<p>Function <code><ahref="../reference/portion.html">rsi_df()</a></code> to transform a <code>data.frame</code> to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combinations of existing functions <code><ahref="../reference/count.html">count_df()</a></code> and <code><ahref="../reference/portion.html">portion_df()</a></code> to immediately show resistance percentages and number of available isolates:</p>
<li><p>Function <code><ahref="../reference/mo_property.html">mo_info()</a></code> as an analogy to <code><ahref="../reference/ab_property.html">ab_info()</a></code>. The <code><ahref="../reference/mo_property.html">mo_info()</a></code> prints a list with the full taxonomy, authors, and the URL to the online database of a microorganism</p></li>
</ul>
@ -254,6 +265,7 @@
<h4class="hasAnchor">
<ahref="#changed"class="anchor"></a>Changed</h4>
<ul>
<li>Column names of output <code><ahref="../reference/count.html">count_df()</a></code> and <code><ahref="../reference/portion.html">portion_df()</a></code> are now lowercase</li>
<li>Fixed bug in translation of microorganism names</li>
<li>Fixed bug in determining taxonomic kingdoms</li>
<li>Algorithm improvements for <code><ahref="../reference/as.ab.html">as.ab()</a></code> and <code><ahref="../reference/as.mo.html">as.mo()</a></code> to understand even more severely misspelled input</li>
@ -270,6 +282,7 @@
</li>
<li>Fixed bug where not all old taxonomic named would not be printed when using a vector as input for <code><ahref="../reference/as.mo.html">as.mo()</a></code>
</li>
<li>Manually added <em>Trichomonas vaginalis</em> from the kingdom of Protozoa, which is missing from the Catalogue of Life</li>
<p>The <code>antibiotics</code> data set will be searched, after which the input data will be checked for column names with a value in any abbreviations, codes or official names found in the <code>antibiotics</code> data set. For example:</p>
<aclass="sourceLine"id="cb4-4"title="4"><spanclass="co"># Filtering on glycopeptide antibacterials: all of `vanc` and `teic` is R</span></a></code></pre></div>
<aclass="sourceLine"id="cb5-4"title="4"><spanclass="co"># Filtering on glycopeptide antibacterials: all of `vanc` and `teic` is R</span></a></code></pre></div>
</li>
<li>
<p>All <code>ab_*</code> functions are deprecated and replaced by <code>atc_*</code> functions:</p>
These functions use <code><ahref="../reference/as.atc.html">as.atc()</a></code> internally. The old <code>atc_property</code> has been renamed <code><ahref="../reference/atc_online.html">atc_online_property()</a></code>. This is done for two reasons: firstly, not all ATC codes are of antibiotics (ab) but can also be of antivirals or antifungals. Secondly, the input must have class <code>atc</code> or must be coerable to this class. Properties of these classes should start with the same class name, analogous to <code><ahref="../reference/as.mo.html">as.mo()</a></code> and e.g.<code>mo_genus</code>.</li>
<li>New functions <code><ahref="../reference/mo_source.html">set_mo_source()</a></code> and <code><ahref="../reference/mo_source.html">get_mo_source()</a></code> to use your own predefined MO codes as input for <code><ahref="../reference/as.mo.html">as.mo()</a></code> and consequently all <code>mo_*</code> functions</li>
<li>Support for the upcoming <ahref="https://dplyr.tidyverse.org"><code>dplyr</code></a> version 0.8.0</li>
@ -467,20 +480,20 @@ These functions use <code><a href="../reference/as.atc.html">as.atc()</a></code>
<li>New function <code><ahref="../reference/age_groups.html">age_groups()</a></code> to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.</li>
<li>
<p>New function <code><ahref="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> as well as the base R <code><ahref="https://www.rdocumentation.org/packages/graphics/topics/plot">plot()</a></code> function can now be used for resistance prediction calculated with <code><ahref="../reference/resistance_predict.html">resistance_predict()</a></code>:</p>
<p>Functions <code><ahref="../reference/first_isolate.html">filter_first_isolate()</a></code> and <code><ahref="../reference/first_isolate.html">filter_first_weighted_isolate()</a></code> to shorten and fasten filtering on data sets with antimicrobial results, e.g.:</p>
<li>New function <code><ahref="../reference/availability.html">availability()</a></code> to check the number of available (non-empty) results in a <code>data.frame</code>
</li>
@ -509,33 +522,33 @@ These functions use <code><a href="../reference/as.atc.html">as.atc()</a></code>
<ul>
<li>
<p>Now handles incorrect spelling, like <code>i</code> instead of <code>y</code> and <code>f</code> instead of <code>ph</code>:</p>
<p>Uncertainty of the algorithm is now divided into four levels, 0 to 3, where the default <code>allow_uncertain = TRUE</code> is equal to uncertainty level 2. Run <code><ahref="../reference/as.mo.html">?as.mo</a></code> for more info about these levels.</p>
Using <code><ahref="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a></code> could lead to very unreliable results.</li>
<li>Implemented the latest publication of Becker <em>et al.</em> (2019), for categorising coagulase-negative <em>Staphylococci</em>
</li>
<li>All microbial IDs that found are now saved to a local file <code>~/.Rhistory_mo</code>. Use the new function <code>clean_mo_history()</code> to delete this file, which resets the algorithms.</li>
<li>
<p>Incoercible results will now be considered ‘unknown’, MO code <code>UNKNOWN</code>. On foreign systems, properties of these will be translated to all languages already previously supported: German, Dutch, French, Italian, Spanish and Portuguese:</p>
<aclass="sourceLine"id="cb11-3"title="3"><spanclass="co"># one unique value (^= 100.0%) could not be coerced and is considered 'unknown': "qwerty". Use mo_failures() to review it.</span></a>
<aclass="sourceLine"id="cb12-3"title="3"><spanclass="co"># one unique value (^= 100.0%) could not be coerced and is considered 'unknown': "qwerty". Use mo_failures() to review it.</span></a>
<li>Fix for vector containing only empty values</li>
<li>Finds better results when input is in other languages</li>
@ -581,19 +594,19 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<ul>
<li>
<p>Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:</p>
<divclass="sourceCode"id="cb12"><preclass="sourceCode r"><codeclass="sourceCode r"><aclass="sourceLine"id="cb12-1"title="1"><spanclass="co"># Determine genus of microorganisms (mo) in `septic_patients` data set:</span></a>
<aclass="sourceLine"id="cb12-2"title="2"><spanclass="co"># OLD WAY</span></a>
<divclass="sourceCode"id="cb13"><preclass="sourceCode r"><codeclass="sourceCode r"><aclass="sourceLine"id="cb13-1"title="1"><spanclass="co"># Determine genus of microorganisms (mo) in `septic_patients` data set:</span></a>
<aclass="sourceLine"id="cb13-2"title="2"><spanclass="co"># OLD WAY</span></a>
<li>Added parameter <code>combine_IR</code> (TRUE/FALSE) to functions <code>portion_df</code> and <code>count_df</code>, to indicate that all values of I and R must be merged into one, so the output only consists of S vs.IR (susceptible vs.non-susceptible)</li>
<li>Fix for <code>portion_*(..., as_percent = TRUE)</code> when minimal number of isolates would not be met</li>
@ -684,15 +697,15 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<aclass="sourceLine"id="cb15-3"title="3"><spanclass="st"></span><spanclass="kw">select</span>(<spanclass="op">-</span>count, <spanclass="op">-</span>cum_count) <spanclass="co"># only get item, percent, cum_percent</span></a></code></pre></div>
<aclass="sourceLine"id="cb16-3"title="3"><spanclass="st"></span><spanclass="kw">select</span>(<spanclass="op">-</span>count, <spanclass="op">-</span>cum_count) <spanclass="co"># only get item, percent, cum_percent</span></a></code></pre></div>
</li>
<li>Check for <code><ahref="https://www.rdocumentation.org/packages/hms/topics/hms">hms::is.hms</a></code>
</li>
@ -772,18 +785,18 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
</li>
</ul>
<p>They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:</p>
<li>Functions <code>count_R</code>, <code>count_IR</code>, <code>count_I</code>, <code>count_SI</code> and <code>count_S</code> to selectively count resistant or susceptible isolates
<ul>
@ -794,18 +807,18 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
</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 intelligent rules:</p>
<li>Added parameter <code>reference_df</code> for <code>as.mo</code>, so users can supply their own microbial IDs, name or codes as a reference table</li>
<li>Renamed all previous references to <code>bactid</code> to <code>mo</code>, like:
@ -833,12 +846,12 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<li>Added three antimicrobial agents to the <code>antibiotics</code> data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)</li>
<li>
<p>Added 163 trade names to the <code>antibiotics</code> data set, it now contains 298 different trade names in total, e.g.:</p>
<li>For <code>first_isolate</code>, rows will be ignored when there’s no species available</li>
<li>Function <code>ratio</code> is now deprecated and will be removed in a future release, as it is not really the scope of this package</li>
@ -849,13 +862,13 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
</li>
<li>
<p>Support for quasiquotation in the functions series <code>count_*</code> and <code>portions_*</code>, and <code>n_rsi</code>. This allows to check for more than 2 vectors or columns.</p>
<li>Edited <code>ggplot_rsi</code> and <code>geom_rsi</code> so they can cope with <code>count_df</code>. The new <code>fun</code> parameter has value <code>portion_df</code> at default, but can be set to <code>count_df</code>.</li>
<li>Fix for <code>ggplot_rsi</code> when the <code>ggplot2</code> package was not loaded</li>
@ -869,12 +882,12 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
</li>
<li>
<p>Support for types (classes) list and matrix for <code>freq</code></p>
<p>These functions are meant to count isolates. Use the <code><ahref='portion.html'>portion</a>_*</code> functions to calculate microbial resistance.</p>
<p><code>n_rsi</code> is an alias of <code>count_all</code>. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to <code><ahref='https://dplyr.tidyverse.org/reference/n_distinct.html'>n_distinct</a></code>. Their function is equal to <code>count_S(...) + count_IR(...)</code>.</p>
<p><code>count_df</code> takes any variable from <code>data</code> that has an <code>"rsi"</code> class (created with <code><ahref='as.rsi.html'>as.rsi</a></code>) and counts the amounts of R, I and S. The resulting <em>tidy data</em> (see Source) <code>data.frame</code> will have three rows (S/I/R) and a column for each variable with class <code>"rsi"</code>.</p>
<p>The function <code>n_rsi</code> is an alias of <code>count_all</code>. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to <code><ahref='https://dplyr.tidyverse.org/reference/n_distinct.html'>n_distinct</a></code>. Their function is equal to <code>count_S(...) + count_IR(...)</code>.</p>
<p>The function <code>count_df</code> takes any variable from <code>data</code> that has an <code>"rsi"</code> class (created with <code><ahref='as.rsi.html'>as.rsi</a></code>) and counts the amounts of S, I and R. The resulting <em>tidy data</em> (see Source) <code>data.frame</code> will have three rows (S/I/R) and a column for each variable with class <code>"rsi"</code>.</p>
<p>The function <code>rsi_df</code> works exactly like <code>count_df</code>, but add the percentage of S, I and R.</p>
<h2class="hasAnchor"id="interpretation-of-s-i-and-r"><aclass="anchor"href="#interpretation-of-s-i-and-r"></a>Interpretation of S, I and R</h2>
<td><p>variable to show on x axis, either <code>"Antibiotic"</code> (default) or <code>"Interpretation"</code> or a grouping variable</p></td>
<td><p>variable to show on x axis, either <code>"antibiotic"</code> (default) or <code>"interpretation"</code> or a grouping variable</p></td>
</tr>
<tr>
<th>fill</th>
<td><p>variable to categorise using the plots legend, either <code>"Antibiotic"</code> (default) or <code>"Interpretation"</code> or a grouping variable</p></td>
<td><p>variable to categorise using the plots legend, either <code>"antibiotic"</code> (default) or <code>"interpretation"</code> or a grouping variable</p></td>
</tr>
<tr>
<th>facet</th>
<td><p>variable to split plots by, either <code>"Interpretation"</code> (default) or <code>"Antibiotic"</code> or a grouping variable</p></td>
<td><p>variable to split plots by, either <code>"interpretation"</code> (default) or <code>"antibiotic"</code> or a grouping variable</p></td>
<p>A <code><ahref='https://www.rdocumentation.org/packages/base/topics/data.frame'>data.frame</a></code> with 67,903 observations and 16 variables:</p><dlclass='dl-horizontal'>
<p>A <code><ahref='https://www.rdocumentation.org/packages/base/topics/data.frame'>data.frame</a></code> with 67,906 observations and 16 variables:</p><dlclass='dl-horizontal'>
<dt><code>mo</code></dt><dd><p>ID of microorganism as used by this package</p></dd>
<dt><code>col_id</code></dt><dd><p>Catalogue of Life ID</p></dd>
<dt><code>fullname</code></dt><dd><p>Full name, like <code>"Escherichia coli"</code></p></dd>
@ -323,7 +320,8 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
<p><strong>Remember that you should filter your table to let it contain only first isolates!</strong> Use <code><ahref='first_isolate.html'>first_isolate</a></code> to determine them in your data set.</p>
<p>These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the <code><ahref='count.html'>count</a></code> functions to count isolates. <em>Low counts can infuence the outcome - these <code>portion</code> functions may camouflage this, since they only return the portion albeit being dependent on the <code>minimum</code> parameter.</em></p>
<p><code>portion_df</code> takes any variable from <code>data</code> that has an <code>"rsi"</code> class (created with <code><ahref='as.rsi.html'>as.rsi</a></code>) and calculates the portions R, I and S. The resulting <em>tidy data</em> (see Source) <code>data.frame</code> will have three rows (S/I/R) and a column for each variable with class <code>"rsi"</code>.
<p>The function <code>portion_df</code> takes any variable from <code>data</code> that has an <code>"rsi"</code> class (created with <code><ahref='as.rsi.html'>as.rsi</a></code>) and calculates the portions R, I and S. The resulting <em>tidy data</em> (see Source) <code>data.frame</code> will have three rows (S/I/R) and a column for each group and each variable with class <code>"rsi"</code>.</p>
<p>The function <code>rsi_df</code> works exactly like <code>portion_df</code>, but add the number of isolates.
<br/><br/>
To calculate the probability (<em>p</em>) of susceptibility of one antibiotic, we use this formula:
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