* Data set `intrinsic_resistant`. This data set contains all bug-drug combinations where the 'bug' is intrinsic resistant to the 'drug' according to the latest EUCAST insights. It contains just two columns: `microorganism` and `antibiotic`.
<p>The <ahref="https://cran.r-project.org/">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 GitHub. So there may even be a lot more than 14,000 packages out there.</p>
<p>The <ahref="https://cran.r-project.org/">official R network (CRAN)</a> features more than 16,000 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 GitHub. So there may even be a lot more than 14,000 packages out there.</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>
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<li>
@ -221,7 +228,7 @@
@@ -221,7 +228,7 @@
</li>
<li>
<p><strong>R has a huge community.</strong></p>
<p>Many R users just ask questions on websites like <ahref="https://stackoverflow.com">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, more than <ahref="https://stackoverflow.com/questions/tagged/r?sort=votes">300,000 R-related questions</a> have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
<p>Many R users just ask questions on websites like <ahref="https://stackoverflow.com">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, more than <ahref="https://stackoverflow.com/questions/tagged/r?sort=votes">360,000 R-related questions</a> have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
</li>
<li>
<p><strong>R understands any data type, including SPSS/SAS/Stata.</strong></p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
<p>Looking up arbitrary codes of less prevalent microorganisms costs the most time. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
<p>Uncommon microorganisms take some more time than common microorganisms. To further improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
<p>To improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
<p>Repetitive results are unique values that are present more than once. Unique values will only be calculated once by <code><ahref="../reference/as.mo.html">as.mo()</a></code>. We will use <code><ahref="../reference/mo_property.html">mo_name()</a></code> for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><ahref="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<p>What about precalculated results? If the input is an already precalculated result of a helper function like <code><ahref="../reference/mo_property.html">mo_name()</a></code>, it almost doesn’t take any time at all (see ‘C’ below):</p>
B = <spanclass="fu"><ahref="../reference/mo_property.html">mo_name</a></span>(<spanclass="st">"S. aureus"</span>),
C = <spanclass="fu"><ahref="../reference/mo_property.html">mo_name</a></span>(<spanclass="st">"Staphylococcus aureus"</span>),
times = <spanclass="fl">10</span>)
<spanclass="fu"><ahref="https://rdrr.io/r/base/print.html">print</a></span>(<spanclass="kw">run_it</span>, unit = <spanclass="st">"ms"</span>, signif = <spanclass="fl">3</span>)
<spanclass="co"># Unit: milliseconds</span>
<spanclass="co"># expr min lq mean median uq max neval</span>
<spanclass="co"># A 5.90 6.16 6.93 7.24 7.43 7.89 10</span>
<spanclass="co"># B 11.40 12.00 16.20 13.20 14.20 45.20 10</span>
<spanclass="co"># C 1.05 1.07 1.18 1.13 1.28 1.40 10</span>
<spanclass="co"># expr min lq mean median uq max neval</span>
<spanclass="co"># A 5.080 5.220 5.81 5.66 6.46 7.16 10</span>
<spanclass="co"># B 10.000 10.200 14.40 10.60 11.30 49.00 10</span>
<spanclass="co"># C 0.862 0.875 1.04 1.05 1.14 1.40 10</span>
</pre></div>
<p>So going from <code><ahref="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0011 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>
<p>So going from <code><ahref="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.001 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>
B = <spanclass="fu"><ahref="../reference/mo_property.html">mo_genus</a></span>(<spanclass="st">"Staphylococcus"</span>),
C = <spanclass="fu"><ahref="../reference/mo_property.html">mo_name</a></span>(<spanclass="st">"Staphylococcus aureus"</span>),
@ -332,15 +311,15 @@
@@ -332,15 +311,15 @@
times = <spanclass="fl">10</span>)
<spanclass="fu"><ahref="https://rdrr.io/r/base/print.html">print</a></span>(<spanclass="kw">run_it</span>, unit = <spanclass="st">"ms"</span>, signif = <spanclass="fl">3</span>)
<spanclass="co"># Unit: milliseconds</span>
<spanclass="co"># expr min lq mean median uq max neval</span>
<spanclass="co"># A 0.976 0.993 1.13 1.07 1.29 1.34 10</span>
<spanclass="co"># B 1.000 1.060 1.13 1.07 1.15 1.40 10</span>
<spanclass="co"># C 0.927 1.030 1.08 1.06 1.11 1.35 10</span>
<spanclass="co"># D 0.896 0.983 1.11 1.08 1.27 1.41 10</span>
<spanclass="co"># E 0.866 1.020 1.10 1.07 1.23 1.37 10</span>
<spanclass="co"># F 0.967 0.993 1.16 1.05 1.32 1.61 10</span>
<spanclass="co"># G 0.855 1.020 1.07 1.07 1.17 1.31 10</span>
<spanclass="co"># H 0.966 1.010 1.12 1.06 1.19 1.36 10</span>
<spanclass="co"># expr min lq mean median uq max neval</span>
<spanclass="co"># A 0.869 0.889 0.951 0.904 1.010 1.19 10</span>
<spanclass="co"># B 0.837 0.873 0.977 0.937 1.010 1.36 10</span>
<spanclass="co"># C 0.869 0.874 1.020 0.921 1.130 1.40 10</span>
<spanclass="co"># D 0.829 0.858 0.898 0.862 0.873 1.21 10</span>
<spanclass="co"># E 0.862 0.870 0.983 0.918 1.050 1.36 10</span>
<spanclass="co"># F 0.841 0.850 0.915 0.867 0.907 1.24 10</span>
<spanclass="co"># G 0.842 0.851 0.940 0.898 1.000 1.16 10</span>
<spanclass="co"># H 0.854 0.864 1.030 0.920 1.170 1.60 10</span>
</pre></div>
<p>Of course, when running <code><ahref="../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> anyway, 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>
@ -348,7 +327,7 @@
@@ -348,7 +327,7 @@
<h3class="hasAnchor">
<ahref="#results-in-other-languages"class="anchor"></a>Results in other languages</h3>
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost does’t take extra time:</p>
<spanclass="fu"><ahref="../reference/mo_property.html">mo_name</a></span>(<spanclass="st">"CoNS"</span>, language = <spanclass="st">"en"</span>) <spanclass="co"># or just mo_name("CoNS") on an English system</span>
<p>This package contains a lot of reference data sets that are all reliable, up-to-date and free to download. You can even use them outside of R, for example to train your laboratory information system (LIS) about intrinsic resistance!</p>
<p>We included them in our <code>AMR</code> package, but also automatically ‘mirror’ them to our public repository in different software formats. On this page, we explain how to download them and how the structure of the data sets look like. The tab separated files <strong>allow for machine reading taxonomic data and EUCAST and CLSI interpretation guidelines</strong>, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI.</p>
<p><em>Note: Years and dates of updates mentioned on this page, are from on <code>AMR</code> package version 1.3.0.9004, online released on 16 August 2020. <strong>If you are reading this page from within R, please <ahref="https://msberends.github.io/AMR/articles/datasets.html">visit our website</a> for the latest update.</strong></em></p>
<divid="microorganisms"class="section level2">
<p>This package contains a lot of reference data sets that are all reliable, up-to-date and free to download. You can even use them outside of R, for example to teach your laboratory information system (LIS) about intrinsic resistance!</p>
<p>We included them in our <code>AMR</code> package, but also automatically ‘mirror’ them to our public repository in different software formats. On this page, we explain how to download them and how the structure of the data sets look like. The tab separated files <strong>allow for machine reading taxonomic data and EUCAST and CLSI interpretation guidelines</strong>, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. We also offer all data sets in formats for R, SPSS, SAS, Stata and Excel.</p>
<p><em>Note: Years and dates of updates mentioned on this page, are from on <code>AMR</code> package version 1.3.0.9005, online released on 17 August 2020. <strong>If you are reading this page from within R, please <ahref="https://msberends.github.io/AMR/articles/datasets.html">visit our website</a> for the latest update.</strong></em></p>
<p>A data set with 67,151 rows and 16 columns, containing the following column names:<br><em>mo, fullname, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, species_id, source, prevalence, snomed</em>.</p>
<p>Included (sub)species per taxonomic kingdom:</p>
<p>Download the data set preferably in the software you use, so the data file already has the correct data structure. Below files were updated on 28 July 2020 20:52:40 CEST.</p>
<p>A data set with 12,708 rows and 4 columns, containing the following column names:<br><em>fullname, fullname_new, ref, prevalence</em>.</p>
<p><strong>Note:</strong> remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e.of column <em>fullname</em>. For the scientific reference of the new names, i.e.of column <em>fullname_new</em>, see the <code>microorganisms</code> data set.</p>
<p>Example rows when filtering on <em>Escherichia</em>:</p>
<p>This data set contains all EARS-Net and ATC codes gathered from WHO and WHONET, and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.</p>
<p>Download the data set preferably in the software you use, so the data file already has the correct data structure. Below files were updated on 31 July 2020 12:12:13 CEST.</p>
<p>A data set with 456 rows and 14 columns, containing the following column names:<br><em>ab, atc, cid, name, group, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc</em>.</p>
<p>This data set contains all ATC codes gathered from WHO and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.</p>
<ul>
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@ -610,28 +633,10 @@
@@ -610,28 +633,10 @@
<li><ahref="https://pubchem.ncbi.nlm.nih.gov">PubChem by the US National Library of Medicine</a></li>
<p>Download the data set preferably in the software you use, so the data file already has the correct data structure. Below files were updated on 23 November 2019 19:03:43 CET.</p>
<p>A data set with 102 rows and 9 columns, containing the following column names:<br><em>atc, cid, name, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units</em>.</p>
<p>Download the data set preferably in the software you use, so the data file already has the correct data structure. Below files were updated on 14 August 2020 14:18:20 CEST.</p>
<p>This data set contains interpretation rules for MIC values and disk diffusion diameters. Included guidelines are CLSI (2010-2019) and EUCAST (2011-2020).</p>
<p>Download the data set preferably in the software you use, so the data file already has the correct data structure. Below files were updated on 29 July 2020 13:12:34 CEST.</p>
<p>A data set with 18,650 rows and 10 columns, containing the following column names:<br><em>guideline, method, site, mo, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti</em>.</p>
