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163 new trade names, added ab_tradenames

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dr. M.S. (Matthijs) Berends 2018-08-29 12:27:37 +02:00
parent 972fc4f6c7
commit 029157b3be
20 changed files with 139 additions and 314 deletions
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1
.Rbuildignore
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@ -4,3 +4,4 @@
.zenodo.json
^cran-comments\.md$
^appveyor\.yml$
_noinclude

1
.gitignore vendored
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@ -9,6 +9,7 @@ inst/doc
/src/*.o-*
/src/*.d
/src/*.so
_noinclude
*.dll
vignettes/*.R
.DS_Store

2
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 0.3.0.9005
Date: 2018-08-28
Date: 2018-08-29
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(

1
NAMESPACE
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@ -41,6 +41,7 @@ export(ab_certe)
export(ab_official)
export(ab_official_nl)
export(ab_property)
export(ab_tradenames)
export(ab_trivial_nl)
export(ab_umcg)
export(abname)

15
NEWS.md
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@ -2,14 +2,25 @@
#### New
* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptible isolates
* New function `count_df` to get all counts of S, I and R of a data set with antibiotic columns, with support for grouped variables
* Extra function `count_df` (which works like `portion_df`) to get all counts of S, I and R of a data set with antibiotic columns, with support for grouped variables
* Function `is.rsi.eligible` to check for columns that have valid antimicrobial results, but do not have the `rsi` class yet. Transform the columns of your raw data with: `data %>% mutate_if(is.rsi.eligible, as.rsi)`
* Functions `as.atc` and `is.atc` to transform/look up antibiotic ATC codes as defined by the WHO. The existing function `guess_atc` is now an alias of `as.atc`.
* Aliases for existing function `mo_property`: `mo_aerobic`, `mo_family`, `mo_fullname`, `mo_genus`, `mo_gramstain`, `mo_gramstain_nl`, `mo_property`, `mo_species`, `mo_subspecies`, `mo_type`, `mo_type_nl`
* Function `ab_property` and its aliases: `ab_certe`, `ab_official`, `ab_official_nl`, `ab_property`, `ab_trivial_nl`, `ab_umcg`
* Introduction to AMR as a vignette
#### Changed
* Added 182 microorganisms to the `microorganisms` data set, now n = 2,646 (2,207 bacteria, 285 fungi/yeasts, 153 parasites, 1 other)
* Added 182 microorganisms to the `microorganisms` data set, now *n* = 2,646 (2,207 bacteria, 285 fungi/yeasts, 153 parasites, 1 other)
* Added three antimicrobial agents to the `antibiotics` data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)
* Added 163 trade names to the `antibiotics` data set, it now contains 298 different trade names in total, e.g.:
```r
ab_official("Bactroban")
# [1] "Mupirocin"
ab_official(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))
# [1] "Mupirocin" "Amoxicillin" "Azithromycin" "Flucloxacillin"
ab_atc(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))
# [1] "R01AX06" "J01CA04" "J01FA10" "J01CF05"
```
* Removed function `ratio` as it is not really the scope of this package
* Fix in `as.mic` for values ending in zeroes after a real number
* Huge speed improvement for `as.bactid`

14
R/ab_property.R
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@ -22,6 +22,7 @@
#' @param x a (vector of a) valid \code{\link{atc}} code or any text that can be coerced to a valid atc with \code{\link{as.atc}}
#' @param property one of the column names of one of the \code{\link{antibiotics}} data set, like \code{"atc"} and \code{"official"}
#' @rdname ab_property
#' @return A vector of values. In case of \code{ab_tradenames}, if \code{x} is of length one, a vector will be returned. Otherwise a \code{\link{list}}, with \code{x} as names.
#' @export
#' @importFrom dplyr %>% left_join pull
#' @seealso \code{\link{antibiotics}}
@ -82,3 +83,16 @@ ab_certe <- function(x) {
ab_umcg <- function(x) {
ab_property(x, "umcg")
}
#' @rdname ab_property
#' @export
ab_tradenames <- function(x) {
res <- ab_property(x, "trade_name")
res <- strsplit(res, "|", fixed = TRUE)
if (length(x) == 1) {
res <- unlist(res)
} else {
names(res) <- x
}
res
}

11
R/abname.R
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@ -23,6 +23,7 @@
#' @param from,to type to transform from and to. See \code{\link{antibiotics}} for its column names. WIth \code{from = "guess"} the from will be guessed from \code{"atc"}, \code{"certe"} and \code{"umcg"}. When using \code{to = "atc"}, the ATC code will be searched using \code{\link{as.atc}}.
#' @param textbetween text to put between multiple returned texts
#' @param tolower return output as lower case with function \code{\link{tolower}}.
#' @details \strong{The \code{\link{ab_property}} functions are faster and more concise}, but do not support concatenated strings, like \code{abname("AMCL+GENT"}.
#' @keywords ab antibiotics
#' @source \code{\link{antibiotics}}
#' @export
@ -100,29 +101,29 @@ abname <- function(abcode,
}
if (from %in% c("atc", "guess")) {
if (abcode[i] %in% abx$atc) {
abcode[i] <- abx[which(abx$atc == abcode[i]),] %>% pull(to)
abcode[i] <- abx[which(abx$atc == abcode[i]),] %>% pull(to) %>% .[1]
next
}
}
if (from %in% c("certe", "guess")) {
if (abcode[i] %in% abx$certe) {
abcode[i] <- abx[which(abx$certe == abcode[i]),] %>% pull(to)
abcode[i] <- abx[which(abx$certe == abcode[i]),] %>% pull(to) %>% .[1]
next
}
}
if (from %in% c("umcg", "guess")) {
if (abcode[i] %in% abx$umcg) {
abcode[i] <- abx[which(abx$umcg == abcode[i]),] %>% pull(to)
abcode[i] <- abx[which(abx$umcg == abcode[i]),] %>% pull(to) %>% .[1]
next
}
}
if (from %in% c("trade_name", "guess")) {
if (abcode[i] %in% abx$trade_name) {
abcode[i] <- abx[which(abx$trade_name == abcode[i]),] %>% pull(to)
abcode[i] <- abx[which(abx$trade_name == abcode[i]),] %>% pull(to) %>% .[1]
next
}
if (sum(abx$trade_name %like% abcode[i]) > 0) {
abcode[i] <- abx[which(abx$trade_name %like% abcode[i]),] %>% pull(to)
abcode[i] <- abx[which(abx$trade_name %like% abcode[i]),] %>% pull(to) %>% .[1]
next
}
}

14
R/atc.R
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@ -64,6 +64,13 @@ as.atc <- function(x) {
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
}
# try ATC in code form, even if it does not exist in the antibiotics data set YET
if (length(found) == 0 & x[i] %like% '[A-Z][0-9][0-9][A-Z][A-Z][0-9][0-9]') {
warning("ATC code ", x[i], " is not yet in the `antibiotics` data set.")
fail <- FALSE
x.new[is.na(x.new) & x.bak == x[i]] <- x[i]
}
# try abbreviation of certe and glims
found <- AMR::antibiotics[which(tolower(AMR::antibiotics$certe) == tolower(x[i])),]$atc
if (length(found) > 0) {
@ -83,6 +90,13 @@ as.atc <- function(x) {
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
}
# try exact official Dutch
found <- AMR::antibiotics[which(tolower(AMR::antibiotics$official_nl) == tolower(x[i])),]$atc
if (length(found) > 0) {
fail <- FALSE
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
}
# try trade name
found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$trade_name, ")") %like% x[i]),]$atc
if (length(found) > 0) {

4
R/bactid.R
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@ -205,12 +205,12 @@ as.bactid <- function(x, Becker = FALSE, Lancefield = FALSE) {
failures <- c(failures, x_backup[i])
next
}
if (x_backup[i] %in% MOs$bactid) {
if (x_backup[i] %in% AMR::microorganisms$bactid) {
# is already a valid bactid
x[i] <- x_backup[i]
next
}
if (x_trimmed[i] %in% MOs$bactid) {
if (x_trimmed[i] %in% AMR::microorganisms$bactid) {
# is already a valid bactid
x[i] <- x_trimmed[i]
next

231
R/data.R
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@ -16,10 +16,10 @@
# GNU General Public License for more details. #
# ==================================================================== #
#' Dataset with 420 antibiotics
#' Dataset with 423 antibiotics
#'
#' A dataset containing all antibiotics with a J0 code, with their DDD's. Properties were downloaded from the WHO, see Source.
#' @format A data.frame with 420 observations and 18 variables:
#' A dataset containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDD's. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
#' @format A data.frame with 423 observations and 18 variables:
#' \describe{
#' \item{\code{atc}}{ATC code, like \code{J01CR02}}
#' \item{\code{certe}}{Certe code, like \code{amcl}}
@ -28,7 +28,7 @@
#' \item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and beta-lactamase inhibitor"}}
#' \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
#' \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
#' \item{\code{trade_name}}{Trade name as used by many countries, used internally by \code{\link{as.atc}}}
#' \item{\code{trade_name}}{Trade name as used by many countries (a total of 294), used internally by \code{\link{as.atc}}}
#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
#' \item{\code{oral_units}}{Units of \code{ddd_units}}
#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
@ -61,177 +61,7 @@
# paste() %>%
# .[. %like% "StringValueList"] %>%
# gsub("[</]+StringValueList[>]", "", .)
# abbr and trade_name created with:
# https://hs.unr.edu/Documents/dhs/chs/NVPHTC/antibiotic_refeference_guide.pdf
# antibiotics %>%
# mutate(abbr =
# case_when(
# official == 'Amikacin' ~ 'Ak|AN|AMI|AMK',
# official == 'Amoxicillin' ~ 'AMX|AMOX|AC',
# official == 'Amoxicillin and beta-lactamase inhibitor' ~ 'AUG|A/C|XL|AML',
# official == 'Ampicillin' ~ 'AM|AMP',
# official == 'Ampicillin and beta-lactamase inhibitor' ~ 'A/S|SAM|AMS|AB',
# official == 'Azithromycin' ~ 'Azi|AZM|AZ',
# official == 'Azlocillin' ~ 'AZ|AZL',
# official == 'Aztreonam' ~ 'Azt|ATM|AT|AZM',
# official == 'Carbenicillin' ~ 'Cb|BAR',
# official == 'Cefaclor' ~ 'Ccl|CEC|Cfr|FAC|CF',
# official == 'Cefadroxil' ~ 'CFR|FAD',
# official == 'Cefazolin' ~ 'Cfz|CZ|FAZ|KZ',
# official == 'Cefdinir' ~ 'Cdn|CDR|DIN|CD|CFD',
# official == 'Cefditoren' ~ 'CDN',
# official == 'Cefepime' ~ 'Cpe|FEP|PM|CPM',
# official == 'Cefixime' ~ 'Cfe|DCFM|FIX|IX',
# official == 'Cefoperazone' ~ 'Cfp|CPZ|PER|FOP|CP',
# official == 'Cefotaxime' ~ 'Cft|CTX|TAX|FOT|CT',
# official == 'Cefotetan' ~ 'Ctn|CTT|CTE|TANS|CN',
# official == 'Cefoxitin' ~ 'Cfx|FOX|CX|FX',
# official == 'Cefpodoxime' ~ 'Cpd|POD|PX',
# official == 'Cefprozil' ~ 'Cpz|CPR|FP',
# official == 'Ceftaroline' ~ 'CPT',
# official == 'Ceftazidime' ~ 'Caz|TAZ|TZ',
# official == 'Ceftibuten' ~ 'CTB|TIB|CB',
# official == 'Ceftizoxime' ~ 'Cz|ZOX|CZX|CZ|CTZ|TIZ',
# official == 'Ceftriaxone' ~ 'Cax|CRO|CTR|FRX|AXO|TX',
# official == 'Cefuroxime' ~ 'Crm|CXM|CFX|ROX|FUR|XM',
# official == 'Cephalexin' ~ 'CN|LX|CFL',
# official == 'Cephalothin' ~ 'Cf',
# official == 'Chloramphenicol' ~ 'C|CHL|CL',
# official == 'Ciprofloxacin' ~ 'Cp|CIP|CI',
# official == 'Clarithromycin' ~ 'Cla|CLR|CLM|CH',
# official == 'Clindamycin' ~ 'Cd|CC|CM|CLI|DA',
# official == 'Colistin' ~ 'CL|CS|CT',
# official == 'Daptomycin' ~ 'Dap',
# official == 'Doxycycline' ~ 'Dox',
# official == 'Doripenem' ~ 'DOR|Dor',
# official == 'Ertapenem' ~ 'Etp',
# official == 'Erythromycin' ~ 'E|ERY|EM',
# official == 'Fosfomycin' ~ 'FOS|FF|FO|FM',
# official == 'Flucloxacillin' ~ 'CLOX',
# official == 'Gentamicin' ~ 'Gm|CN|GEN',
# official == 'Imipenem' ~ 'Imp|IPM|IMI|IP',
# official == 'Kanamycin' ~ 'K|KAN|HLK|KM',
# official == 'Levofloxacin' ~ 'Lvx|LEV|LEVO|LE',
# official == 'Linezolid' ~ 'Lzd|LNZ|LZ',
# official == 'Lomefloxacin' ~ 'Lmf|LOM',
# official == 'Meropenem' ~ 'Mer|MEM|MERO|MRP|MP',
# official == 'Metronidazole' ~ 'MNZ',
# official == 'Mezlocillin' ~ 'Mz|MEZ',
# official == 'Minocycline' ~ 'Min|MI|MN|MNO|MC|MH',
# official == 'Moxifloxacin' ~ 'Mox|MXF',
# official == 'Mupirocin' ~ 'MUP',
# official == 'Nafcillin' ~ 'Naf|NF',
# official == 'Nalidixic acid' ~ 'NA|NAL',
# official == 'Nitrofurantoin' ~ 'Fd|F/M|FT|NIT|NI|F',
# official == 'Norfloxacin' ~ 'Nxn|NOR|NX',
# official == 'Ofloxacin' ~ 'Ofl|OFX|OF',
# official == 'Oxacillin' ~ 'Ox|OXS|OXA',
# official == 'Benzylpenicillin' ~ 'P|PEN|PV',
# official == 'Penicillins, combinations with other antibacterials' ~ 'P|PEN|PV',
# official == 'Piperacillin' ~ 'Pi|PIP|PP',
# official == 'Piperacillin and beta-lactamase inhibitor' ~ 'PT|TZP|PTZ|P/T|PTc',
# official == 'Polymyxin B' ~ 'PB',
# official == 'Quinupristin/dalfopristin' ~ 'Syn|Q/D|QDA|RP',
# official == 'Rifampin' ~ 'Rif|RA|RI|RD',
# official == 'Spectinomycin' ~ 'SPT|SPE|SC',
# official == 'Streptomycin' ~ 'S|STR',
# official == 'Teicoplanin' ~ 'Tei|TEC|TPN|TP|TPL',
# official == 'Telavancin' ~ 'TLV',
# official == 'Telithromcyin' ~ 'Tel',
# official == 'Tetracycline' ~ 'Te|TET|TC',
# official == 'Ticarcillin' ~ 'Ti|TIC|TC',
# official == 'Ticarcillin and beta-lactamase inhibitor' ~ 'Tim|T/C|TCC|TLc',
# official == 'Tigecycline' ~ 'TGC',
# official == 'Tobramycin' ~ 'To|NN|TM|TOB',
# official == 'Trimethoprim' ~ 'T|TMP|TR|W',
# official == 'Sulfamethoxazole and trimethoprim' ~ 'T/S|SXT|SxT|TS|COT',
# official == 'Vancomycin' ~ 'Va|VAN',
# TRUE ~ NA_character_),
#
# trade_name =
# case_when(
# official == 'Amikacin' ~ 'Amikin',
# official == 'Amoxicillin' ~ 'Amoxil|Dispermox|Larotid|Trimox',
# official == 'Amoxicillin and beta-lactamase inhibitor' ~ 'Augmentin',
# official == 'Ampicillin' ~ 'Pfizerpen-A|Principen',
# official == 'Ampicillin and beta-lactamase inhibitor' ~ 'Unasyn',
# official == 'Azithromycin' ~ 'Zithromax',
# official == 'Azlocillin' ~ 'Azlin',
# official == 'Aztreonam' ~ 'Azactam',
# official == 'Carbenicillin' ~ 'Geocillin',
# official == 'Cefaclor' ~ 'Ceclor',
# official == 'Cefadroxil' ~ 'Duricef',
# official == 'Cefazolin' ~ 'Ancef',
# official == 'Cefdinir' ~ 'Omnicef',
# official == 'Cefditoren' ~ 'Spectracef',
# official == 'Cefepime' ~ 'Maxipime',
# official == 'Cefixime' ~ 'Suprax',
# official == 'Cefoperazone' ~ 'Cefobid',
# official == 'Cefotaxime' ~ 'Claforan',
# official == 'Cefotetan' ~ 'Cefotan',
# official == 'Cefoxitin' ~ 'Mefoxin',
# official == 'Cefpodoxime' ~ 'Vantin',
# official == 'Cefprozil' ~ 'Cefzil',
# official == 'Ceftaroline' ~ 'Teflaro',
# official == 'Ceftazidime' ~ 'Fortaz|Tazicef|Tazidime',
# official == 'Ceftibuten' ~ 'Cedax',
# official == 'Ceftizoxime' ~ 'Cefizox',
# official == 'Ceftriaxone' ~ 'Rocephin',
# official == 'Cefuroxime' ~ 'Ceftin|Zinacef',
# official == 'Cephalexin' ~ 'Keflex|Panixine',
# official == 'Cephalothin' ~ 'Keflin',
# official == 'Chloramphenicol' ~ 'Chloromycetin',
# official == 'Ciprofloxacin' ~ 'Cipro|Ciloxan|Ciproxin',
# official == 'Clarithromycin' ~ 'Biaxin',
# official == 'Clindamycin' ~ 'Cleocin|Clinda-Derm|Clindagel|Clindesse|Clindets|Evoclin',
# official == 'Colistin' ~ 'Coly-Mycin',
# official == 'Daptomycin' ~ 'Cubicin',
# official == 'Doxycycline' ~ 'Doryx|Monodox|Vibramycin|Atridox|Oracea|Periostat|Vibra-Tabs',
# official == 'Doripenem' ~ 'Doribax',
# official == 'Ertapenem' ~ 'Invanz',
# official == 'Erythromycin' ~ 'Eryc|EryPed|Erythrocin|E-Base|E-Glades|E-Mycin|E.E.S.|Ery-Tab|Eryderm|Erygel|Erythra-derm|Eryzole|Pediamycin',
# official == 'Fosfomycin' ~ 'Monurol',
# official == 'Flucloxacillin' ~ 'Flopen|Floxapen|Fluclox|Sesamol|Softapen|Staphylex',
# official == 'Gentamicin' ~ 'Garamycin|Genoptic',
# official == 'Imipenem' ~ 'Primaxin',
# official == 'Kanamycin' ~ 'Kantrex',
# official == 'Levofloxacin' ~ 'Levaquin|Quixin',
# official == 'Linezolid' ~ 'Zyvox',
# official == 'Lomefloxacin' ~ 'Maxaquin',
# official == 'Meropenem' ~ 'Merrem',
# official == 'Metronidazole' ~ 'Flagyl|MetroGel|MetroCream|MetroLotion',
# official == 'Mezlocillin' ~ 'Mezlin',
# official == 'Minocycline' ~ 'Arestin|Solodyn',
# official == 'Moxifloxacin' ~ 'Avelox|Vigamox',
# official == 'Mupirocin' ~ 'Bactroban|Centany',
# official == 'Nafcillin' ~ 'Unipen',
# official == 'Nalidixic acid' ~ 'NegGram',
# official == 'Nitrofurantoin' ~ 'Furadantin|Macrobid|Macrodantin',
# official == 'Norfloxacin' ~ 'Noroxin',
# official == 'Ofloxacin' ~ 'Floxin|Ocuflox|Ophthalmic',
# official == 'Oxacillin' ~ 'Bactocill',
# official == 'Benzylpenicillin' ~ 'Permapen|Pfizerpen|Veetids',
# official == 'Penicillins, combinations with other antibacterials' ~ 'Permapen|Pfizerpen|Veetids',
# official == 'Piperacillin' ~ 'Pipracil',
# official == 'Piperacillin and beta-lactamase inhibitor' ~ 'Zosyn',
# official == 'Polymyxin B' ~ 'Poly-RX',
# official == 'Quinupristin/dalfopristin' ~ 'Synercid',
# official == 'Rifampin' ~ 'Rifadin|Rifamate|Rimactane',
# official == 'Spectinomycin' ~ 'Trobicin',
# official == 'Streptomycin' ~ 'Streptomycin Sulfate',
# official == 'Teicoplanin' ~ 'Targocid',
# official == 'Telavancin' ~ 'Vibativ',
# official == 'Telithromcyin' ~ 'Ketek',
# official == 'Tetracycline' ~ 'Sumycin|Bristacycline|Tetrex',
# official == 'Ticarcillin' ~ 'Ticar',
# official == 'Ticarcillin and beta-lactamase inhibitor' ~ 'Timentin',
# official == 'Tigecycline' ~ 'Tygacil',
# official == 'Tobramycin' ~ 'Tobi|Aktob|Tobre',
# official == 'Trimethoprim' ~ 'Primsol|Proloprim',
# official == 'Sulfamethoxazole and trimethoprim' ~ 'Bactrim|Septra|Sulfatrim',
# official == 'Vancomycin' ~ 'Vancocin|Vancomycin Hydrochloride',
# TRUE ~ NA_character_)
# )
# last two columns created with:
# antibiotics %>%
# mutate(useful_gramnegative =
@ -251,21 +81,44 @@
# NA
# )
# )
#
# ADD NEW TRADE NAMES FROM OTHER DATAFRAME
# antibiotics_add_to_property <- function(ab_df, atc, property, value) {
# if (length(atc) > 1L) {
# stop("only one atc at a time")
# }
# if (!property %in% c("abbr", "trade_name")) {
# stop("only possible for abbr and trade_name")
# }
#
# value <- gsub(ab_df[which(ab_df$atc == atc),] %>% pull("official"), "", value, fixed = TRUE)
# value <- gsub("||", "|", value, fixed = TRUE)
# value <- gsub("[äáàâ]", "a", value)
# value <- gsub("[ëéèê]", "e", value)
# value <- gsub("[ïíìî]", "i", value)
# value <- gsub("[öóòô]", "o", value)
# value <- gsub("[üúùû]", "u", value)
# if (!atc %in% ab_df$atc) {
# message("SKIPPING - UNKNOWN ATC: ", atc)
# }
# if (is.na(value)) {
# message("SKIPPING - VALUE MISSES: ", atc)
# }
# if (atc %in% ab_df$atc & !is.na(value)) {
# current <- ab_df[which(ab_df$atc == atc),] %>% pull(property)
# if (!is.na(current)) {
# value <- paste(current, value, sep = "|")
# }
# value <- strsplit(value, "|", fixed = TRUE) %>% unlist() %>% unique() %>% paste(collapse = "|")
# value <- gsub("||", "|", value, fixed = TRUE)
# # print(value)
# ab_df[which(ab_df$atc == atc), property] <- value
# message("Added ", value, " to ", ab_official(atc), " (", atc, ", ", ab_certe(atc), ")")
# }
# ab_df
# }
#
"antibiotics"
antibiotics_add_to_property <- function(antibiotics, atc, property, value) {
if (length(atc) > 1L) {
stop("only one atc at a time")
}
if (!property %in% c("abbr", "trade_name")) {
stop("only possible for abbr and trade_name")
}
if (atc %in% antibiotics$atc) {
current <- antibiotics[which(antibiotics$atc == atc), property]
antibiotics[which(antibiotics$atc == atc), property] <- paste(current, value, sep = "|")
message("done")
}
antibiotics
}
#' Dataset with ~2650 microorganisms
#'

10
README.md
View File

@ -42,12 +42,12 @@ This R package was intended to make microbial epidemiology easier. Most function
This `AMR` package basically does four important things:
1. It **cleanses existing data**, by transforming it to reproducible and profound *classes*, making the most efficient use of R. These functions all use artificial intelligence to get 'more expected' results:
1. It **cleanses existing data**, by transforming it to reproducible and profound *classes*, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:
* Use `as.bactid` to get an ID of a microorganism. The IDs are quite obvious - the ID of *E. coli* is "ESCCOL" and the ID of *S. aureus* is "STAAUR". This `as.bactid` function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even `as.bactid("MRSA")` will return the ID of *S. aureus*. Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. To find bacteria based on your input, this package contains a freely available database of ~2,650 different (potential) human pathogenic microorganisms.
* Use `as.bactid` to get an ID of a microorganism. The IDs are quite obvious - the ID of *E. coli* is "ESCCOL" and the ID of *S. aureus* is "STAAUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even `as.bactid("MRSA")` will return the ID of *S. aureus*. Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. To find bacteria based on your input, this package contains a freely available database of ~2,650 different (potential) human pathogenic microorganisms.
* Use `as.rsi` to transform values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
* Use `as.mic` to cleanse your MIC values. It produces a so-called factor (called *ordinal* in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
* Use `as.atc` to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantine", "nitro" all return the ATC code of Nitrofurantoine.
* Use `as.atc` to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantin", "nitro" all return the ATC code of Nitrofurantoine.
2. It **enhances existing data** and **adds new data** from data sets included in this package.
@ -55,8 +55,8 @@ This `AMR` package basically does four important things:
* 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.
* The data set `microorganisms` contains the family, genus, species, subspecies, colloquial name and Gram stain of almost 2,650 microorganisms (2,207 bacteria, 285 fungi/yeasts, 153 parasites, 1 other). This enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set. For example, to get properties of a bacteria ID, use `mo_genus`, `mo_family` or `mo_gramstain`. These functions can be used to add new variables to your data.
* The data set `antibiotics` contains the ATC code, LIS codes, official name, trivial name, trade name and DDD of both oral and parenteral administration.
* The data set `microorganisms` contains the family, genus, species, subspecies, colloquial name and Gram stain of almost 2,650 microorganisms (2,207 bacteria, 285 fungi/yeasts, 153 parasites, 1 other). This 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` or `mo_gramstain`. Since it uses `as.bactid` internally, AI is supported. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. These functions can be used to add new variables to your data.
* The data set `antibiotics` contains the ATC code, LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains a total of 298 trade names. Use functions like `ab_official` and `ab_tradenames` to look up values. As the `mo_*` functions use `as.bactid` internally, the `ab_*` functions use `as.atc` internally so it uses AI to guess your expected result. For example, `ab_official("Fluclox")`, `ab_official("Floxapen")` and `ab_official("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.

BIN
data/antibiotics.rda
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Binary file not shown.

6
man/ab_property.Rd
View File

@ -8,6 +8,7 @@
\alias{ab_trivial_nl}
\alias{ab_certe}
\alias{ab_umcg}
\alias{ab_tradenames}
\title{Property of an antibiotic}
\usage{
ab_property(x, property = "official")
@ -23,12 +24,17 @@ ab_trivial_nl(x)
ab_certe(x)
ab_umcg(x)
ab_tradenames(x)
}
\arguments{
\item{x}{a (vector of a) valid \code{\link{atc}} code or any text that can be coerced to a valid atc with \code{\link{as.atc}}}
\item{property}{one of the column names of one of the \code{\link{antibiotics}} data set, like \code{"atc"} and \code{"official"}}
}
\value{
A vector of values. In case of \code{ab_tradenames}, if \code{x} is of length one, a vector will be returned. Otherwise a \code{\link{list}}, with \code{x} as names.
}
\description{
Use these functions to return a specific property of an antibiotic from the \code{\link{antibiotics}} data set, based on their ATC code. Get such a code with \code{\link{as.atc}}.
}

3
man/abname.Rd
View File

@ -22,6 +22,9 @@ abname(abcode, from = c("guess", "atc", "certe", "umcg"),
\description{
Convert antibiotic codes to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{antibiotics}}.
}
\details{
\strong{The \code{\link{ab_property}} functions are faster and more concise}, but do not support concatenated strings, like \code{abname("AMCL+GENT"}.
}
\examples{
abname("AMCL")
# "Amoxicillin and beta-lactamase inhibitor"

8
man/antibiotics.Rd
View File

@ -3,8 +3,8 @@
\docType{data}
\name{antibiotics}
\alias{antibiotics}
\title{Dataset with 420 antibiotics}
\format{A data.frame with 420 observations and 18 variables:
\title{Dataset with 423 antibiotics}
\format{A data.frame with 423 observations and 18 variables:
\describe{
\item{\code{atc}}{ATC code, like \code{J01CR02}}
\item{\code{certe}}{Certe code, like \code{amcl}}
@ -13,7 +13,7 @@
\item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and beta-lactamase inhibitor"}}
\item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
\item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
\item{\code{trade_name}}{Trade name as used by many countries, used internally by \code{\link{as.atc}}}
\item{\code{trade_name}}{Trade name as used by many countries (a total of 294), used internally by \code{\link{as.atc}}}
\item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
\item{\code{oral_units}}{Units of \code{ddd_units}}
\item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
@ -32,7 +32,7 @@
antibiotics
}
\description{
A dataset containing all antibiotics with a J0 code, with their DDD's. Properties were downloaded from the WHO, see Source.
A dataset containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDD's. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
}
\seealso{
\code{\link{microorganisms}}

2
tests/testthat/test-ab_property.R
View File

@ -6,4 +6,6 @@ test_that("ab_property works", {
expect_equal(ab_official_nl("amox"), "Amoxicilline")
expect_equal(ab_trivial_nl("amox"), "Amoxicilline")
expect_equal(ab_umcg("amox"), "AMOX")
expect_equal(class(ab_tradenames("amox")), "character")
expect_equal(class(ab_tradenames(c("amox", "amox"))), "list")
})

4
tests/testthat/test-atc.R
View File

@ -34,6 +34,10 @@ test_that("guess_atc works", {
expect_identical(class(as.atc("amox")), "atc")
expect_identical(ab_trivial_nl("Cefmenoxim"), "Cefmenoxim")
expect_warning(as.atc("Z00ZZ00")) # not yet available in data set
# first 5 chars of official name
expect_equal(as.character(as.atc(c("nitro", "cipro"))),
c("J01XE01", "J01MA02"))

31
vignettes/AMR.Rmd
View File

@ -21,37 +21,42 @@ This R package was intended to make microbial epidemiology easier. Most function
This `AMR` package basically does four important things:
1. It **cleanses existing data**, by transforming it to reproducible and profound *classes*, making the most efficient use of R. These function all use artificial intelligence to get expected results:
1. It **cleanses existing data**, by transforming it to reproducible and profound *classes*, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:
* Use `as.bactid` to get an ID of a microorganism. It takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. This package has a database of ~2500 different (potential) human pathogenic microorganisms.
* Use `as.bactid` to get an ID of a microorganism. The IDs are quite obvious - the ID of *E. coli* is "ESCCOL" and the ID of *S. aureus* is "STAAUR". This `as.bactid` function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even `as.bactid("MRSA")` will return the ID of *S. aureus*. Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. To find bacteria based on your input, this package contains a freely available database of ~2,650 different (potential) human pathogenic microorganisms.
* Use `as.rsi` to transform values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
* Use `as.mic` to cleanse your MIC values. It produces a so-called factor (in SPSS calls this *ordinal*) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
* Use `as.atc` to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantine", "nitro" will return the ATC code of Nitrofurantoine.
* Use `as.mic` to cleanse your MIC values. It produces a so-called factor (called *ordinal* in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
* Use `as.atc` to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantine", "nitro" all return the ATC code of Nitrofurantoine.
2. It **enhances existing data** and **adds new data** from data sets included in this package.
* Use `EUCAST_rules` to apply [EUCAST expert rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/).
* Use `MDRO` (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines with or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
* Data set `microorganisms` contains the family, genus, species, subspecies, colloqual name and Gram stain of almost 2500 microorganisms. This enables e.g. resistance analysis of different antibiotics per Gram stain.
* Data set `antibiotics` contains the ATC code, LIS codes, official name, trivial name, trade name and DDD of both oral and parenteral administration.
* 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 `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.
* The data set `microorganisms` contains the family, genus, species, subspecies, colloquial name and Gram stain of almost 2,650 microorganisms (2,207 bacteria, 285 fungi/yeasts, 153 parasites, 1 other). This 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` or `mo_gramstain`. Since it uses `as.bactid` internally, AI is supported. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. These functions can be used to add new variables to your data.
* The data set `antibiotics` contains the ATC code, LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains a total of 298 trade names. Use functions like `ab_official` and `ab_tradenames` to look up values. As the `mo_*` functions use `as.bactid` internally, the `ab_*` functions use `as.atc` internally so it uses AI to guess your expected result. For example, `ab_official("Fluclox")`, `ab_official("Floxapen")` and `ab_official("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.
* Calculate the resistance (and even co-resistance) of microbial isolates with the `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` functions, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`)
* Calculate the resistance (and even co-resistance) of microbial isolates with the `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` functions. Similarly, the *amount* of isolates can be determined with the `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` functions. All these functions can be used [with the `dplyr` package](https://dplyr.tidyverse.org/#usage) (e.g. in conjunction with [`summarise`](https://dplyr.tidyverse.org/reference/summarise.html))
* Plot AMR results with `geom_rsi`, a function made for the `ggplot2` package
* Predict antimicrobial resistance for the nextcoming years using logistic regression models with the `resistance_predict` function
* Conduct descriptive statistics to enhance base R: calculate kurtosis, skewness and create frequency tables
4. It **teaches the user** how to use all the above actions, by showing many examples in the help pages. The package contains an example data set called `septic_patients`. This data set, consisting of 2000 blood culture isolates from anonymised septic patients between 2001 and 2017 in the Northern Netherlands, is real and genuine data.
4. It **teaches the user** how to use all the above actions.
* The package contains extensive help pages with many examples.
* It also contains an example data set called `septic_patients`. 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 of 38,414 antimicrobial results
* Real and genuine data
----
```{r, echo = FALSE}
# this will print "2018" in 2018, and "2018-yyyy" after 2018.
yrs <- c(2018:format(Sys.Date(), "%Y"))
yrs <- c(min(yrs), max(yrs))
yrs <- paste(unique(yrs), collapse = "-")
yrs <- paste(unique(c(2018, format(Sys.Date(), "%Y"))), collapse = "-")
```
AMR, (c) `r yrs`, `r packageDescription("AMR")$URL`

89
vignettes/freq.R
View File

@ -1,89 +0,0 @@
## ----setup, include = FALSE, results = 'markup'--------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
)
library(dplyr)
library(AMR)
## ---- echo = TRUE, results = 'hide'--------------------------------------
# just using base R
freq(septic_patients$sex)
# using base R to select the variable and pass it on with a pipe from the dplyr package
septic_patients$sex %>% freq()
# do it all with pipes, using the `select` function from the dplyr package
septic_patients %>%
select(sex) %>%
freq()
# or the preferred way: using a pipe to pass the variable on to the freq function
septic_patients %>% freq(sex) # this also shows 'age' in the title
## ---- echo = TRUE--------------------------------------------------------
freq(septic_patients$sex)
## ---- echo = TRUE, results = 'hide'--------------------------------------
my_patients <- septic_patients %>% left_join_microorganisms()
## ---- echo = TRUE--------------------------------------------------------
colnames(microorganisms)
## ---- echo = TRUE--------------------------------------------------------
dim(septic_patients)
dim(my_patients)
## ---- echo = TRUE--------------------------------------------------------
my_patients %>% freq(genus, species)
## ---- echo = TRUE--------------------------------------------------------
# # get age distribution of unique patients
septic_patients %>%
distinct(patient_id, .keep_all = TRUE) %>%
freq(age, nmax = 5)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
freq(hospital_id)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
freq(hospital_id, sort.count = TRUE)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
select(amox) %>%
freq()
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
select(date) %>%
freq(nmax = 5)
## ---- echo = TRUE--------------------------------------------------------
my_df <- septic_patients %>% freq(age)
class(my_df)
## ---- echo = TRUE--------------------------------------------------------
dim(my_df)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
freq(amox, na.rm = FALSE)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
freq(hospital_id, row.names = FALSE)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
freq(hospital_id, markdown = TRUE)
## ---- echo = FALSE-------------------------------------------------------
# this will print "2018" in 2018, and "2018-yyyy" after 2018.
yrs <- c(2018:format(Sys.Date(), "%Y"))
yrs <- c(min(yrs), max(yrs))
yrs <- paste(unique(yrs), collapse = "-")

4
vignettes/freq.Rmd
View File

@ -181,9 +181,7 @@ septic_patients %>%
----
```{r, echo = FALSE}
# this will print "2018" in 2018, and "2018-yyyy" after 2018.
yrs <- c(2018:format(Sys.Date(), "%Y"))
yrs <- c(min(yrs), max(yrs))
yrs <- paste(unique(yrs), collapse = "-")
yrs <- paste(unique(c(2018, format(Sys.Date(), "%Y"))), collapse = "-")
```
AMR, (c) `r yrs`, `r packageDescription("AMR")$URL`