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(v0.8.0.9033) antivirals data set, cleanup

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dr. M.S. (Matthijs) Berends 2019-11-18 12:10:47 +01:00
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 0.8.0.9032
Date: 2019-11-15
Version: 0.8.0.9033
Date: 2019-11-18
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(role = c("aut", "cre"),

8
NEWS.md
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@ -1,5 +1,5 @@
# AMR 0.8.0.9032
<small>Last updated: 15-Nov-2019</small>
# AMR 0.8.0.9033
## <small>Last updated: 18-Nov-2019</small>
### Breaking
* Adopted Adeolu *et al.* (2016), [PMID 27620848](https://www.ncbi.nlm.nih.gov/pubmed/27620848) for the `microorganisms` data set, which means that the new order Enterobacterales now consists of a part of the existing family Enterobacteriaceae, but that this family has been split into other families as well (like *Morganellaceae* and *Yersiniaceae*). Although published in 2016, this information is not yet in the Catalogue of Life version of 2019. All MDRO determinations with `mdro()` will now use the Enterobacterales order for all guidelines before 2016 that were dependent on the Enterobacteriaceae family.
@ -25,6 +25,7 @@
* Support for a new MDRO guideline: Magiorakos AP, Srinivasan A *et al.* "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).
* This is now the new default guideline for the `mdro()` function
* The new Verbose mode (`mdro(...., verbose = TRUE)`) returns an informative data set where the reason for MDRO determination is given for every isolate, and an list of the resistant antimicrobial agents
* Data set `antivirals`, containing all entries from the ATC J05 group with their DDDs for oral and parenteral treatment
### Changes
* Improvements to algorithm in `as.mo()`:
@ -48,13 +49,14 @@
* When running `as.rsi()` over a data set, it will now print the guideline that will be used if it is not specified by the user
* Improvements for `eucast_rules()`:
* Fix where *Stenotrophomonas maltophilia* would always become ceftazidime R (following EUCAST v3.1)
* Fix where *Leuconostoc* and *Pediococcus* would not always become glyopeptides R
* Fix where *Leuconostoc* and *Pediococcus* would not always become glycopeptides R
* non-EUCAST rules in `eucast_rules()` are now applied first and not as last anymore. This is to improve the dependency on certain antibiotics for the official EUCAST rules. Please see `?eucast_rules`.
* Fix for interpreting MIC values with `as.rsi()` where the input is `NA`
* Added "imi" and "imp" as allowed abbreviation for Imipenem (IPM)
* Fix for automatically determining columns with antibiotic results in `mdro()` and `eucast_rules()`
* Added ATC codes for ceftaroline, ceftobiprole and faropenem and fixed two typos in the `antibiotics` data set
* More robust way of determining valid MIC values
* Small changed to the `example_isolates` data set to better reflect reality
### Other
* Change dependency on `clean` to `cleaner`, as this package was renamed accordingly upon CRAN request

41
R/data.R
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@ -34,7 +34,7 @@
#' \item{\code{abbr}}{List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)}
#' \item{\code{synonyms}}{Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID}
#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
#' \item{\code{oral_units}}{Units of \code{ddd_units}}
#' \item{\code{oral_units}}{Units of \code{oral_ddd}}
#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
#' \item{\code{iv_units}}{Units of \code{iv_ddd}}
#' }
@ -48,9 +48,28 @@
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
#' @inheritSection WHOCC WHOCC
#' @inheritSection AMR Read more on our website!
#' @seealso \code{\link{microorganisms}}
#' @seealso \code{\link{antivirals}} \code{\link{microorganisms}}
"antibiotics"
#' Data set with ~100 antivirals
#'
#' A data set containing all antivirals, according to the ATC code group 'J05' (Antivirals for systemic use).
#' @format A \code{\link{data.frame}} with 102 observations and 7 variables:
#' \describe{
#' \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC}
#' \item{\code{name}}{Official name as used by WHONET/EARS-Net or the WHO}
#' \item{\code{atc_group}}{Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC}
#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
#' \item{\code{oral_units}}{Units of \code{oral_ddd}}
#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
#' \item{\code{iv_units}}{Units of \code{iv_ddd}}
#' }
#' @source World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): \url{https://www.whocc.no/atc_ddd_index/}
#' @inheritSection WHOCC WHOCC
#' @inheritSection AMR Read more on our website!
#' @seealso \code{\link{antibiotics}} \code{\link{microorganisms}}
"antivirals"
#' Data set with ~70,000 microorganisms
#'
#' A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using \code{\link{as.mo}}.
@ -126,9 +145,9 @@ catalogue_of_life <- list(
#' @seealso \code{\link{as.mo}} \code{\link{microorganisms}}
"microorganisms.codes"
#' Data set with 2,000 blood culture isolates
#' Data set with 2,000 example isolates
#'
#' An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found 4 different hospitals in the Netherlands, between 2001 and 2017. This \code{data.frame} can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
#' A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
#' @format A \code{\link{data.frame}} with 2,000 observations and 49 variables:
#' \describe{
#' \item{\code{date}}{date of receipt at the laboratory}
@ -138,7 +157,7 @@ catalogue_of_life <- list(
#' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
#' \item{\code{age}}{age of the patient}
#' \item{\code{gender}}{gender of the patient}
#' \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
#' \item{\code{patient_id}}{ID of the patient}
#' \item{\code{mo}}{ID of microorganism created with \code{\link{as.mo}}, see also \code{\link{microorganisms}}}
#' \item{\code{PEN:RIF}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{ab_name}}}
#' }
@ -183,17 +202,17 @@ catalogue_of_life <- list(
#' Data set for RSI interpretation
#'
#' Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use \code{\link{as.rsi}} to transform MICs or disks measurements to RSI values.
#' @format A \code{\link{data.frame}} with 11,559 observations and 9 variables:
#' @format A \code{\link{data.frame}} with 13,975 observations and 9 variables:
#' \describe{
#' \item{\code{guideline}}{Name of the guideline}
#' \item{\code{method}}{Either "MIC" or "DISK"}
#' \item{\code{site}}{Body site, e.g. "Oral" or "Respiratory"}
#' \item{\code{mo}}{Microbial ID, see \code{\link{as.mo}}}
#' \item{\code{ab}}{Antibiotic ID, see \code{\link{as.ab}}}
#' \item{\code{ref_tbl}}{Info about where the guideline rule can be found}
#' \item{\code{S_mic}}{Lowest MIC value that leads to "S"}
#' \item{\code{R_mic}}{Highest MIC value that leads to "R"}
#' \item{\code{dose_disk}}{Dose of the used disk diffusion method}
#' \item{\code{S_disk}}{Lowest number of millimeters that leads to "S"}
#' \item{\code{R_disk}}{Highest number of millimeters that leads to "R"}
#' \item{\code{disk_dose}}{Dose of the used disk diffusion method}
#' \item{\code{breakpoint_S}}{Lowest MIC value or highest number of millimeters that leads to "S"}
#' \item{\code{breakpoint_R}}{Highest MIC value or lowest number of millimeters that leads to "R"}
#' }
#' @inheritSection AMR Read more on our website!
"rsi_translation"

2
R/eucast_rules.R
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@ -163,6 +163,7 @@ EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016"
#' }
#' @inheritSection AMR Read more on our website!
#' @examples
#' \donttest{
#' a <- data.frame(mo = c("Staphylococcus aureus",
#' "Enterococcus faecalis",
#' "Escherichia coli",
@ -198,7 +199,6 @@ EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016"
#' # 5 Pseudomonas aeruginosa R R - - R R R
#'
#'
#' \donttest{
#' # do not apply EUCAST rules, but rather get a data.frame
#' # with 18 rows, containing all details about the transformations:
#' c <- eucast_rules(a, verbose = TRUE)

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R/mdro.R
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@ -54,29 +54,24 @@
#' @rdname mdro
#' @aliases MDR XDR PDR BRMO 3MRGN 4MRGN
#' @importFrom dplyr %>% filter_at vars all_vars pull mutate_at
#' @importFrom crayon blue bold italic
#' @importFrom crayon blue bold italic red
#' @importFrom cleaner percentage
#' @export
#' @inheritSection AMR Read more on our website!
#' @source
#' Please see Details for the list of publications used for this function.
#' @examples
#' \donttest{
#' library(dplyr)
#'
#' example_isolates %>%
#' mdro() %>%
#' freq()
#'
#' \donttest{
#' example_isolates %>%
#' mutate(EUCAST = eucast_exceptional_phenotypes(.),
#' BRMO = brmo(.),
#' MRGN = mrgn(.))
#'
#' example_isolates %>%
#' rename(PIP = TZP) %>% # no piperacillin, so take piperacillin/tazobactam
#' mrgn() %>% # check German guideline
#' freq() # check frequencies
#' }
mdro <- function(x,
guideline = NULL,
@ -155,7 +150,7 @@ mdro <- function(x,
guideline$name <- "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance."
guideline$author <- "Magiorakos AP, Srinivasan A, Carey RB, ..., Vatopoulos A, Weber JT, Monnet DL"
guideline$version <- "N/A"
guideline$source <- "Magiorakos et al. (2012) Clinical Microbiology and Infection 18:3. DOI: 10.1111/j.1469-0691.2011.03570.x"
guideline$source <- "Clinical Microbiology and Infection 18:3, 2012. DOI: 10.1111/j.1469-0691.2011.03570.x"
} else if (guideline$code == "eucast") {
guideline$name <- "EUCAST Expert Rules, \"Intrinsic Resistance and Exceptional Phenotypes Tables\""
@ -174,7 +169,7 @@ mdro <- function(x,
guideline$name <- "Cross-border comparison of the Dutch and German guidelines on multidrug-resistant Gram-negative microorganisms"
guideline$author <- "M\u00fcller J, Voss A, K\u00f6ck R, ..., Kern WV, Wendt C, Friedrich AW"
guideline$version <- "N/A"
guideline$source <- "M\u00fcller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6"
guideline$source <- "Antimicrobial Resistance and Infection Control 4:7, 2015. DOI: 10.1186/s13756-015-0047-6"
} else if (guideline$code == "brmo") {
guideline$name <- "WIP-Richtlijn Bijzonder Resistente Micro-organismen (BRMO)"
@ -422,9 +417,9 @@ mdro <- function(x,
if (info == TRUE) {
if (combine_SI == TRUE) {
cat("\nOnly results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.\n")
cat(red("\nOnly results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.\n"))
} else {
cat("\nResults with 'R' or 'I' are considered as resistance. Use `combine_SI = TRUE` to only consider 'R' as resistance.\n")
cat(red("\nResults with 'R' or 'I' are considered as resistance. Use `combine_SI = TRUE` to only consider 'R' as resistance.\n"))
}
cat("\nDetermining multidrug-resistant organisms (MDRO), according to:\n",
bold("Guideline: "), italic(guideline$name), "\n",

22
R/mo.R
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@ -572,22 +572,21 @@ exec_as.mo <- function(x,
# add start en stop regex
x <- paste0("^", x, "$")
x_withspaces_start_only <- paste0("^", x_withspaces)
x_withspaces_end_only <- paste0(x_withspaces, "$")
x_withspaces_start_end <- paste0("^", x_withspaces, "$")
if (isTRUE(debug)) {
cat(paste0(blue('x'), ' "', x, '"\n'))
cat(paste0(blue('x_species'), ' "', x_species, '"\n'))
cat(paste0(blue('x_withspaces_start_only'), ' "', x_withspaces_start_only, '"\n'))
cat(paste0(blue('x_withspaces_end_only'), ' "', x_withspaces_end_only, '"\n'))
cat(paste0(blue('x_withspaces_start_end'), ' "', x_withspaces_start_end, '"\n'))
cat(paste0(blue('x_backup'), ' "', x_backup, '"\n'))
cat(paste0(blue('x_backup_without_spp'), ' "', x_backup_without_spp, '"\n'))
cat(paste0(blue('x_trimmed'), ' "', x_trimmed, '"\n'))
cat(paste0(blue('x_trimmed_species'), ' "', x_trimmed_species, '"\n'))
cat(paste0(blue('x_trimmed_without_group'), ' "', x_trimmed_without_group, '"\n'))
cat(paste0(blue("x"), ' "', x, '"\n'))
cat(paste0(blue("x_species"), ' "', x_species, '"\n'))
cat(paste0(blue("x_withspaces_start_only"), ' "', x_withspaces_start_only, '"\n'))
cat(paste0(blue("x_withspaces_end_only"), ' "', x_withspaces_end_only, '"\n'))
cat(paste0(blue("x_withspaces_start_end"), ' "', x_withspaces_start_end, '"\n'))
cat(paste0(blue("x_backup"), ' "', x_backup, '"\n'))
cat(paste0(blue("x_backup_without_spp"), ' "', x_backup_without_spp, '"\n'))
cat(paste0(blue("x_trimmed"), ' "', x_trimmed, '"\n'))
cat(paste0(blue("x_trimmed_species"), ' "', x_trimmed_species, '"\n'))
cat(paste0(blue("x_trimmed_without_group"), ' "', x_trimmed_without_group, '"\n'))
}
progress <- progress_estimated(n = length(x), min_time = 3)
@ -1782,6 +1781,7 @@ pillar_shaft.mo <- function(x, ...) {
out[is.na(x)] <- pillar::style_na(" NA")
out[x == "UNKNOWN"] <- pillar::style_na(" UNKNOWN")
# make it always fit exactly
pillar::new_pillar_shaft_simple(out, align = "left", width = max(nchar(x)))
}

2
R/whocc.R
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@ -24,7 +24,7 @@
#' All antimicrobial drugs and their official names, ATC codes, ATC groups and defined daily dose (DDD) are included in this package, using the WHO Collaborating Centre for Drug Statistics Methodology.
#' @section WHOCC:
#' \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
#' This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
#' This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
#'
#' These have become the gold standard for international drug utilisation monitoring and research.
#'

114
_pkgdown.yml
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@ -83,6 +83,62 @@ navbar:
href: "LICENSE-text.html"
reference:
- title: "Cleaning your data"
desc: >
Functions for cleaning and optimising your data, to be able to add
variables later on (like taxonomic properties) or to fix and extend
antibiotic interpretations by applying [EUCAST rules](http://www.eucast.org/expert_rules_and_intrinsic_resistance/).
contents:
- starts_with("as.")
- "`eucast_rules`"
- "`guess_ab_col`"
- "`mo_source`"
- "`read.4D`"
- "`rsi_translation`"
- title: "Enhancing your data"
desc: >
Functions to add new data to your existing data, such as the determination
of first isolates, multi-drug resistant microorganisms (MDRO), getting
properties of microorganisms or antibiotics and determining the age of
patients or divide ages into age groups.
contents:
- "`ab_property`"
- "`age_groups`"
- "`age`"
- "`atc_online_property`"
- "`first_isolate`"
- "`join`"
- "`key_antibiotics`"
- "`mdro`"
- "`mo_property`"
- "`p_symbol`"
- title: "Analysing your data"
desc: >
Functions for conducting AMR analysis, like counting isolates, calculating
resistance or susceptibility, or make plots.
contents:
- "`availability`"
- "`bug_drug_combinations`"
- "`count`"
- "`filter_ab_class`"
- "`g.test`"
- "`ggplot_rsi`"
- "`kurtosis`"
- "`portion`"
- "`resistance_predict`"
- "`skewness`"
- title: "Included data sets"
desc: >
Scientifically reliable references for microorganisms and
antibiotics, and example data sets to use for practise.
contents:
- "`antibiotics`"
- "`antivirals`"
- "`example_isolates`"
- "`microorganisms.codes`"
- "`microorganisms.old`"
- "`microorganisms`"
- "`WHONET`"
- title: "Background information"
desc: >
Some pages about our package and its external sources. Be sure to read our [How To's](./../articles/index.html)
@ -92,61 +148,6 @@ reference:
- "`catalogue_of_life`"
- "`catalogue_of_life_version`"
- "`WHOCC`"
- title: "Cleaning your data"
desc: >
Functions for cleaning and optimising your data, to be able to add
variables later on (like taxonomic properties) or to fix and extend
antibiotic interpretations by applying [EUCAST rules](http://www.eucast.org/expert_rules_and_intrinsic_resistance/).
contents:
- starts_with("as.")
- "`mo_source`"
- "`eucast_rules`"
- "`rsi_translation`"
- "`guess_ab_col`"
- "`read.4D`"
- title: "Adding variables to your data"
desc: >
Functions to add new data to existing data, like the determination
of first isolates, multi-drug resistant microorganisms (MDRO), getting
properties of microorganisms or antibiotics and determining the age of
patients or divide ages into age groups.
contents:
- "`first_isolate`"
- "`mdro`"
- "`key_antibiotics`"
- "`mo_property`"
- "`ab_property`"
- "`age`"
- "`age_groups`"
- "`p_symbol`"
- "`join`"
- "`atc_online_property`"
- title: "Analysing your data"
desc: >
Functions for conducting AMR analysis, like counting isolates, calculating
resistance or susceptibility, or make plots.
contents:
- "`availability`"
- "`bug_drug_combinations`"
- "`count`"
- "`portion`"
- "`filter_ab_class`"
- "`g.test`"
- "`ggplot_rsi`"
- "`kurtosis`"
- "`resistance_predict`"
- "`skewness`"
- title: "Included data sets"
desc: >
References for microorganisms and antibiotics, and even a
genuine data set with isolates from septic patients.
contents:
- "`antibiotics`"
- "`microorganisms`"
- "`example_isolates`"
- "`WHONET`"
- "`microorganisms.codes`"
- "`microorganisms.old`"
- title: Other functions
desc: >
These functions are mostly for internal use, but some of
@ -160,9 +161,10 @@ reference:
These functions are extensions of functions in other packages.
contents:
- "`extended-functions`"
- "`reexports`"
- title: functions
desc: >
These functions are deprecated, meaning that they still
These functions are deprecated, meaning that they will still
work but show a warning with every use and will be removed
in a future version.
contents:

20
data-raw/reproduction_of_antibiotics.R
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@ -1,3 +1,23 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# 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. #
# #
# This R package was created for academic research and was publicly #
# released in the hope that it will be useful, but it comes WITHOUT #
# ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.gitlab.io/AMR. #
# ==================================================================== #
library(dplyr)

62
data-raw/reproduction_of_antivirals.R Normal file
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@ -0,0 +1,62 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# 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. #
# #
# This R package was created for academic research and was publicly #
# released in the hope that it will be useful, but it comes WITHOUT #
# ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.gitlab.io/AMR. #
# ==================================================================== #
# get all data from the WHOCC website
get_atc_table <- function(atc_group) {
# give as input J0XXX, like atc_group = "J05AB"
downloaded <- read_html(paste0("https://www.whocc.no/atc_ddd_index/?code=", atc_group, "&showdescription=no"))
table_title <- downloaded %>% html_nodes(paste0('a[href="./?code=', atc_group, '"]')) %>% html_text()
table_content <- downloaded %>%
html_nodes("table") %>%
html_table(header = TRUE) %>%
# returns list, so make data.frame out of it
as.data.frame(stringsAsFactors = FALSE) %>%
# select right columns
select(atc = ATC.code, name = Name, ddd = DDD, unit = U, ddd_type = Adm.R) %>%
# fill empty rows
mutate(atc = ifelse(atc == "", lag(atc), atc), name = ifelse(name == "", lag(name), name)) %>%
pivot_wider(names_from = ddd_type, values_from = c(ddd, unit)) %>%
mutate(atc_group = table_title)
if (!"ddd_O" %in% colnames(table_content)) {
table_content <- table_content %>% mutate(ddd_O = NA_real_, unit_O = NA_character_)
}
if (!"ddd_P" %in% colnames(table_content)) {
table_content <- table_content %>% mutate(ddd_P = NA_real_, unit_P = NA_character_)
}
table_content %>% select(atc, name, atc_group,
oral_ddd = ddd_O, oral_units = unit_O,
iv_ddd = ddd_P, iv_units = unit_P)
}
# these are the relevant groups for input: https://www.whocc.no/atc_ddd_index/?code=J05A (J05 only contains J05A)
atc_groups <- c("J05AA", "J05AB", "J05AC", "J05AD", "J05AE", "J05AF", "J05AG", "J05AH", "J05AP", "J05AR", "J05AX")
# get the first
antivirals <- get_atc_table(atc_groups[1])
# bind all others to it
for (i in 2:length(atc_groups)) {
antivirals <- rbind(antivirals, get_atc_table(atc_groups[i]))
}
# arrange on name, untibble it and save
antivirals <- antivirals %>% arrange(name) %>% as.data.frame(stringsAsFactors = FALSE)
usethis::use_data(antivirals, overwrite = TRUE)

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2
docs/404.html
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@ -84,7 +84,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="https://msberends.gitlab.io/AMR/index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9033</span>
</span>
</div>

2
docs/LICENSE-text.html
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@ -84,7 +84,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9033</span>
</span>
</div>

454
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@ -41,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -187,7 +187,7 @@
<h1>How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">15 November 2019</h4>
<h4 class="date">18 November 2019</h4>
<div class="hidden name"><code>AMR.Rmd</code></div>
@ -196,7 +196,7 @@
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 15 November 2019.</p>
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 18 November 2019.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
@ -212,21 +212,21 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2019-11-15</td>
<td align="center">2019-11-18</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2019-11-15</td>
<td align="center">2019-11-18</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2019-11-15</td>
<td align="center">2019-11-18</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -321,8 +321,8 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2015-09-02</td>
<td align="center">V3</td>
<td align="center">2011-03-27</td>
<td align="center">O9</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
@ -332,41 +332,30 @@
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2017-06-27</td>
<td align="center">H4</td>
<td align="center">2015-09-08</td>
<td align="center">U5</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2012-05-31</td>
<td align="center">X3</td>
<td align="center">Hospital B</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2013-06-08</td>
<td align="center">K2</td>
<td align="center">Hospital B</td>
<tr class="odd">
<td align="center">2014-09-10</td>
<td align="center">L5</td>
<td align="center">Hospital C</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2012-02-10</td>
<td align="center">M5</td>
<tr class="even">
<td align="center">2011-07-28</td>
<td align="center">D7</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
@ -375,16 +364,27 @@
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2013-04-12</td>
<td align="center">N6</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2010-06-25</td>
<td align="center">N7</td>
<td align="center">Hospital D</td>
<td align="center">2017-03-29</td>
<td align="center">F1</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">M</td>
</tr>
</tbody>
</table>
@ -406,8 +406,8 @@
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------- -------- ----------- -------------
# 1 M 10,309 51.55% 10,309 51.55%
# 2 F 9,691 48.46% 20,000 100.00%</code></pre>
# 1 M 10,413 52.06% 10,413 52.06%
# 2 F 9,587 47.94% 20,000 100.00%</code></pre>
<p>So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values <code>M</code> and <code>F</code>. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.</p>
<p>The data is already quite clean, but we still need to transform some variables. The <code>bacteria</code> column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code> function of the <code>dplyr</code> package makes this really easy:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1">data &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span></a>
@ -422,8 +422,8 @@
<a class="sourceLine" id="cb14-3" data-line-number="3"><span class="co"># Other rules by this AMR package</span></a>
<a class="sourceLine" id="cb14-4" data-line-number="4"><span class="co"># Non-EUCAST: inherit amoxicillin results for unavailable ampicillin (no changes)</span></a>
<a class="sourceLine" id="cb14-5" data-line-number="5"><span class="co"># Non-EUCAST: inherit ampicillin results for unavailable amoxicillin (no changes)</span></a>
<a class="sourceLine" id="cb14-6" data-line-number="6"><span class="co"># Non-EUCAST: set amoxicillin/clav acid = S where ampicillin = S (3,022 values changed)</span></a>
<a class="sourceLine" id="cb14-7" data-line-number="7"><span class="co"># Non-EUCAST: set ampicillin = R where amoxicillin/clav acid = R (151 values changed)</span></a>
<a class="sourceLine" id="cb14-6" data-line-number="6"><span class="co"># Non-EUCAST: set amoxicillin/clav acid = S where ampicillin = S (2,986 values changed)</span></a>
<a class="sourceLine" id="cb14-7" data-line-number="7"><span class="co"># Non-EUCAST: set ampicillin = R where amoxicillin/clav acid = R (176 values changed)</span></a>
<a class="sourceLine" id="cb14-8" data-line-number="8"><span class="co"># Non-EUCAST: set piperacillin = R where piperacillin/tazobactam = R (no changes)</span></a>
<a class="sourceLine" id="cb14-9" data-line-number="9"><span class="co"># Non-EUCAST: set piperacillin/tazobactam = S where piperacillin = S (no changes)</span></a>
<a class="sourceLine" id="cb14-10" data-line-number="10"><span class="co"># Non-EUCAST: set trimethoprim = R where trimethoprim/sulfa = R (no changes)</span></a>
@ -448,14 +448,14 @@
<a class="sourceLine" id="cb14-29" data-line-number="29"><span class="co"># Pasteurella multocida (no changes)</span></a>
<a class="sourceLine" id="cb14-30" data-line-number="30"><span class="co"># Staphylococcus (no changes)</span></a>
<a class="sourceLine" id="cb14-31" data-line-number="31"><span class="co"># Streptococcus groups A, B, C, G (no changes)</span></a>
<a class="sourceLine" id="cb14-32" data-line-number="32"><span class="co"># Streptococcus pneumoniae (1,071 values changed)</span></a>
<a class="sourceLine" id="cb14-32" data-line-number="32"><span class="co"># Streptococcus pneumoniae (1,015 values changed)</span></a>
<a class="sourceLine" id="cb14-33" data-line-number="33"><span class="co"># Viridans group streptococci (no changes)</span></a>
<a class="sourceLine" id="cb14-34" data-line-number="34"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-35" data-line-number="35"><span class="co"># EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)</span></a>
<a class="sourceLine" id="cb14-36" data-line-number="36"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1,282 values changed)</span></a>
<a class="sourceLine" id="cb14-36" data-line-number="36"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1,284 values changed)</span></a>
<a class="sourceLine" id="cb14-37" data-line-number="37"><span class="co"># Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)</span></a>
<a class="sourceLine" id="cb14-38" data-line-number="38"><span class="co"># Table 03: Intrinsic resistance in other Gram-negative bacteria (no changes)</span></a>
<a class="sourceLine" id="cb14-39" data-line-number="39"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2,783 values changed)</span></a>
<a class="sourceLine" id="cb14-39" data-line-number="39"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2,796 values changed)</span></a>
<a class="sourceLine" id="cb14-40" data-line-number="40"><span class="co"># Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)</span></a>
<a class="sourceLine" id="cb14-41" data-line-number="41"><span class="co"># Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)</span></a>
<a class="sourceLine" id="cb14-42" data-line-number="42"><span class="co"># Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes)</span></a>
@ -463,15 +463,15 @@
<a class="sourceLine" id="cb14-44" data-line-number="44"><span class="co"># Table 13: Interpretive rules for quinolones (no changes)</span></a>
<a class="sourceLine" id="cb14-45" data-line-number="45"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-46" data-line-number="46"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb14-47" data-line-number="47"><span class="co"># EUCAST rules affected 6,586 out of 20,000 rows, making a total of 8,309 edits</span></a>
<a class="sourceLine" id="cb14-47" data-line-number="47"><span class="co"># EUCAST rules affected 6,569 out of 20,000 rows, making a total of 8,257 edits</span></a>
<a class="sourceLine" id="cb14-48" data-line-number="48"><span class="co"># =&gt; added 0 test results</span></a>
<a class="sourceLine" id="cb14-49" data-line-number="49"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-50" data-line-number="50"><span class="co"># =&gt; changed 8,309 test results</span></a>
<a class="sourceLine" id="cb14-51" data-line-number="51"><span class="co"># - 129 test results changed from S to I</span></a>
<a class="sourceLine" id="cb14-52" data-line-number="52"><span class="co"># - 4,834 test results changed from S to R</span></a>
<a class="sourceLine" id="cb14-53" data-line-number="53"><span class="co"># - 1,222 test results changed from I to S</span></a>
<a class="sourceLine" id="cb14-54" data-line-number="54"><span class="co"># - 324 test results changed from I to R</span></a>
<a class="sourceLine" id="cb14-55" data-line-number="55"><span class="co"># - 1,800 test results changed from R to S</span></a>
<a class="sourceLine" id="cb14-50" data-line-number="50"><span class="co"># =&gt; changed 8,257 test results</span></a>
<a class="sourceLine" id="cb14-51" data-line-number="51"><span class="co"># - 123 test results changed from S to I</span></a>
<a class="sourceLine" id="cb14-52" data-line-number="52"><span class="co"># - 4,779 test results changed from S to R</span></a>
<a class="sourceLine" id="cb14-53" data-line-number="53"><span class="co"># - 1,134 test results changed from I to S</span></a>
<a class="sourceLine" id="cb14-54" data-line-number="54"><span class="co"># - 369 test results changed from I to R</span></a>
<a class="sourceLine" id="cb14-55" data-line-number="55"><span class="co"># - 1,852 test results changed from R to S</span></a>
<a class="sourceLine" id="cb14-56" data-line-number="56"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb14-57" data-line-number="57"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-58" data-line-number="58"><span class="co"># Use eucast_rules(..., verbose = TRUE) (on your original data) to get a data.frame with all specified edits instead.</span></a></code></pre></div>
@ -499,8 +499,8 @@
<a class="sourceLine" id="cb16-3" data-line-number="3"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `bacteria` as input for `col_mo`.</span></a>
<a class="sourceLine" id="cb16-4" data-line-number="4"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a>
<a class="sourceLine" id="cb16-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<a class="sourceLine" id="cb16-6" data-line-number="6"><span class="co"># =&gt; Found 5,688 first isolates (28.4% of total)</span></a></code></pre></div>
<p>So only 28.4% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<a class="sourceLine" id="cb16-6" data-line-number="6"><span class="co"># =&gt; Found 5,699 first isolates (28.5% of total)</span></a></code></pre></div>
<p>So only 28.5% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(first <span class="op">==</span><span class="st"> </span><span class="ot">TRUE</span>)</a></code></pre></div>
<p>For future use, the above two syntaxes can be shortened with the <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
@ -510,7 +510,7 @@
<div id="first-weighted-isolates" class="section level2">
<h2 class="hasAnchor">
<a href="#first-weighted-isolates" class="anchor"></a>First <em>weighted</em> isolates</h2>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient I9, sorted on date:</p>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient X7, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -526,19 +526,19 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-02-08</td>
<td align="center">I9</td>
<td align="center">2010-03-18</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-05</td>
<td align="center">I9</td>
<td align="center">2010-04-28</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -548,30 +548,30 @@
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-05-14</td>
<td align="center">I9</td>
<td align="center">2010-06-26</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-12-10</td>
<td align="center">I9</td>
<td align="center">2010-08-06</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-12-17</td>
<td align="center">I9</td>
<td align="center">2010-08-31</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -581,19 +581,19 @@
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2011-04-18</td>
<td align="center">I9</td>
<td align="center">2010-10-15</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-04-25</td>
<td align="center">I9</td>
<td align="center">2010-11-19</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
@ -603,8 +603,8 @@
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-06-06</td>
<td align="center">I9</td>
<td align="center">2011-01-20</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -614,23 +614,23 @@
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-07-14</td>
<td align="center">I9</td>
<td align="center">2011-04-10</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-07-31</td>
<td align="center">I9</td>
<td align="center">2011-04-12</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
@ -647,7 +647,7 @@
<a class="sourceLine" id="cb19-7" data-line-number="7"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<a class="sourceLine" id="cb19-8" data-line-number="8"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.</span></a>
<a class="sourceLine" id="cb19-9" data-line-number="9"><span class="co"># [Criterion] Inclusion based on key antibiotics, ignoring I</span></a>
<a class="sourceLine" id="cb19-10" data-line-number="10"><span class="co"># =&gt; Found 15,051 first weighted isolates (75.3% of total)</span></a></code></pre></div>
<a class="sourceLine" id="cb19-10" data-line-number="10"><span class="co"># =&gt; Found 15,085 first weighted isolates (75.4% of total)</span></a></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -664,80 +664,68 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-02-08</td>
<td align="center">I9</td>
<td align="center">2010-03-18</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-05</td>
<td align="center">I9</td>
<td align="center">2010-04-28</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-05-14</td>
<td align="center">I9</td>
<td align="center">2010-06-26</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-12-10</td>
<td align="center">I9</td>
<td align="center">2010-08-06</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-12-17</td>
<td align="center">I9</td>
<td align="center">2010-08-31</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2011-04-18</td>
<td align="center">I9</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-04-25</td>
<td align="center">I9</td>
<td align="center">2010-10-15</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
@ -746,10 +734,22 @@
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2010-11-19</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-06-06</td>
<td align="center">I9</td>
<td align="center">2011-01-20</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -760,35 +760,35 @@
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-07-14</td>
<td align="center">I9</td>
<td align="center">2011-04-10</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-07-31</td>
<td align="center">I9</td>
<td align="center">2011-04-12</td>
<td align="center">X7</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
</tbody>
</table>
<p>Instead of 2, now 9 isolates are flagged. In total, 75.3% of all isolates are marked ‘first weighted’ - 46.8% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>Instead of 2, now 6 isolates are flagged. In total, 75.4% of all isolates are marked ‘first weighted’ - 46.9% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>As with <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code>, there’s a shortcut for this new algorithm too:</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb20-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/first_isolate.html">filter_first_weighted_isolate</a></span>()</a></code></pre></div>
<p>So we end up with 15,051 isolates for analysis.</p>
<p>So we end up with 15,085 isolates for analysis.</p>
<p>We can remove unneeded columns:</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data_1st <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="op">-</span><span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(first, keyab))</a></code></pre></div>
@ -813,13 +813,29 @@
</tr></thead>
<tbody>
<tr class="odd">
<td>2</td>
<td align="center">2017-06-27</td>
<td align="center">H4</td>
<td align="center">Hospital C</td>
<td>1</td>
<td align="center">2011-03-27</td>
<td align="center">O9</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</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>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>4</td>
<td align="center">2011-07-28</td>
<td align="center">D7</td>
<td align="center">Hospital A</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
@ -828,59 +844,27 @@
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>3</td>
<td align="center">2012-05-31</td>
<td align="center">X3</td>
<td align="center">Hospital B</td>
<tr class="odd">
<td>8</td>
<td align="center">2015-08-23</td>
<td align="center">M1</td>
<td align="center">Hospital D</td>
<td align="center">B_STRPT_PNMN</td>
<td align="center">I</td>
<td align="center">I</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">F</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
<td align="center">Streptococcus</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>4</td>
<td align="center">2013-06-08</td>
<td align="center">K2</td>
<td align="center">Hospital B</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>5</td>
<td align="center">2012-02-10</td>
<td align="center">M5</td>
<td align="center">Hospital A</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>7</td>
<td align="center">2015-10-03</td>
<td align="center">B7</td>
<td align="center">Hospital A</td>
<td>9</td>
<td align="center">2017-03-08</td>
<td align="center">G2</td>
<td align="center">Hospital B</td>
<td align="center">B_KLBSL_PNMN</td>
<td align="center">R</td>
<td align="center">S</td>
@ -892,20 +876,36 @@
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>9</td>
<td align="center">2016-04-27</td>
<td align="center">O7</td>
<td align="center">Hospital B</td>
<td align="center">B_STRPT_PNMN</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<tr class="odd">
<td>10</td>
<td align="center">2013-11-09</td>
<td align="center">Q8</td>
<td align="center">Hospital A</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-positive</td>
<td align="center">Streptococcus</td>
<td align="center">pneumoniae</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>16</td>
<td align="center">2010-06-08</td>
<td align="center">O4</td>
<td align="center">Hospital A</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
</tbody>
@ -927,7 +927,7 @@
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb24-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(genus, species)</a></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 15,051 (of which NA: 0 = 0%)<br>
Length: 15,085 (of which NA: 0 = 0%)<br>
Unique: 4</p>
<p>Shortest: 16<br>
Longest: 24</p>
@ -944,33 +944,33 @@ Longest: 24</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">7,443</td>
<td align="right">49.45%</td>
<td align="right">7,443</td>
<td align="right">49.45%</td>
<td align="right">7,426</td>
<td align="right">49.23%</td>
<td align="right">7,426</td>
<td align="right">49.23%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Staphylococcus aureus</td>
<td align="right">3,689</td>
<td align="right">24.51%</td>
<td align="right">11,132</td>
<td align="right">73.96%</td>
<td align="right">3,769</td>
<td align="right">24.99%</td>
<td align="right">11,195</td>
<td align="right">74.21%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">2,398</td>
<td align="right">15.93%</td>
<td align="right">13,530</td>
<td align="right">89.89%</td>
<td align="right">2,340</td>
<td align="right">15.51%</td>
<td align="right">13,535</td>
<td align="right">89.72%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">1,521</td>
<td align="right">10.11%</td>
<td align="right">15,051</td>
<td align="right">1,550</td>
<td align="right">10.28%</td>
<td align="right">15,085</td>
<td align="right">100.00%</td>
</tr>
</tbody>
@ -982,7 +982,7 @@ Longest: 24</p>
<p>The functions <code><a href="../reference/proportion.html">resistance()</a></code> and <code><a href="../reference/proportion.html">susceptibility()</a></code> can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions <code><a href="../reference/proportion.html">proportion_S()</a></code>, <code><a href="../reference/proportion.html">proportion_SI()</a></code>, <code><a href="../reference/proportion.html">proportion_I()</a></code>, <code><a href="../reference/proportion.html">proportion_IR()</a></code> and <code><a href="../reference/proportion.html">proportion_R()</a></code> can be used to determine the proportion of a specific antimicrobial outcome.</p>
<p>As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (<code><a href="../reference/proportion.html">proportion_R()</a></code>, equal to <code><a href="../reference/proportion.html">resistance()</a></code>) and susceptibility as the proportion of S and I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equal to <code><a href="../reference/proportion.html">susceptibility()</a></code>). These functions can be used on their own:</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/proportion.html">resistance</a></span>(AMX)</a>
<a class="sourceLine" id="cb25-2" data-line-number="2"><span class="co"># [1] 0.4655505</span></a></code></pre></div>
<a class="sourceLine" id="cb25-2" data-line-number="2"><span class="co"># [1] 0.4684123</span></a></code></pre></div>
<p>Or can be used in conjuction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb26-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(hospital) <span class="op">%&gt;%</span><span class="st"> </span></a>
@ -995,19 +995,19 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4651671</td>
<td align="center">0.4666964</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4687618</td>
<td align="center">0.4711845</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4569626</td>
<td align="center">0.4627451</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4668462</td>
<td align="center">0.4704102</td>
</tr>
</tbody>
</table>
@ -1025,23 +1025,23 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4651671</td>
<td align="center">4579</td>
<td align="center">0.4666964</td>
<td align="center">4489</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4687618</td>
<td align="center">5282</td>
<td align="center">0.4711845</td>
<td align="center">5327</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4569626</td>
<td align="center">2219</td>
<td align="center">0.4627451</td>
<td align="center">2295</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4668462</td>
<td align="center">2971</td>
<td align="center">0.4704102</td>
<td align="center">2974</td>
</tr>
</tbody>
</table>
@ -1061,27 +1061,27 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Escherichia</td>
<td align="center">0.9183125</td>
<td align="center">0.9039366</td>
<td align="center">0.9934166</td>
<td align="center">0.9243200</td>
<td align="center">0.8955023</td>
<td align="center">0.9934016</td>
</tr>
<tr class="even">
<td align="center">Klebsiella</td>
<td align="center">0.9119001</td>
<td align="center">0.8954635</td>
<td align="center">0.9960552</td>
<td align="center">0.9174194</td>
<td align="center">0.9019355</td>
<td align="center">0.9967742</td>
</tr>
<tr class="odd">
<td align="center">Staphylococcus</td>
<td align="center">0.9262673</td>
<td align="center">0.9227433</td>
<td align="center">0.9951206</td>
<td align="center">0.9193420</td>
<td align="center">0.9211993</td>
<td align="center">0.9936323</td>
</tr>
<tr class="even">
<td align="center">Streptococcus</td>
<td align="center">0.6092577</td>
<td align="center">0.6128205</td>
<td align="center">0.0000000</td>
<td align="center">0.6092577</td>
<td align="center">0.6128205</td>
</tr>
</tbody>
</table>
@ -1155,7 +1155,7 @@ Longest: 24</p>
<div id="independence-test" class="section level2">
<h2 class="hasAnchor">
<a href="#independence-test" class="anchor"></a>Independence test</h2>
<p>The next example uses the included <code>example_isolates</code>, which is an anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. This <code>data.frame</code> can be used to practice AMR analysis.</p>
<p>The next example uses the <code>example_isolates</code> data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis.</p>
<p>We will compare the resistance to fosfomycin (column <code>FOS</code>) in hospital A and D. The input for the <code><a href="https://rdrr.io/r/stats/fisher.test.html">fisher.test()</a></code> can be retrieved with a transformation like this:</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb34-1" data-line-number="1"><span class="co"># use package 'tidyr' to pivot data; </span></a>
<a class="sourceLine" id="cb34-2" data-line-number="2"><span class="co"># it gets installed with this 'AMR' package</span></a>

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@ -41,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9029</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -187,7 +187,7 @@
<h1>How to apply EUCAST rules</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">10 November 2019</h4>
<h4 class="date">18 November 2019</h4>
<div class="hidden name"><code>EUCAST.Rmd</code></div>

63
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@ -41,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9029</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -187,7 +187,7 @@
<h1>How to determine multi-drug resistance (MDR)</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">10 November 2019</h4>
<h4 class="date">18 November 2019</h4>
<div class="hidden name"><code>MDR.Rmd</code></div>
@ -196,11 +196,11 @@
<p>With the function <code><a href="../reference/mdro.html">mdro()</a></code>, you can determine multi-drug resistant organisms (MDRO).</p>
<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 id="type-of-input" class="section level4">
<h4 class="hasAnchor">
<a href="#type-of-input" class="anchor"></a>Type of input</h4>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> takes a data set as input, such as a regular <code>data.frame</code>. It automatically determines the right columns for info about your isolates, like 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>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, like 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 id="guidelines" class="section level4">
@ -229,7 +229,8 @@ The German national guideline - Mueller et al. (2015) Antimicrobial Resistance a
<div id="examples" class="section level4">
<h4 class="hasAnchor">
<a href="#examples" class="anchor"></a>Examples</h4>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered <code>factor</code>. 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. If we test that guideline on the included <code>example_isolates</code> data set, we get:</p>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered <code>factor</code>. 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 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(dplyr) <span class="co"># to support pipes: %&gt;%</span></a></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">example_isolates <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/mdro.html">mdro</a></span>() <span class="op">%&gt;%</span><span class="st"> </span></a>
@ -306,14 +307,14 @@ Unique: 2</p>
<p>The data set now looks like this:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(my_TB_data)</a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="co"># 1 S R S R I R</span></a>
<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="co"># 2 I S R I R R</span></a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="co"># 3 S R S S S R</span></a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co"># 4 R S S S R S</span></a>
<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># 5 S S S S I R</span></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># 6 I S R I S S</span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="co"># 1 S R S S S S</span></a>
<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="co"># 2 S S S S R S</span></a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="co"># 3 S R R S S R</span></a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co"># 4 S R S S I S</span></a>
<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># 5 R S S I R S</span></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># 6 R R S S R R</span></a>
<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># kanamycin</span></a>
<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># 1 S</span></a>
<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># 1 R</span></a>
<a class="sourceLine" id="cb5-11" data-line-number="11"><span class="co"># 2 R</span></a>
<a class="sourceLine" id="cb5-12" data-line-number="12"><span class="co"># 3 R</span></a>
<a class="sourceLine" id="cb5-13" data-line-number="13"><span class="co"># 4 S</span></a>
@ -335,7 +336,7 @@ Unique: 2</p>
<a class="sourceLine" id="cb7-11" data-line-number="11"><span class="co"># Author: WHO (World Health Organization)</span></a>
<a class="sourceLine" id="cb7-12" data-line-number="12"><span class="co"># Source: https://www.who.int/tb/publications/pmdt_companionhandbook/en/</span></a>
<a class="sourceLine" id="cb7-13" data-line-number="13"><span class="co"># </span></a>
<a class="sourceLine" id="cb7-14" data-line-number="14"><span class="co"># =&gt; Found 4320 MDROs out of 5000 tested isolates (86.4%)</span></a></code></pre></div>
<a class="sourceLine" id="cb7-14" data-line-number="14"><span class="co"># =&gt; Found 4341 MDROs out of 5000 tested isolates (86.8%)</span></a></code></pre></div>
<p>Create a frequency table of the results:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(my_TB_data<span class="op">$</span>mdr)</a></code></pre></div>
<p><strong>Frequency table</strong></p>
@ -356,40 +357,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3243</td>
<td align="right">64.86%</td>
<td align="right">3243</td>
<td align="right">64.86%</td>
<td align="right">3281</td>
<td align="right">65.62%</td>
<td align="right">3281</td>
<td align="right">65.62%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">680</td>
<td align="right">13.60%</td>
<td align="right">3923</td>
<td align="right">78.46%</td>
<td align="right">659</td>
<td align="right">13.18%</td>
<td align="right">3940</td>
<td align="right">78.80%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">587</td>
<td align="right">11.74%</td>
<td align="right">4510</td>
<td align="right">90.20%</td>
<td align="right">571</td>
<td align="right">11.42%</td>
<td align="right">4511</td>
<td align="right">90.22%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">302</td>
<td align="right">6.04%</td>
<td align="right">4812</td>
<td align="right">96.24%</td>
<td align="right">278</td>
<td align="right">5.56%</td>
<td align="right">4789</td>
<td align="right">95.78%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">188</td>
<td align="right">3.76%</td>
<td align="right">211</td>
<td align="right">4.22%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>

4
docs/articles/SPSS.html
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@ -41,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -187,7 +187,7 @@
<h1>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">15 November 2019</h4>
<h4 class="date">18 November 2019</h4>
<div class="hidden name"><code>SPSS.Rmd</code></div>

4
docs/articles/WHONET.html
View File

@ -41,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9029</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -187,7 +187,7 @@
<h1>How to work with WHONET data</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">10 November 2019</h4>
<h4 class="date">18 November 2019</h4>
<div class="hidden name"><code>WHONET.Rmd</code></div>

102
docs/articles/benchmarks.html
View File

@ -41,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -187,7 +187,7 @@
<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">16 October 2019</h4>
<h4 class="date">18 November 2019</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -221,21 +221,21 @@
<a class="sourceLine" id="cb2-16" data-line-number="16"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(S.aureus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 9.9 10 14 11 12 35 10</span></a>
<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 33.0 33 39 38 39 52 10</span></a>
<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 32.0 36 44 38 56 68 10</span></a>
<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 10.0 10 13 11 11 34 10</span></a>
<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 10.0 11 19 12 33 42 10</span></a>
<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 25.0 26 32 28 32 53 10</span></a>
<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 24.0 25 31 27 30 52 10</span></a>
<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 31.0 32 39 34 38 84 10</span></a>
<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 610.0 640 680 680 710 770 10</span></a>
<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 330.0 340 350 350 360 370 10</span></a>
<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 9.8 10 13 11 12 34 10</span></a>
<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 20.0 22 29 24 31 57 10</span></a>
<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 20.0 20 29 23 42 47 10</span></a>
<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 21.0 23 30 25 43 47 10</span></a></code></pre></div>
<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 10 10 16 11 13 58 10</span></a>
<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 33 34 50 45 67 80 10</span></a>
<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 34 38 44 39 43 72 10</span></a>
<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 10 11 16 12 15 44 10</span></a>
<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 11 11 17 12 14 59 10</span></a>
<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 26 30 37 33 48 54 10</span></a>
<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 26 27 31 28 32 48 10</span></a>
<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 31 35 37 37 40 44 10</span></a>
<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 650 690 750 710 840 900 10</span></a>
<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 380 410 430 430 440 500 10</span></a>
<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 10 11 20 11 37 44 10</span></a>
<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 20 22 51 29 47 220 10</span></a>
<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 21 23 32 25 41 57 10</span></a>
<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 20 21 23 22 23 27 10</span></a></code></pre></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-5-1.png" width="562.5"></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 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
@ -247,19 +247,19 @@
<a class="sourceLine" id="cb3-6" data-line-number="6"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(M.semesiae, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1343.00 1385.00 1398.00 1403.00 1418.0</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1299.00 1356.00 1397.00 1396.00 1442.0</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 1892.00 2028.00 2052.00 2041.00 2084.0</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 1990.00 2017.00 2062.00 2032.00 2094.0</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 32.63 33.24 38.58 35.82 40.2</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1415.00 1467.00 1505.00 1493.00 1537.00</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1447.00 1488.00 1530.00 1521.00 1555.00</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 2216.00 2239.00 2293.00 2284.00 2351.00</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 2143.00 2212.00 2304.00 2307.00 2332.00</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 32.44 35.59 43.59 38.19 45.88</span></a>
<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co"># max neval</span></a>
<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1437.00 10</span></a>
<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1488.00 10</span></a>
<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 2169.00 10</span></a>
<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 2245.00 10</span></a>
<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 57.04 10</span></a></code></pre></div>
<p>That takes 14.3 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are almost fast - these are the most probable input from most data sets.</p>
<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1627.00 10</span></a>
<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1628.00 10</span></a>
<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 2395.00 10</span></a>
<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 2535.00 10</span></a>
<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 69.48 10</span></a></code></pre></div>
<p>That takes 13.8 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are almost fast - these are the most probable input from most data sets.</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-9-1.png" width="562.5"></p>
<p>In reality, the <code><a href="../reference/as.mo.html">as.mo()</a></code> functions <strong>learns from its own output to speed up determinations for next times</strong>. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use <code><a href="../reference/as.mo.html">as.mo()</a></code> yourself:</p>
@ -296,8 +296,8 @@
<a class="sourceLine" id="cb4-24" data-line-number="24"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 645 661 683 672 686 771 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.67 seconds (671 ms). You only lose time on your unique input values.</p>
<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 642 674 702 687 713 843 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.69 seconds (686 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -309,10 +309,10 @@
<a class="sourceLine" id="cb5-4" data-line-number="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.380 6.530 7.750 7.180 8.21 11.30 10</span></a>
<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 24.300 25.500 30.400 26.900 31.60 54.80 10</span></a>
<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.803 0.827 0.926 0.869 0.95 1.22 10</span></a></code></pre></div>
<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.350 6.420 6.880 6.530 7.180 8.56 10</span></a>
<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 24.600 25.100 31.300 25.700 27.900 62.80 10</span></a>
<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.798 0.867 0.914 0.892 0.931 1.14 10</span></a></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0009 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="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1">run_it &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),</a>
<a class="sourceLine" id="cb6-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</a>
@ -326,14 +326,14 @@
<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb6-11" data-line-number="11"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb6-12" data-line-number="12"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.509 0.530 0.681 0.565 0.661 1.630 10</span></a>
<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.518 0.526 0.598 0.553 0.564 0.875 10</span></a>
<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.848 0.882 1.100 0.990 1.180 1.920 10</span></a>
<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.566 0.592 0.734 0.714 0.765 1.120 10</span></a>
<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.486 0.522 0.555 0.542 0.551 0.681 10</span></a>
<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.466 0.493 0.598 0.553 0.586 1.110 10</span></a>
<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.462 0.498 0.598 0.525 0.671 0.921 10</span></a>
<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.480 0.489 0.566 0.508 0.628 0.756 10</span></a></code></pre></div>
<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.446 0.459 0.471 0.475 0.481 0.485 10</span></a>
<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.502 0.506 0.518 0.513 0.521 0.568 10</span></a>
<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.774 0.802 0.811 0.815 0.823 0.838 10</span></a>
<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.505 0.511 0.532 0.515 0.534 0.627 10</span></a>
<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.467 0.469 0.495 0.478 0.481 0.676 10</span></a>
<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.457 0.467 0.478 0.479 0.484 0.509 10</span></a>
<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.452 0.460 0.467 0.471 0.472 0.478 10</span></a>
<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.460 0.469 0.473 0.473 0.480 0.486 10</span></a></code></pre></div>
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
@ -359,14 +359,14 @@
<a class="sourceLine" id="cb7-17" data-line-number="17"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 20.01 20.76 28.07 22.47 29.04 54.04 10</span></a>
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 21.95 22.28 26.91 22.72 24.24 58.36 10</span></a>
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 27.57 27.96 47.86 31.50 54.01 149.80 10</span></a>
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 22.08 22.13 28.59 24.11 26.45 58.61 10</span></a>
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 21.73 22.33 25.39 25.30 26.90 29.57 10</span></a>
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 22.23 22.98 24.95 23.45 23.70 34.08 10</span></a>
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 22.02 23.03 24.80 24.37 27.14 28.33 10</span></a></code></pre></div>
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 20.49 21.64 27.36 23.44 27.96 53.91 10</span></a>
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 23.26 23.37 28.22 24.42 26.13 60.60 10</span></a>
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 28.62 29.74 36.90 30.18 33.12 72.41 10</span></a>
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 21.77 23.08 27.71 24.75 26.98 55.14 10</span></a>
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 22.54 23.37 25.19 25.25 26.41 29.08 10</span></a>
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 23.04 23.99 30.97 26.14 30.78 52.61 10</span></a>
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 22.40 23.07 24.26 23.14 24.44 31.60 10</span></a></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9033</span>
</span>
</div>

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@ -41,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -187,7 +187,7 @@
<h1>How to predict antimicrobial resistance</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">16 October 2019</h4>
<h4 class="date">18 November 2019</h4>
<div class="hidden name"><code>resistance_predict.Rmd</code></div>

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9033</span>
</span>
</div>

5
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@ -139,6 +139,11 @@ help {
font-size: 1.5em;
}
/* Beautify manually added 'Last updated' text of NEWS */
div[id^=last-updated] h2 {
padding-top: 10px;
}
.version.label {
display: none;
}

20
docs/index.html
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@ -45,7 +45,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9033</span>
</span>
</div>
@ -200,8 +200,8 @@ A methods paper about this package has been preprinted at bioRxiv. It was update
<div id="what-is-amr-for-r" class="section level3">
<h3 class="hasAnchor">
<a href="#what-is-amr-for-r" class="anchor"></a>What is <code>AMR</code> (for R)?</h3>
<p><code>AMR</code> is a free and open-source <a href="https://www.r-project.org">R package</a> to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. Since its first public release in early 2018, this package has been downloaded over 25,000 times from more than 60 countries <small>(source: <a href="https://cran-logs.rstudio.com">CRAN logs, 2019</a>)</small>.</p>
<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~70,000 microorganisms</strong></a> (distinct microbial species) and <a href="./reference/antibiotics.html"><strong>~450 antibiotics</strong></a> by name and code, and knows all about valid RSI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
<p><code>AMR</code> is a free and open-source <a href="https://www.r-project.org">R package</a> to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. Since its first public release in early 2018, this package has been downloaded over 25,000 times from more than 70 countries <small>(source: <a href="https://cran-logs.rstudio.com">CRAN logs, 2019</a>)</small>.</p>
<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~70,000 distinct microbial species</strong></a> and all <a href="./reference/antibiotics.html"><strong>~550 antibiotic, antimycotic and antiviral drugs</strong></a> by name and code, and knows all about valid RSI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
<p>We created this package for both routine analysis and academic research (as part of our PhD theses) at the Faculty of Medical Sciences of the University of Groningen, the Netherlands, and the Medical Microbiology &amp; Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is <a href="./news">actively maintained</a> and is free software (see <a href="#copyright">Copyright</a>).</p>
<p><strong>Used to SPSS?</strong> Read our <a href="./articles/SPSS.html">tutorial on how to import data from SPSS, SAS or Stata</a>.</p>
<div id="partners" class="section level4">
@ -297,7 +297,7 @@ A methods paper about this package has been preprinted at bioRxiv. It was update
<div id="antimicrobial-reference-data" class="section level4">
<h4 class="hasAnchor">
<a href="#antimicrobial-reference-data" class="anchor"></a>Antimicrobial reference data</h4>
<p>This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href="https://www.whocc.no" class="uri">https://www.whocc.no</a>) and the <a href="http://ec.europa.eu/health/documents/community-register/html/atc.htm">Pharmaceuticals Community Register of the European Commission</a>.</p>
<p>This package contains <strong>all ~550 antibiotic, antimycotic and antiviral drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href="https://www.whocc.no" class="uri">https://www.whocc.no</a>) and the <a href="http://ec.europa.eu/health/documents/community-register/html/atc.htm">Pharmaceuticals Community Register of the European Commission</a>.</p>
<p><strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href="https://www.whocc.no/copyright_disclaimer/" class="uri">https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>
<p>Read more about the data from WHOCC <a href="./reference/WHOCC.html">in our manual</a>.</p>
</div>
@ -330,9 +330,9 @@ A methods paper about this package has been preprinted at bioRxiv. It was update
<li>You can also identify first <em>weighted</em> isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.</li>
</ul>
</li>
<li>Use <code><a href="reference/mdro.html">mdro()</a></code> (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.</li>
<li>Use <code><a href="reference/mdro.html">mdro()</a></code> to determine which micro-organisms are multi-drug resistant organisms (MDRO). It supports a variety of international guidelines, such as the MDR-paper by Magiorakos <em>et al.</em> (2012, <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=21793988">PMID 21793988</a>), the exceptional phenotype definitions of EUCAST and the WHO guideline on multi-drug resistant TB. It also supports the national guidelines of the Netherlands and Germany.</li>
<li>The <a href="./reference/microorganisms.html">data set <code>microorganisms</code></a> contains the complete taxonomic tree of ~70,000 microorganisms. Furthermore, some colloquial names and all Gram stains are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like <code><a href="reference/mo_property.html">mo_genus()</a></code>, <code><a href="reference/mo_property.html">mo_family()</a></code>, <code><a href="reference/mo_property.html">mo_gramstain()</a></code> or even <code><a href="reference/mo_property.html">mo_phylum()</a></code>. As they use <code><a href="reference/as.mo.html">as.mo()</a></code> internally, they also use the same intelligent rules for determination. For example, <code><a href="reference/mo_property.html">mo_genus("MRSA")</a></code> and <code><a href="reference/mo_property.html">mo_genus("S. aureus")</a></code> will both return <code>"Staphylococcus"</code>. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.</li>
<li>The <a href="./reference/antibiotics.html">data set <code>antibiotics</code></a> contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. The function <code><a href="reference/ab_property.html">ab_atc()</a></code> will return the ATC code of an antibiotic as defined by the WHO. Use functions like <code><a href="reference/ab_property.html">ab_name()</a></code>, <code><a href="reference/ab_property.html">ab_group()</a></code> and <code><a href="reference/ab_property.html">ab_tradenames()</a></code> to look up values. The <code>ab_*</code> functions use <code><a href="reference/as.ab.html">as.ab()</a></code> internally so they support the same intelligent rules to guess the most probable result. For example, <code><a href="reference/ab_property.html">ab_name("Fluclox")</a></code>, <code><a href="reference/ab_property.html">ab_name("Floxapen")</a></code> and <code><a href="reference/ab_property.html">ab_name("J01CF05")</a></code> will all return <code>"Flucloxacillin"</code>. These functions can again be used to add new variables to your data.</li>
<li>The <a href="./reference/antibiotics.html">data set <code>antibiotics</code></a> contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. Use functions like <code><a href="reference/ab_property.html">ab_name()</a></code>, <code><a href="reference/ab_property.html">ab_group()</a></code>, <code><a href="reference/ab_property.html">ab_atc()</a></code> and <code><a href="reference/ab_property.html">ab_tradenames()</a></code> to look up values. The <code>ab_*</code> functions use <code><a href="reference/as.ab.html">as.ab()</a></code> internally so they support the same intelligent rules to guess the most probable result. For example, <code><a href="reference/ab_property.html">ab_name("Fluclox")</a></code>, <code><a href="reference/ab_property.html">ab_name("Floxapen")</a></code> and <code><a href="reference/ab_property.html">ab_name("J01CF05")</a></code> will all return <code>"Flucloxacillin"</code>. These functions can again be used to add new variables to your data.</li>
</ul>
</li>
<li>
@ -349,13 +349,7 @@ A methods paper about this package has been preprinted at bioRxiv. It was update
<li>Aside from this website with many tutorials, the package itself contains extensive help pages with many examples for all functions.</li>
<li>The package also contains example data sets:
<ul>
<li>The <a href="./reference/example_isolates.html"><code>example_isolates</code> data set</a>. This data set contains:
<ul>
<li>2,000 blood culture isolates from anonymised septic patients between 2001 and 2017 in the Northern Netherlands</li>
<li>Results of 40 antibiotics (each antibiotic in its own column) with a total ~40,000 antimicrobial results</li>
<li>Real and genuine data</li>
</ul>
</li>
<li>The <a href="./reference/example_isolates.html"><code>example_isolates</code> data set</a>. This data set contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis.</li>
<li>The <a href="./reference/WHONET.html"><code>WHONET</code> data set</a>. This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET <a href="./articles/WHONET.html">on its tutorial page</a>.</li>
</ul>
</li>

18
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@ -84,7 +84,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9033</span>
</span>
</div>
@ -231,11 +231,14 @@
</div>
<div id="amr-0-8-0-9032" class="section level1">
<div id="amr-0-8-0-9033" class="section level1">
<h1 class="page-header">
<a href="#amr-0-8-0-9032" class="anchor"></a>AMR 0.8.0.9032<small> Unreleased </small>
<a href="#amr-0-8-0-9033" class="anchor"></a>AMR 0.8.0.9033<small> Unreleased </small>
</h1>
<p><small>Last updated: 15-Nov-2019</small></p>
<div id="last-updated-18-nov-2019" class="section level2">
<h2 class="hasAnchor">
<a href="#last-updated-18-nov-2019" class="anchor"></a><small>Last updated: 18-Nov-2019</small>
</h2>
<div id="breaking" class="section level3">
<h3 class="hasAnchor">
<a href="#breaking" class="anchor"></a>Breaking</h3>
@ -271,6 +274,7 @@
<li>The new Verbose mode (<code><a href="../reference/mdro.html">mdro(...., verbose = TRUE)</a></code>) returns an informative data set where the reason for MDRO determination is given for every isolate, and an list of the resistant antimicrobial agents</li>
</ul>
</li>
<li><p>Data set <code>antivirals</code>, containing all entries from the ATC J05 group with their DDDs for oral and parenteral treatment</p></li>
</ul>
</div>
<div id="changes" class="section level3">
@ -303,7 +307,7 @@
<li>Improvements for <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>:
<ul>
<li>Fix where <em>Stenotrophomonas maltophilia</em> would always become ceftazidime R (following EUCAST v3.1)</li>
<li>Fix where <em>Leuconostoc</em> and <em>Pediococcus</em> would not always become glyopeptides R</li>
<li>Fix where <em>Leuconostoc</em> and <em>Pediococcus</em> would not always become glycopeptides R</li>
<li>non-EUCAST rules in <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> are now applied first and not as last anymore. This is to improve the dependency on certain antibiotics for the official EUCAST rules. Please see <code><a href="../reference/eucast_rules.html">?eucast_rules</a></code>.</li>
</ul>
</li>
@ -314,6 +318,7 @@
</li>
<li>Added ATC codes for ceftaroline, ceftobiprole and faropenem and fixed two typos in the <code>antibiotics</code> data set</li>
<li>More robust way of determining valid MIC values</li>
<li>Small changed to the <code>example_isolates</code> data set to better reflect reality</li>
</ul>
</div>
<div id="other" class="section level3">
@ -324,6 +329,7 @@
<li>Added Dr. Sofia Ny as contributor</li>
</ul>
</div>
</div>
</div>
<div id="amr-0-8-0" class="section level1">
<h1 class="page-header">
@ -1385,7 +1391,7 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
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<h2>Contents</h2>
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<li><a href="#amr-0-8-0-9032">0.8.0.9032</a></li>
<li><a href="#amr-0-8-0-9033">0.8.0.9033</a></li>
<li><a href="#amr-0-8-0">0.8.0</a></li>
<li><a href="#amr-0-7-1">0.7.1</a></li>
<li><a href="#amr-0-7-0">0.7.0</a></li>

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</span>
</div>
@ -244,7 +244,7 @@
<p><img src='figures/logo_who.png' height=60px style=margin-bottom:5px /> <br />
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
This package contains <strong>all ~550 antibiotic, antimycotic and antiviral drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
<p>These have become the gold standard for international drug utilisation monitoring and research.</p>
<p>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.</p>
<p><strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href='https://www.whocc.no/copyright_disclaimer/'>https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>

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</button>
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</span>
</div>
@ -253,7 +253,7 @@
<dt><code>abbr</code></dt><dd><p>List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)</p></dd>
<dt><code>synonyms</code></dt><dd><p>Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID</p></dd>
<dt><code>oral_ddd</code></dt><dd><p>Defined Daily Dose (DDD), oral treatment</p></dd>
<dt><code>oral_units</code></dt><dd><p>Units of <code>ddd_units</code></p></dd>
<dt><code>oral_units</code></dt><dd><p>Units of <code>oral_ddd</code></p></dd>
<dt><code>iv_ddd</code></dt><dd><p>Defined Daily Dose (DDD), parenteral treatment</p></dd>
<dt><code>iv_units</code></dt><dd><p>Units of <code>iv_ddd</code></p></dd>
@ -272,7 +272,7 @@
<p><img src='figures/logo_who.png' height=60px style=margin-bottom:5px /> <br />
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
This package contains <strong>all ~550 antibiotic, antimycotic and antiviral drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
<p>These have become the gold standard for international drug utilisation monitoring and research.</p>
<p>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.</p>
<p><strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href='https://www.whocc.no/copyright_disclaimer/'>https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>
@ -283,7 +283,7 @@ This package contains <strong>all ~450 antimicrobial drugs</strong> and their An
<p>On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitlab.io/AMR</a> you can find <a href='https://msberends.gitlab.io/AMR/articles/AMR.html'>a tutorial</a> about how to conduct AMR analysis, the <a href='https://msberends.gitlab.io/AMR/reference'>complete documentation of all functions</a> (which reads a lot easier than here in R) and <a href='https://msberends.gitlab.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>.</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p><code><a href='microorganisms.html'>microorganisms</a></code></p></div>
<div class='dont-index'><p><code>antivirals</code> <code><a href='microorganisms.html'>microorganisms</a></code></p></div>
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<h1>Data set with ~100 antivirals</h1>
<div class="hidden name"><code>antivirals.Rd</code></div>
</div>
<div class="ref-description">
<p>A data set containing all antivirals, according to the ATC code group 'J05' (Antivirals for systemic use).</p>
</div>
<pre class="usage"><span class='no'>antivirals</span></pre>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>A <code><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with 102 observations and 7 variables:</p><dl class='dl-horizontal'>
<dt><code>atc</code></dt><dd><p>ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC</p></dd>
<dt><code>name</code></dt><dd><p>Official name as used by WHONET/EARS-Net or the WHO</p></dd>
<dt><code>atc_group</code></dt><dd><p>Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC</p></dd>
<dt><code>oral_ddd</code></dt><dd><p>Defined Daily Dose (DDD), oral treatment</p></dd>
<dt><code>oral_units</code></dt><dd><p>Units of <code>oral_ddd</code></p></dd>
<dt><code>iv_ddd</code></dt><dd><p>Defined Daily Dose (DDD), parenteral treatment</p></dd>
<dt><code>iv_units</code></dt><dd><p>Units of <code>iv_ddd</code></p></dd>
</dl>
<h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
<p>World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): <a href='https://www.whocc.no/atc_ddd_index/'>https://www.whocc.no/atc_ddd_index/</a></p>
<h2 class="hasAnchor" id="whocc"><a class="anchor" href="#whocc"></a>WHOCC</h2>
<p><img src='figures/logo_who.png' height=60px style=margin-bottom:5px /> <br />
This package contains <strong>all ~550 antibiotic, antimycotic and antiviral drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
<p>These have become the gold standard for international drug utilisation monitoring and research.</p>
<p>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.</p>
<p><strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href='https://www.whocc.no/copyright_disclaimer/'>https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2>
<p>On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitlab.io/AMR</a> you can find <a href='https://msberends.gitlab.io/AMR/articles/AMR.html'>a tutorial</a> about how to conduct AMR analysis, the <a href='https://msberends.gitlab.io/AMR/reference'>complete documentation of all functions</a> (which reads a lot easier than here in R) and <a href='https://msberends.gitlab.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>.</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p><code><a href='antibiotics.html'>antibiotics</a></code> <code><a href='microorganisms.html'>microorganisms</a></code></p></div>
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<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#format">Format</a></li>
<li><a href="#source">Source</a></li>
<li><a href="#whocc">WHOCC</a></li>
<li><a href="#read-more-on-our-website-">Read more on our website!</a></li>
<li><a href="#see-also">See also</a></li>
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<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
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@ -273,7 +273,7 @@
<p><img src='figures/logo_who.png' height=60px style=margin-bottom:5px /> <br />
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
This package contains <strong>all ~550 antibiotic, antimycotic and antiviral drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
<p>These have become the gold standard for international drug utilisation monitoring and research.</p>
<p>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.</p>
<p><strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href='https://www.whocc.no/copyright_disclaimer/'>https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>

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@ -85,7 +85,7 @@
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@ -85,7 +85,7 @@
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@ -85,7 +85,7 @@
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@ -86,7 +86,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
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@ -86,7 +86,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
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@ -394,7 +394,8 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
<p>On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitlab.io/AMR</a> you can find <a href='https://msberends.gitlab.io/AMR/articles/AMR.html'>a tutorial</a> about how to conduct AMR analysis, the <a href='https://msberends.gitlab.io/AMR/reference'>complete documentation of all functions</a> (which reads a lot easier than here in R) and <a href='https://msberends.gitlab.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='no'>a</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></span>(<span class='kw'>mo</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"Staphylococcus aureus"</span>,
<pre class="examples"><span class='co'># \donttest{</span>
<span class='no'>a</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></span>(<span class='kw'>mo</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></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>,
@ -429,7 +430,6 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
<span class='co'># 5 Pseudomonas aeruginosa R R - - R R R</span>
<span class='co'># \donttest{</span>
<span class='co'># do not apply EUCAST rules, but rather get a data.frame</span>
<span class='co'># with 18 rows, containing all details about the transformations:</span>
<span class='no'>c</span> <span class='kw'>&lt;-</span> <span class='fu'>eucast_rules</span>(<span class='no'>a</span>, <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)

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@ -6,7 +6,7 @@
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Data set with 2,000 blood culture isolates — example_isolates • AMR (for R)</title>
<title>Data set with 2,000 example isolates — example_isolates • AMR (for R)</title>
<!-- favicons -->
<link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png">
@ -50,8 +50,8 @@
<link href="../extra.css" rel="stylesheet">
<script src="../extra.js"></script>
<meta property="og:title" content="Data set with 2,000 blood culture isolates — example_isolates" />
<meta property="og:description" content="An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found 4 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis. For examples, please read the tutorial on our website." />
<meta property="og:title" content="Data set with 2,000 example isolates — example_isolates" />
<meta property="og:description" content="A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read the tutorial on our website." />
<meta property="og:image" content="https://msberends.gitlab.io/AMR/logo.png" />
<meta name="twitter:card" content="summary" />
@ -85,7 +85,7 @@
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
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</span>
</div>
@ -228,13 +228,13 @@
<div class="row">
<div class="col-md-9 contents">
<div class="page-header">
<h1>Data set with 2,000 blood culture isolates</h1>
<h1>Data set with 2,000 example isolates</h1>
<div class="hidden name"><code>example_isolates.Rd</code></div>
</div>
<div class="ref-description">
<p>An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found 4 different hospitals in the Netherlands, between 2001 and 2017. This <code>data.frame</code> can be used to practice AMR analysis. For examples, please read <a href='https://msberends.gitlab.io/AMR/articles/AMR.html'>the tutorial on our website</a>.</p>
<p>A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read <a href='https://msberends.gitlab.io/AMR/articles/AMR.html'>the tutorial on our website</a>.</p>
</div>
<pre class="usage"><span class='no'>example_isolates</span></pre>
@ -250,7 +250,7 @@
<dt><code>ward_outpatient</code></dt><dd><p>logical to determine if ward is an outpatient clinic</p></dd>
<dt><code>age</code></dt><dd><p>age of the patient</p></dd>
<dt><code>gender</code></dt><dd><p>gender of the patient</p></dd>
<dt><code>patient_id</code></dt><dd><p>ID of the patient, first 10 characters of an SHA hash containing irretrievable information</p></dd>
<dt><code>patient_id</code></dt><dd><p>ID of the patient</p></dd>
<dt><code>mo</code></dt><dd><p>ID of microorganism created with <code><a href='as.mo.html'>as.mo</a></code>, see also <code><a href='microorganisms.html'>microorganisms</a></code></p></dd>
<dt><code>PEN:RIF</code></dt><dd><p>40 different antibiotics with class <code>rsi</code> (see <code><a href='as.rsi.html'>as.rsi</a></code>); these column names occur in <code><a href='antibiotics.html'>antibiotics</a></code> data set and can be translated with <code><a href='ab_property.html'>ab_name</a></code></p></dd>

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@ -85,7 +85,7 @@
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@ -85,7 +85,7 @@
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@ -85,7 +85,7 @@
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2
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@ -85,7 +85,7 @@
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2
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@ -85,7 +85,7 @@
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212
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@ -84,7 +84,7 @@
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
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</span>
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@ -239,38 +239,6 @@
</colgroup>
<tbody>
<tr>
<th colspan="2">
<h2 id="section-background-information" class="hasAnchor"><a href="#section-background-information" class="anchor"></a>Background information</h2>
<p class="section-desc"><p>Some pages about our package and its external sources. Be sure to read our <a href="./../articles/index.html">How To’s</a> for more information about how to work with functions in this package.</p></p>
</th>
</tr>
<tr>
<td>
<p><code><a href="AMR.html">AMR</a></code> </p>
</td>
<td><p>The <code>AMR</code> Package</p></td>
</tr><tr>
<td>
<p><code><a href="catalogue_of_life.html">catalogue_of_life</a></code> </p>
</td>
<td><p>The Catalogue of Life</p></td>
</tr><tr>
<td>
<p><code><a href="catalogue_of_life_version.html">catalogue_of_life_version()</a></code> </p>
</td>
<td><p>Version info of included Catalogue of Life</p></td>
</tr><tr>
<td>
<p><code><a href="WHOCC.html">WHOCC</a></code> </p>
</td>
<td><p>WHOCC: WHO Collaborating Centre for Drug Statistics Methodology</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-cleaning-your-data" class="hasAnchor"><a href="#section-cleaning-your-data" class="anchor"></a>Cleaning your data</h2>
@ -309,80 +277,50 @@
<td><p>Class 'rsi'</p></td>
</tr><tr>
<td>
<p><code><a href="mo_source.html">set_mo_source()</a></code> <code><a href="mo_source.html">get_mo_source()</a></code> </p>
</td>
<td><p>Use predefined reference data set</p></td>
</tr><tr>
<td>
<p><code><a href="eucast_rules.html">eucast_rules()</a></code> </p>
</td>
<td><p>EUCAST rules</p></td>
</tr><tr>
<td>
<p><code><a href="rsi_translation.html">rsi_translation</a></code> </p>
</td>
<td><p>Data set for RSI interpretation</p></td>
</tr><tr>
<td>
<p><code><a href="guess_ab_col.html">guess_ab_col()</a></code> </p>
</td>
<td><p>Guess antibiotic column</p></td>
</tr><tr>
<td>
<p><code><a href="mo_source.html">set_mo_source()</a></code> <code><a href="mo_source.html">get_mo_source()</a></code> </p>
</td>
<td><p>Use predefined reference data set</p></td>
</tr><tr>
<td>
<p><code><a href="read.4D.html">read.4D()</a></code> </p>
</td>
<td><p>Read data from 4D database</p></td>
</tr><tr>
<td>
<p><code><a href="rsi_translation.html">rsi_translation</a></code> </p>
</td>
<td><p>Data set for RSI interpretation</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-adding-variables-to-your-data" class="hasAnchor"><a href="#section-adding-variables-to-your-data" class="anchor"></a>Adding variables to your data</h2>
<p class="section-desc"><p>Functions to add new data to existing data, like the determination of first isolates, multi-drug resistant microorganisms (MDRO), getting properties of microorganisms or antibiotics and determining the age of patients or divide ages into age groups.</p></p>
<h2 id="section-enhancing-your-data" class="hasAnchor"><a href="#section-enhancing-your-data" class="anchor"></a>Enhancing your data</h2>
<p class="section-desc"><p>Functions to add new data to your existing data, such as the determination of first isolates, multi-drug resistant microorganisms (MDRO), getting properties of microorganisms or antibiotics and determining the age of patients or divide ages into age groups.</p></p>
</th>
</tr>
<tr>
<td>
<p><code><a href="first_isolate.html">first_isolate()</a></code> <code><a href="first_isolate.html">filter_first_isolate()</a></code> <code><a href="first_isolate.html">filter_first_weighted_isolate()</a></code> </p>
</td>
<td><p>Determine first (weighted) isolates</p></td>
</tr><tr>
<td>
<p><code><a href="mdro.html">mdro()</a></code> <code><a href="mdro.html">brmo()</a></code> <code><a href="mdro.html">mrgn()</a></code> <code><a href="mdro.html">mdr_tb()</a></code> <code><a href="mdro.html">mdr_cmi2012()</a></code> <code><a href="mdro.html">eucast_exceptional_phenotypes()</a></code> </p>
</td>
<td><p>Determine multidrug-resistant organisms (MDRO)</p></td>
</tr><tr>
<td>
<p><code><a href="key_antibiotics.html">key_antibiotics()</a></code> <code><a href="key_antibiotics.html">key_antibiotics_equal()</a></code> </p>
</td>
<td><p>Key antibiotics for first <em>weighted</em> isolates</p></td>
</tr><tr>
<td>
<p><code><a href="mo_property.html">mo_name()</a></code> <code><a href="mo_property.html">mo_fullname()</a></code> <code><a href="mo_property.html">mo_shortname()</a></code> <code><a href="mo_property.html">mo_subspecies()</a></code> <code><a href="mo_property.html">mo_species()</a></code> <code><a href="mo_property.html">mo_genus()</a></code> <code><a href="mo_property.html">mo_family()</a></code> <code><a href="mo_property.html">mo_order()</a></code> <code><a href="mo_property.html">mo_class()</a></code> <code><a href="mo_property.html">mo_phylum()</a></code> <code><a href="mo_property.html">mo_kingdom()</a></code> <code><a href="mo_property.html">mo_type()</a></code> <code><a href="mo_property.html">mo_gramstain()</a></code> <code><a href="mo_property.html">mo_ref()</a></code> <code><a href="mo_property.html">mo_authors()</a></code> <code><a href="mo_property.html">mo_year()</a></code> <code><a href="mo_property.html">mo_rank()</a></code> <code><a href="mo_property.html">mo_taxonomy()</a></code> <code><a href="mo_property.html">mo_synonyms()</a></code> <code><a href="mo_property.html">mo_info()</a></code> <code><a href="mo_property.html">mo_url()</a></code> <code><a href="mo_property.html">mo_property()</a></code> </p>
</td>
<td><p>Property of a microorganism</p></td>
</tr><tr>
<td>
<p><code><a href="ab_property.html">ab_name()</a></code> <code><a href="ab_property.html">ab_atc()</a></code> <code><a href="ab_property.html">ab_cid()</a></code> <code><a href="ab_property.html">ab_synonyms()</a></code> <code><a href="ab_property.html">ab_tradenames()</a></code> <code><a href="ab_property.html">ab_group()</a></code> <code><a href="ab_property.html">ab_atc_group1()</a></code> <code><a href="ab_property.html">ab_atc_group2()</a></code> <code><a href="ab_property.html">ab_ddd()</a></code> <code><a href="ab_property.html">ab_info()</a></code> <code><a href="ab_property.html">ab_property()</a></code> </p>
</td>
<td><p>Property of an antibiotic</p></td>
</tr><tr>
<td>
<p><code><a href="age.html">age()</a></code> </p>
</td>
<td><p>Age in years of individuals</p></td>
</tr><tr>
<td>
<p><code><a href="age_groups.html">age_groups()</a></code> </p>
</td>
@ -390,9 +328,21 @@
</tr><tr>
<td>
<p><code><a href="p_symbol.html">p_symbol()</a></code> </p>
<p><code><a href="age.html">age()</a></code> </p>
</td>
<td><p>Symbol of a p value</p></td>
<td><p>Age in years of individuals</p></td>
</tr><tr>
<td>
<p><code><a href="atc_online.html">atc_online_property()</a></code> <code><a href="atc_online.html">atc_online_groups()</a></code> <code><a href="atc_online.html">atc_online_ddd()</a></code> </p>
</td>
<td><p>Get ATC properties from WHOCC website</p></td>
</tr><tr>
<td>
<p><code><a href="first_isolate.html">first_isolate()</a></code> <code><a href="first_isolate.html">filter_first_isolate()</a></code> <code><a href="first_isolate.html">filter_first_weighted_isolate()</a></code> </p>
</td>
<td><p>Determine first (weighted) isolates</p></td>
</tr><tr>
<td>
@ -402,9 +352,27 @@
</tr><tr>
<td>
<p><code><a href="atc_online.html">atc_online_property()</a></code> <code><a href="atc_online.html">atc_online_groups()</a></code> <code><a href="atc_online.html">atc_online_ddd()</a></code> </p>
<p><code><a href="key_antibiotics.html">key_antibiotics()</a></code> <code><a href="key_antibiotics.html">key_antibiotics_equal()</a></code> </p>
</td>
<td><p>Get ATC properties from WHOCC website</p></td>
<td><p>Key antibiotics for first <em>weighted</em> isolates</p></td>
</tr><tr>
<td>
<p><code><a href="mdro.html">mdro()</a></code> <code><a href="mdro.html">brmo()</a></code> <code><a href="mdro.html">mrgn()</a></code> <code><a href="mdro.html">mdr_tb()</a></code> <code><a href="mdro.html">mdr_cmi2012()</a></code> <code><a href="mdro.html">eucast_exceptional_phenotypes()</a></code> </p>
</td>
<td><p>Determine multidrug-resistant organisms (MDRO)</p></td>
</tr><tr>
<td>
<p><code><a href="mo_property.html">mo_name()</a></code> <code><a href="mo_property.html">mo_fullname()</a></code> <code><a href="mo_property.html">mo_shortname()</a></code> <code><a href="mo_property.html">mo_subspecies()</a></code> <code><a href="mo_property.html">mo_species()</a></code> <code><a href="mo_property.html">mo_genus()</a></code> <code><a href="mo_property.html">mo_family()</a></code> <code><a href="mo_property.html">mo_order()</a></code> <code><a href="mo_property.html">mo_class()</a></code> <code><a href="mo_property.html">mo_phylum()</a></code> <code><a href="mo_property.html">mo_kingdom()</a></code> <code><a href="mo_property.html">mo_type()</a></code> <code><a href="mo_property.html">mo_gramstain()</a></code> <code><a href="mo_property.html">mo_ref()</a></code> <code><a href="mo_property.html">mo_authors()</a></code> <code><a href="mo_property.html">mo_year()</a></code> <code><a href="mo_property.html">mo_rank()</a></code> <code><a href="mo_property.html">mo_taxonomy()</a></code> <code><a href="mo_property.html">mo_synonyms()</a></code> <code><a href="mo_property.html">mo_info()</a></code> <code><a href="mo_property.html">mo_url()</a></code> <code><a href="mo_property.html">mo_property()</a></code> </p>
</td>
<td><p>Property of a microorganism</p></td>
</tr><tr>
<td>
<p><code><a href="p_symbol.html">p_symbol()</a></code> </p>
</td>
<td><p>Symbol of a p value</p></td>
</tr>
</tbody><tbody>
<tr>
@ -433,12 +401,6 @@
<td><p>Count isolates</p></td>
</tr><tr>
<td>
<p><code><a href="proportion.html">resistance()</a></code> <code><a href="proportion.html">susceptibility()</a></code> <code><a href="proportion.html">proportion_R()</a></code> <code><a href="proportion.html">proportion_IR()</a></code> <code><a href="proportion.html">proportion_I()</a></code> <code><a href="proportion.html">proportion_SI()</a></code> <code><a href="proportion.html">proportion_S()</a></code> <code><a href="proportion.html">proportion_df()</a></code> <code><a href="proportion.html">rsi_df()</a></code> </p>
</td>
<td><p>Calculate microbial resistance</p></td>
</tr><tr>
<td>
<p><code><a href="filter_ab_class.html">filter_ab_class()</a></code> <code><a href="filter_ab_class.html">filter_aminoglycosides()</a></code> <code><a href="filter_ab_class.html">filter_carbapenems()</a></code> <code><a href="filter_ab_class.html">filter_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_1st_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_2nd_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_3rd_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_4th_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_5th_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_fluoroquinolones()</a></code> <code><a href="filter_ab_class.html">filter_glycopeptides()</a></code> <code><a href="filter_ab_class.html">filter_macrolides()</a></code> <code><a href="filter_ab_class.html">filter_tetracyclines()</a></code> </p>
</td>
@ -463,6 +425,12 @@
<td><p>Kurtosis of the sample</p></td>
</tr><tr>
<td>
<p><code><a href="proportion.html">resistance()</a></code> <code><a href="proportion.html">susceptibility()</a></code> <code><a href="proportion.html">proportion_R()</a></code> <code><a href="proportion.html">proportion_IR()</a></code> <code><a href="proportion.html">proportion_I()</a></code> <code><a href="proportion.html">proportion_SI()</a></code> <code><a href="proportion.html">proportion_S()</a></code> <code><a href="proportion.html">proportion_df()</a></code> <code><a href="proportion.html">rsi_df()</a></code> </p>
</td>
<td><p>Calculate microbial resistance</p></td>
</tr><tr>
<td>
<p><code><a href="resistance_predict.html">resistance_predict()</a></code> <code><a href="resistance_predict.html">rsi_predict()</a></code> <code><a href="resistance_predict.html">plot(<i>&lt;resistance_predict&gt;</i>)</a></code> <code><a href="resistance_predict.html">ggplot_rsi_predict()</a></code> </p>
</td>
@ -478,7 +446,7 @@
<tr>
<th colspan="2">
<h2 id="section-included-data-sets" class="hasAnchor"><a href="#section-included-data-sets" class="anchor"></a>Included data sets</h2>
<p class="section-desc"><p>References for microorganisms and antibiotics, and even a genuine data set with isolates from septic patients.</p></p>
<p class="section-desc"><p>Scientifically reliable references for microorganisms and antibiotics, and example data sets to use for practise.</p></p>
</th>
</tr>
<tr>
@ -490,21 +458,15 @@
</tr><tr>
<td>
<p><code><a href="microorganisms.html">microorganisms</a></code> </p>
<p><code><a href="antivirals.html">antivirals</a></code> </p>
</td>
<td><p>Data set with ~70,000 microorganisms</p></td>
<td><p>Data set with ~100 antivirals</p></td>
</tr><tr>
<td>
<p><code><a href="example_isolates.html">example_isolates</a></code> </p>
</td>
<td><p>Data set with 2,000 blood culture isolates</p></td>
</tr><tr>
<td>
<p><code><a href="WHONET.html">WHONET</a></code> </p>
</td>
<td><p>Data set with 500 isolates - WHONET example</p></td>
<td><p>Data set with 2,000 example isolates</p></td>
</tr><tr>
<td>
@ -517,6 +479,50 @@
<p><code><a href="microorganisms.old.html">microorganisms.old</a></code> </p>
</td>
<td><p>Data set with previously accepted taxonomic names</p></td>
</tr><tr>
<td>
<p><code><a href="microorganisms.html">microorganisms</a></code> </p>
</td>
<td><p>Data set with ~70,000 microorganisms</p></td>
</tr><tr>
<td>
<p><code><a href="WHONET.html">WHONET</a></code> </p>
</td>
<td><p>Data set with 500 isolates - WHONET example</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-background-information" class="hasAnchor"><a href="#section-background-information" class="anchor"></a>Background information</h2>
<p class="section-desc"><p>Some pages about our package and its external sources. Be sure to read our <a href="./../articles/index.html">How To’s</a> for more information about how to work with functions in this package.</p></p>
</th>
</tr>
<tr>
<td>
<p><code><a href="AMR.html">AMR</a></code> </p>
</td>
<td><p>The <code>AMR</code> Package</p></td>
</tr><tr>
<td>
<p><code><a href="catalogue_of_life.html">catalogue_of_life</a></code> </p>
</td>
<td><p>The Catalogue of Life</p></td>
</tr><tr>
<td>
<p><code><a href="catalogue_of_life_version.html">catalogue_of_life_version()</a></code> </p>
</td>
<td><p>Version info of included Catalogue of Life</p></td>
</tr><tr>
<td>
<p><code><a href="WHOCC.html">WHOCC</a></code> </p>
</td>
<td><p>WHOCC: WHO Collaborating Centre for Drug Statistics Methodology</p></td>
</tr>
</tbody><tbody>
<tr>
@ -551,12 +557,18 @@
<p><code><a href="extended-functions.html">scale_type.mo()</a></code> <code><a href="extended-functions.html">scale_type.ab()</a></code> </p>
</td>
<td><p>Extended functions</p></td>
</tr><tr>
<td>
<p><code><a href="reexports.html">reexports</a></code> </p>
</td>
<td><p>Objects exported from other packages</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-functions" class="hasAnchor"><a href="#section-functions" class="anchor"></a>functions</h2>
<p class="section-desc"><p>These functions are deprecated, meaning that they still work but show a warning with every use and will be removed in a future version.</p></p>
<p class="section-desc"><p>These functions are deprecated, meaning that they will still work but show a warning with every use and will be removed in a future version.</p></p>
</th>
</tr>
<tr>
@ -573,11 +585,11 @@
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#section-background-information">Background information</a></li>
<li><a href="#section-cleaning-your-data">Cleaning your data</a></li>
<li><a href="#section-adding-variables-to-your-data">Adding variables to your data</a></li>
<li><a href="#section-enhancing-your-data">Enhancing your data</a></li>
<li><a href="#section-analysing-your-data">Analysing your data</a></li>
<li><a href="#section-included-data-sets">Included data sets</a></li>
<li><a href="#section-background-information">Background information</a></li>
<li><a href="#section-other-functions">Other functions</a></li>
<li><a href="#section-extended-functions">Extended functions</a></li>
<li><a href="#section-functions">functions</a></li>

2
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@ -85,7 +85,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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@ -85,7 +85,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9030</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -419,22 +419,17 @@
<p>On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitlab.io/AMR</a> you can find <a href='https://msberends.gitlab.io/AMR/articles/AMR.html'>a tutorial</a> about how to conduct AMR analysis, the <a href='https://msberends.gitlab.io/AMR/reference'>complete documentation of all functions</a> (which reads a lot easier than here in R) and <a href='https://msberends.gitlab.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>dplyr</span>)
<pre class="examples"><span class='co'># \donttest{</span>
<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>dplyr</span>)
<span class='no'>example_isolates</span> <span class='kw'>%&gt;%</span>
<span class='fu'>mdro</span>() <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://rdrr.io/pkg/cleaner/man/freq.html'>freq</a></span>()
<span class='co'># \donttest{</span>
<span class='no'>example_isolates</span> <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span>(<span class='kw'>EUCAST</span> <span class='kw'>=</span> <span class='fu'>eucast_exceptional_phenotypes</span>(<span class='no'>.</span>),
<span class='kw'>BRMO</span> <span class='kw'>=</span> <span class='fu'>brmo</span>(<span class='no'>.</span>),
<span class='kw'>MRGN</span> <span class='kw'>=</span> <span class='fu'>mrgn</span>(<span class='no'>.</span>))
<span class='no'>example_isolates</span> <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/select.html'>rename</a></span>(<span class='kw'>PIP</span> <span class='kw'>=</span> <span class='no'>TZP</span>) <span class='kw'>%&gt;%</span> <span class='co'># no piperacillin, so take piperacillin/tazobactam</span>
<span class='fu'>mrgn</span>() <span class='kw'>%&gt;%</span> <span class='co'># check German guideline</span>
<span class='fu'><a href='https://rdrr.io/pkg/cleaner/man/freq.html'>freq</a></span>() <span class='co'># check frequencies</span>
<span class='co'># }</span></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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@ -86,7 +86,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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@ -86,7 +86,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9030</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9021</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>
@ -242,16 +242,16 @@
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>A <code><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with 11,559 observations and 9 variables:</p><dl class='dl-horizontal'>
<p>A <code><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with 13,975 observations and 9 variables:</p><dl class='dl-horizontal'>
<dt><code>guideline</code></dt><dd><p>Name of the guideline</p></dd>
<dt><code>method</code></dt><dd><p>Either "MIC" or "DISK"</p></dd>
<dt><code>site</code></dt><dd><p>Body site, e.g. "Oral" or "Respiratory"</p></dd>
<dt><code>mo</code></dt><dd><p>Microbial ID, see <code><a href='as.mo.html'>as.mo</a></code></p></dd>
<dt><code>ab</code></dt><dd><p>Antibiotic ID, see <code><a href='as.ab.html'>as.ab</a></code></p></dd>
<dt><code>ref_tbl</code></dt><dd><p>Info about where the guideline rule can be found</p></dd>
<dt><code>S_mic</code></dt><dd><p>Lowest MIC value that leads to "S"</p></dd>
<dt><code>R_mic</code></dt><dd><p>Highest MIC value that leads to "R"</p></dd>
<dt><code>dose_disk</code></dt><dd><p>Dose of the used disk diffusion method</p></dd>
<dt><code>S_disk</code></dt><dd><p>Lowest number of millimeters that leads to "S"</p></dd>
<dt><code>R_disk</code></dt><dd><p>Highest number of millimeters that leads to "R"</p></dd>
<dt><code>disk_dose</code></dt><dd><p>Dose of the used disk diffusion method</p></dd>
<dt><code>breakpoint_S</code></dt><dd><p>Lowest MIC value or highest number of millimeters that leads to "S"</p></dd>
<dt><code>breakpoint_R</code></dt><dd><p>Highest MIC value or lowest number of millimeters that leads to "R"</p></dd>
</dl>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2>

2
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@ -86,7 +86,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

2
docs/reference/translate.html
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@ -85,7 +85,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
</span>
</div>

3
docs/sitemap.xml
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@ -27,6 +27,9 @@
<url>
<loc>https://msberends.gitlab.io/AMR/reference/antibiotics.html</loc>
</url>
<url>
<loc>https://msberends.gitlab.io/AMR/reference/antivirals.html</loc>
</url>
<url>
<loc>https://msberends.gitlab.io/AMR/reference/as.ab.html</loc>
</url>

2
git_premaster.sh
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@ -37,7 +37,7 @@ sed -i -- "s/^Version: .*/Version: ${new_version}/" DESCRIPTION
# update 1st line of NEWS.md
sed -i -- "1s/.*/# AMR ${new_version}/" NEWS.md
# add date to 2nd line of NEWS.md
sed -i -- "2s/.*/\<small\>Last updated: $(date '+%d-%b-%Y')\<\/small\>/" NEWS.md
sed -i -- "2s/.*/## \<small\>Last updated: $(date '+%d-%b-%Y')\<\/small\>/" NEWS.md
rm *-- || true
echo "• First 3 lines of DESCRIPTION:"
head -3 DESCRIPTION

15
index.md
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@ -10,9 +10,9 @@
### What is `AMR` (for R)?
`AMR` is a free and open-source [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. Since its first public release in early 2018, this package has been downloaded over 25,000 times from more than 60 countries <small>(source: [CRAN logs, 2019](https://cran-logs.rstudio.com))</small>.
`AMR` is a free and open-source [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. Since its first public release in early 2018, this package has been downloaded over 25,000 times from more than 70 countries <small>(source: [CRAN logs, 2019](https://cran-logs.rstudio.com))</small>.
After installing this package, R knows [**~70,000 microorganisms**](./reference/microorganisms.html) (distinct microbial species) and [**~450 antibiotics**](./reference/antibiotics.html) by name and code, and knows all about valid RSI and MIC values. It supports any data format, including WHONET/EARS-Net data.
After installing this package, R knows [**~70,000 distinct microbial species**](./reference/microorganisms.html) and all [**~550 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code, and knows all about valid RSI and MIC values. It supports any data format, including WHONET/EARS-Net data.
We created this package for both routine analysis and academic research (as part of our PhD theses) at the Faculty of Medical Sciences of the University of Groningen, the Netherlands, and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is [actively maintained](./news) and is free software (see [Copyright](#copyright)).
@ -109,7 +109,7 @@ Read more about which data from the Catalogue of Life [in our manual](./referenc
#### Antimicrobial reference data
This package contains **all ~450 antimicrobial drugs** and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the [Pharmaceuticals Community Register of the European Commission](http://ec.europa.eu/health/documents/community-register/html/atc.htm).
This package contains **all ~550 antibiotic, antimycotic and antiviral drugs** and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the [Pharmaceuticals Community Register of the European Commission](http://ec.europa.eu/health/documents/community-register/html/atc.htm).
**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/.**
@ -137,9 +137,9 @@ The `AMR` package basically does four important things:
* Use `eucast_rules()` to apply [EUCAST expert rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) (not the translation from MIC to RSI values, use `as.rsi()` for that).
* Use `first_isolate()` to identify the first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute).
* You can also identify first *weighted* isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
* Use `mdro()` (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
* Use `mdro()` to determine which micro-organisms are multi-drug resistant organisms (MDRO). It supports a variety of international guidelines, such as the MDR-paper by Magiorakos *et al.* (2012, [PMID 21793988](https://www.ncbi.nlm.nih.gov/pubmed/?term=21793988)), the exceptional phenotype definitions of EUCAST and the WHO guideline on multi-drug resistant TB. It also supports the national guidelines of the Netherlands and Germany.
* The [data set `microorganisms`](./reference/microorganisms.html) contains the complete taxonomic tree of ~70,000 microorganisms. Furthermore, some colloquial names and all Gram stains are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus()`, `mo_family()`, `mo_gramstain()` or even `mo_phylum()`. As they use `as.mo()` internally, they also use the same intelligent rules for determination. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
* The [data set `antibiotics`](./reference/antibiotics.html) contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. The function `ab_atc()` will return the ATC code of an antibiotic as defined by the WHO. Use functions like `ab_name()`, `ab_group()` and `ab_tradenames()` to look up values. The `ab_*` functions use `as.ab()` internally so they support the same intelligent rules to guess the most probable result. For example, `ab_name("Fluclox")`, `ab_name("Floxapen")` and `ab_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
* The [data set `antibiotics`](./reference/antibiotics.html) contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. Use functions like `ab_name()`, `ab_group()`, `ab_atc()` and `ab_tradenames()` to look up values. The `ab_*` functions use `as.ab()` internally so they support the same intelligent rules to guess the most probable result. For example, `ab_name("Fluclox")`, `ab_name("Floxapen")` and `ab_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
3. It **analyses the data** with convenient functions that use well-known methods.
@ -151,10 +151,7 @@ The `AMR` package basically does four important things:
* Aside from this website with many tutorials, the package itself contains extensive help pages with many examples for all functions.
* The package also contains example data sets:
* The [`example_isolates` data set](./reference/example_isolates.html). This data set contains:
* 2,000 blood culture isolates from anonymised septic patients between 2001 and 2017 in the Northern Netherlands
* Results of 40 antibiotics (each antibiotic in its own column) with a total ~40,000 antimicrobial results
* Real and genuine data
* The [`example_isolates` data set](./reference/example_isolates.html). This data set contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis.
* The [`WHONET` data set](./reference/WHONET.html). This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET [on its tutorial page](./articles/WHONET.html).
### Copyright

2
man/WHOCC.Rd
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@ -9,7 +9,7 @@ All antimicrobial drugs and their official names, ATC codes, ATC groups and defi
\section{WHOCC}{
\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
These have become the gold standard for international drug utilisation monitoring and research.

6
man/antibiotics.Rd
View File

@ -16,7 +16,7 @@
\item{\code{abbr}}{List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)}
\item{\code{synonyms}}{Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID}
\item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
\item{\code{oral_units}}{Units of \code{ddd_units}}
\item{\code{oral_units}}{Units of \code{oral_ddd}}
\item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
\item{\code{iv_units}}{Units of \code{iv_ddd}}
}}
@ -41,7 +41,7 @@ Synonyms (i.e. trade names) are derived from the Compound ID (\code{cid}) and co
\section{WHOCC}{
\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
These have become the gold standard for international drug utilisation monitoring and research.
@ -56,6 +56,6 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
}
\seealso{
\code{\link{microorganisms}}
\code{\link{antivirals}} \code{\link{microorganisms}}
}
\keyword{datasets}

46
man/antivirals.Rd Normal file
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@ -0,0 +1,46 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{antivirals}
\alias{antivirals}
\title{Data set with ~100 antivirals}
\format{A \code{\link{data.frame}} with 102 observations and 7 variables:
\describe{
\item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC}
\item{\code{name}}{Official name as used by WHONET/EARS-Net or the WHO}
\item{\code{atc_group}}{Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC}
\item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
\item{\code{oral_units}}{Units of \code{oral_ddd}}
\item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
\item{\code{iv_units}}{Units of \code{iv_ddd}}
}}
\source{
World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): \url{https://www.whocc.no/atc_ddd_index/}
}
\usage{
antivirals
}
\description{
A data set containing all antivirals, according to the ATC code group 'J05' (Antivirals for systemic use).
}
\section{WHOCC}{
\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
These have become the gold standard for international drug utilisation monitoring and research.
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.
\strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.}
}
\section{Read more on our website!}{
On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
}
\seealso{
\code{\link{antibiotics}} \code{\link{microorganisms}}
}
\keyword{datasets}

2
man/as.ab.Rd
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@ -37,7 +37,7 @@ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{htt
\section{WHOCC}{
\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
These have become the gold standard for international drug utilisation monitoring and research.

2
man/eucast_rules.Rd
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@ -158,6 +158,7 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
}
\examples{
\donttest{
a <- data.frame(mo = c("Staphylococcus aureus",
"Enterococcus faecalis",
"Escherichia coli",
@ -193,7 +194,6 @@ b
# 5 Pseudomonas aeruginosa R R - - R R R
\donttest{
# do not apply EUCAST rules, but rather get a data.frame
# with 18 rows, containing all details about the transformations:
c <- eucast_rules(a, verbose = TRUE)

6
man/example_isolates.Rd
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@ -3,7 +3,7 @@
\docType{data}
\name{example_isolates}
\alias{example_isolates}
\title{Data set with 2,000 blood culture isolates}
\title{Data set with 2,000 example isolates}
\format{A \code{\link{data.frame}} with 2,000 observations and 49 variables:
\describe{
\item{\code{date}}{date of receipt at the laboratory}
@ -13,7 +13,7 @@
\item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
\item{\code{age}}{age of the patient}
\item{\code{gender}}{gender of the patient}
\item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
\item{\code{patient_id}}{ID of the patient}
\item{\code{mo}}{ID of microorganism created with \code{\link{as.mo}}, see also \code{\link{microorganisms}}}
\item{\code{PEN:RIF}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{ab_name}}}
}}
@ -21,7 +21,7 @@
example_isolates
}
\description{
An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found 4 different hospitals in the Netherlands, between 2001 and 2017. This \code{data.frame} can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
}
\section{Read more on our website!}{

7
man/mdro.Rd
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@ -184,21 +184,16 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
}
\examples{
\donttest{
library(dplyr)
example_isolates \%>\%
mdro() \%>\%
freq()
\donttest{
example_isolates \%>\%
mutate(EUCAST = eucast_exceptional_phenotypes(.),
BRMO = brmo(.),
MRGN = mrgn(.))
example_isolates \%>\%
rename(PIP = TZP) \%>\% # no piperacillin, so take piperacillin/tazobactam
mrgn() \%>\% # check German guideline
freq() # check frequencies
}
}

12
man/rsi_translation.Rd
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@ -4,17 +4,17 @@
\name{rsi_translation}
\alias{rsi_translation}
\title{Data set for RSI interpretation}
\format{A \code{\link{data.frame}} with 11,559 observations and 9 variables:
\format{A \code{\link{data.frame}} with 13,975 observations and 9 variables:
\describe{
\item{\code{guideline}}{Name of the guideline}
\item{\code{method}}{Either "MIC" or "DISK"}
\item{\code{site}}{Body site, e.g. "Oral" or "Respiratory"}
\item{\code{mo}}{Microbial ID, see \code{\link{as.mo}}}
\item{\code{ab}}{Antibiotic ID, see \code{\link{as.ab}}}
\item{\code{ref_tbl}}{Info about where the guideline rule can be found}
\item{\code{S_mic}}{Lowest MIC value that leads to "S"}
\item{\code{R_mic}}{Highest MIC value that leads to "R"}
\item{\code{dose_disk}}{Dose of the used disk diffusion method}
\item{\code{S_disk}}{Lowest number of millimeters that leads to "S"}
\item{\code{R_disk}}{Highest number of millimeters that leads to "R"}
\item{\code{disk_dose}}{Dose of the used disk diffusion method}
\item{\code{breakpoint_S}}{Lowest MIC value or highest number of millimeters that leads to "S"}
\item{\code{breakpoint_R}}{Highest MIC value or lowest number of millimeters that leads to "R"}
}}
\usage{
rsi_translation

5
pkgdown/extra.css
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@ -139,6 +139,11 @@ help {
font-size: 1.5em;
}
/* Beautify manually added 'Last updated' text of NEWS */
div[id^=last-updated] h2 {
padding-top: 10px;
}
.version.label {
display: none;
}

2
tests/testthat/test-first_isolate.R
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@ -99,7 +99,7 @@ test_that("first isolates work", {
info = TRUE,
icu_exclude = TRUE),
na.rm = TRUE),
1163)
906)
# set 1500 random observations to be of specimen type 'Urine'
random_rows <- sample(x = 1:2000, size = 1500, replace = FALSE)

2
vignettes/AMR.Rmd
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@ -459,7 +459,7 @@ data_1st %>%
## Independence test
The next example uses the included `example_isolates`, which is an anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. This `data.frame` can be used to practice AMR analysis.
The next example uses the `example_isolates` data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis.
We will compare the resistance to fosfomycin (column `FOS`) in hospital A and D. The input for the `fisher.test()` can be retrieved with a transformation like this:

10
vignettes/MDR.Rmd
View File

@ -21,11 +21,11 @@ knitr::opts_chunk$set(
library(AMR)
```
With the function `mdro()`, you can determine multi-drug resistant organisms (MDRO).
With the function `mdro()`, you can determine which micro-organisms are multi-drug resistant organisms (MDRO).
#### Type of input
The `mdro()` takes a data set as input, such as a regular `data.frame`. It automatically determines the right columns for info about your isolates, like 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 `?mdro`.
The `mdro()` function takes a data set as input, such as a regular `data.frame`. It tries to automatically determine the right columns for info about your isolates, like 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 `?mdro`.
For WHONET data (and most other data), all settings are automatically set correctly.
@ -51,7 +51,9 @@ The function support multiple guidelines. You can select a guideline with the `g
#### Examples
The `mdro()` function always returns an ordered `factor`. For example, the output of the default guideline by Magiorakos *et al.* returns a `factor` with levels 'Negative', 'MDR', 'XDR' or 'PDR' in that order. If we test that guideline on the included `example_isolates` data set, we get:
The `mdro()` function always returns an ordered `factor`. For example, the output of the default guideline by Magiorakos *et al.* returns a `factor` with levels 'Negative', 'MDR', 'XDR' or 'PDR' in that order.
The next example uses the `example_isolates` data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:
```{r, message = FALSE}
library(dplyr) # to support pipes: %>%
@ -62,7 +64,7 @@ example_isolates %>%
freq() # show frequency table of the result
```
```{r, echo = FALSE, results = 'asis', message = FALSE, warning = FALSE}
library(dplyr) # to support pipes: %>%
library(dplyr)
example_isolates %>%
mdro(info = FALSE) %>%
freq() # show frequency table of the result