* Support for EUCAST Clinical Breakpoints v11.0 (2021), effective in the `eucast_rules()` function and in `as.rsi()` to interpret MIC and disk diffusion values. This is now the default guideline in this package.
@ -43,6 +43,20 @@
#> [1] "Hongos" "Levaduras"
```
* Added Pretomanid (PMD, J04AK08) to the `antibiotics` data set
* MIC values (see `as.mic()`) can now be used in any mathematical processing, such as usage inside functions `min()`, `max()`, `range()`, and with binary operators (`+`, `-`, etc.). This allows for easy distribution analysis and fast filtering on MIC values:
```r
x <-random_mic(10)
x
#> Class <mic>
#> [1] 128 0.5 2 0.125 64 0.25 >=256 8 16 4
x[x > 4]
#> Class <mic>
#> [1] 128 64 >=256 8 16
range(x)
#> [1] 0.125 256.000
range(log2(x))
#> [1] -3 8
```
### Changed
* Updated the bacterial taxonomy to 3 March 2021 (using [LSPN](https://lpsn.dsmz.de))
@ -53,19 +67,10 @@
* All colours were updated to colour-blind friendly versions for values R, S and I for all plot methods (also applies to tibble printing)
* Interpretation of MIC and disk diffusion values to R/SI will now be translated if the system language is German, Dutch or Spanish (see `translate`)
* Plotting is now possible with base R using `plot()` and with ggplot2 using `ggplot()` on any vector of MIC and disk diffusion values
* Updated SNOMED codes to US Edition of SNOMED CT from 1 September 2020 and added the source to the help page of the `microorganisms` data set
* `is.rsi()` and `is.rsi.eligible()` now return a vector of `TRUE`/`FALSE` when the input is a data set, by iterating over all columns
* Using functions without setting a data set (e.g., `mo_is_gram_negative()`, `mo_is_gram_positive()`, `mo_is_intrinsic_resistant()`, `first_isolate()`, `mdro()`) now work with `dplyr`s `group_by()` again
* `first_isolate()` can be used with `group_by()` (also when using a dot `.` as input for the data) and now returns the names of the groups
* MIC values (see `as.mic()`) can now be used in any mathematical processing, such as usage inside functions `min()`, `max()`, `range()`, and with binary operators (+, -, etc.). This allows easy distribution analysis and fast filtering on MIC values:
* Updated the data set `microorganisms.codes` (which contains popular LIS and WHONET codes for microorganisms) for some species of *Mycobacterium* that previously incorrectly returned *M. africanum*
* WHONET code `"PNV"` will now correctly be interpreted as `PHN`, the antibiotic code for phenoxymethylpenicillin ('peni V')
* Fix for verbose output of `mdro(..., verbose = TRUE)` for German guideline (3MGRN and 4MGRN) and Dutch guideline (BRMO, only *P. aeruginosa*)
#' Data Set with `r format(nrow(microorganisms), big.mark = ",")` Microorganisms
#'
#' A data set containing the microbial taxonomy, last updated in `r catalogue_of_life$yearmonth_LPSN`, of six kingdoms from the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). MO codes can be looked up using [as.mo()].
#' A data set containing the microbial taxonomy, last updated in `r CATALOGUE_OF_LIFE$yearmonth_LPSN`, of six kingdoms from the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). MO codes can be looked up using [as.mo()].
#' @inheritSection catalogue_of_life Catalogue of Life
#' @format A [data.frame] with `r format(nrow(microorganisms), big.mark = ",")` observations and `r ncol(microorganisms)` variables:
#' - `mo`\cr ID of microorganism as used by this package
@ -94,7 +94,7 @@
#' - `species_id`\cr ID of the species as used by the Catalogue of Life
#' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
#' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
#' - `snomed`\cr SNOMED code of the microorganism. Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
#' - `snomed`\cr Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, according to the `r SNOMED_VERSION$current_source` (see *Source*). Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
#' @details
#' Please note that entries are only based on the Catalogue of Life and the LPSN (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
#'
@ -124,29 +124,25 @@
#'
#' As of February 2020, the regularly augmented LPSN database at DSMZ is the basis of the new LPSN service. The new database was implemented for the Type-Strain Genome Server and augmented in 2018 to store all kinds of nomenclatural information. Data from the previous version of LPSN and from the Prokaryotic Nomenclature Up-to-date (PNU) service were imported into the new system. PNU had been established in 1993 as a service of the Leibniz Institute DSMZ, and was curated by Norbert Weiss, Manfred Kracht and Dorothea Gleim.
#' List of Prokaryotic names with Standing in Nomenclature: `r catalogue_of_life$yearmonth_LPSN`
#' List of Prokaryotic names with Standing in Nomenclature (`r CATALOGUE_OF_LIFE$yearmonth_LPSN`) as currently implemented in this `AMR` package:
#'
#' * Parte, A.C., Sarda Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Goker, M. (2020). List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}
#' * Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
#' * Parte, A.C. (2014). LPSN — List of Prokaryotic names with Standing in Nomenclature. Nucleic Acids Research, 42, Issue D1, D613–D616; \doi{10.1093/nar/gkt1111}
#' * Euzeby, J.P. (1997). List of Bacterial Names with Standing in Nomenclature: a Folder Available on the Internet. International Journal of Systematic Bacteriology, 47, 590-592; \doi{10.1099/00207713-47-2-590}
#'
#' `r SNOMED_VERSION$current_source` as currently implemented in this `AMR` package:
#'
#' * Retrieved from the `r SNOMED_VERSION$title`, OID `r SNOMED_VERSION$current_oid`, version `r SNOMED_VERSION$current_version`; url: <`r SNOMED_VERSION$url`>
#' @inheritSection AMR Reference Data Publicly Available
version="Catalogue of Life: {year} Annual Checklist",
url_CoL="http://www.catalogueoflife.org/col/",
url_LPSN="https://lpsn.dsmz.de",
yearmonth_LPSN="March 2021"
)
#' Data Set with Previously Accepted Taxonomic Names
#'
#' A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by [as.mo()].
#' @param verbose a [logical] to turn Verbose mode on and off (default is off). In Verbose mode, the function does not apply rules to the data, but instead returns a data set in logbook form with extensive info about which rows and columns would be effected and in which way. Using Verbose mode takes a lot more time.
#' @param version_breakpoints the version number to use for the EUCAST Clinical Breakpoints guideline. Can be either `r vector_or(names(EUCAST_VERSION_BREAKPOINTS), reverse = TRUE)`.
#' @param version_expertrules the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be either `r vector_or(names(EUCAST_VERSION_EXPERT_RULES), reverse = TRUE)`.
#' @param ampc_cephalosporin_resistance a character value that should be applied for AmpC de-repressed cephalosporin-resistant mutants, defaults to `NA`. Currently only works when `version_expertrules` is `3.2`; '*EUCAST Expert Rules v3.2 on Enterobacterales*' states that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these agents. A value of `NA` for this argument will remove results for these agents, while e.g. a value of `"R"` will make the results for these agents resistant. Use `NULL` to not alter the results for AmpC de-repressed cephalosporin-resistant mutants. \cr For *EUCAST Expert Rules* v3.2, this rule applies to: `r vector_and(gsub("[^a-zA-Z ]+", "", unlist(strsplit(eucast_rules_file[which(eucast_rules_file$reference.version == 3.2 & eucast_rules_file$reference.rule %like% "ampc"), "this_value"][1], "|", fixed = TRUE))), quotes = "*")`.
#' @param ampc_cephalosporin_resistance a character value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants, defaults to `NA`. Currently only works when `version_expertrules` is `3.2`; '*EUCAST Expert Rules v3.2 on Enterobacterales*' states that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three agents. A value of `NA` (the default) for this argument will remove results for these three agents, while e.g. a value of `"R"` will make the results for these agents resistant. Use `NULL` or `FALSE` to not alter results for these three agents of AmpC de-repressed cephalosporin-resistant mutants. Using `TRUE` is equal to using `"R"`. \cr For *EUCAST Expert Rules* v3.2, this rule applies to: `r vector_and(gsub("[^a-zA-Z ]+", "", unlist(strsplit(eucast_rules_file[which(eucast_rules_file$reference.version == 3.2 & eucast_rules_file$reference.rule %like% "ampc"), "this_value"][1], "|", fixed = TRUE))), quotes = "*")`.
#' @param ... column name of an antibiotic, see section *Antibiotics* below
#' @param ab any (vector of) text that can be coerced to a valid antibiotic code with [as.ab()]
#' @param administration route of administration, either `r vector_or(dosage$administration)`
#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were [transformed to class `<rsi>`]([rsi]) on beforehand (defaults to `FALSE`)
#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
#' @inheritParams first_isolate
#' @details
#' **Note:** This function does not translate MIC values to RSI values. Use [as.rsi()] for that. \cr
#' The following antibiotics are used for the functions [eucast_rules()] and [mdro()]. These are shown below in the format 'name (`antimicrobial ID`, [ATC code](https://www.whocc.no/atc/structure_and_principles/))', sorted alphabetically:
#' @param ab_class an antimicrobial class, like `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
#' @param result an antibiotic result: S, I or R (or a combination of more of them)
#' @param scope the scope to check which variables to check, can be `"any"` (default) or `"all"`
#' @param only_rsi_columns a logical to indicate whether only columns must be included that were [transformed to class `<rsi>`]([rsi]) on beforehand (defaults to `FALSE`)
#' @param only_rsi_columns a logical to indicate whether only columns must be included that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
#' @param ... arguments passed on to [filter_ab_class()]
#' @details All columns of `x` will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.). This means that a filter function like e.g. [filter_aminoglycosides()] will include column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
#' @param search_string a text to search `x` for, will be checked with [as.ab()] if this value is not a column in `x`
#' @param verbose a logical to indicate whether additional info should be printed
#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were [transformed to class `<rsi>`]([rsi]) on beforehand (defaults to `FALSE`)
#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
#' @details You can look for an antibiotic (trade) name or abbreviation and it will search `x` and the [antibiotics] data set for any column containing a name or code of that antibiotic. **Longer columns names take precedence over shorter column names.**
#' @return A column name of `x`, or `NULL` when no result is found.
# NOTE TO SELF: could also have done this with the 'lifecycle' package, but why add a package dependency for such an easy job??
###############
#' Lifecycles of Functions in the `amr` Package
#' Lifecycles of Functions in the `AMR` Package
#' @name lifecycle
#' @rdname lifecycle
#' @description Functions in this `AMR` package are categorised using [the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle](https://www.Tidyverse.org/lifecycle).
#' @description Functions in this `AMR` package are categorised using [the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle](https://lifecycle.r-lib.org/articles/stages.html).
#' This page contains a section for every lifecycle (with text borrowed from the aforementioned Tidyverse website), so they can be used in the manual pages of the functions.
#' 2. Becker K *et al.* **Implications of identifying the recently defined members of the *S. aureus* complex, *S. argenteus* and *S. schweitzeri*: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).** 2019. Clin Microbiol Infect; \doi{10.1016/j.cmi.2019.02.028}
#' 3. Becker K *et al.* **Emergence of coagulase-negative staphylococci** 2020. Expert Rev Anti Infect Ther. 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
#' 4. Lancefield RC **A serological differentiation of human and other groups of hemolytic streptococci**. 1933. J Exp Med. 57(4): 571–95; \doi{10.1084/jem.57.4.571}
#' 5. Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
#' 6. List of Prokaryotic names with Standing in Nomenclature (`r CATALOGUE_OF_LIFE$yearmonth_LPSN`), \doi{10.1099/ijsem.0.004332}
#' 7. `r SNOMED_VERSION$current_source`, retrieved from the `r SNOMED_VERSION$title`, OID `r SNOMED_VERSION$current_oid`, version `r SNOMED_VERSION$current_version`; url: <`r SNOMED_VERSION$url`>
#' @export
#' @return A [character] [vector] with additional class [`mo`]
#' @seealso [microorganisms] for the [data.frame] that is being used to determine ID's.
#' * \ifelse{html}{\out{<i>p<sub>n</sub></i> is the human pathogenic prevalence group of <i>n</i>, as described below;}}{p_n is the human pathogenic prevalence group of \eqn{n}, as described below;}
#' * \ifelse{html}{\out{<i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}}{l_n is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}
#'
#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales. **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Absidia*, *Acremonium*, *Actinotignum*, *Alternaria*, *Anaerosalibacter*, *Apophysomyces*, *Arachnia*, *Aspergillus*, *Aureobacterium*, *Aureobasidium*, *Bacteroides*, *Basidiobolus*, *Beauveria*, *Blastocystis*, *Branhamella*, *Calymmatobacterium*, *Candida*, *Capnocytophaga*, *Catabacter*, *Chaetomium*, *Chryseobacterium*, *Chryseomonas*, *Chrysonilia*, *Cladophialophora*, *Cladosporium*, *Conidiobolus*, *Cryptococcus*, *Curvularia*, *Exophiala*, *Exserohilum*, *Flavobacterium*, *Fonsecaea*, *Fusarium*, *Fusobacterium*, *Hendersonula*, *Hypomyces*, *Koserella*, *Lelliottia*, *Leptosphaeria*, *Leptotrichia*, *Malassezia*, *Malbranchea*, *Mortierella*, *Mucor*, *Mycocentrospora*, *Mycoplasma*, *Nectria*, *Ochroconis*, *Oidiodendron*, *Phoma*, *Piedraia*, *Pithomyces*, *Pityrosporum*, *Prevotella*,\\*Pseudallescheria*, *Rhizomucor*, *Rhizopus*, *Rhodotorula*, *Scolecobasidium*, *Scopulariopsis*, *Scytalidium*,*Sporobolomyces*, *Stachybotrys*, *Stomatococcus*, *Treponema*, *Trichoderma*, *Trichophyton*, *Trichosporon*, *Tritirachium* or *Ureaplasma*. **Group 3** consists of all other microorganisms.
#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales. **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Absidia*, *Acremonium*, *Actinotignum*, *Alternaria*, *Anaerosalibacter*, *Apophysomyces*, *Arachnia*, *Aspergillus*, *Aureobacterium*, *Aureobasidium*, *Bacteroides*, *Basidiobolus*, *Beauveria*, *Blastocystis*, *Branhamella*, *Calymmatobacterium*, *Candida*, *Capnocytophaga*, *Catabacter*, *Chaetomium*, *Chryseobacterium*, *Chryseomonas*, *Chrysonilia*, *Cladophialophora*, *Cladosporium*, *Conidiobolus*, *Cryptococcus*, *Curvularia*, *Exophiala*, *Exserohilum*, *Flavobacterium*, *Fonsecaea*, *Fusarium*, *Fusobacterium*, *Hendersonula*, *Hypomyces*, *Koserella*, *Lelliottia*, *Leptosphaeria*, *Leptotrichia*, *Malassezia*, *Malbranchea*, *Mortierella*, *Mucor*, *Mycocentrospora*, *Mycoplasma*, *Nectria*, *Ochroconis*, *Oidiodendron*, *Phoma*, *Piedraia*, *Pithomyces*, *Pityrosporum*, *Prevotella*,*Pseudallescheria*, *Rhizomucor*, *Rhizopus*, *Rhodotorula*, *Scolecobasidium*, *Scopulariopsis*, *Scytalidium*,*Sporobolomyces*, *Stachybotrys*, *Stomatococcus*, *Treponema*, *Trichoderma*, *Trichophyton*, *Trichosporon*, *Tritirachium* or *Ureaplasma*. **Group 3** consists of all other microorganisms.
#'
#' All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., `"E. coli"` will return the microbial ID of *Escherichia coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Escherichia coli"), 3)`}, a highly prevalent microorganism found in humans) and not *Entamoeba coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Entamoeba coli"), 3)`}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
#' All output [will be translated][translate] where possible.
#'
#' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
#'
#' SNOMED codes - [mo_snomed()] - are from the `r SNOMED_VERSION$current_source`. See the [microorganisms] data set for more info.
#' @inheritSection mo_matching_score Matching Score for Microorganisms
#' @inheritSection catalogue_of_life Catalogue of Life
#' @inheritSection as.mo Source
@ -60,7 +62,7 @@
#' - An [integer] in case of [mo_year()]
#' - A [list] in case of [mo_taxonomy()] and [mo_info()]
#' - A named [character] in case of [mo_url()]
#' - A [double] in case of [mo_snomed()]
#' - A [numeric] in case of [mo_snomed()]
#' - A [character] in all other cases
#' @export
#' @seealso [microorganisms]
@ -161,7 +163,8 @@
#'
#' # get a list with the complete taxonomy (from kingdom to subspecies)
#' mo_taxonomy("E. coli")
#' # get a list with the taxonomy, the authors, Gram-stain and URL to the online database
#' # get a list with the taxonomy, the authors, Gram-stain,
<p>A data set with 70,026 rows and 16 columns, containing the following column names:<br><em>class</em>, <em>family</em>, <em>fullname</em>, <em>genus</em>, <em>kingdom</em>, <em>mo</em>, <em>order</em>, <em>phylum</em>, <em>prevalence</em>, <em>rank</em>, <em>ref</em>, <em>snomed</em>, <em>source</em>, <em>species</em>, <em>species_id</em> and <em>subspecies</em>.</p>
<p>A data set with 70,026 rows and 16 columns, containing the following column names:<br><em>mo</em>, <em>fullname</em>, <em>kingdom</em>, <em>phylum</em>, <em>class</em>, <em>order</em>, <em>family</em>, <em>genus</em>, <em>species</em>, <em>subspecies</em>, <em>rank</em>, <em>ref</em>, <em>species_id</em>, <em>source</em>, <em>prevalence</em> and <em>snomed</em>.</p>
<p>This data set is in R available as <code>microorganisms</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 5 March 2021 10:46:55 CET. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
<p>It was last updated on 11 March 2021 09:32:27 UTC. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.rds">R file</a> (2.2 MB)<br>
</li>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.xlsx">Excel file</a> (6.3 MB)<br>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.xlsx">Excel file</a> (6.4 MB)<br>
</li>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.txt">plain text file</a> (13.9 MB)<br>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.txt">plain text file</a> (14.8 MB)<br>
</li>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sas">SAS file</a> (27.4 MB)<br>
</li>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sav">SPSS file</a> (29.9 MB)<br>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sav">SPSS file</a> (27.8 MB)<br>
</li>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.dta">Stata file</a> (26.9 MB)</li>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.dta">Stata file</a> (25.1 MB)</li>
</ul>
<p><strong>NOTE: The exported files for SAS, SPSS and Stata do not contain SNOMED codes, as their file size would exceed 100 MB; the file size limit of GitHub.</strong> Advice? Use R instead.</p>
<divid="source"class="section level3">
<h3class="hasAnchor">
<ahref="#source"class="anchor"></a>Source</h3>
@ -235,6 +236,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
<ahref="http://www.catalogueoflife.org">Catalogue of Life</a> (included version: 2019)</li>
<li>
<ahref="https://lpsn.dsmz.de">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: March 2021)</li>
<li>US Edition of SNOMED CT from 1 September 2020, retrieved from the <ahref="https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009">Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)</a>, OID 2.16.840.1.114222.4.11.1009, version 12</li>
</ul>
</div>
<divid="example-content"class="section level3">
@ -276,22 +278,22 @@ If you are reading this page from within R, please <a href="https://msberends.gi
<p>Example rows when filtering on genus <em>Escherichia</em>:</p>
<tableclass="table">
<colgroup>
<colwidth="5%">
<colwidth="9%">
<colwidth="4%">
<colwidth="8%">
<colwidth="3%">
<colwidth="5%">
<colwidth="7%">
<colwidth="6%">
<colwidth="8%">
<colwidth="6%">
<colwidth="7%">
<colwidth="5%">
<colwidth="4%">
<colwidth="4%">
<colwidth="3%">
<colwidth="9%">
<colwidth="13%">
<colwidth="3%">
<colwidth="4%">
<colwidth="3%">
<colwidth="8%">
<colwidth="11%">
<colwidth="2%">
<colwidth="4%">
<colwidth="15%">
</colgroup>
<thead><trclass="header">
<thalign="center">mo</th>
@ -364,7 +366,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
<p>A data set with 14,100 rows and 4 columns, containing the following column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>prevalence</em> and <em>ref</em>.</p>
<p>A data set with 14,100 rows and 4 columns, containing the following column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>ref</em> and <em>prevalence</em>.</p>
<p><strong>Note:</strong> remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e.of column <em>‘fullname’</em>. For the scientific reference of the new names, i.e.of column <em>‘fullname_new’</em>, see the <code>microorganisms</code> data set.</p>
<p>This data set is in R available as <code>microorganisms.old</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 5 March 2021 10:46:55 CET. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
<p>It was last updated on 5 March 2021 09:46:55 UTC. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.old.rds">R file</a> (0.2 MB)<br>
@ -493,9 +495,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
<p>A data set with 456 rows and 14 columns, containing the following column names:<br><em>ab</em>, <em>abbreviations</em>, <em>atc</em>, <em>atc_group1</em>, <em>atc_group2</em>, <em>cid</em>, <em>group</em>, <em>iv_ddd</em>, <em>iv_units</em>, <em>loinc</em>, <em>name</em>, <em>oral_ddd</em>, <em>oral_units</em> and <em>synonyms</em>.</p>
<p>A data set with 456 rows and 14 columns, containing the following column names:<br><em>ab</em>, <em>atc</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em>, <em>iv_units</em> and <em>loinc</em>.</p>
<p>This data set is in R available as <code>antibiotics</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 14 January 2021 16:04:41 CET. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p>It was last updated on 8 March 2021 18:20:46 UTC. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antibiotics.rds">R file</a> (32 kB)<br>
@ -661,9 +663,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
<p>A data set with 102 rows and 9 columns, containing the following column names:<br><em>atc</em>, <em>atc_group</em>, <em>cid</em>, <em>iv_ddd</em>, <em>iv_units</em>, <em>name</em>, <em>oral_ddd</em>, <em>oral_units</em> and <em>synonyms</em>.</p>
<p>A data set with 102 rows and 9 columns, containing the following column names:<br><em>atc</em>, <em>cid</em>, <em>name</em>, <em>atc_group</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em> and <em>iv_units</em>.</p>
<p>This data set is in R available as <code>antivirals</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 29 August 2020 21:53:07 CEST. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p>It was last updated on 29 August 2020 19:53:07 UTC. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antivirals.rds">R file</a> (5 kB)<br>
@ -788,9 +790,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
<p>A data set with 93,892 rows and 2 columns, containing the following column names:<br><em>antibiotic</em> and <em>microorganism</em>.</p>
<p>A data set with 93,892 rows and 2 columns, containing the following column names:<br><em>microorganism</em> and <em>antibiotic</em>.</p>
<p>This data set is in R available as <code>intrinsic_resistant</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 5 March 2021 10:46:55 CET. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
<p>It was last updated on 5 March 2021 09:46:55 UTC. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.rds">R file</a> (69 kB)<br>
@ -1003,9 +1005,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
<ahref="#interpretation-from-mic-values-disk-diameters-to-rsi"class="anchor"></a>Interpretation from MIC values / disk diameters to R/SI</h2>
<p>A data set with 20,486 rows and 10 columns, containing the following column names:<br><em>ab</em>, <em>breakpoint_R</em>, <em>breakpoint_S</em>, <em>disk_dose</em>, <em>guideline</em>, <em>method</em>, <em>mo</em>, <em>ref_tbl</em>, <em>site</em> and <em>uti</em>.</p>
<p>A data set with 20,486 rows and 10 columns, containing the following column names:<br><em>guideline</em>, <em>method</em>, <em>site</em>, <em>mo</em>, <em>ab</em>, <em>ref_tbl</em>, <em>disk_dose</em>, <em>breakpoint_S</em>, <em>breakpoint_R</em> and <em>uti</em>.</p>
<p>This data set is in R available as <code>rsi_translation</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 5 March 2021 10:46:55 CET. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
<p>It was last updated on 5 March 2021 09:46:55 UTC. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/rsi_translation.rds">R file</a> (34 kB)<br>
@ -1133,9 +1135,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
<ahref="#dosage-guidelines-from-eucast"class="anchor"></a>Dosage guidelines from EUCAST</h2>
<p>A data set with 169 rows and 9 columns, containing the following column names:<br><em>ab</em>, <em>administration</em>, <em>dose</em>, <em>dose_times</em>, <em>eucast_version</em>, <em>name</em>, <em>notes</em>, <em>original_txt</em> and <em>type</em>.</p>
<p>A data set with 169 rows and 9 columns, containing the following column names:<br><em>ab</em>, <em>name</em>, <em>type</em>, <em>dose</em>, <em>dose_times</em>, <em>administration</em>, <em>notes</em>, <em>original_txt</em> and <em>eucast_version</em>.</p>
<p>This data set is in R available as <code>dosage</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 25 January 2021 21:58:20 CET. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
<p>It was last updated on 25 January 2021 20:58:20 UTC. Find more info about the structure of this data set <ahref="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <ahref="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/dosage.rds">R file</a> (3 kB)<br>
