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small as.mo fix

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dr. M.S. (Matthijs) Berends 2019-03-06 14:39:02 +01:00
parent 5c7a061a5d
commit 0c0c6e289b
15 changed files with 212 additions and 150 deletions
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1
.gitlab-ci.yml
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@ -95,7 +95,6 @@ pages:
only:
- master
script:
#- Rscript -e "install.packages('pkgdown', repos = 'https://cran.rstudio.com')"
- Rscript -e "devtools::install(build = TRUE, upgrade = FALSE)"
- R -e "pkgdown::build_site(examples = FALSE, lazy = TRUE, override = list(destination = 'public'))"
artifacts:

2
NEWS.md
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@ -282,7 +282,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptible isolates
* 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.mo` and `is.mo` as replacements for `as.bactid` and `is.bactid` (since the `microoganisms` data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The `as.mo` function determines microbial IDs using Artificial Intelligence (AI):
* Functions `as.mo` and `is.mo` as replacements for `as.bactid` and `is.bactid` (since the `microoganisms` data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The `as.mo` function determines microbial IDs using intelligent rules:
```r
as.mo("E. coli")
# [1] B_ESCHR_COL

12
R/mo.R
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@ -21,7 +21,7 @@
#' Transform to microorganism ID
#'
#' Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (see Source), so the input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
#' Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using intelligent rules and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (see Source), so the input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
#' @param x a character vector or a \code{data.frame} with one or two columns
#' @param Becker a logical to indicate whether \emph{Staphylococci} should be categorised into Coagulase Negative \emph{Staphylococci} ("CoNS") and Coagulase Positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1].
#'
@ -55,8 +55,8 @@
#'
#' Use the \code{\link{mo_property}} functions to get properties based on the returned code, see Examples.
#'
#' \strong{Artificial Intelligence} \cr
#' This function uses Artificial Intelligence (AI) to help getting fast and logical results. It tries to find matches in this order:
#' \strong{Intelligent rules} \cr
#' This function uses intelligent rules to help getting fast and logical results. It tries to find matches in this order:
#' \itemize{
#' \item{Taxonomic kingdom: it first searches in Bacteria, then Fungi, then Protozoa}
#' \item{Human pathogenic prevalence: it first searches in more prevalent microorganisms, then less prevalent ones (see section \emph{Microbial prevalence of pathogens in humans})}
@ -73,7 +73,7 @@
#' This means that looking up human pathogenic microorganisms takes less time than looking up human non-pathogenic microorganisms.
#'
#' \strong{Uncertain results} \cr
#' When using \code{allow_uncertain = TRUE} (which is the default setting), it will use additional rules if all previous AI rules failed to get valid results. These are:
#' When using \code{allow_uncertain = TRUE} (which is the default setting), it will use additional rules if all previous rules failed to get valid results. These are:
#' \itemize{
#' \item{It tries to look for previously accepted (but now invalid) taxonomic names}
#' \item{It strips off values between brackets and the brackets itself, and re-evaluates the input with all previous rules}
@ -96,7 +96,7 @@
#' Use \code{mo_renamed()} to get a vector with all values that could be coerced based on an old, previously accepted taxonomic name.
#'
#' \strong{Microbial prevalence of pathogens in humans} \cr
#' The artificial intelligence takes into account microbial prevalence of pathogens in humans. It uses three groups and every (sub)species is in the group it matches first. These groups are:
#' The intelligent rules takes into account microbial prevalence of pathogens in humans. It uses three groups and every (sub)species is in the group it matches first. These groups are:
#' \itemize{
#' \item{1 (most prevalent): class is Gammaproteobacteria \strong{or} genus is one of: \emph{Enterococcus}, \emph{Staphylococcus}, \emph{Streptococcus}.}
#' \item{2: phylum is one of: Proteobacteria, Firmicutes, Actinobacteria, Sarcomastigophora \strong{or} genus is one of: \emph{Aspergillus}, \emph{Bacteroides}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Chryseobacterium}, \emph{Cryptococcus}, \emph{Elisabethkingia}, \emph{Flavobacterium}, \emph{Fusobacterium}, \emph{Giardia}, \emph{Leptotrichia}, \emph{Mycoplasma}, \emph{Prevotella}, \emph{Rhodotorula}, \emph{Treponema}, \emph{Trichophyton}, \emph{Ureaplasma}.}
@ -326,7 +326,7 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE,
}
# all empty
if (all(identical(trimws(x_input), "") | is.na(x_input))) {
if (all(identical(trimws(x_input), "") | is.na(x_input) | length(x) == 0)) {
if (property == "mo") {
return(structure(rep(NA_character_, length(x_input)),
class = "mo"))

1
_pkgdown.yml
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@ -128,6 +128,7 @@ reference:
- '`availability`'
- '`count`'
- '`portion`'
- '`filter_ab_class`'
- '`freq`'
- '`g.test`'
- '`ggplot_rsi`'

176
docs/articles/SPSS.html
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@ -192,7 +192,7 @@
<h1>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">14 February 2019</h4>
<h4 class="date">06 March 2019</h4>
<div class="hidden name"><code>SPSS.Rmd</code></div>
@ -214,7 +214,7 @@
<li>
<p><strong>R is highly modular.</strong></p>
<p>The <a href="https://cran.r-project.org/web/packages/">official R network (CRAN)</a> features almost 14,000 packages at the time of writing, our <code>AMR</code> package being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitLab or GitHub. So there may even be a lot more than 14,000 packages out there.</p>
<p>Bottomline is, you can really extend it yourself or ask somebody to do this for you. Take for example our <code>AMR</code> package. SPSS, SAS and Stata will never know what a valid MIC value is (so data might not be clean) or what the Gram stain of <em>E. coli</em> is. Or the fact that all species of <em>Klebiella</em> are resistant to amoxicillin.</p>
<p>Bottom line is, you can really extend it yourself or ask somebody to do this for you. Take for example our <code>AMR</code> package. Among other things, it adds reliable reference data to R to help you with the data cleaning and analysis. SPSS, SAS and Stata will never know what a valid MIC value is or what the Gram stain of <em>E. coli</em> is. Or that all species of <em>Klebiella</em> are resistant to amoxicillin and that Floxapen<sup>®</sup> is a trade name of flucloxacillin. These facts and properties are often needed to clean existing data, which would be very inconvenient in a software package without reliable reference data. See below for a demonstration.</p>
</li>
<li>
<p><strong>R is extremely flexible.</strong></p>
@ -241,6 +241,38 @@
</li>
</ul>
<p>If you sometimes write syntaxes in SPSS to run a complete analysis or to ‘automate’ some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS.</p>
<p>To demonstrate the first point:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="co"># not all values are valid MIC values:</span></a>
<a class="sourceLine" id="cb1-2" title="2"><span class="kw"><a href="../reference/as.mic.html">as.mic</a></span>(<span class="fl">0.125</span>)</a>
<a class="sourceLine" id="cb1-3" title="3"><span class="co">#&gt; Class 'mic'</span></a>
<a class="sourceLine" id="cb1-4" title="4"><span class="co">#&gt; [1] 0.125</span></a>
<a class="sourceLine" id="cb1-5" title="5"><span class="kw"><a href="../reference/as.mic.html">as.mic</a></span>(<span class="st">"testvalue"</span>)</a>
<a class="sourceLine" id="cb1-6" title="6"><span class="co">#&gt; Class 'mic'</span></a>
<a class="sourceLine" id="cb1-7" title="7"><span class="co">#&gt; [1] &lt;NA&gt;</span></a>
<a class="sourceLine" id="cb1-8" title="8"></a>
<a class="sourceLine" id="cb1-9" title="9"><span class="co"># the Gram stain is avaiable for all bacteria:</span></a>
<a class="sourceLine" id="cb1-10" title="10"><span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="st">"E. coli"</span>)</a>
<a class="sourceLine" id="cb1-11" title="11"><span class="co">#&gt; [1] "Gram negative"</span></a>
<a class="sourceLine" id="cb1-12" title="12"></a>
<a class="sourceLine" id="cb1-13" title="13"><span class="co"># Klebsiella is intrinsic resistant to amoxicllin, according to EUCAST:</span></a>
<a class="sourceLine" id="cb1-14" title="14">klebsiella_test &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/data.frame">data.frame</a></span>(<span class="dt">mo =</span> <span class="st">"klebsiella"</span>, </a>
<a class="sourceLine" id="cb1-15" title="15"> <span class="dt">amox =</span> <span class="st">"S"</span>,</a>
<a class="sourceLine" id="cb1-16" title="16"> <span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb1-17" title="17">klebsiella_test</a>
<a class="sourceLine" id="cb1-18" title="18"><span class="co">#&gt; mo amox</span></a>
<a class="sourceLine" id="cb1-19" title="19"><span class="co">#&gt; 1 klebsiella S</span></a>
<a class="sourceLine" id="cb1-20" title="20"><span class="kw"><a href="../reference/eucast_rules.html">eucast_rules</a></span>(klebsiella_test, <span class="dt">info =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb1-21" title="21"><span class="co">#&gt; mo amox</span></a>
<a class="sourceLine" id="cb1-22" title="22"><span class="co">#&gt; 1 klebsiella R</span></a>
<a class="sourceLine" id="cb1-23" title="23"></a>
<a class="sourceLine" id="cb1-24" title="24"><span class="co"># hundreds of trade names can be translated to an ATC or name:</span></a>
<a class="sourceLine" id="cb1-25" title="25"><span class="kw"><a href="../reference/atc_property.html">atc_name</a></span>(<span class="st">"floxapen"</span>)</a>
<a class="sourceLine" id="cb1-26" title="26"><span class="co">#&gt; [1] "Flucloxacillin"</span></a>
<a class="sourceLine" id="cb1-27" title="27"><span class="kw"><a href="../reference/as.atc.html">as.atc</a></span>(<span class="st">"floxapen"</span>)</a>
<a class="sourceLine" id="cb1-28" title="28"><span class="co">#&gt; Class 'atc'</span></a>
<a class="sourceLine" id="cb1-29" title="29"><span class="co">#&gt; [1] J01CF05</span></a>
<a class="sourceLine" id="cb1-30" title="30"><span class="kw"><a href="../reference/atc_property.html">atc_tradenames</a></span>(<span class="st">"floxapen"</span>)</a>
<a class="sourceLine" id="cb1-31" title="31"><span class="co">#&gt; [1] "Flopen" "Floxapen" "Fluclox" "Sesamol" "Softapen" "Staphylex"</span></a></code></pre></div>
</div>
<div id="import-data-from-spsssasstata" class="section level2">
<h2 class="hasAnchor">
@ -248,105 +280,105 @@
<div id="rstudio" class="section level3">
<h3 class="hasAnchor">
<a href="#rstudio" class="anchor"></a>RStudio</h3>
<p>To work with R, probably the best option is to use <a href="https://www.rstudio.com/products/rstudio/">RStudio</a>. It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menu to work with other data sources. You can also run <a href="https://www.rstudio.com/products/rstudio/">RStudio Server</a>, which is nothing less than the complete RStudio software available as a website (e.g. in your corporate network or at home).</p>
<p>To work with R, probably the best option is to use <a href="https://www.rstudio.com/products/rstudio/">RStudio</a>. It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menus to work with other data sources. You can also run <a href="https://www.rstudio.com/products/rstudio/">RStudio Server</a>, which is nothing less than the complete RStudio software available as a website (e.g. in your corporate network or at home).</p>
<p>To import a data file, just click <em>Import Dataset</em> in the Environment tab:</p>
<p><img src="../import1.png"></p>
<p><img src="https://gitlab.com/msberends/AMR/raw/master/docs/import1.png"></p>
<p>If additional packages are needed, RStudio will ask you if they should be installed on beforehand.</p>
<p>In the the window that opens, you can define all options (parameters) that should be used for import and you’re ready to go:</p>
<p><img src="../import2.png"></p>
<p><img src="https://gitlab.com/msberends/AMR/raw/master/docs/import2.png"></p>
<p>If you want named variables to be imported as factors so it resembles SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>.</p>
<p>The difference is this:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1">SPSS_data</a>
<a class="sourceLine" id="cb1-2" title="2"><span class="co"># # A tibble: 4,203 x 4</span></a>
<a class="sourceLine" id="cb1-3" title="3"><span class="co"># v001 sex status statusage</span></a>
<a class="sourceLine" id="cb1-4" title="4"><span class="co"># &lt;dbl&gt; &lt;dbl+lbl&gt; &lt;dbl+lbl&gt; &lt;dbl&gt;</span></a>
<a class="sourceLine" id="cb1-5" title="5"><span class="co"># 1 10002 1 1 76.6</span></a>
<a class="sourceLine" id="cb1-6" title="6"><span class="co"># 2 10004 0 1 59.1</span></a>
<a class="sourceLine" id="cb1-7" title="7"><span class="co"># 3 10005 1 1 54.5</span></a>
<a class="sourceLine" id="cb1-8" title="8"><span class="co"># 4 10006 1 1 54.1</span></a>
<a class="sourceLine" id="cb1-9" title="9"><span class="co"># 5 10007 1 1 57.7</span></a>
<a class="sourceLine" id="cb1-10" title="10"><span class="co"># 6 10008 1 1 62.8</span></a>
<a class="sourceLine" id="cb1-11" title="11"><span class="co"># 7 10010 0 1 63.7</span></a>
<a class="sourceLine" id="cb1-12" title="12"><span class="co"># 8 10011 1 1 73.1</span></a>
<a class="sourceLine" id="cb1-13" title="13"><span class="co"># 9 10017 1 1 56.7</span></a>
<a class="sourceLine" id="cb1-14" title="14"><span class="co"># 10 10018 0 1 66.6</span></a>
<a class="sourceLine" id="cb1-15" title="15"><span class="co"># # … with 4,193 more rows</span></a>
<a class="sourceLine" id="cb1-16" title="16"></a>
<a class="sourceLine" id="cb1-17" title="17"><span class="kw">as_factor</span>(SPSS_data)</a>
<a class="sourceLine" id="cb1-18" title="18"><span class="co"># # A tibble: 4,203 x 4</span></a>
<a class="sourceLine" id="cb1-19" title="19"><span class="co"># v001 sex status statusage</span></a>
<a class="sourceLine" id="cb1-20" title="20"><span class="co"># &lt;dbl&gt; &lt;fct&gt; &lt;fct&gt; &lt;dbl&gt;</span></a>
<a class="sourceLine" id="cb1-21" title="21"><span class="co"># 1 10002 Male alive 76.6</span></a>
<a class="sourceLine" id="cb1-22" title="22"><span class="co"># 2 10004 Female alive 59.1</span></a>
<a class="sourceLine" id="cb1-23" title="23"><span class="co"># 3 10005 Male alive 54.5</span></a>
<a class="sourceLine" id="cb1-24" title="24"><span class="co"># 4 10006 Male alive 54.1</span></a>
<a class="sourceLine" id="cb1-25" title="25"><span class="co"># 5 10007 Male alive 57.7</span></a>
<a class="sourceLine" id="cb1-26" title="26"><span class="co"># 6 10008 Male alive 62.8</span></a>
<a class="sourceLine" id="cb1-27" title="27"><span class="co"># 7 10010 Female alive 63.7</span></a>
<a class="sourceLine" id="cb1-28" title="28"><span class="co"># 8 10011 Male alive 73.1</span></a>
<a class="sourceLine" id="cb1-29" title="29"><span class="co"># 9 10017 Male alive 56.7</span></a>
<a class="sourceLine" id="cb1-30" title="30"><span class="co"># 10 10018 Female alive 66.6</span></a>
<a class="sourceLine" id="cb1-31" title="31"><span class="co"># # … with 4,193 more rows</span></a></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">SPSS_data</a>
<a class="sourceLine" id="cb2-2" title="2"><span class="co"># # A tibble: 4,203 x 4</span></a>
<a class="sourceLine" id="cb2-3" title="3"><span class="co"># v001 sex status statusage</span></a>
<a class="sourceLine" id="cb2-4" title="4"><span class="co"># &lt;dbl&gt; &lt;dbl+lbl&gt; &lt;dbl+lbl&gt; &lt;dbl&gt;</span></a>
<a class="sourceLine" id="cb2-5" title="5"><span class="co"># 1 10002 1 1 76.6</span></a>
<a class="sourceLine" id="cb2-6" title="6"><span class="co"># 2 10004 0 1 59.1</span></a>
<a class="sourceLine" id="cb2-7" title="7"><span class="co"># 3 10005 1 1 54.5</span></a>
<a class="sourceLine" id="cb2-8" title="8"><span class="co"># 4 10006 1 1 54.1</span></a>
<a class="sourceLine" id="cb2-9" title="9"><span class="co"># 5 10007 1 1 57.7</span></a>
<a class="sourceLine" id="cb2-10" title="10"><span class="co"># 6 10008 1 1 62.8</span></a>
<a class="sourceLine" id="cb2-11" title="11"><span class="co"># 7 10010 0 1 63.7</span></a>
<a class="sourceLine" id="cb2-12" title="12"><span class="co"># 8 10011 1 1 73.1</span></a>
<a class="sourceLine" id="cb2-13" title="13"><span class="co"># 9 10017 1 1 56.7</span></a>
<a class="sourceLine" id="cb2-14" title="14"><span class="co"># 10 10018 0 1 66.6</span></a>
<a class="sourceLine" id="cb2-15" title="15"><span class="co"># # … with 4,193 more rows</span></a>
<a class="sourceLine" id="cb2-16" title="16"></a>
<a class="sourceLine" id="cb2-17" title="17"><span class="kw">as_factor</span>(SPSS_data)</a>
<a class="sourceLine" id="cb2-18" title="18"><span class="co"># # A tibble: 4,203 x 4</span></a>
<a class="sourceLine" id="cb2-19" title="19"><span class="co"># v001 sex status statusage</span></a>
<a class="sourceLine" id="cb2-20" title="20"><span class="co"># &lt;dbl&gt; &lt;fct&gt; &lt;fct&gt; &lt;dbl&gt;</span></a>
<a class="sourceLine" id="cb2-21" title="21"><span class="co"># 1 10002 Male alive 76.6</span></a>
<a class="sourceLine" id="cb2-22" title="22"><span class="co"># 2 10004 Female alive 59.1</span></a>
<a class="sourceLine" id="cb2-23" title="23"><span class="co"># 3 10005 Male alive 54.5</span></a>
<a class="sourceLine" id="cb2-24" title="24"><span class="co"># 4 10006 Male alive 54.1</span></a>
<a class="sourceLine" id="cb2-25" title="25"><span class="co"># 5 10007 Male alive 57.7</span></a>
<a class="sourceLine" id="cb2-26" title="26"><span class="co"># 6 10008 Male alive 62.8</span></a>
<a class="sourceLine" id="cb2-27" title="27"><span class="co"># 7 10010 Female alive 63.7</span></a>
<a class="sourceLine" id="cb2-28" title="28"><span class="co"># 8 10011 Male alive 73.1</span></a>
<a class="sourceLine" id="cb2-29" title="29"><span class="co"># 9 10017 Male alive 56.7</span></a>
<a class="sourceLine" id="cb2-30" title="30"><span class="co"># 10 10018 Female alive 66.6</span></a>
<a class="sourceLine" id="cb2-31" title="31"><span class="co"># # … with 4,193 more rows</span></a></code></pre></div>
</div>
<div id="base-r" class="section level3">
<h3 class="hasAnchor">
<a href="#base-r" class="anchor"></a>Base R</h3>
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/">great <code>haven</code> package</a> yourself:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="co"># download and install the latest version:</span></a>
<a class="sourceLine" id="cb2-2" title="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages</a></span>(<span class="st">"haven"</span>)</a>
<a class="sourceLine" id="cb2-3" title="3"><span class="co"># load the package you just installed:</span></a>
<a class="sourceLine" id="cb2-4" title="4"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(haven) </a></code></pre></div>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="co"># download and install the latest version:</span></a>
<a class="sourceLine" id="cb3-2" title="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages</a></span>(<span class="st">"haven"</span>)</a>
<a class="sourceLine" id="cb3-3" title="3"><span class="co"># load the package you just installed:</span></a>
<a class="sourceLine" id="cb3-4" title="4"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(haven) </a></code></pre></div>
<p>You can now import files as follows:</p>
<div id="spss" class="section level4">
<h4 class="hasAnchor">
<a href="#spss" class="anchor"></a>SPSS</h4>
<p>To read files from SPSS into R:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="co"># read any SPSS file based on file extension (best way):</span></a>
<a class="sourceLine" id="cb3-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_spss</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a>
<a class="sourceLine" id="cb3-3" title="3"></a>
<a class="sourceLine" id="cb3-4" title="4"><span class="co"># read .sav or .zsav file:</span></a>
<a class="sourceLine" id="cb3-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_sav</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a>
<a class="sourceLine" id="cb3-6" title="6"></a>
<a class="sourceLine" id="cb3-7" title="7"><span class="co"># read .por file:</span></a>
<a class="sourceLine" id="cb3-8" title="8"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a></code></pre></div>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="co"># read any SPSS file based on file extension (best way):</span></a>
<a class="sourceLine" id="cb4-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_spss</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a>
<a class="sourceLine" id="cb4-3" title="3"></a>
<a class="sourceLine" id="cb4-4" title="4"><span class="co"># read .sav or .zsav file:</span></a>
<a class="sourceLine" id="cb4-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_sav</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a>
<a class="sourceLine" id="cb4-6" title="6"></a>
<a class="sourceLine" id="cb4-7" title="7"><span class="co"># read .por file:</span></a>
<a class="sourceLine" id="cb4-8" title="8"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a></code></pre></div>
<p>Do not forget about <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>, as mentioned above.</p>
<p>To export your R objects to the SPSS file format:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="co"># save as .sav file:</span></a>
<a class="sourceLine" id="cb4-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>)</a>
<a class="sourceLine" id="cb4-3" title="3"></a>
<a class="sourceLine" id="cb4-4" title="4"><span class="co"># save as compressed .zsav file:</span></a>
<a class="sourceLine" id="cb4-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>, <span class="dt">compress =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="co"># save as .sav file:</span></a>
<a class="sourceLine" id="cb5-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>)</a>
<a class="sourceLine" id="cb5-3" title="3"></a>
<a class="sourceLine" id="cb5-4" title="4"><span class="co"># save as compressed .zsav file:</span></a>
<a class="sourceLine" id="cb5-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>, <span class="dt">compress =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
</div>
<div id="sas" class="section level4">
<h4 class="hasAnchor">
<a href="#sas" class="anchor"></a>SAS</h4>
<p>To read files from SAS into R:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="co"># read .sas7bdat + .sas7bcat files:</span></a>
<a class="sourceLine" id="cb5-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_sas.html">read_sas</a></span>(<span class="dt">data_file =</span> <span class="st">"path/to/file"</span>, <span class="dt">catalog_file =</span> <span class="ot">NULL</span>)</a>
<a class="sourceLine" id="cb5-3" title="3"></a>
<a class="sourceLine" id="cb5-4" title="4"><span class="co"># read SAS transport files (version 5 and version 8):</span></a>
<a class="sourceLine" id="cb5-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a></code></pre></div>
<p>To export your R objects to the SAS file format:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="co"># save as regular SAS file:</span></a>
<a class="sourceLine" id="cb6-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_sas.html">write_sas</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>)</a>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="co"># read .sas7bdat + .sas7bcat files:</span></a>
<a class="sourceLine" id="cb6-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_sas.html">read_sas</a></span>(<span class="dt">data_file =</span> <span class="st">"path/to/file"</span>, <span class="dt">catalog_file =</span> <span class="ot">NULL</span>)</a>
<a class="sourceLine" id="cb6-3" title="3"></a>
<a class="sourceLine" id="cb6-4" title="4"><span class="co"># the SAS transport format is an open format </span></a>
<a class="sourceLine" id="cb6-5" title="5"><span class="co"># (required for submission of the data to the FDA)</span></a>
<a class="sourceLine" id="cb6-6" title="6"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>, <span class="dt">version =</span> <span class="dv">8</span>)</a></code></pre></div>
<a class="sourceLine" id="cb6-4" title="4"><span class="co"># read SAS transport files (version 5 and version 8):</span></a>
<a class="sourceLine" id="cb6-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a></code></pre></div>
<p>To export your R objects to the SAS file format:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="co"># save as regular SAS file:</span></a>
<a class="sourceLine" id="cb7-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_sas.html">write_sas</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>)</a>
<a class="sourceLine" id="cb7-3" title="3"></a>
<a class="sourceLine" id="cb7-4" title="4"><span class="co"># the SAS transport format is an open format </span></a>
<a class="sourceLine" id="cb7-5" title="5"><span class="co"># (required for submission of the data to the FDA)</span></a>
<a class="sourceLine" id="cb7-6" title="6"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>, <span class="dt">version =</span> <span class="dv">8</span>)</a></code></pre></div>
</div>
<div id="stata" class="section level4">
<h4 class="hasAnchor">
<a href="#stata" class="anchor"></a>Stata</h4>
<p>To read files from Stata into R:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="co"># read .dta file:</span></a>
<a class="sourceLine" id="cb7-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_stata</a></span>(<span class="dt">file =</span> <span class="st">"/path/to/file"</span>)</a>
<a class="sourceLine" id="cb7-3" title="3"></a>
<a class="sourceLine" id="cb7-4" title="4"><span class="co"># works exactly the same:</span></a>
<a class="sourceLine" id="cb7-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span>(<span class="dt">file =</span> <span class="st">"/path/to/file"</span>)</a></code></pre></div>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1"><span class="co"># read .dta file:</span></a>
<a class="sourceLine" id="cb8-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_stata</a></span>(<span class="dt">file =</span> <span class="st">"/path/to/file"</span>)</a>
<a class="sourceLine" id="cb8-3" title="3"></a>
<a class="sourceLine" id="cb8-4" title="4"><span class="co"># works exactly the same:</span></a>
<a class="sourceLine" id="cb8-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span>(<span class="dt">file =</span> <span class="st">"/path/to/file"</span>)</a></code></pre></div>
<p>To export your R objects to the Stata file format:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1"><span class="co"># save as .dta file, Stata version 14:</span></a>
<a class="sourceLine" id="cb8-2" title="2"><span class="co"># (supports Stata v8 until v15 at the time of writing)</span></a>
<a class="sourceLine" id="cb8-3" title="3"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"/path/to/file"</span>, <span class="dt">version =</span> <span class="dv">14</span>)</a></code></pre></div>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="co"># save as .dta file, Stata version 14:</span></a>
<a class="sourceLine" id="cb9-2" title="2"><span class="co"># (supports Stata v8 until v15 at the time of writing)</span></a>
<a class="sourceLine" id="cb9-3" title="3"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"/path/to/file"</span>, <span class="dt">version =</span> <span class="dv">14</span>)</a></code></pre></div>
</div>
</div>
</div>

78
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@ -192,7 +192,7 @@
<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">05 March 2019</h4>
<h4 class="date">06 March 2019</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -201,12 +201,12 @@
<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://catalogueoflife.org">Catalogue of Life</a>. We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using AI (Artificial Intelligence) combined with the taxonomic tree of Catalogue of Life.</p>
<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://catalogueoflife.org">Catalogue of Life</a>. We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark()</a></code> runs different input expressions independently of each other and measures their time-to-result.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(microbenchmark)</a>
<a class="sourceLine" id="cb1-2" title="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR)</a></code></pre></div>
<p>In the next test, we try to ‘coerce’ different input values for <em>Staphylococcus aureus</em>. The actual result is the same every time: it returns its MO code <code>B_STPHY_AUR</code> (<em>B</em> stands for <em>Bacteria</em>, the taxonomic kingdom).</p>
<p>But the calculation time differs a lot. Here, the AI effect can be reviewed best:</p>
<p>But the calculation time differs a lot:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">S.aureus &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"sau"</span>),</a>
<a class="sourceLine" id="cb2-2" title="2"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"stau"</span>),</a>
<a class="sourceLine" id="cb2-3" title="3"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"staaur"</span>),</a>
@ -217,14 +217,14 @@
<a class="sourceLine" id="cb2-8" title="8"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb2-9" title="9"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(S.aureus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb2-10" title="10"><span class="co">#&gt; Unit: milliseconds</span></a>
<a class="sourceLine" id="cb2-11" title="11"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-12" title="12"><span class="co">#&gt; as.mo("sau") 16.80 17.20 25.80 18.10 32.10 52.8 10</span></a>
<a class="sourceLine" id="cb2-13" title="13"><span class="co">#&gt; as.mo("stau") 32.20 33.10 43.30 34.00 42.40 82.3 10</span></a>
<a class="sourceLine" id="cb2-14" title="14"><span class="co">#&gt; as.mo("staaur") 16.70 16.80 25.40 17.20 19.80 60.4 10</span></a>
<a class="sourceLine" id="cb2-15" title="15"><span class="co">#&gt; as.mo("STAAUR") 16.70 16.80 22.40 17.90 20.20 61.7 10</span></a>
<a class="sourceLine" id="cb2-16" title="16"><span class="co">#&gt; as.mo("S. aureus") 24.90 25.10 42.00 26.20 67.50 88.8 10</span></a>
<a class="sourceLine" id="cb2-17" title="17"><span class="co">#&gt; as.mo("S. aureus") 24.50 25.00 43.70 25.30 67.90 78.2 10</span></a>
<a class="sourceLine" id="cb2-18" title="18"><span class="co">#&gt; as.mo("Staphylococcus aureus") 7.47 7.48 9.63 7.53 7.69 28.3 10</span></a></code></pre></div>
<a class="sourceLine" id="cb2-11" title="11"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-12" title="12"><span class="co">#&gt; as.mo("sau") 16.50 16.60 17.0 16.70 17.00 19.0 10</span></a>
<a class="sourceLine" id="cb2-13" title="13"><span class="co">#&gt; as.mo("stau") 31.70 31.90 51.5 32.10 49.10 166.0 10</span></a>
<a class="sourceLine" id="cb2-14" title="14"><span class="co">#&gt; as.mo("staaur") 16.60 16.70 21.8 16.80 17.00 65.7 10</span></a>
<a class="sourceLine" id="cb2-15" title="15"><span class="co">#&gt; as.mo("STAAUR") 16.70 16.70 27.1 16.80 17.60 77.3 10</span></a>
<a class="sourceLine" id="cb2-16" title="16"><span class="co">#&gt; as.mo("S. aureus") 24.60 24.70 30.2 25.30 31.80 57.8 10</span></a>
<a class="sourceLine" id="cb2-17" title="17"><span class="co">#&gt; as.mo("S. aureus") 24.60 24.60 37.7 25.10 66.00 67.4 10</span></a>
<a class="sourceLine" id="cb2-18" title="18"><span class="co">#&gt; as.mo("Staphylococcus aureus") 7.47 7.48 11.1 7.65 8.02 41.1 10</span></a></code></pre></div>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Thermus islandicus</em> (<code>B_THERMS_ISL</code>), a bug probably never found before in humans:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1">T.islandicus &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"theisl"</span>),</a>
@ -235,13 +235,13 @@
<a class="sourceLine" id="cb3-6" title="6"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb3-7" title="7"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(T.islandicus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb3-8" title="8"><span class="co">#&gt; Unit: milliseconds</span></a>
<a class="sourceLine" id="cb3-9" title="9"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb3-10" title="10"><span class="co">#&gt; as.mo("theisl") 275.0 304.0 310 309.0 316 337 10</span></a>
<a class="sourceLine" id="cb3-11" title="11"><span class="co">#&gt; as.mo("THEISL") 263.0 304.0 311 306.0 312 391 10</span></a>
<a class="sourceLine" id="cb3-12" title="12"><span class="co">#&gt; as.mo("T. islandicus") 142.0 143.0 168 148.0 188 220 10</span></a>
<a class="sourceLine" id="cb3-13" title="13"><span class="co">#&gt; as.mo("T. islandicus") 142.0 142.0 169 143.0 185 312 10</span></a>
<a class="sourceLine" id="cb3-14" title="14"><span class="co">#&gt; as.mo("Thermus islandicus") 68.1 68.6 101 89.8 122 179 10</span></a></code></pre></div>
<p>That takes 7 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Thermus islandicus</em>) are almost fast - these are the most probable input from most data sets.</p>
<a class="sourceLine" id="cb3-9" title="9"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb3-10" title="10"><span class="co">#&gt; as.mo("theisl") 269.0 270.0 294.0 293.0 317 320 10</span></a>
<a class="sourceLine" id="cb3-11" title="11"><span class="co">#&gt; as.mo("THEISL") 272.0 313.0 327.0 316.0 321 476 10</span></a>
<a class="sourceLine" id="cb3-12" title="12"><span class="co">#&gt; as.mo("T. islandicus") 142.0 142.0 159.0 144.0 191 205 10</span></a>
<a class="sourceLine" id="cb3-13" title="13"><span class="co">#&gt; as.mo("T. islandicus") 142.0 143.0 166.0 164.0 188 196 10</span></a>
<a class="sourceLine" id="cb3-14" title="14"><span class="co">#&gt; as.mo("Thermus islandicus") 68.4 68.6 86.8 69.2 113 116 10</span></a></code></pre></div>
<p>That takes 7.4 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Thermus islandicus</em>) are almost fast - these are the most probable input from most data sets.</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Thermus islandicus</em> (which is very uncommon):</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/par">par</a></span>(<span class="dt">mar =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">5</span>, <span class="dv">16</span>, <span class="dv">4</span>, <span class="dv">2</span>)) <span class="co"># set more space for left margin text (16)</span></a>
<a class="sourceLine" id="cb4-2" title="2"></a>
@ -287,8 +287,8 @@
<a class="sourceLine" id="cb5-24" title="24"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb5-25" title="25"><span class="co">#&gt; Unit: milliseconds</span></a>
<a class="sourceLine" id="cb5-26" title="26"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb5-27" title="27"><span class="co">#&gt; mo_fullname(x) 687 738 767 770 774 887 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.77 seconds (770 ms). You only lose time on your unique input values.</p>
<a class="sourceLine" id="cb5-27" title="27"><span class="co">#&gt; mo_fullname(x) 688 757 800 758 919 921 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.76 seconds (758 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -300,10 +300,10 @@
<a class="sourceLine" id="cb6-4" title="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb6-5" title="5"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb6-6" title="6"><span class="co">#&gt; Unit: milliseconds</span></a>
<a class="sourceLine" id="cb6-7" title="7"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-8" title="8"><span class="co">#&gt; A 11.100 11.300 15.600 11.400 11.600 53.4 10</span></a>
<a class="sourceLine" id="cb6-9" title="9"><span class="co">#&gt; B 22.200 22.500 22.800 22.800 23.000 23.7 10</span></a>
<a class="sourceLine" id="cb6-10" title="10"><span class="co">#&gt; C 0.322 0.326 0.518 0.563 0.578 0.8 10</span></a></code></pre></div>
<a class="sourceLine" id="cb6-7" title="7"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-8" title="8"><span class="co">#&gt; A 11.200 11.300 11.900 12.200 12.300 12.600 10</span></a>
<a class="sourceLine" id="cb6-9" title="9"><span class="co">#&gt; B 22.300 22.500 23.300 23.100 23.900 24.700 10</span></a>
<a class="sourceLine" id="cb6-10" title="10"><span class="co">#&gt; C 0.339 0.519 0.612 0.564 0.768 0.776 10</span></a></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_fullname("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0006 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1">run_it &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),</a>
<a class="sourceLine" id="cb7-2" title="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</a>
@ -317,14 +317,14 @@
<a class="sourceLine" id="cb7-10" title="10"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb7-11" title="11"><span class="co">#&gt; Unit: milliseconds</span></a>
<a class="sourceLine" id="cb7-12" title="12"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb7-13" title="13"><span class="co">#&gt; A 0.330 0.345 0.421 0.441 0.461 0.525 10</span></a>
<a class="sourceLine" id="cb7-14" title="14"><span class="co">#&gt; B 0.355 0.378 0.453 0.435 0.464 0.754 10</span></a>
<a class="sourceLine" id="cb7-15" title="15"><span class="co">#&gt; C 0.315 0.374 0.506 0.516 0.633 0.700 10</span></a>
<a class="sourceLine" id="cb7-16" title="16"><span class="co">#&gt; D 0.273 0.329 0.353 0.365 0.385 0.391 10</span></a>
<a class="sourceLine" id="cb7-17" title="17"><span class="co">#&gt; E 0.279 0.322 0.330 0.335 0.343 0.366 10</span></a>
<a class="sourceLine" id="cb7-18" title="18"><span class="co">#&gt; F 0.247 0.263 0.315 0.319 0.355 0.429 10</span></a>
<a class="sourceLine" id="cb7-19" title="19"><span class="co">#&gt; G 0.276 0.320 0.342 0.328 0.371 0.447 10</span></a>
<a class="sourceLine" id="cb7-20" title="20"><span class="co">#&gt; H 0.251 0.258 0.315 0.329 0.360 0.368 10</span></a></code></pre></div>
<a class="sourceLine" id="cb7-13" title="13"><span class="co">#&gt; A 0.321 0.434 0.481 0.501 0.519 0.665 10</span></a>
<a class="sourceLine" id="cb7-14" title="14"><span class="co">#&gt; B 0.352 0.414 0.482 0.475 0.513 0.715 10</span></a>
<a class="sourceLine" id="cb7-15" title="15"><span class="co">#&gt; C 0.394 0.648 0.670 0.679 0.770 0.839 10</span></a>
<a class="sourceLine" id="cb7-16" title="16"><span class="co">#&gt; D 0.354 0.371 0.393 0.395 0.410 0.428 10</span></a>
<a class="sourceLine" id="cb7-17" title="17"><span class="co">#&gt; E 0.286 0.353 0.370 0.369 0.399 0.443 10</span></a>
<a class="sourceLine" id="cb7-18" title="18"><span class="co">#&gt; F 0.317 0.373 0.377 0.380 0.392 0.437 10</span></a>
<a class="sourceLine" id="cb7-19" title="19"><span class="co">#&gt; G 0.272 0.307 0.352 0.348 0.387 0.431 10</span></a>
<a class="sourceLine" id="cb7-20" title="20"><span class="co">#&gt; H 0.293 0.338 0.366 0.361 0.412 0.441 10</span></a></code></pre></div>
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
@ -351,13 +351,13 @@
<a class="sourceLine" id="cb8-18" title="18"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
<a class="sourceLine" id="cb8-19" title="19"><span class="co">#&gt; Unit: milliseconds</span></a>
<a class="sourceLine" id="cb8-20" title="20"><span class="co">#&gt; expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb8-21" title="21"><span class="co">#&gt; en 14.98 15.35 19.58 15.51 15.58 56.78 10</span></a>
<a class="sourceLine" id="cb8-22" title="22"><span class="co">#&gt; de 27.26 27.69 28.05 27.85 28.01 29.44 10</span></a>
<a class="sourceLine" id="cb8-23" title="23"><span class="co">#&gt; nl 27.07 27.73 31.89 27.87 28.00 69.50 10</span></a>
<a class="sourceLine" id="cb8-24" title="24"><span class="co">#&gt; es 26.98 27.43 31.98 27.84 28.09 69.30 10</span></a>
<a class="sourceLine" id="cb8-25" title="25"><span class="co">#&gt; it 27.26 27.57 28.38 27.82 28.92 32.14 10</span></a>
<a class="sourceLine" id="cb8-26" title="26"><span class="co">#&gt; fr 27.21 27.72 36.16 27.83 28.19 70.08 10</span></a>
<a class="sourceLine" id="cb8-27" title="27"><span class="co">#&gt; pt 27.21 27.78 36.17 27.96 28.54 69.43 10</span></a></code></pre></div>
<a class="sourceLine" id="cb8-21" title="21"><span class="co">#&gt; en 14.97 15.49 15.46 15.56 15.60 15.65 10</span></a>
<a class="sourceLine" id="cb8-22" title="22"><span class="co">#&gt; de 27.29 27.76 44.46 27.98 69.27 71.00 10</span></a>
<a class="sourceLine" id="cb8-23" title="23"><span class="co">#&gt; nl 27.22 27.97 28.53 28.03 29.40 31.36 10</span></a>
<a class="sourceLine" id="cb8-24" title="24"><span class="co">#&gt; es 27.45 27.96 32.24 28.00 28.08 69.99 10</span></a>
<a class="sourceLine" id="cb8-25" title="25"><span class="co">#&gt; it 27.21 27.32 28.01 27.89 28.05 29.41 10</span></a>
<a class="sourceLine" id="cb8-26" title="26"><span class="co">#&gt; fr 27.26 27.94 28.16 28.12 28.25 29.47 10</span></a>
<a class="sourceLine" id="cb8-27" title="27"><span class="co">#&gt; pt 27.08 27.45 36.14 27.92 28.07 70.34 10</span></a></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>

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@ -311,9 +311,9 @@
<p>The <code>AMR</code> package basically does four important things:</p>
<ol>
<li>
<p>It <strong>cleanses existing data</strong> by providing new <em>classes</em> for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use artificial intelligence to guess results that you would expect:</p>
<p>It <strong>cleanses existing data</strong> by providing new <em>classes</em> for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:</p>
<ul>
<li>Use <code><a href="reference/as.mo.html">as.mo()</a></code> to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of <em>Klebsiella pneumoniae</em> is “B_KLBSL_PNE” (B stands for Bacteria) and the ID of <em>S. aureus</em> is “B_STPHY_AUR”. The function takes almost any text as input that looks like the name or code of a microorganism like “E. coli”, “esco” or “esccol” and tries to find expected results using artificial intelligence on the included Catalogue of Life data set, consisting of almost 60,000 microorganisms. It only takes milliseconds to find results, please see our <a href="./articles/benchmarks.html">benchmarks</a>. Moreover, it can group <em>Staphylococci</em> into coagulase negative and positive (CoNS and CoPS, see <a href="./reference/as.mo.html#source">source</a>) and can categorise <em>Streptococci</em> into Lancefield groups (like beta-haemolytic <em>Streptococcus</em> Group B, <a href="./reference/as.mo.html#source">source</a>).</li>
<li>Use <code><a href="reference/as.mo.html">as.mo()</a></code> to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of <em>Klebsiella pneumoniae</em> is “B_KLBSL_PNE” (B stands for Bacteria) and the ID of <em>S. aureus</em> is “B_STPHY_AUR”. The function takes almost any text as input that looks like the name or code of a microorganism like “E. coli”, “esco” or “esccol” and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set, consisting of almost 60,000 microorganisms. It only takes milliseconds to find results, please see our <a href="./articles/benchmarks.html">benchmarks</a>. Moreover, it can group <em>Staphylococci</em> into coagulase negative and positive (CoNS and CoPS, see <a href="./reference/as.mo.html#source">source</a>) and can categorise <em>Streptococci</em> into Lancefield groups (like beta-haemolytic <em>Streptococcus</em> Group B, <a href="./reference/as.mo.html#source">source</a>).</li>
<li>Use <code><a href="reference/as.rsi.html">as.rsi()</a></code> 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 “&lt;=0.002; S” (combined MIC/RSI) will result in “S”.</li>
<li>Use <code><a href="reference/as.mic.html">as.mic()</a></code> to cleanse your MIC values. It produces a so-called factor (called <em>ordinal</em> in SPSS) with valid MIC values as levels. A value like “&lt;=0.002; S” (combined MIC/RSI) will result in “&lt;=0.002”.</li>
<li>Use <code><a href="reference/as.atc.html">as.atc()</a></code> 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.</li>
@ -329,8 +329,8 @@
</ul>
</li>
<li>Use <code><a href="reference/mdro.html">mdro()</a></code> (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.</li>
<li>The <a href="./reference/microorganisms.html">data set <code>microorganisms</code></a> contains the complete taxonomic tree of almost 60,000 microorganisms. Furthermore, the colloquial name and Gram stain are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like <code><a href="reference/mo_property.html">mo_genus()</a></code>, <code><a href="reference/mo_property.html">mo_family()</a></code>, <code><a href="reference/mo_property.html">mo_gramstain()</a></code> or even <code><a href="reference/mo_property.html">mo_phylum()</a></code>. As they use <code><a href="reference/as.mo.html">as.mo()</a></code> internally, they also use artificial intelligence. For example, <code><a href="reference/mo_property.html">mo_genus("MRSA")</a></code> and <code><a href="reference/mo_property.html">mo_genus("S. aureus")</a></code> will both return <code>"Staphylococcus"</code>. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.</li>
<li>The <a href="./reference/antibiotics.html">data set <code>antibiotics</code></a> contains almost 500 antimicrobial drugs with their ATC code, EARS-Net code, common LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains hundreds of trade names. Use functions like <code><a href="reference/atc_property.html">atc_name()</a></code> and <code><a href="reference/atc_property.html">atc_tradenames()</a></code> to look up values. The <code>atc_*</code> functions use <code><a href="reference/as.atc.html">as.atc()</a></code> internally so they support AI to guess your expected result. For example, <code><a href="reference/atc_property.html">atc_name("Fluclox")</a></code>, <code><a href="reference/atc_property.html">atc_name("Floxapen")</a></code> and <code><a href="reference/atc_property.html">atc_name("J01CF05")</a></code> will all return <code>"Flucloxacillin"</code>. These functions can again be used to add new variables to your data.</li>
<li>The <a href="./reference/microorganisms.html">data set <code>microorganisms</code></a> contains the complete taxonomic tree of almost 60,000 microorganisms. Furthermore, the colloquial name and Gram stain are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like <code><a href="reference/mo_property.html">mo_genus()</a></code>, <code><a href="reference/mo_property.html">mo_family()</a></code>, <code><a href="reference/mo_property.html">mo_gramstain()</a></code> or even <code><a href="reference/mo_property.html">mo_phylum()</a></code>. As they use <code><a href="reference/as.mo.html">as.mo()</a></code> internally, they also use the same intelligent rules for determination. For example, <code><a href="reference/mo_property.html">mo_genus("MRSA")</a></code> and <code><a href="reference/mo_property.html">mo_genus("S. aureus")</a></code> will both return <code>"Staphylococcus"</code>. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.</li>
<li>The <a href="./reference/antibiotics.html">data set <code>antibiotics</code></a> contains almost 500 antimicrobial drugs with their ATC code, EARS-Net code, common LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains hundreds of trade names. Use functions like <code><a href="reference/atc_property.html">atc_name()</a></code> and <code><a href="reference/atc_property.html">atc_tradenames()</a></code> to look up values. The <code>atc_*</code> functions use <code><a href="reference/as.atc.html">as.atc()</a></code> internally so they support the same intelligent rules to guess the most probable result. For example, <code><a href="reference/atc_property.html">atc_name("Fluclox")</a></code>, <code><a href="reference/atc_property.html">atc_name("Floxapen")</a></code> and <code><a href="reference/atc_property.html">atc_name("J01CF05")</a></code> will all return <code>"Flucloxacillin"</code>. These functions can again be used to add new variables to your data.</li>
</ul>
</li>
<li>
@ -349,14 +349,14 @@
<li>Aside from this website with many tutorials, the package itself contains extensive help pages with many examples for all functions.</li>
<li>The package also contains example data sets:
<ul>
<li>The <a href=".reference/septic_patients.html"><code>septic_patients</code> data set</a>. This data set contains:
<li>The <a href="./reference/septic_patients.html"><code>septic_patients</code> data set</a>. This data set contains:
<ul>
<li>2,000 blood culture isolates from anonymised septic patients between 2001 and 2017 in the Northern Netherlands</li>
<li>Results of 40 antibiotics (each antibiotic in its own column) with a total ~40,000 antimicrobial results</li>
<li>Real and genuine data</li>
</ul>
</li>
<li>The <a href=".reference/WHONET.html"><code>WHONET</code> data set</a>. This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET <a href="./articles/WHONET.html">on its tutorial page</a>.</li>
<li>The <a href="./reference/WHONET.html"><code>WHONET</code> data set</a>. This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET <a href="./articles/WHONET.html">on its tutorial page</a>.</li>
</ul>
</li>
</ul>

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@ -611,7 +611,7 @@ These functions use <code><a href="../reference/as.atc.html">as.atc()</a></code>
<li>Function <code>is.rsi.eligible</code> to check for columns that have valid antimicrobial results, but do not have the <code>rsi</code> class yet. Transform the columns of your raw data with: <code>data %&gt;% mutate_if(is.rsi.eligible, as.rsi)</code>
</li>
<li>
<p>Functions <code>as.mo</code> and <code>is.mo</code> as replacements for <code>as.bactid</code> and <code>is.bactid</code> (since the <code>microoganisms</code> data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The <code>as.mo</code> function determines microbial IDs using Artificial Intelligence (AI):</p>
<p>Functions <code>as.mo</code> and <code>is.mo</code> as replacements for <code>as.bactid</code> and <code>is.bactid</code> (since the <code>microoganisms</code> data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The <code>as.mo</code> function determines microbial IDs using intelligent rules:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1"><span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"E. coli"</span>)</a>
<a class="sourceLine" id="cb14-2" title="2"><span class="co"># [1] B_ESCHR_COL</span></a>
<a class="sourceLine" id="cb14-3" title="3"><span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"MRSA"</span>)</a>

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@ -47,7 +47,7 @@
<script src="../extra.js"></script>
<meta property="og:title" content="Transform to microorganism ID — as.mo" />
<meta property="og:description" content="Use this function to determine a valid microorganism ID (mo). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (see Source), so the input can be almost anything: a full name (like &quot;Staphylococcus aureus&quot;), an abbreviated name (like &quot;S. aureus&quot;), an abbreviation known in the field (like &quot;MRSA&quot;), or just a genus. You could also select a genus and species column, zie Examples." />
<meta property="og:description" content="Use this function to determine a valid microorganism ID (mo). Determination is done using intelligent rules and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (see Source), so the input can be almost anything: a full name (like &quot;Staphylococcus aureus&quot;), an abbreviated name (like &quot;S. aureus&quot;), an abbreviation known in the field (like &quot;MRSA&quot;), or just a genus. You could also select a genus and species column, zie Examples." />
<meta property="og:image" content="https://msberends.gitlab.io/AMR/logo.png" />
<meta name="twitter:card" content="summary" />
@ -237,7 +237,7 @@
<div class="ref-description">
<p>Use this function to determine a valid microorganism ID (<code>mo</code>). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (see Source), so the input can be almost anything: a full name (like <code>"Staphylococcus aureus"</code>), an abbreviated name (like <code>"S. aureus"</code>), an abbreviation known in the field (like <code>"MRSA"</code>), or just a genus. You could also <code>select</code> a genus and species column, zie Examples.</p>
<p>Use this function to determine a valid microorganism ID (<code>mo</code>). Determination is done using intelligent rules and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (see Source), so the input can be almost anything: a full name (like <code>"Staphylococcus aureus"</code>), an abbreviated name (like <code>"S. aureus"</code>), an abbreviation known in the field (like <code>"MRSA"</code>), or just a genus. You could also <code>select</code> a genus and species column, zie Examples.</p>
</div>
@ -301,8 +301,8 @@
</pre>
<p>Values that cannot be coered will be considered 'unknown' and have an MO code <code>UNKNOWN</code>.</p>
<p>Use the <code><a href='mo_property.html'>mo_property</a></code> functions to get properties based on the returned code, see Examples.</p>
<p><strong>Artificial Intelligence</strong> <br />
This function uses Artificial Intelligence (AI) to help getting fast and logical results. It tries to find matches in this order:</p><ul>
<p><strong>Intelligent rules</strong> <br />
This function uses intelligent rules to help getting fast and logical results. It tries to find matches in this order:</p><ul>
<li><p>Taxonomic kingdom: it first searches in Bacteria, then Fungi, then Protozoa</p></li>
<li><p>Human pathogenic prevalence: it first searches in more prevalent microorganisms, then less prevalent ones (see section <em>Microbial prevalence of pathogens in humans</em>)</p></li>
<li><p>Valid MO codes and full names: it first searches in already valid MO code and known genus/species combinations</p></li>
@ -314,7 +314,7 @@ This function uses Artificial Intelligence (AI) to help getting fast and logical
<li><p>Something like <code>"stau"</code> or <code>"S aur"</code> will return the ID of <em>Staphylococcus aureus</em> and not <em>Staphylococcus auricularis</em></p></li>
</ul><p>This means that looking up human pathogenic microorganisms takes less time than looking up human non-pathogenic microorganisms.</p>
<p><strong>Uncertain results</strong> <br />
When using <code>allow_uncertain = TRUE</code> (which is the default setting), it will use additional rules if all previous AI rules failed to get valid results. These are:</p><ul>
When using <code>allow_uncertain = TRUE</code> (which is the default setting), it will use additional rules if all previous rules failed to get valid results. These are:</p><ul>
<li><p>It tries to look for previously accepted (but now invalid) taxonomic names</p></li>
<li><p>It strips off values between brackets and the brackets itself, and re-evaluates the input with all previous rules</p></li>
<li><p>It strips off words from the end one by one and re-evaluates the input with all previous rules</p></li>
@ -330,7 +330,7 @@ When using <code>allow_uncertain = TRUE</code> (which is the default setting), i
<p>Use <code>mo_uncertainties()</code> to get a data.frame with all values that were coerced to a valid value, but with uncertainty.</p>
<p>Use <code>mo_renamed()</code> to get a vector with all values that could be coerced based on an old, previously accepted taxonomic name.</p>
<p><strong>Microbial prevalence of pathogens in humans</strong> <br />
The artificial intelligence takes into account microbial prevalence of pathogens in humans. It uses three groups and every (sub)species is in the group it matches first. These groups are:</p><ul>
The intelligent rules takes into account microbial prevalence of pathogens in humans. It uses three groups and every (sub)species is in the group it matches first. These groups are:</p><ul>
<li><p>1 (most prevalent): class is Gammaproteobacteria <strong>or</strong> genus is one of: <em>Enterococcus</em>, <em>Staphylococcus</em>, <em>Streptococcus</em>.</p></li>
<li><p>2: phylum is one of: Proteobacteria, Firmicutes, Actinobacteria, Sarcomastigophora <strong>or</strong> genus is one of: <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>, <em>Ureaplasma</em>.</p></li>
<li><p>3 (least prevalent): all others.</p></li>

6
docs/reference/index.html
View File

@ -428,6 +428,12 @@
<td><p>Calculate resistance of isolates</p></td>
</tr><tr>
<td>
<p><code><a href="filter_ab_class.html">filter_ab_class()</a></code> <code><a href="filter_ab_class.html">filter_aminoglycosides()</a></code> <code><a href="filter_ab_class.html">filter_carbapenems()</a></code> <code><a href="filter_ab_class.html">filter_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_1st_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_2nd_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_3rd_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_4th_cephalosporins()</a></code> <code><a href="filter_ab_class.html">filter_fluoroquinolones()</a></code> <code><a href="filter_ab_class.html">filter_glycopeptides()</a></code> <code><a href="filter_ab_class.html">filter_macrolides()</a></code> <code><a href="filter_ab_class.html">filter_tetracyclines()</a></code> </p>
</td>
<td><p>Filter on antibiotic class</p></td>
</tr><tr>
<td>
<p><code><a href="freq.html">frequency_tbl()</a></code> <code><a href="freq.html">freq()</a></code> <code><a href="freq.html">top_freq()</a></code> <code><a href="freq.html">header()</a></code> <code><a href="freq.html">print(<i>&lt;frequency_tbl&gt;</i>)</a></code> </p>
</td>

12
index.md
View File

@ -130,9 +130,9 @@ Read our tutorial about [how to work with WHONET data here](./articles/WHONET.ht
The `AMR` package basically does four important things:
1. It **cleanses existing data** by providing new *classes* for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use artificial intelligence to guess results that you would expect:
1. It **cleanses existing data** by providing new *classes* for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:
* Use `as.mo()` to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" or "esccol" and tries to find expected results using artificial intelligence on the included Catalogue of Life data set, consisting of almost 60,000 microorganisms. It only takes milliseconds to find results, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
* Use `as.mo()` to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" or "esccol" and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set, consisting of almost 60,000 microorganisms. It only takes milliseconds to find results, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
* 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", "Furadantin", "nitro" all return the ATC code of Nitrofurantoine.
@ -143,8 +143,8 @@ The `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`](./reference/microorganisms.html) contains the complete taxonomic tree of almost 60,000 microorganisms. Furthermore, the colloquial name and Gram stain are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus()`, `mo_family()`, `mo_gramstain()` or even `mo_phylum()`. As they use `as.mo()` internally, they also use artificial intelligence. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
* The [data set `antibiotics`](./reference/antibiotics.html) contains almost 500 antimicrobial drugs with their ATC code, EARS-Net code, common LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains hundreds of trade names. Use functions like `atc_name()` and `atc_tradenames()` to look up values. The `atc_*` functions use `as.atc()` internally so they support AI to guess your expected result. For example, `atc_name("Fluclox")`, `atc_name("Floxapen")` and `atc_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
* The [data set `microorganisms`](./reference/microorganisms.html) contains the complete taxonomic tree of almost 60,000 microorganisms. Furthermore, the colloquial name and Gram stain are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus()`, `mo_family()`, `mo_gramstain()` or even `mo_phylum()`. As they use `as.mo()` internally, they also use the same intelligent rules for determination. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
* The [data set `antibiotics`](./reference/antibiotics.html) contains almost 500 antimicrobial drugs with their ATC code, EARS-Net code, common LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains hundreds of trade names. Use functions like `atc_name()` and `atc_tradenames()` to look up values. The `atc_*` functions use `as.atc()` internally so they support the same intelligent rules to guess the most probable result. For example, `atc_name("Fluclox")`, `atc_name("Floxapen")` and `atc_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
3. It **analyses the data** with convenient functions that use well-known methods.
@ -157,11 +157,11 @@ The `AMR` package basically does four important things:
* Aside from this website with many tutorials, the package itself contains extensive help pages with many examples for all functions.
* The package also contains example data sets:
* The [`septic_patients` data set](.reference/septic_patients.html). This data set contains:
* The [`septic_patients` data set](./reference/septic_patients.html). This data set contains:
* 2,000 blood culture isolates from anonymised septic patients between 2001 and 2017 in the Northern Netherlands
* Results of 40 antibiotics (each antibiotic in its own column) with a total ~40,000 antimicrobial results
* Real and genuine data
* The [`WHONET` data set](.reference/WHONET.html). This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET [on its tutorial page](./articles/WHONET.html).
* The [`WHONET` data set](./reference/WHONET.html). This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET [on its tutorial page](./articles/WHONET.html).
#### Partners

10
man/as.mo.Rd
View File

@ -39,7 +39,7 @@ mo_renamed()
Character (vector) with class \code{"mo"}. Unknown values will return \code{NA}.
}
\description{
Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (see Source), so the input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using intelligent rules and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (see Source), so the input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
}
\details{
A microbial ID from this package (class: \code{mo}) typically looks like these examples:\cr
@ -62,8 +62,8 @@ Values that cannot be coered will be considered 'unknown' and have an MO code \c
Use the \code{\link{mo_property}} functions to get properties based on the returned code, see Examples.
\strong{Artificial Intelligence} \cr
This function uses Artificial Intelligence (AI) to help getting fast and logical results. It tries to find matches in this order:
\strong{Intelligent rules} \cr
This function uses intelligent rules to help getting fast and logical results. It tries to find matches in this order:
\itemize{
\item{Taxonomic kingdom: it first searches in Bacteria, then Fungi, then Protozoa}
\item{Human pathogenic prevalence: it first searches in more prevalent microorganisms, then less prevalent ones (see section \emph{Microbial prevalence of pathogens in humans})}
@ -80,7 +80,7 @@ A couple of effects because of these rules:
This means that looking up human pathogenic microorganisms takes less time than looking up human non-pathogenic microorganisms.
\strong{Uncertain results} \cr
When using \code{allow_uncertain = TRUE} (which is the default setting), it will use additional rules if all previous AI rules failed to get valid results. These are:
When using \code{allow_uncertain = TRUE} (which is the default setting), it will use additional rules if all previous rules failed to get valid results. These are:
\itemize{
\item{It tries to look for previously accepted (but now invalid) taxonomic names}
\item{It strips off values between brackets and the brackets itself, and re-evaluates the input with all previous rules}
@ -103,7 +103,7 @@ Use \code{mo_uncertainties()} to get a data.frame with all values that were coer
Use \code{mo_renamed()} to get a vector with all values that could be coerced based on an old, previously accepted taxonomic name.
\strong{Microbial prevalence of pathogens in humans} \cr
The artificial intelligence takes into account microbial prevalence of pathogens in humans. It uses three groups and every (sub)species is in the group it matches first. These groups are:
The intelligent rules takes into account microbial prevalence of pathogens in humans. It uses three groups and every (sub)species is in the group it matches first. These groups are:
\itemize{
\item{1 (most prevalent): class is Gammaproteobacteria \strong{or} genus is one of: \emph{Enterococcus}, \emph{Staphylococcus}, \emph{Streptococcus}.}
\item{2: phylum is one of: Proteobacteria, Firmicutes, Actinobacteria, Sarcomastigophora \strong{or} genus is one of: \emph{Aspergillus}, \emph{Bacteroides}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Chryseobacterium}, \emph{Cryptococcus}, \emph{Elisabethkingia}, \emph{Flavobacterium}, \emph{Fusobacterium}, \emph{Giardia}, \emph{Leptotrichia}, \emph{Mycoplasma}, \emph{Prevotella}, \emph{Rhodotorula}, \emph{Treponema}, \emph{Trichophyton}, \emph{Ureaplasma}.}

34
vignettes/SPSS.Rmd
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@ -1,7 +1,7 @@
---
title: "How to import data from SPSS / SAS / Stata"
author: "Matthijs S. Berends"
date: "14 February 2019"
date: '`r format(Sys.Date(), "%d %B %Y")`'
output:
rmarkdown::html_vignette:
toc: true
@ -20,6 +20,7 @@ knitr::opts_chunk$set(
)
# set to original language (English)
Sys.setlocale(locale = "C")
library(AMR)
```
## SPSS / SAS / Stata
@ -34,7 +35,7 @@ As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major
The [official R network (CRAN)](https://cran.r-project.org/web/packages/) features almost 14,000 packages at the time of writing, our `AMR` package being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitLab or GitHub. So there may even be a lot more than 14,000 packages out there.
Bottomline is, you can really extend it yourself or ask somebody to do this for you. Take for example our `AMR` package. SPSS, SAS and Stata will never know what a valid MIC value is (so data might not be clean) or what the Gram stain of *E. coli* is. Or the fact that all species of *Klebiella* are resistant to amoxicillin.
Bottom line is, you can really extend it yourself or ask somebody to do this for you. Take for example our `AMR` package. Among other things, it adds reliable reference data to R to help you with the data cleaning and analysis. SPSS, SAS and Stata will never know what a valid MIC value is or what the Gram stain of *E. coli* is. Or that all species of *Klebiella* are resistant to amoxicillin and that Floxapen^&reg;^ is a trade name of flucloxacillin. These facts and properties are often needed to clean existing data, which would be very inconvenient in a software package without reliable reference data. See below for a demonstration.
* **R is extremely flexible.**
@ -65,20 +66,43 @@ As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major
If you sometimes write syntaxes in SPSS to run a complete analysis or to 'automate' some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS.
To demonstrate the first point:
```{r, warning = FALSE, message = FALSE}
# not all values are valid MIC values:
as.mic(0.125)
as.mic("testvalue")
# the Gram stain is avaiable for all bacteria:
mo_gramstain("E. coli")
# Klebsiella is intrinsic resistant to amoxicllin, according to EUCAST:
klebsiella_test <- data.frame(mo = "klebsiella",
amox = "S",
stringsAsFactors = FALSE)
klebsiella_test
eucast_rules(klebsiella_test, info = FALSE)
# hundreds of trade names can be translated to an ATC or name:
atc_name("floxapen")
as.atc("floxapen")
atc_tradenames("floxapen")
```
## Import data from SPSS/SAS/Stata
### RStudio
To work with R, probably the best option is to use [RStudio](https://www.rstudio.com/products/rstudio/). It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menu to work with other data sources. You can also run [RStudio Server](https://www.rstudio.com/products/rstudio/), which is nothing less than the complete RStudio software available as a website (e.g. in your corporate network or at home).
To work with R, probably the best option is to use [RStudio](https://www.rstudio.com/products/rstudio/). It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menus to work with other data sources. You can also run [RStudio Server](https://www.rstudio.com/products/rstudio/), which is nothing less than the complete RStudio software available as a website (e.g. in your corporate network or at home).
To import a data file, just click *Import Dataset* in the Environment tab:
![](../import1.png)
![](https://gitlab.com/msberends/AMR/raw/master/docs/import1.png)
If additional packages are needed, RStudio will ask you if they should be installed on beforehand.
In the the window that opens, you can define all options (parameters) that should be used for import and you're ready to go:
![](../import2.png)
![](https://gitlab.com/msberends/AMR/raw/master/docs/import2.png)
If you want named variables to be imported as factors so it resembles SPSS more, use `as_factor()`.

4
vignettes/benchmarks.Rmd
View File

@ -23,7 +23,7 @@ knitr::opts_chunk$set(
)
```
One of the most important features of this package is the complete microbial taxonomic database, supplied by the [Catalogue of Life](http://catalogueoflife.org). We created a function `as.mo()` that transforms any user input value to a valid microbial ID by using AI (Artificial Intelligence) combined with the taxonomic tree of Catalogue of Life.
One of the most important features of this package is the complete microbial taxonomic database, supplied by the [Catalogue of Life](http://catalogueoflife.org). We created a function `as.mo()` that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.
Using the `microbenchmark` package, we can review the calculation performance of this function. Its function `microbenchmark()` runs different input expressions independently of each other and measures their time-to-result.
@ -38,7 +38,7 @@ library(AMR)
In the next test, we try to 'coerce' different input values for *Staphylococcus aureus*. The actual result is the same every time: it returns its MO code `B_STPHY_AUR` (*B* stands for *Bacteria*, the taxonomic kingdom).
But the calculation time differs a lot. Here, the AI effect can be reviewed best:
But the calculation time differs a lot:
```{r}
S.aureus <- microbenchmark(as.mo("sau"),