* Function `ab_from_text()` to retrieve antimicrobial drug names, doses and forms of administration from clinical texts in e.g. health care records, which also corrects for misspelling since it uses `as.ab()` internally
@ -19,8 +19,11 @@
* Added Monuril as trade name for fosfomycin
### Changed
* Big speed improvement for using any function on microorganism codes from earlier package versions (prior to `AMR` v1.2.0), such as `as.mo()`, `mo_name()`, `first_isolate()`, `eucast_rules()`, `mdro()`, etc.
As a consequence, very old microbial codes (from `AMR` v0.5.0 and lower) **are not supported anymore**. Use `as.mo()` on your microorganism names or codes to transform them to current abbreviations used in this package.
* Improvements for `susceptibility()` and `resistance()` and all `count_*()`, `proportion_*()` functions:
* 95% speed improvement (!) by using other base R functions for calculation
* 95% speed improvement by using other base R functions for calculation
* Using unexisting columns wil now return an error instead of dropping them silently
* Using variables for column names (as well as selectors like `dplyr::all_of()`) now works again
* Improvements for `as.ab()`:
@ -33,7 +36,7 @@
* Fixed a bug for using grouped versions of `rsi_df()`, `proportion_df()` and `count_df()`, and fixed a bug where not all different antimicrobial results were added as rows
* Improved auto-determination for columns of types `<mo>` and `<Date>`
* Fixed a bug in `bug_drug_combinations()` for when only one antibiotic was in the input data
* Changed the summary for class `<mo>`, to highlight the %SI vs. %R
* Changed the summary for class `<rsi>`, to highlight the %SI vs. %R
* Improved error handling, giving more useful info when functions return an error
* Any progress bar will now only show in interactive mode (i.e. not in R Markdown)
* Speed improvement for `mdro()` and `filter_ab_class()`
#' @section About the records from DSMZ (see source):
#' Names of prokaryotes are defined as being validly published by the International Code of Nomenclature of Bacteria. Validly published are all names which are included in the Approved Lists of Bacterial Names and the names subsequently published in the International Journal of Systematic Bacteriology (IJSB) and, from January 2000, in the International Journal of Systematic and Evolutionary Microbiology (IJSEM) as original articles or in the validation lists.
#' In February 2020, the DSMZ records were merged with the List of Prokaryotic names with Standing in Nomenclature (LPSN).
#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
#'
#' 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
#'
#' Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, <https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date> (check included version with [catalogue_of_life_version()]).
#' Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, <https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date> and <https://lpsn.dsmz.de> (check included version with [catalogue_of_life_version()]).
#' - `breakpoint_S`\cr Lowest MIC value or highest number of millimetres that leads to "S"
#' - `breakpoint_R`\cr Highest MIC value or lowest number of millimetres that leads to "R"
#' - `uti`\cr A logical value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
#' @details The repository of this `AMR` package contains a file comprising this exact data set: <https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt>. This file **allows for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. This file is updated automatically.
#' @details The repository of this `AMR` package contains a file comprising this exact data set: <https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt>. This file **allows for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. The file is updated automatically.
#' Values that cannot be coered will be considered 'unknown' and will get the MO code `UNKNOWN`.
#' Values that cannot be coerced will be considered 'unknown' and will get the MO code `UNKNOWN`.
#'
#' Use the [`mo_*`][mo_property()] functions to get properties based on the returned code, see Examples.
#'
@ -77,27 +77,27 @@
#' - Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;
#' - Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.
#'
#' This leads to e.g.:
#' The level of uncertainty can be set using the argument `allow_uncertain`. The default is `allow_uncertain = TRUE`, which is equal to uncertainty level 2. Using `allow_uncertain = FALSE` is equal to uncertainty level 0 and will skip all rules. You can also use e.g. `as.mo(..., allow_uncertain = 1)` to only allow up to level 1 uncertainty.
#'
#' With the default setting (`allow_uncertain = TRUE`, level 2), below examples will lead to valid results:
#' - `"Streptococcus group B (known as S. agalactiae)"`. The text between brackets will be removed and a warning will be thrown that the result *Streptococcus group B* (``r as.mo("Streptococcus group B")``) needs review.
#' - `"S. aureus - please mind: MRSA"`. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result *Staphylococcus aureus* (``r as.mo("Staphylococcus aureus")``) needs review.
#' - `"Fluoroquinolone-resistant Neisseria gonorrhoeae"`. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result *Neisseria gonorrhoeae* (``r as.mo("Neisseria gonorrhoeae")``) needs review.
#'
#' The level of uncertainty can be set using the argument `allow_uncertain`. The default is `allow_uncertain = TRUE`, which is equal to uncertainty level 2. Using `allow_uncertain = FALSE` is equal to uncertainty level 0 and will skip all rules. You can also use e.g. `as.mo(..., allow_uncertain = 1)` to only allow up to level 1 uncertainty.
#'
#' There are three helper functions that can be run after then [as.mo()] function:
#' - Use [mo_uncertainties()] to get a [`data.frame`] with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \eqn{(n - 0.5 * L) / n}, where *n* is the number of characters of the returned full name of the microorganism, and *L* is the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between that full name and the user input.
#' - Use [mo_failures()] to get a [`vector`] with all values that could not be coerced to a valid value.
#' - Use [mo_renamed()] to get a [`data.frame`] with all values that could be coerced based on an old, previously accepted taxonomic name.
#' There are three helper functions that can be run after using the [as.mo()] function:
#' - Use [mo_uncertainties()] to get a [`data.frame`] with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \eqn{(n - 0.5 * L) / n}, where *n* is the number of characters of the full taxonomic name of the microorganism, and *L* is the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between that full name and the user input.
#' - Use [mo_failures()] to get a [`character`] [`vector`] with all values that could not be coerced to a valid value.
#' - Use [mo_renamed()] to get a [`data.frame`] with all values that could be coerced based on old, previously accepted taxonomic names.
#'
#' ## Microbial prevalence of pathogens in humans
#'
#' The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the `prevalence` columns in the [microorganisms] and [microorganisms.old] data sets. The grouping into prevalence groups 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 Enterobacteriales.
#' 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 *Klebsiella*, *Pseudomonas* and *Legionella*.
#'
#' Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Aspergillus*, *Bacteroides*, *Candida*, *Capnocytophaga*, *Chryseobacterium*, *Cryptococcus*, *Elisabethkingia*, *Flavobacterium*, *Fusobacterium*, *Giardia*, *Leptotrichia*, *Mycoplasma*, *Prevotella*, *Rhodotorula*, *Treponema*, *Trichophyton* or *Ureaplasma*.
#' Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Aspergillus*, *Bacteroides*, *Candida*, *Capnocytophaga*, *Chryseobacterium*, *Cryptococcus*, *Elisabethkingia*, *Flavobacterium*, *Fusobacterium*, *Giardia*, *Leptotrichia*, *Mycoplasma*, *Prevotella*, *Rhodotorula*, *Treponema*, *Trichophyton* or *Ureaplasma*. This group consequently contains all less common and rare human pathogens.
#'
#' Group 3 (least prevalent microorganisms) consists of all other microorganisms.
#' Group 3 (least prevalent microorganisms) consists of all other microorganisms. This group contains microorganisms most probably not found in humans.
#' @inheritSection catalogue_of_life Catalogue of Life
# (source as a section here, so it can be inherited by other man pages:)
#' @section Source:
@ -106,7 +106,7 @@
#' 3. Lancefield RC **A serological differentiation of human and other groups of hemolytic streptococci**. 1933. J Exp Med. 57(4): 571–95. <https://dx.doi.org/10.1084/jem.57.4.571>
#' 4. Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
#' @export
#' @return A [`character`] vector with class [`mo`]
#' @return A [`character`] [`vector`] with additional class [`mo`]
#' @seealso [microorganisms] for the [`data.frame`] that is being used to determine ID's.
#'
#' The [mo_property()] functions (like [mo_genus()], [mo_gramstain()]) to get properties based on the returned code.
@ -177,7 +177,9 @@ as.mo <- function(x,
# start off with replaced language-specific non-ASCII characters with ASCII characters
<ahref="#last-updated-22-july-2020" class="anchor"></a><small>Last updated: 22 July 2020</small>
</h2>
<divid="new"class="section level3">
<h3class="hasAnchor">
@ -262,14 +262,20 @@
<h3class="hasAnchor">
<ahref="#changed"class="anchor"></a>Changed</h3>
<ul>
<li>Improvements for <code><ahref="../reference/proportion.html">susceptibility()</a></code> and <code><ahref="../reference/proportion.html">resistance()</a></code> and all <code>count_*()</code>, <code>proportion_*()</code> functions:
<li>
<p>Big speed improvement for using any function on microorganism codes from earlier package versions (prior to <code>AMR</code> v1.2.0), such as <code><ahref="../reference/as.mo.html">as.mo()</a></code>, <code><ahref="../reference/mo_property.html">mo_name()</a></code>, <code><ahref="../reference/first_isolate.html">first_isolate()</a></code>, <code><ahref="../reference/eucast_rules.html">eucast_rules()</a></code>, <code><ahref="../reference/mdro.html">mdro()</a></code>, etc.</p>
<p>As a consequence, very old microbial codes (from <code>AMR</code> v0.5.0 and lower) <strong>are not supported anymore</strong>. Use <code><ahref="../reference/as.mo.html">as.mo()</a></code> on your microorganism names or codes to transform them to current abbreviations used in this package.</p>
</li>
<li>
<p>Improvements for <code><ahref="../reference/proportion.html">susceptibility()</a></code> and <code><ahref="../reference/proportion.html">resistance()</a></code> and all <code>count_*()</code>, <code>proportion_*()</code> functions:</p>
<ul>
<li>95% speed improvement (!) by using other base R functions for calculation</li>
<li>95% speed improvement by using other base R functions for calculation</li>
<li>Using unexisting columns wil now return an error instead of dropping them silently</li>
<li>Using variables for column names (as well as selectors like <code><ahref="https://dplyr.tidyverse.org/reference/reexports.html">dplyr::all_of()</a></code>) now works again</li>
</ul>
</li>
<li>Improvements for <code><ahref="../reference/as.ab.html">as.ab()</a></code>:
<li>
<p>Improvements for <code><ahref="../reference/as.ab.html">as.ab()</a></code>:</p>
<ul>
<li>Dramatic improvement of the algorithm behind <code><ahref="../reference/as.ab.html">as.ab()</a></code>, making many more input errors translatable, such as digitalised health care records, using too few or too many vowels or consonants and many more</li>
<li>Added progress bar</li>
@ -277,17 +283,15 @@
<li>The <code><ahref="../reference/as.ab.html">as.ab()</a></code> function will now throw a note if more than 1 antimicrobial drug could be retrieved from a single input value.</li>
</ul>
</li>
<li>Fixed a bug where <code><ahref="../reference/eucast_rules.html">eucast_rules()</a></code> would not work on a tibble when the <code>tibble</code> or <code>dplyr</code> package was loaded</li>
<li>All <code>*_join_microorganisms()</code> functions and <code><ahref="../reference/bug_drug_combinations.html">bug_drug_combinations()</a></code> now return the original data class (e.g.<code>tibble</code>s and <code>data.table</code>s)</li>
<li>Fixed a bug for using grouped versions of <code><ahref="../reference/proportion.html">rsi_df()</a></code>, <code><ahref="../reference/proportion.html">proportion_df()</a></code> and <code><ahref="../reference/count.html">count_df()</a></code>, and fixed a bug where not all different antimicrobial results were added as rows</li>
<li>Improved auto-determination for columns of types <code><mo></code> and <code><Date></code>
</li>
<li>Fixed a bug in <code><ahref="../reference/bug_drug_combinations.html">bug_drug_combinations()</a></code> for when only one antibiotic was in the input data</li>
<li>Changed the summary for class <code><mo></code>, to highlight the %SI vs.%R</li>
<li>Improved error handling, giving more useful info when functions return an error</li>
<li>Any progress bar will now only show in interactive mode (i.e.not in R Markdown)</li>
<li>Speed improvement for <code><ahref="../reference/mdro.html">mdro()</a></code> and <code><ahref="../reference/filter_ab_class.html">filter_ab_class()</a></code>
</li>
<li><p>Fixed a bug where <code><ahref="../reference/eucast_rules.html">eucast_rules()</a></code> would not work on a tibble when the <code>tibble</code> or <code>dplyr</code> package was loaded</p></li>
<li><p>All <code>*_join_microorganisms()</code> functions and <code><ahref="../reference/bug_drug_combinations.html">bug_drug_combinations()</a></code> now return the original data class (e.g.<code>tibble</code>s and <code>data.table</code>s)</p></li>
<li><p>Fixed a bug for using grouped versions of <code><ahref="../reference/proportion.html">rsi_df()</a></code>, <code><ahref="../reference/proportion.html">proportion_df()</a></code> and <code><ahref="../reference/count.html">count_df()</a></code>, and fixed a bug where not all different antimicrobial results were added as rows</p></li>
<li><p>Improved auto-determination for columns of types <code><mo></code> and <code><Date></code></p></li>
<li><p>Fixed a bug in <code><ahref="../reference/bug_drug_combinations.html">bug_drug_combinations()</a></code> for when only one antibiotic was in the input data</p></li>
<li><p>Changed the summary for class <code><rsi></code>, to highlight the %SI vs.%R</p></li>
<li><p>Improved error handling, giving more useful info when functions return an error</p></li>
<li><p>Any progress bar will now only show in interactive mode (i.e.not in R Markdown)</p></li>
<li><p>Speed improvement for <code><ahref="../reference/mdro.html">mdro()</a></code> and <code><ahref="../reference/filter_ab_class.html">filter_ab_class()</a></code></p></li>
<p>A <code><ahref='https://rdrr.io/r/base/character.html'>character</a></code> vector with class <code>mo</code></p>
<p>A <code><ahref='https://rdrr.io/r/base/character.html'>character</a></code><code><ahref='https://rdrr.io/r/base/vector.html'>vector</a></code> with additional class <code>mo</code></p>
<p>Values that cannot be coered will be considered 'unknown' and will get the MO code <code>UNKNOWN</code>.</p>
<p>Values that cannot be coerced will be considered 'unknown' and will get the MO code <code>UNKNOWN</code>.</p>
<p>Use the <code><ahref='mo_property.html'>mo_*</a></code> functions to get properties based on the returned code, see Examples.</p>
<p>The algorithm uses data from the Catalogue of Life (see below) and from one other source (see <ahref='microorganisms.html'>microorganisms</a>).</p>
<p>The <code>as.mo()</code> function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:</p><ol>
@ -327,17 +327,17 @@
<li><p>Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.</p></li>
</ul>
<p>This leads to e.g.:</p><ul>
<p>The level of uncertainty can be set using the argument <code>allow_uncertain</code>. The default is <code>allow_uncertain = TRUE</code>, which is equal to uncertainty level 2. Using <code>allow_uncertain = FALSE</code> is equal to uncertainty level 0 and will skip all rules. You can also use e.g. <code>as.mo(..., allow_uncertain = 1)</code> to only allow up to level 1 uncertainty.</p>
<p>With the default setting (<code>allow_uncertain = TRUE</code>, level 2), below examples will lead to valid results:</p><ul>
<li><p><code>"Streptococcus group B (known as S. agalactiae)"</code>. The text between brackets will be removed and a warning will be thrown that the result <em>Streptococcus group B</em> (<code>B_STRPT_GRPB</code>) needs review.</p></li>
<li><p><code>"S. aureus - please mind: MRSA"</code>. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result <em>Staphylococcus aureus</em> (<code>B_STPHY_AURS</code>) needs review.</p></li>
<li><p><code>"Fluoroquinolone-resistant Neisseria gonorrhoeae"</code>. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result <em>Neisseria gonorrhoeae</em> (<code>B_NESSR_GNRR</code>) needs review.</p></li>
</ul>
<p>The level of uncertainty can be set using the argument <code>allow_uncertain</code>. The default is <code>allow_uncertain = TRUE</code>, which is equal to uncertainty level 2. Using <code>allow_uncertain = FALSE</code> is equal to uncertainty level 0 and will skip all rules. You can also use e.g. <code>as.mo(..., allow_uncertain = 1)</code> to only allow up to level 1 uncertainty.</p>
<p>There are three helper functions that can be run after then <code>as.mo()</code> function:</p><ul>
<li><p>Use <code>mo_uncertainties()</code> to get a <code><ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \((n - 0.5 * L) / n\), where <em>n</em> is the number of characters of the returned full name of the microorganism, and <em>L</em> is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance</a> between that full name and the user input.</p></li>
<li><p>Use <code>mo_failures()</code> to get a <code><ahref='https://rdrr.io/r/base/vector.html'>vector</a></code> with all values that could not be coerced to a valid value.</p></li>
<li><p>Use <code>mo_renamed()</code> to get a <code><ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with all values that could be coerced based on an old, previously accepted taxonomic name.</p></li>
<p>There are three helper functions that can be run after using the <code>as.mo()</code> function:</p><ul>
<li><p>Use <code>mo_uncertainties()</code> to get a <code><ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \((n - 0.5 * L) / n\), where <em>n</em> is the number of characters of the full taxonomic name of the microorganism, and <em>L</em> is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance</a> between that full name and the user input.</p></li>
<li><p>Use <code>mo_failures()</code> to get a <code><ahref='https://rdrr.io/r/base/character.html'>character</a></code><code><ahref='https://rdrr.io/r/base/vector.html'>vector</a></code> with all values that could not be coerced to a valid value.</p></li>
<li><p>Use <code>mo_renamed()</code> to get a <code><ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with all values that could be coerced based on old, previously accepted taxonomic names.</p></li>
</ul>
@ -345,9 +345,9 @@
<p>The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the <code>prevalence</code> columns in the <ahref='microorganisms.html'>microorganisms</a> and <ahref='microorganisms.old.html'>microorganisms.old</a> data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence.</p>
<p>Group 1 (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Pseudomonas</em> and <em>Legionella</em> and all species within the order Enterobacteriales.</p>
<p>Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Aspergillus</em>, <em>Bacteroides</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Chryseobacterium</em>, <em>Cryptococcus</em>, <em>Elisabethkingia</em>, <em>Flavobacterium</em>, <em>Fusobacterium</em>, <em>Giardia</em>, <em>Leptotrichia</em>, <em>Mycoplasma</em>, <em>Prevotella</em>, <em>Rhodotorula</em>, <em>Treponema</em>, <em>Trichophyton</em> or <em>Ureaplasma</em>.</p>
<p>Group 3 (least prevalent microorganisms) consists of all other microorganisms.</p>
<p>Group 1 (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Klebsiella</em>, <em>Pseudomonas</em> and <em>Legionella</em>.</p>
<p>Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Aspergillus</em>, <em>Bacteroides</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Chryseobacterium</em>, <em>Cryptococcus</em>, <em>Elisabethkingia</em>, <em>Flavobacterium</em>, <em>Fusobacterium</em>, <em>Giardia</em>, <em>Leptotrichia</em>, <em>Mycoplasma</em>, <em>Prevotella</em>, <em>Rhodotorula</em>, <em>Treponema</em>, <em>Trichophyton</em> or <em>Ureaplasma</em>. This group consequently contains all less common and rare human pathogens.</p>
<p>Group 3 (least prevalent microorganisms) consists of all other microorganisms. This group contains microorganisms most probably not found in humans.</p>
<metaproperty="og:title"content="Data set with 67,150 microorganisms — microorganisms" />
<metaproperty="og:title"content="Data set with 67,151 microorganisms — microorganisms" />
<metaproperty="og:description"content="A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using as.mo()."/>
<p>A <code><ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with 67,150 observations and 16 variables:</p><ul>
<p>A <code><ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with 67,151 observations and 16 variables:</p><ul>
<li><p><code>mo</code><br/> ID of microorganism as used by this package</p></li>
<li><p><code>fullname</code><br/> Full name, like <code>"Escherichia coli"</code></p></li>
<li><p><code>kingdom</code>, <code>phylum</code>, <code>class</code>, <code>order</code>, <code>family</code>, <code>genus</code>, <code>species</code>, <code>subspecies</code><br/> Taxonomic rank of the microorganism</p></li>
@ -256,13 +256,14 @@
<p>Catalogue of Life: Annual Checklist (public online taxonomic database), <ahref='http://www.catalogueoflife.org'>http://www.catalogueoflife.org</a> (check included annual version with <code><ahref='catalogue_of_life_version.html'>catalogue_of_life_version()</a></code>).</p>
<p>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</p>
<p>Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, <ahref='https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date'>https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date</a> (check included version with <code><ahref='catalogue_of_life_version.html'>catalogue_of_life_version()</a></code>).</p>
<p>Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, <ahref='https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date'>https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date</a>and <ahref='https://lpsn.dsmz.de'>https://lpsn.dsmz.de</a>(check included version with <code><ahref='catalogue_of_life_version.html'>catalogue_of_life_version()</a></code>).</p>
<li><p>11 entries of <em>Streptococcus</em> (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)</p></li>
<li><p>2 entries of <em>Staphylococcus</em> (coagulase-negative (CoNS) and coagulase-positive (CoPS))</p></li>
<li><p>3 entries of <em>Trichomonas</em> (<em>Trichomonas vaginalis</em>, and its family and genus)</p></li>
<li><p>1 entry of <em>Candida</em> (<em>Candida krusei</em>), that is not (yet) in the Catalogue of Life</p></li>
<li><p>1 entry of <em>Blastocystis</em> (<em>Blastocystis hominis</em>), although it officially does not exist (Noel <em>et al.</em> 2005, PMID 15634993)</p></li>
<li><p>5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)</p></li>
