<span class="co"># 10 10018 0 1 66.6</span>
<span class="co"># # ... with 4,193 more rows</span>
<span class="fu">as_factor</span><span class="op">(</span><span class="va">SPSS_data</span><span class="op">)</span>
<span class="co"># # A tibble: 4,203 x 4</span>
<span class="co"># v001 sex status statusage</span>
<span class="co"># &lt;dbl&gt; &lt;fct&gt; &lt;fct&gt; &lt;dbl&gt;</span>
<span class="co"># 1 10002 Male alive 76.6</span>
<span class="co"># 2 10004 Female alive 59.1</span>
<span class="co"># 3 10005 Male alive 54.5</span>
<span class="co"># 4 10006 Male alive 54.1</span>
<span class="co"># 5 10007 Male alive 57.7</span>
<span class="co"># 6 10008 Male alive 62.8</span>
<span class="co"># 7 10010 Female alive 63.7</span>
<span class="co"># 8 10011 Male alive 73.1</span>
<span class="co"># 9 10017 Male alive 56.7</span>
<span class="co"># 10 10018 Female alive 66.6</span>
<span class="co"># # ... with 4,193 more rows</span></code></pre></div>
</div>
<div class="section level3">
<h3 id="base-r">Base R<a class="anchor" aria-label="anchor" href="#base-r"></a>
</h3>
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/" class="external-link">great <code>haven</code> package</a>
yourself:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># download and install the latest version:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"haven"</span><span class="op">)</span>
<span class="co"># load the package you just installed:</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://haven.tidyverse.org" class="external-link">haven</a></span><span class="op">)</span> </code></pre></div>
<p>You can now import files as follows:</p>
<div class="section level4">
<h4 id="spss">SPSS<a class="anchor" aria-label="anchor" href="#spss"></a>
</h4>
<p>To read files from SPSS into R:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read any SPSS file based on file extension (best way):</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">read_spss</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># read .sav or .zsav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">read_sav</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># read .por file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">read_por</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
<p>Do not forget about <code><a href="https://haven.tidyverse.org/reference/as_factor.html" class="external-link">as_factor()</a></code>, as mentioned above.</p>
<p>To export your R objects to the SPSS file format:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as .sav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># save as compressed .zsav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, compress <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
</div>
<div class="section level4">
<h4 id="sas">SAS<a class="anchor" aria-label="anchor" href="#sas"></a>
</h4>
<p>To read files from SAS into R:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read .sas7bdat + .sas7bcat files:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html" class="external-link">read_sas</a></span><span class="op">(</span>data_file <span class="op">=</span> <span class="st">"path/to/file"</span>, catalog_file <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span>
<span class="co"># read SAS transport files (version 5 and version 8):</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html" class="external-link">read_xpt</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
<p>To export your R objects to the SAS file format:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as regular SAS file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html" class="external-link">write_sas</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># the SAS transport format is an open format </span>
<span class="co"># (required for submission of the data to the FDA)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html" class="external-link">write_xpt</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, version <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></code></pre></div>
</div>
<div class="section level4">
<h4 id="stata">Stata<a class="anchor" aria-label="anchor" href="#stata"></a>
</h4>
<p>To read files from Stata into R:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read .dta file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html" class="external-link">read_stata</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span>
<span class="co"># works exactly the same:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html" class="external-link">read_dta</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span></code></pre></div>
<p>To export your R objects to the Stata file format:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as .dta file, Stata version 14:</span>
<span class="co"># (supports Stata v8 until v15 at the time of writing)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html" class="external-link">write_dta</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"/path/to/file"</span>, version <span class="op">=</span> <span class="fl">14</span><span class="op">)</span></code></pre></div>
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<h1 data-toc-skip>How to work with WHONET data</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
<div class="hidden name"><code>WHONET.Rmd</code></div>
</div>
<div class="section level3">
<h3 id="import-of-data">Import of data<a class="anchor" aria-label="anchor" href="#import-of-data"></a>
</h3>
<p>This tutorial assumes you already imported the WHONET data with
e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code>
package</a>. In RStudio, this can be done using the menu button ‘Import
Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
select your exported file. Make sure date fields are imported
correctly.</p>
<p>An example syntax could look like this:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></code></pre></div>
<p>This package comes with an <a href="https://msberends.github.io/AMR/reference/WHONET.html">example
data set <code>WHONET</code></a>. We will use it for this analysis.</p>
</div>
<div class="section level3">
<h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
</h3>
<p>First, load the relevant packages if you did not yet did this. I use
the tidyverse for all of my analyses. All of them. If you don’t know it
yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
<p>We will have to transform some variables to simplify and automate the
analysis:</p>
<ul>
<li>Microorganisms should be transformed to our own microorganism codes
(called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our
Catalogue of Life reference data set</a>, which contains all ~70,000
microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
recognises almost all WHONET abbreviations of microorganisms.</li>
<li>Antimicrobial results or interpretations have to be clean and valid.
In other words, they should only contain values <code>"S"</code>,
<code>"I"</code> or <code>"R"</code>. That is exactly where the
<code><a href="../reference/as.rsi.html">as.rsi()</a></code> function is for.</li>
</ul>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># transform variables</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="va">WHONET</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="co"># get microbial ID based on given organism</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></code></pre></div>
<p>No errors or warnings, so all values are transformed succesfully.</p>
<p>We also created a package dedicated to data cleaning and checking,
called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq()</a></code>
function can be used to create frequency tables.</p>
<p>So let’s check our data, with a couple of frequency tables:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
<span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 500<br>
Available: 500 (100%, NA: 0 = 0%)<br>
Unique: 37</p>
<p>Shortest: 11<br>
Longest: 40</p>
<table class="table">
<colgroup>
<col width="4%">
<col width="47%">
<col width="7%">
<col width="10%">
<col width="13%">
<col width="15%">
</colgroup>
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">245</td>
<td align="right">49.0%</td>
<td align="right">245</td>
<td align="right">49.0%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Coagulase-negative Staphylococcus (CoNS)</td>
<td align="right">74</td>
<td align="right">14.8%</td>
<td align="right">319</td>
<td align="right">63.8%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Staphylococcus epidermidis</td>
<td align="right">38</td>
<td align="right">7.6%</td>
<td align="right">357</td>
<td align="right">71.4%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">31</td>
<td align="right">6.2%</td>
<td align="right">388</td>
<td align="right">77.6%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Staphylococcus hominis</td>
<td align="right">21</td>
<td align="right">4.2%</td>
<td align="right">409</td>
<td align="right">81.8%</td>
</tr>
<tr class="even">
<td align="left">6</td>
<td align="left">Proteus mirabilis</td>
<td align="right">9</td>
<td align="right">1.8%</td>
<td align="right">418</td>
<td align="right">83.6%</td>
</tr>
<tr class="odd">
<td align="left">7</td>
<td align="left">Enterococcus faecium</td>
<td align="right">8</td>
<td align="right">1.6%</td>
<td align="right">426</td>
<td align="right">85.2%</td>
</tr>
<tr class="even">
<td align="left">8</td>
<td align="left">Staphylococcus capitis</td>
<td align="right">8</td>
<td align="right">1.6%</td>
<td align="right">434</td>
<td align="right">86.8%</td>
</tr>
<tr class="odd">
<td align="left">9</td>
<td align="left">Enterobacter cloacae</td>
<td align="right">5</td>
<td align="right">1.0%</td>
<td align="right">439</td>
<td align="right">87.8%</td>
</tr>
<tr class="even">
<td align="left">10</td>
<td align="left">Streptococcus anginosus</td>
<td align="right">5</td>
<td align="right">1.0%</td>
<td align="right">444</td>
<td align="right">88.8%</td>
</tr>
</tbody>
</table>
<p>(omitted 27 entries, n = 56 [11.2%])</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># our transformed antibiotic columns</span>
<span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span>
<span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered &gt; rsi (numeric)<br>
Length: 500<br>
Levels: 3: S &lt; I &lt; R<br>
Available: 481 (96.2%, NA: 19 = 3.8%)<br>
Unique: 3</p>
<p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02)<br>
Drug group: Beta-lactams/penicillins<br>
%SI: 78.59%</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">S</td>
<td align="right">356</td>
<td align="right">74.01%</td>
<td align="right">356</td>
<td align="right">74.01%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">R</td>
<td align="right">103</td>
<td align="right">21.41%</td>
<td align="right">459</td>
<td align="right">95.43%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">I</td>
<td align="right">22</td>
<td align="right">4.57%</td>
<td align="right">481</td>
<td align="right">100.00%</td>
</tr>
</tbody>
</table>
</div>
<div class="section level3">
<h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
</h3>
<p>An easy <code>ggplot</code> will already give a lot of information,
using the included <code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> function:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">'ab'</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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<h1 data-toc-skip>Benchmarks</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/benchmarks.Rmd" class="external-link"><code>vignettes/benchmarks.Rmd</code></a></small>
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<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> (CoL) and the <a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN). We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the microbial taxonomy.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark()</a></code> runs different input expressions independently of each other and measures their time-to-result.</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/joshuaulrich/microbenchmark/" class="external-link">microbenchmark</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></code></pre></div>
<p>In the next test, we try to ‘coerce’ different input values into the microbial code of <em>Staphylococcus aureus</em>. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.</p>
<p>The actual result is the same every time: it returns its microorganism code <code>B_STPHY_AURS</code> (<em>B</em> stands for <em>Bacteria</em>, its taxonomic kingdom).</p>
<p>But the calculation time differs a lot:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">S.aureus</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"sau"</span><span class="op">)</span>, <span class="co"># WHONET code</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"stau"</span><span class="op">)</span>,
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"STAU"</span><span class="op">)</span>,
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"staaur"</span><span class="op">)</span>,
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>,
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"S aureus"</span><span class="op">)</span>,
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>, <span class="co"># official taxonomic name</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus (MRSA)"</span><span class="op">)</span>, <span class="co"># additional text</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"Sthafilokkockus aaureuz"</span><span class="op">)</span>, <span class="co"># incorrect spelling</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"MRSA"</span><span class="op">)</span>, <span class="co"># Methicillin Resistant S. aureus</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"VISA"</span><span class="op">)</span>, <span class="co"># Vancomycin Intermediate S. aureus</span>
times <span class="op">=</span> <span class="fl">25</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 19.0 20.0 25.0 20.0 26.0 55 25</span>
<span class="co"># as.mo("stau") 94.0 95.0 110.0 100.0 130.0 140 25</span>
<span class="co"># as.mo("STAU") 92.0 97.0 110.0 110.0 120.0 140 25</span>
<span class="co"># as.mo("staaur") 19.0 19.0 24.0 20.0 21.0 56 25</span>
<span class="co"># as.mo("STAAUR") 19.0 20.0 21.0 20.0 20.0 49 25</span>
<span class="co"># as.mo("S. aureus") 54.0 57.0 72.0 64.0 86.0 96 25</span>
<span class="co"># as.mo("S aureus") 55.0 55.0 72.0 57.0 90.0 100 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 5.6 5.7 8.5 5.8 6.2 40 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 360.0 370.0 400.0 400.0 420.0 550 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 280.0 290.0 300.0 300.0 320.0 350 25</span>
<span class="co"># as.mo("MRSA") 19.0 20.0 24.0 20.0 21.0 51 25</span>
<span class="co"># as.mo("VISA") 34.0 34.0 48.0 36.0 65.0 73 25</span></code></pre></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="750"></p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 69 times slower to determine.</p>
<p>To improve performance, we implemented two important algorithms to save unnecessary calculations: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
<div class="section level3">
<h3 id="repetitive-results">Repetitive results<a class="anchor" aria-label="anchor" href="#repetitive-results"></a>
</h3>
<p>Repetitive results are values that are present more than once in a vector. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. So running <code>as.mo(c("E. coli", "E. coli"))</code> will check the value <code>"E. coli"</code> only once.</p>
<p>To prove this, we will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for testing - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># start with the example_isolates data set</span>
<span class="va">x</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="co"># take all MO codes from the 'mo' column</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/pull.html" class="external-link">pull</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="co"># and copy them a thousand times</span>
<span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="co"># then scramble them</span>
<span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># what do these values look like? They are of class &lt;mo&gt;:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># Class &lt;mo&gt;</span>
<span class="co"># [1] B_ENTRBC_CLOC B_ESCHR_COLI B_STRPT_PYGN B_STPHY_AURS B_ESCHR_COLI </span>
<span class="co"># [6] B_STRPT_PNMN</span>
<span class="co"># as the example_isolates data set has 2,000 rows, we should have 2 million items</span>
<span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># [1] 2000000</span>
<span class="co"># and how many unique values do we have?</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/n_distinct.html" class="external-link">n_distinct</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># [1] 90</span>
<span class="co"># now let's see:</span>
<span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># mo_name(x) 259 264 357 299 451 509 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.299 seconds. That is 149 nanoseconds on average. You only lose time on your unique input values.</p>
</div>
<div class="section level3">
<h3 id="precalculated-results">Precalculated results<a class="anchor" aria-label="anchor" href="#precalculated-results"></a>
</h3>
<p>What about precalculated results? If the input is an already precalculated result of a helper function such as <code><a href="../reference/mo_property.html">mo_name()</a></code>, it almost doesn’t take any time at all. In other words, if you run <code><a href="../reference/mo_property.html">mo_name()</a></code> on a valid taxonomic name, it will return the results immediately (see ‘C’ below):</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 11.90 12.10 13.0 13.50 13.70 13.80 10</span>
<span class="co"># B 60.90 61.20 67.7 66.20 66.90 99.70 10</span>
<span class="co"># C 2.91 2.94 3.2 3.32 3.38 3.46 10</span></code></pre></div>
<p>So going from <code>mo_name("Staphylococcus aureus")</code> to <code>"Staphylococcus aureus"</code> takes 0.0033 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"Staphylococcus"</span><span class="op">)</span>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
D <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_family</a></span><span class="op">(</span><span class="st">"Staphylococcaceae"</span><span class="op">)</span>,
E <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="st">"Bacillales"</span><span class="op">)</span>,
F <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_class</a></span><span class="op">(</span><span class="st">"Bacilli"</span><span class="op">)</span>,
G <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_phylum</a></span><span class="op">(</span><span class="st">"Firmicutes"</span><span class="op">)</span>,
H <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_kingdom</a></span><span class="op">(</span><span class="st">"Bacteria"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 2.92 2.93 3.02 2.94 3.02 3.40 10</span>
<span class="co"># B 2.87 2.90 3.14 3.09 3.32 3.71 10</span>
<span class="co"># C 2.91 2.94 3.15 3.12 3.33 3.46 10</span>
<span class="co"># D 2.86 2.90 3.05 2.96 3.27 3.30 10</span>
<span class="co"># E 2.87 2.88 3.03 2.96 3.16 3.29 10</span>
<span class="co"># F 2.92 2.95 3.08 2.98 3.29 3.35 10</span>
<span class="co"># G 2.89 2.96 3.04 2.99 3.11 3.29 10</span>
<span class="co"># H 2.85 2.95 3.11 3.08 3.31 3.38 10</span></code></pre></div>
<p>Of course, when running <code>mo_phylum("Firmicutes")</code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package contains all phyla of all known bacteria, it can just return the initial value immediately.</p>
</div>
<div class="section level3">
<h3 id="results-in-other-languages">Results in other languages<a class="anchor" aria-label="anchor" href="#results-in-other-languages"></a>
</h3>
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost does’t take extra time (compare “en” from the table below with the other languages):</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">CoNS</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"CoNS"</span><span class="op">)</span>
<span class="va">CoNS</span>
<span class="co"># Class &lt;mo&gt;</span>
<span class="co"># [1] B_STPHY_CONS</span>
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</span> <span class="co"># or just mo_name(CoNS) on an English system</span>
<span class="co"># [1] "Coagulase-negative Staphylococcus (CoNS)"</span>
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span> <span class="co"># or just mo_name(CoNS) on a Spanish system</span>
<span class="co"># [1] "Staphylococcus coagulasa negativo (SCN)"</span>
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"nl"</span><span class="op">)</span> <span class="co"># or just mo_name(CoNS) on a Dutch system</span>
<span class="co"># [1] "Coagulase-negatieve Staphylococcus (CNS)"</span>
<span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span>da <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"da"</span><span class="op">)</span>,
de <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"de"</span><span class="op">)</span>,
en <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</span>,
es <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span>,
fr <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"fr"</span><span class="op">)</span>,
it <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"it"</span><span class="op">)</span>,
nl <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"nl"</span><span class="op">)</span>,
pt <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"pt"</span><span class="op">)</span>,
ru <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"ru"</span><span class="op">)</span>,
sv <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"sv"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># da 3.546 3.643 4.072 3.704 3.832 35.930 100</span>
<span class="co"># de 3.597 3.659 4.422 3.734 3.839 36.400 100</span>
<span class="co"># en 1.672 1.726 1.804 1.767 1.794 2.259 100</span>
<span class="co"># es 3.609 3.685 4.496 3.760 3.843 36.540 100</span>
<span class="co"># fr 3.484 3.567 3.725 3.654 3.713 6.281 100</span>
<span class="co"># it 3.523 3.615 4.419 3.720 3.787 36.720 100</span>
<span class="co"># nl 3.614 3.676 3.805 3.732 3.838 4.703 100</span>
<span class="co"># pt 3.512 3.595 4.077 3.659 3.789 37.310 100</span>
<span class="co"># ru 3.556 3.647 4.057 3.680 3.812 35.230 100</span>
<span class="co"># sv 3.540 3.642 4.093 3.732 3.803 36.340 100</span></code></pre></div>
<p>Currently supported languages are Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish and Swedish.</p>
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<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/resistance_predict.Rmd" class="external-link"><code>vignettes/resistance_predict.Rmd</code></a></small>
<div class="hidden name"><code>resistance_predict.Rmd</code></div>
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<h2 id="needed-r-packages">Needed R packages<a class="anchor" aria-label="anchor" href="#needed-r-packages"></a>
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<p>As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="co"># (if not yet installed, install with:)</span>
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></code></pre></div>
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<h2 id="prediction-analysis">Prediction analysis<a class="anchor" aria-label="anchor" href="#prediction-analysis"></a>
</h2>
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./AMR.html">other AMR data analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
<p>It is basically as easy as:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true"></a><span class="kw">resistance_predict</span>(<span class="dt">tbl =</span> example_isolates, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true"></a><span class="co"># or:</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true"></a>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true"></a> model <span class="st">"binomial"</span>)</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true"></a>predict_TZP &lt;-<span class="st"> </span>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true"></a> <span class="dt">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model will be printed unless using <code>resistance_predict(..., info = FALSE)</code>.</p>
<pre><code><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></code></pre>
<p>This text is only a printed summary - the actual result (output) of the function is a <code>data.frame</code> containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">predict_TZP</span>
<span class="co"># year value se_min se_max observations observed estimated</span>
<span class="co"># 1 2002 0.20000000 NA NA 15 0.20000000 0.05616378</span>
<span class="co"># 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839</span>
<span class="co"># 3 2004 0.08536585 NA NA 82 0.08536585 0.06760841</span>
<span class="co"># 4 2005 0.05000000 NA NA 60 0.05000000 0.07411100</span>
<span class="co"># 5 2006 0.05084746 NA NA 59 0.05084746 0.08118454</span>
<span class="co"># 6 2007 0.12121212 NA NA 66 0.12121212 0.08886843</span>
<span class="co"># 7 2008 0.04166667 NA NA 72 0.04166667 0.09720264</span>
<span class="co"># 8 2009 0.01639344 NA NA 61 0.01639344 0.10622731</span>
<span class="co"># 9 2010 0.05660377 NA NA 53 0.05660377 0.11598223</span>
<span class="co"># 10 2011 0.18279570 NA NA 93 0.18279570 0.12650615</span>
<span class="co"># 11 2012 0.30769231 NA NA 65 0.30769231 0.13783610</span>
<span class="co"># 12 2013 0.06896552 NA NA 58 0.06896552 0.15000651</span>
<span class="co"># 13 2014 0.10000000 NA NA 60 0.10000000 0.16304829</span>
<span class="co"># 14 2015 0.23636364 NA NA 55 0.23636364 0.17698785</span>
<span class="co"># 15 2016 0.22619048 NA NA 84 0.22619048 0.19184597</span>
<span class="co"># 16 2017 0.16279070 NA NA 86 0.16279070 0.20763675</span>
<span class="co"># 17 2018 0.22436641 0.1938710 0.2548618 NA NA 0.22436641</span>
<span class="co"># 18 2019 0.24203228 0.2062911 0.2777735 NA NA 0.24203228</span>
<span class="co"># 19 2020 0.26062172 0.2191758 0.3020676 NA NA 0.26062172</span>
<span class="co"># 20 2021 0.28011130 0.2325557 0.3276669 NA NA 0.28011130</span>
<span class="co"># 21 2022 0.30046606 0.2464567 0.3544755 NA NA 0.30046606</span>
<span class="co"># 22 2023 0.32163907 0.2609011 0.3823771 NA NA 0.32163907</span>
<span class="co"># 23 2024 0.34357130 0.2759081 0.4112345 NA NA 0.34357130</span>
<span class="co"># 24 2025 0.36619175 0.2914934 0.4408901 NA NA 0.36619175</span>
<span class="co"># 25 2026 0.38941799 0.3076686 0.4711674 NA NA 0.38941799</span>
<span class="co"># 26 2027 0.41315710 0.3244399 0.5018743 NA NA 0.41315710</span>
<span class="co"># 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688</span>
<span class="co"># 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755</span>
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span>
<span class="co"># 30 2031 0.51109592 0.3973697 0.6248221 NA NA 0.51109592</span>
<span class="co"># 31 2032 0.53574417 0.4169574 0.6545309 NA NA 0.53574417</span></code></pre></div>
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
<p>This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.</p>
<p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R">
<span class="co"># choose for error bars instead of a ribbon</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
<div class="section level3">
<h3 id="choosing-the-right-model">Choosing the right model<a class="anchor" aria-label="anchor" href="#choosing-the-right-model"></a>
</h3>
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># ℹ Using column 'date' as input for `col_date`.</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
<p>You can define the model with the <code>model</code> parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
<p>Valid values are:</p>
<table class="table">
<colgroup>
<col width="32%">
<col width="25%">
<col width="42%">
</colgroup>
<thead><tr class="header">
<th>Input values</th>
<th>Function used by R</th>
<th>Type of model</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>
<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
</td>
<td><code>glm(..., family = binomial)</code></td>
<td>Generalised linear model with binomial distribution</td>
</tr>
<tr class="even">
<td>
<code>"loglin"</code> or <code>"poisson"</code>
</td>
<td><code>glm(..., family = poisson)</code></td>
<td>Generalised linear model with poisson distribution</td>
</tr>
<tr class="odd">
<td>
<code>"lin"</code> or <code>"linear"</code>
</td>
<td><code><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm()</a></code></td>
<td>Linear model</td>
</tr>
</tbody>
</table>
<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># ℹ Using column 'date' as input for `col_date`.</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
<p>This seems more likely, doesn’t it?</p>
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
<span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">family</span>
<span class="co"># </span>
<span class="co"># Family: binomial </span>
<span class="co"># Link function: logit</span>
<span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span>
<span class="co"># Estimate Std. Error z value Pr(&gt;|z|)</span>
<span class="co"># (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span>
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></code></pre></div>
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