<ahref="#needed-r-packages"class="anchor"></a>Needed R packages</h2>
<p>As with many uses in R, we need some additional packages for AMR analysis. The most important one is <ahref="https://dplyr.tidyverse.org/"><code>dplyr</code></a>, which tremendously improves the way we work with data - it allows for a very natural way of writing syntaxes in R. Another important dependency is <ahref="https://ggplot2.tidyverse.org/"><code>ggplot2</code></a>. This package can be used to create beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<divclass="sourceCode"id="cb1"><preclass="sourceCode r"><codeclass="sourceCode r"><aclass="sourceLine"id="cb1-1"data-line-number="1"><spanclass="kw"><ahref="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr) <spanclass="co"># the data science package</span></a>
<aclass="sourceLine"id="cb1-2"data-line-number="2"><spanclass="kw"><ahref="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR) <spanclass="co"># this package, to simplify and automate AMR analysis</span></a>
<aclass="sourceLine"id="cb1-3"data-line-number="3"><spanclass="kw"><ahref="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2) <spanclass="co"># for appealing plots</span></a></code></pre></div>
<divclass="sourceCode"><preclass="sourceCode r"><codeclass="sourceCode r"><spanclass="kw"><ahref="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr) <spanclass="co"># the data science package</span>
<spanclass="kw"><ahref="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR) <spanclass="co"># this package, to simplify and automate AMR analysis</span>
<spanclass="kw"><ahref="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2) <spanclass="co"># for appealing plots</span></code></pre></div>
</div>
<divid="creation-of-data"class="section level2">
<h2class="hasAnchor">
<ahref="#creation-of-data"class="anchor"></a>Creation of data</h2>
<p>We will create some fake example data to use for analysis. For antimicrobial resistance analysis, we need at least: a patients ID, name or code of a microorganism, a date and antimicrobial results (an antibiogram). It could also include a specimen type (e.g.to filter on blood or urine), the ward type (e.g.to filter on ICUs).</p>
<p>We will create some fake example data to use for analysis. For antimicrobial resistance analysis, we need at least: a patient ID, name or code of a microorganism, a date and antimicrobial results (an antibiogram). It could also include a specimen type (e.g.to filter on blood or urine), the ward type (e.g.to filter on ICUs).</p>
<p>With additional columns (like a hospital name, the patients gender of even [well-defined] clinical properties) you can do a comparative analysis, as this tutorial will demonstrate too.</p>
<p>The <code>LETTERS</code> object is available in R - it’s a vector with 26 characters: <code>A</code> to <code>Z</code>. The <code>patients</code> object we just created is now a vector of length 260, with values (patient IDs) varying from <code>A1</code> to <code>Z10</code>.</p>
<p>The <code>LETTERS</code> object is available in R - it’s a vector with 26 characters: <code>A</code> to <code>Z</code>. The <code>patients</code> object we just created is now a vector of length 260, with values (patient IDs) varying from <code>A1</code> to <code>Z10</code>. Now we we also set the gender of our patients, by putting the ID and the gender in a table:</p>
<p>For this tutorial, we will uses four different microorganisms: <em>Escherichia coli</em>, <em>Staphylococcus aureus</em>, <em>Streptococcus pneumoniae</em>, and <em>Klebsiella pneumoniae</em>:</p>
<p>For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:</p>
<p>Using the <code><ahref="http://dplyr.tidyverse.org/reference/sample.html">sample()</a></code> function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the <code>prob</code> parameter.</p>
<p>Using the <code><ahref="http://dplyr.tidyverse.org/reference/join.html">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<p>The resulting data set contains 5,000 blood culture isolates. With the <code><ahref="https://www.rdocumentation.org/packages/utils/topics/head">head()</a></code> function we can preview the first 6 values of this data set:</p>
<ahref="#cleaning-the-data"class="anchor"></a>Cleaning the data</h2>
<p>Use the frequency table function <code><ahref="../reference/freq.html">freq()</a></code> to look specifically for unique values in every variables. For example, for the <code>gender</code> variable:</p>
<divclass="sourceCode"id="cb8"><preclass="sourceCode r"><codeclass="sourceCode r"><aclass="sourceLine"id="cb8-1"data-line-number="1">data <spanclass="op">%>%</span><spanclass="st"></span><spanclass="kw"><ahref="../reference/freq.html">freq</a></span>(gender) <spanclass="co"># this would be the same: freq(data$gender)</span></a></code></pre></div>
<p>Use the frequency table function <code><ahref="../reference/freq.html">freq()</a></code> to look specifically for unique values in any variable. For example, for the <code>gender</code> variable:</p>
<divclass="sourceCode"><preclass="sourceCode r"><codeclass="sourceCode r">data %>%<spanclass="st"></span><spanclass="kw"><ahref="../reference/freq.html">freq</a></span>(gender) <spanclass="co"># this would be the same: freq(data$gender)</span></code></pre></div>
<p>So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values <code>M</code> and <code>F</code>. From a researcher perspective: there are slightly more men. Nothing we didn’t already know.</p>
<p>The data is already quite clean, but we still need to transform some variables. The <code>bacteria</code> column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The <code>mutate</code> function of the dplyr package makes this really easy:</p>
<p>We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The <code><ahref="../reference/as.rsi.html">as.rsi()</a></code> function ensures reliability and reproducibility in these kind of variables. The <code><ahref="http://dplyr.tidyverse.org/reference/summarise_all.html">mutate_at()</a></code> will run the <code><ahref="../reference/as.rsi.html">as.rsi()</a></code> function on defined variables:</p>
<p>Finally, we will apply <ahref="http://www.eucast.org/expert_rules_and_intrinsic_resistance/">EUCAST rules</a> on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the <code><ahref="../reference/eucast_rules.html">eucast_rules()</a></code> function can also apply additional rules, like forcing <helptitle="ATC: J01CA01">ampicillin</help> = R when <helptitle="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
<p>Because the amoxicillin (column <code>amox</code>) and amoxicillin/clavulanic acid (column <code>amcl</code>) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The <code><ahref="../reference/eucast_rules.html">eucast_rules()</a></code> fixes this:</p>
<aclass="sourceLine"id="cb12-3"data-line-number="3"><spanclass="co"># Rules by the European Committee on Antimicrobial Susceptibility Testing (EUCAST)</span></a>
<aclass="sourceLine"id="cb12-22"data-line-number="22"><spanclass="co"># Table 1: Intrinsic resistance in Enterobacteriaceae (342 changes)</span></a>
<aclass="sourceLine"id="cb12-23"data-line-number="23"><spanclass="co"># Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)</span></a>
<aclass="sourceLine"id="cb12-24"data-line-number="24"><spanclass="co"># Table 3: Intrinsic resistance in other Gram-negative bacteria (no changes)</span></a>
<aclass="sourceLine"id="cb12-25"data-line-number="25"><spanclass="co"># Table 4: Intrinsic resistance in Gram-positive bacteria (705 changes)</span></a>
<aclass="sourceLine"id="cb12-26"data-line-number="26"><spanclass="co"># Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)</span></a>
<aclass="sourceLine"id="cb12-27"data-line-number="27"><spanclass="co"># Table 9: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)</span></a>
<aclass="sourceLine"id="cb12-28"data-line-number="28"><spanclass="co"># Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria (no changes)</span></a>
<aclass="sourceLine"id="cb12-29"data-line-number="29"><spanclass="co"># Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes)</span></a>
<aclass="sourceLine"id="cb12-30"data-line-number="30"><spanclass="co"># Table 12: Interpretive rules for aminoglycosides (no changes)</span></a>
<aclass="sourceLine"id="cb12-31"data-line-number="31"><spanclass="co"># Table 13: Interpretive rules for quinolones (no changes)</span></a>
<aclass="sourceLine"id="cb12-33"data-line-number="33"><spanclass="co"># Other rules</span></a>
<aclass="sourceLine"id="cb12-34"data-line-number="34"><spanclass="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (364 changes)</span></a>
<aclass="sourceLine"id="cb12-35"data-line-number="35"><spanclass="co"># Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no changes)</span></a>
<aclass="sourceLine"id="cb12-36"data-line-number="36"><spanclass="co"># Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no changes)</span></a>
<aclass="sourceLine"id="cb12-37"data-line-number="37"><spanclass="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (211 changes)</span></a>
<aclass="sourceLine"id="cb12-38"data-line-number="38"><spanclass="co"># Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)</span></a>
<aclass="sourceLine"id="cb12-39"data-line-number="39"><spanclass="co"># Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)</span></a>
<aclass="sourceLine"id="cb12-41"data-line-number="41"><spanclass="co"># => EUCAST rules affected 4,626 out of 5,000 rows -> changed 2,008 test results.</span></a></code></pre></div>
<p>We also need to know which isolates we can <em>actually</em> use for analysis.</p>
<p>To conduct an analysis of antimicrobial resistance, you <ahref="https://www.ncbi.nlm.nih.gov/pubmed/17304462">must only include the first isolate of every patient per episode</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be .</p>
<p>To conduct an analysis of antimicrobial resistance, you must <ahref="https://www.ncbi.nlm.nih.gov/pubmed/17304462">only include the first isolate of every patient per episode</a> (Hindler <em>et al.</em>, Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be <ahref="https://en.wikipedia.org/wiki/Selection_bias">selection bias</a>.</p>
<p>The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:</p>
<blockquote>
<p><em>(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, <strong>only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype)</strong>. The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.</em> Chapter 6.4, M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. <ahref="https://clsi.org/standards/products/microbiology/documents/m39/"class="uri">https://clsi.org/standards/products/microbiology/documents/m39/</a></p>
<p><em>(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, <strong>only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype)</strong>. The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.</em><br>Chapter 6.4, M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. <ahref="https://clsi.org/standards/products/microbiology/documents/m39/"class="uri">https://clsi.org/standards/products/microbiology/documents/m39/</a></p>
</blockquote>
<p>This <code>AMR</code> package includes this methodology with the <code><ahref="../reference/first_isolate.html">first_isolate()</a></code> function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:</p>
<aclass="sourceLine"id="cb14-3"data-line-number="3"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `bacteria` as input for `col_mo`.</span></a>
<aclass="sourceLine"id="cb14-4"data-line-number="4"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `date` as input for `col_date`.</span></a>
<aclass="sourceLine"id="cb14-5"data-line-number="5"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<aclass="sourceLine"id="cb14-6"data-line-number="6"><spanclass="co"># => Found 2,913 first isolates (58.3% of total)</span></a></code></pre></div>
<p>So only 58.3% is suitable for resistance analysis! We can now filter on is with the <code><ahref="http://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<spanclass="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<spanclass="co"># NOTE: Using column `date` as input for `col_date`.</span>
<spanclass="co"># NOTE: Using column `patient_id` as input for `col_patient_id`.</span>
<spanclass="co"># => Found 2,928 first isolates (58.6% of total)</span></code></pre></div>
<p>So only 58.6% is suitable for resistance analysis! We can now filter on is with the <code><ahref="http://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<p>For future use, the above two syntaxes can be shortened with the <code><ahref="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
<p>Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and show be included too. This is why we weigh isolates, based on their antibiogram. The <code><ahref="../reference/key_antibiotics.html">key_antibiotics()</a></code> function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.</p>
<p>Only 4 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and show be included too. This is why we weigh isolates, based on their antibiogram. The <code><ahref="../reference/key_antibiotics.html">key_antibiotics()</a></code> function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.</p>
<p>If a column exists with a name like ‘key(…)ab’ the <code><ahref="../reference/first_isolate.html">first_isolate()</a></code> function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:</p>
<aclass="sourceLine"id="cb17-4"data-line-number="4"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `bacteria` as input for `col_mo`.</span></a>
<aclass="sourceLine"id="cb17-5"data-line-number="5"><spanclass="co"># Warning: These columns do not exist and will be ignored: cfur, pita, trsu, vanc, teic, tetr, eryt, oxac, rifa, tobr, coli, cfot, cfta, mero.</span></a>
<aclass="sourceLine"id="cb17-6"data-line-number="6"><spanclass="co"># THIS MAY STRONGLY INFLUENCE THE OUTCOME.</span></a>
<aclass="sourceLine"id="cb17-7"data-line-number="7"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `bacteria` as input for `col_mo`.</span></a>
<aclass="sourceLine"id="cb17-8"data-line-number="8"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `date` as input for `col_date`.</span></a>
<aclass="sourceLine"id="cb17-9"data-line-number="9"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<aclass="sourceLine"id="cb17-10"data-line-number="10"><spanclass="co"># </span><spanclass="al">NOTE</span><spanclass="co">: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.</span></a>
<aclass="sourceLine"id="cb17-11"data-line-number="11"><spanclass="co"># [Criterion] Inclusion based on key antibiotics, ignoring I.</span></a>
<aclass="sourceLine"id="cb17-12"data-line-number="12"><spanclass="co"># => Found 4,318 first weighted isolates (86.4% of total)</span></a></code></pre></div>
<spanclass="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<spanclass="co"># Warning: These columns do not exist and will be ignored: cfur, pita, trsu, vanc, teic, tetr, eryt, oxac, rifa, tobr, coli, cfot, cfta, mero.</span>
<spanclass="co"># THIS MAY STRONGLY INFLUENCE THE OUTCOME.</span>
<spanclass="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<spanclass="co"># NOTE: Using column `date` as input for `col_date`.</span>
<spanclass="co"># NOTE: Using column `patient_id` as input for `col_patient_id`.</span>
<spanclass="co"># NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.</span>
<spanclass="co"># [Criterion] Inclusion based on key antibiotics, ignoring I.</span>
<spanclass="co"># => Found 4,412 first weighted isolates (88.2% of total)</span></code></pre></div>
<tableclass="table">
<thead><trclass="header">
<thalign="center">isolate</th>
@ -568,118 +571,118 @@
<tbody>
<trclass="odd">
<tdalign="center">1</td>
<tdalign="center">2010-07-19</td>
<tdalign="center">S6</td>
<tdalign="center">2010-04-08</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">R</td>
<tdalign="center">S</td>
<tdalign="center">TRUE</td>
<tdalign="center">TRUE</td>
</tr>
<trclass="even">
<tdalign="center">2</td>
<tdalign="center">2010-10-13</td>
<tdalign="center">S6</td>
<tdalign="center">2010-07-04</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">R</td>
<tdalign="center">R</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">FALSE</td>
<tdalign="center">R</td>
<tdalign="center">FALSE</td>
<tdalign="center">TRUE</td>
</tr>
<trclass="odd">
<tdalign="center">3</td>
<tdalign="center">2010-12-24</td>
<tdalign="center">S6</td>
<tdalign="center">2010-07-25</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">R</td>
<tdalign="center">I</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">R</td>
<tdalign="center">S</td>
<tdalign="center">FALSE</td>
<tdalign="center">TRUE</td>
</tr>
<trclass="even">
<tdalign="center">4</td>
<tdalign="center">2011-01-02</td>
<tdalign="center">S6</td>
<tdalign="center">2011-08-12</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">R</td>
<tdalign="center">I</td>
<tdalign="center">S</td>
<tdalign="center">R</td>
<tdalign="center">FALSE</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">TRUE</td>
<tdalign="center">TRUE</td>
</tr>
<trclass="odd">
<tdalign="center">5</td>
<tdalign="center">2011-01-23</td>
<tdalign="center">S6</td>
<tdalign="center">2012-03-16</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">FALSE</td>
<tdalign="center">TRUE</td>
<tdalign="center">FALSE</td>
</tr>
<trclass="even">
<tdalign="center">6</td>
<tdalign="center">2011-05-16</td>
<tdalign="center">S6</td>
<tdalign="center">2012-08-15</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">FALSE</td>
<tdalign="center">FALSE</td>
<tdalign="center">R</td>
<tdalign="center">R</td>
<tdalign="center">TRUE</td>
<tdalign="center">TRUE</td>
</tr>
<trclass="odd">
<tdalign="center">7</td>
<tdalign="center">2011-10-13</td>
<tdalign="center">S6</td>
<tdalign="center">2013-01-15</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">R</td>
<tdalign="center">S</td>
<tdalign="center">R</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">TRUE</td>
<tdalign="center">FALSE</td>
<tdalign="center">TRUE</td>
</tr>
<trclass="even">
<tdalign="center">8</td>
<tdalign="center">2012-03-25</td>
<tdalign="center">S6</td>
<tdalign="center">2013-06-15</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">R</td>
<tdalign="center">I</td>
<tdalign="center">R</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">FALSE</td>
<tdalign="center">FALSE</td>
<tdalign="center">TRUE</td>
</tr>
<trclass="odd">
<tdalign="center">9</td>
<tdalign="center">2012-09-01</td>
<tdalign="center">S6</td>
<tdalign="center">2013-09-18</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">R</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">FALSE</td>
<tdalign="center">FALSE</td>
<tdalign="center">S</td>
<tdalign="center">TRUE</td>
<tdalign="center">TRUE</td>
</tr>
<trclass="even">
<tdalign="center">10</td>
<tdalign="center">2012-10-04</td>
<tdalign="center">S6</td>
<tdalign="center">2014-01-13</td>
<tdalign="center">W2</td>
<tdalign="center">B_ESCHR_COL</td>
<tdalign="center">S</td>
<tdalign="center">R</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
<tdalign="center">S</td>
@ -688,28 +691,28 @@
</tr>
</tbody>
</table>
<p>Instead of 2, now 6 isolates are flagged. In total, 86.4% of all isolates are marked ‘first weighted’ - 28.1% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>Instead of 4, now 9 isolates are flagged. In total, 88.2% of all isolates are marked ‘first weighted’ - 29.7% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>As with <code><ahref="../reference/first_isolate.html">filter_first_isolate()</a></code>, there’s a shortcut for this new algorithm too:</p>
<p>It will be downloaded and installed automatically.</p>
</div>
<divid="get-started"class="section level3">
@ -178,17 +178,17 @@
<p>All (sub)species from <strong>the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package</strong>, as well as all previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria. ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists.</p>
<p>The <code>AMR</code> package basically does four important things:</p>
<ol>
<li>
<p>It <strong>cleanses existing data</strong>, by transforming it to reproducible and profound <em>classes</em>, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:</p>
<li>It <strong>cleanses existing data</strong>, by transforming it to reproducible and profound <em>classes</em>, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:</li>
</ol>
<ul>
<li>Use <code><ahref="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” and “esccol”. Even <code><ahref="reference/as.mo.html">as.mo("MRSA")</a></code> will return the ID of <em>S. aureus</em>. Moreover, it can group all coagulase negative and positive <em>Staphylococci</em>, and can transform <em>Streptococci</em> into Lancefield groups. To find bacteria based on your input, it uses Artificial Intelligence to look up values in the included ITIS data, consisting of more than 18,000 microorganisms.</li>
<li>Use <code><ahref="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 “<=0.002; S” (combined MIC/RSI) will result in “S”.</li>
<li>Use <code><ahref="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 “<=0.002; S” (combined MIC/RSI) will result in “<=0.002”.</li>
<li>Use <code><ahref="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>
</ul>
</li>
<li>
<p>It <strong>enhances existing data</strong> and <strong>adds new data</strong> from data sets included in this package.</p>
<ol>
<li>It <strong>enhances existing data</strong> and <strong>adds new data</strong> from data sets included in this package.</li>
</ol>
<ul>
<li>Use <code><ahref="reference/eucast_rules.html">eucast_rules()</a></code> to apply <ahref="http://www.eucast.org/expert_rules_and_intrinsic_resistance/">EUCAST expert rules to isolates</a>.</li>
<li>Use <code><ahref="reference/first_isolate.html">first_isolate()</a></code> to identify the first isolates of every patient <ahref="https://clsi.org/standards/products/microbiology/documents/m39/">using guidelines from the CLSI</a> (Clinical and Laboratory Standards Institute).
@ -200,9 +200,9 @@
<li>The data set <code>microorganisms</code> contains the complete taxonomic tree of more than 18,000 microorganisms (bacteria, fungi/yeasts and protozoa). 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><ahref="reference/mo_property.html">mo_genus()</a></code>, <code><ahref="reference/mo_property.html">mo_family()</a></code>, <code><ahref="reference/mo_property.html">mo_gramstain()</a></code> or even <code><ahref="reference/mo_property.html">mo_phylum()</a></code>. As they use <code><ahref="reference/as.mo.html">as.mo()</a></code> internally, they also use artificial intelligence. For example, <code><ahref="reference/mo_property.html">mo_genus("MRSA")</a></code> and <code><ahref="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 data set <code>antibiotics</code> contains the ATC code, LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains a total of 298 trade names. Use functions like <code><ahref="reference/ab_property.html">ab_name()</a></code> and <code><ahref="reference/ab_property.html">ab_tradenames()</a></code> to look up values. The <code>ab_*</code> functions use <code><ahref="reference/as.atc.html">as.atc()</a></code> internally so they support AI to guess your expected result. For example, <code><ahref="reference/ab_property.html">ab_name("Fluclox")</a></code>, <code><ahref="reference/ab_property.html">ab_name("Floxapen")</a></code> and <code><ahref="reference/ab_property.html">ab_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>
<p>It <strong>analyses the data</strong> with convenient functions that use well-known methods.</p>
<ol>
<li>It <strong>analyses the data</strong> with convenient functions that use well-known methods.</li>
</ol>
<ul>
<li>Calculate the resistance (and even co-resistance) of microbial isolates with the <code><ahref="reference/portion.html">portion_R()</a></code>, <code><ahref="reference/portion.html">portion_IR()</a></code>, <code><ahref="reference/portion.html">portion_I()</a></code>, <code><ahref="reference/portion.html">portion_SI()</a></code> and <code><ahref="reference/portion.html">portion_S()</a></code> functions. Similarly, the <em>number</em> of isolates can be determined with the <code><ahref="reference/count.html">count_R()</a></code>, <code><ahref="reference/count.html">count_IR()</a></code>, <code><ahref="reference/count.html">count_I()</a></code>, <code><ahref="reference/count.html">count_SI()</a></code> and <code><ahref="reference/count.html">count_S()</a></code> functions. All these functions can be used <ahref="https://dplyr.tidyverse.org/#usage">with the <code>dplyr</code> package</a> (e.g.in conjunction with <ahref="https://dplyr.tidyverse.org/reference/summarise.html"><code>summarise</code></a>)</li>
<li>Plot AMR results with <code><ahref="reference/ggplot_rsi.html">geom_rsi()</a></code>, a function made for the <code>ggplot2</code> package</li>
@ -210,9 +210,9 @@
<li>Conduct descriptive statistics to enhance base R: calculate <code><ahref="reference/kurtosis.html">kurtosis()</a></code>, <code><ahref="reference/skewness.html">skewness()</a></code> and create frequency tables with <code><ahref="reference/freq.html">freq()</a></code>
</li>
</ul>
</li>
<li>
<p>It <strong>teaches the user</strong> how to use all the above actions.</p>
<ol>
<li>It <strong>teaches the user</strong> how to use all the above actions.</li>
</ol>
<ul>
<li>The package contains extensive help pages with many examples.</li>
<li>It also contains an example data set called <code>septic_patients</code>. This data set contains:
<strong>BREAKING</strong>: removed deprecated functions, parameters and references to ‘bactid’. Use <code><ahref="../reference/as.mo.html">as.mo()</a></code> to identify an MO code.</li>
<li>New website: <ahref="https://msberends.gitlab.io/AMR"class="uri">https://msberends.gitlab.io/AMR</a> (built with the great <ahref="https://pkgdown.r-lib.org/"><code>pkgdown</code></a>)
<ul>
<li>New website: <ahref="https://msberends.gitlab.io/AMR"class="uri">https://msberends.gitlab.io/AMR</a> (built with the great <ahref="https://pkgdown.r-lib.org/"><code>pkgdown</code></a>)</li>
<li>Contains the complete manual of this package and all of its functions with an explanation of their parameters</li>
</ul>
</li>
<li>Support for <ahref="https://dplyr.tidyverse.org"><code>dplyr</code></a> version 0.8.0</li>
<li>Function <code><ahref="../reference/mo_failures.html">mo_failures()</a></code> to review values that could not be coerced to a valid MO code, using <code><ahref="../reference/as.mo.html">as.mo()</a></code>. This latter function will now only show a maximum of 25 uncoerced values.</li>
<li>Function <code><ahref="../reference/mo_renamed.html">mo_renamed()</a></code> to get a list of all returned values from <code><ahref="../reference/as.mo.html">as.mo()</a></code> that have had taxonomic renaming</li>
<li>Function <code><ahref="../reference/age.html">age()</a></code> to calculate the (patients) age in years</li>
<li>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>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>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.: <code>r septic_patients %>% filter_first_isolate() # or filter_first_isolate(septic_patients)</code> is equal to: <code>r septic_patients %>% mutate(only_firsts = first_isolate(septic_patients, ...)) %>% filter(only_firsts == TRUE) %>% select(-only_firsts)</code>
</li>
</ul>
</div>
@ -197,8 +185,7 @@
<ahref="#changed"class="anchor"></a>Changed</h4>
<ul>
<li>Fixed a critical bug in <code><ahref="../reference/eucast_rules.html">eucast_rules()</a></code> where some rules that depend on previous applied rules would not be applied adequately</li>
<li>Improvements for <code><ahref="../reference/as.mo.html">as.mo()</a></code>:
<ul>
<li>Improvements for <code><ahref="../reference/as.mo.html">as.mo()</a></code>:</li>
<li>Finds better results when input is in other languages</li>
<li>Better handling for subspecies</li>
<li>Better handling for <em>Salmonellae</em>
@ -207,17 +194,12 @@
<li>Manual now contains more info about the algorithms</li>
<li>Progress bar will be shown when it takes more than 3 seconds to get results</li>
<li>Fixed a bug where distances between dates would not be calculated right - in the <code>septic_patients</code> data set this yielded a differences of 0.15% more isolates</li>
<li>Fixed a bug where distances between dates would not be calculated right - in the <code>septic_patients</code> data set this yielded a difference of 0.15% more isolates</li>
<li>Will now use a column named like “patid” for the patient ID (parameter <code>col_patientid</code>), when this parameter was left blank</li>
<li>Will now use a column named like “key(…)ab” or “key(…)antibiotics” for the key antibiotics (parameter <code>col_keyantibiotics()</code>), when this parameter was left blank</li>
<li>Removed parameter <code>output_logical</code>, the function will now always return a logical value</li>
<li>Renamed parameter <code>filter_specimen</code> to <code>specimen_group</code>, although using <code>filter_specimen</code> will still work</li>
</ul>
</li>
<li>A note to the manual pages of the <code>portion</code> functions, that low counts can influence the outcome and that the <code>portion</code> functions may camouflage this, since they only return the portion (albeit being dependent on the <code>minimum</code> parameter)</li>
<li>Function <code><ahref="../reference/mo_property.html">mo_taxonomy()</a></code> now contains the kingdom too</li>
<li>Reduce false positives for <code><ahref="../reference/as.rsi.html">is.rsi.eligible()</a></code>
@ -226,8 +208,7 @@
</li>
<li>Small text updates to summaries of class <code>rsi</code> and <code>mic</code>
<li>Header info is now available as a list, with the <code>header</code> function</li>
<li>Added header info for class <code>mo</code> to show unique count of families, genera and species</li>
<li>Now honours the <code>decimal.mark</code> setting, which just like <code>format</code> defaults to <code><ahref="https://www.rdocumentation.org/packages/base/topics/options">getOption("OutDec")</a></code>
@ -237,8 +218,6 @@
</li>
<li>New parameter <code>droplevels</code> to exclude empty factor levels when input is a factor</li>
<li>Factor levels will be in header when present in input data</li>
</ul>
</li>
<li>Function <code><ahref="../reference/ggplot_rsi.html">scale_y_percent()</a></code> now contains the <code>limits</code> parameter</li>
<li>Automatic parameter filling for <code><ahref="../reference/mdro.html">mdro()</a></code>, <code><ahref="../reference/key_antibiotics.html">key_antibiotics()</a></code> and <code><ahref="../reference/eucast_rules.html">eucast_rules()</a></code>
</li>
@ -280,8 +259,7 @@
</li>
<li>
<code>EUCAST_rules</code> was renamed to <code>eucast_rules</code>, the old function still exists as a deprecated function</li>
<li>Big changes to the <code>eucast_rules</code> function:
<ul>
<li>Big changes to the <code>eucast_rules</code> function:</li>
<li>Now also applies rules from the EUCAST ‘Breakpoint tables for bacteria’, version 8.1, 2018, <ahref="http://www.eucast.org/clinical_breakpoints/"class="uri">http://www.eucast.org/clinical_breakpoints/</a> (see Source of the function)</li>
<li>New parameter <code>rules</code> to specify which rules should be applied (expert rules, breakpoints, others or all)</li>
<li>New parameter <code>verbose</code> which can be set to <code>TRUE</code> to get very specific messages about which columns and rows were affected</li>
@ -290,18 +268,11 @@
<li>Data set <code>septic_patients</code> now reflects these changes</li>
<li>Added parameter <code>pipe</code> for piperacillin (J01CA12), also to the <code>mdro</code> function</li>
<li>Small fixes to EUCAST clinical breakpoint rules</li>
</ul>
</li>
<li>Added column <code>kingdom</code> to the microorganisms data set, and function <code>mo_kingdom</code> to look up values</li>
<li>Tremendous speed improvement for <code>as.mo</code> (and subsequently all <code>mo_*</code> functions), as empty values wil be ignored <em>a priori</em>
</li>
<li>Fewer than 3 characters as input for <code>as.mo</code> will return NA</li>
<li>
<p>Function <code>as.mo</code> (and all <code>mo_*</code> wrappers) now supports genus abbreviations with “species” attached</p>
<li>Function <code>as.mo</code> (and all <code>mo_*</code> wrappers) now supports genus abbreviations with “species” attached <code>r as.mo("E. species") # B_ESCHR mo_fullname("E. spp.") # "Escherichia species" as.mo("S. spp") # B_STPHY mo_fullname("S. species") # "Staphylococcus species"</code>
</li>
<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>
@ -310,19 +281,18 @@
<li>Using <code>portion_*</code> functions now throws a warning when total available isolate is below parameter <code>minimum</code>
</li>
<li>Functions <code>as.mo</code>, <code>as.rsi</code>, <code>as.mic</code>, <code>as.atc</code> and <code>freq</code> will not set package name as attribute anymore</li>
<aclass="sourceLine"id="cb5-3"data-line-number="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>
<spanclass="st"></span><spanclass="kw">select</span>(-count, -cum_count) <spanclass="co"># only get item, percent, cum_percent</span></code></pre></div>
</li>
<li>Check for <code><ahref="https://www.rdocumentation.org/packages/hms/topics/hms">hms::is.hms</a></code>
</li>
@ -333,8 +303,6 @@
<li>New parameter <code>na</code>, to choose which character to print for empty values</li>
<li>New parameter <code>header</code> to turn the header info off (default when <code>markdown = TRUE</code>)</li>
<li>New parameter <code>title</code> to manually setbthe title of the frequency table</li>
</ul>
</li>
<li>
<code>first_isolate</code> now tries to find columns to use as input when parameters are left blank</li>
<li>Improvements for MDRO algorithm (function <code>mdro</code>)</li>
@ -346,8 +314,7 @@
</li>
<li>
<code>ggplot_rsi</code> and <code>scale_y_percent</code> have <code>breaks</code> parameter</li>
<li>Speed improvement for <code>is.rsi.eligible</code>, now 15-20 times faster</li>
<li>In <code>g.test</code>, when <code><ahref="https://www.rdocumentation.org/packages/base/topics/sum">sum(x)</a></code> is below 1000 or any of the expected values is below 5, Fisher’s Exact Test will be suggested</li>
@ -390,8 +355,7 @@
<ahref="#new-2"class="anchor"></a>New</h4>
<ul>
<li>The data set <code>microorganisms</code> now contains <strong>all microbial taxonomic data from ITIS</strong> (kingdoms Bacteria, Fungi and Protozoa), the Integrated Taxonomy Information System, available via <ahref="https://itis.gov"class="uri">https://itis.gov</a>. The data set now contains more than 18,000 microorganisms with all known bacteria, fungi and protozoa according ITIS with genus, species, subspecies, family, order, class, phylum and subkingdom. The new data set <code>microorganisms.old</code> contains all previously known taxonomic names from those kingdoms.</li>
<li>New functions based on the existing function <code>mo_property</code>:
<ul>
<li>New functions based on the existing function <code>mo_property</code>:</li>
<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
<p>They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:<code>r mo_gramstain("E. coli") # [1] "Gram negative" mo_gramstain("E. coli", language = "de") # German # [1] "Gramnegativ" mo_gramstain("E. coli", language = "es") # Spanish # [1] "Gram negativo" mo_fullname("S. group A", language = "pt") # Portuguese # [1] "Streptococcus grupo A"</code></p>
<p>Furthermore, former taxonomic names will give a note about the current taxonomic name: <code>r mo_gramstain("Esc blattae") # Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010) # [1] "Gram negative"</code></p>
<ul>
<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</li>
<li>Extra function <code>count_df</code> (which works like <code>portion_df</code>) to get all counts of S, I and R of a data set with antibiotic columns, with support for grouped variables</li>
</ul>
</li>
<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 %>% 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>
<aclass="sourceLine"id="cb8-5"data-line-number="5"><spanclass="kw"><ahref="../reference/as.mo.html">as.mo</a></span>(<spanclass="st">"S group A"</span>)</a>
<li>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): <code>r as.mo("E. coli") # [1] B_ESCHR_COL as.mo("MRSA") # [1] B_STPHY_AUR as.mo("S group A") # [1] B_STRPTC_GRA</code> And with great speed too - on a quite regular Linux server from 2007 it takes us less than 0.02 seconds to transform 25,000 items: <code>r thousands_of_E_colis <- rep("E. coli", 25000) microbenchmark::microbenchmark(as.mo(thousands_of_E_colis), unit = "s") # Unit: seconds # min median max neval # 0.01817717 0.01843957 0.03878077 100</code>
</li>
<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:
<ul>
<li>Renamed all previous references to <code>bactid</code> to <code>mo</code>, like:</li>
<li>Column names inputs of <code>EUCAST_rules</code>, <code>first_isolate</code> and <code>key_antibiotics</code>
</li>
<li>Column names of datasets <code>microorganisms</code> and <code>septic_patients</code>
</li>
<li>All old syntaxes will still work with this version, but will throw warnings</li>
</ul>
</li>
<li>Function <code>labels_rsi_count</code> to print datalabels on a RSI <code>ggplot2</code> model</li>
<li><p>Functions <code>as.atc</code> and <code>is.atc</code> to transform/look up antibiotic ATC codes as defined by the WHO. The existing function <code>guess_atc</code> is now an alias of <code>as.atc</code>.</p></li>
<li>Function <code>ab_property</code> and its aliases: <code>ab_name</code>, <code>ab_tradenames</code>, <code>ab_certe</code>, <code>ab_umcg</code> and <code>ab_trivial_nl</code>
<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>Added 163 trade names to the <code>antibiotics</code> data set, it now contains 298 different trade names in total, e.g.: <code>r ab_official("Bactroban") # [1] "Mupirocin" ab_name(c("Bactroban", "Amoxil", "Zithromax", "Floxapen")) # [1] "Mupirocin" "Amoxicillin" "Azithromycin" "Flucloxacillin" ab_atc(c("Bactroban", "Amoxil", "Zithromax", "Floxapen")) # [1] "R01AX06" "J01CA04" "J01FA10" "J01CF05"</code>
</li>
<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>
@ -477,36 +404,9 @@
<li>Added <code>prevalence</code> column to the <code>microorganisms</code> data set</li>
<li>Added parameters <code>minimum</code> and <code>as_percent</code> to <code>portion_df</code>
</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>
<li>Added datalabels function <code>labels_rsi_count</code> to <code>ggplot_rsi</code>
</li>
<li>Added possibility to set any parameter to <code>geom_rsi</code> (and <code>ggplot_rsi</code>) so you can set your own preferences</li>
<li>Fix for joins, where predefined suffices would not be honoured</li>
<li>Added parameter <code>quote</code> to the <code>freq</code> function</li>
<li>Added generic function <code>diff</code> for frequency tables</li>
<li>Added longest en shortest character length in the frequency table (<code>freq</code>) header of class <code>character</code>
</li>
<li>
<p>Support for types (classes) list and matrix for <code>freq</code></p>
<li>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. ```r septic_patients %>% select(amox, cipr) %>% count_IR() # which is the same as: septic_patients %>% count_IR(amox, cipr)</li>
</ul>
<p>septic_patients %>% portion_S(amcl) septic_patients %>% portion_S(amcl, gent) septic_patients %>% portion_S(amcl, gent, pita) <code>* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` parameter has value `portion_df` at default, but can be set to `count_df`. * Fix for `ggplot_rsi` when the `ggplot2` package was not loaded * Added datalabels function `labels_rsi_count` to `ggplot_rsi` * Added possibility to set any parameter to `geom_rsi` (and `ggplot_rsi`) so you can set your own preferences * Fix for joins, where predefined suffices would not be honoured * Added parameter `quote` to the `freq` function * Added generic function `diff` for frequency tables * Added longest en shortest character length in the frequency table (`freq`) header of class `character` * Support for types (classes) list and matrix for `freq`</code>r my_matrix = with(septic_patients, matrix(c(age, gender), ncol = 2)) freq(my_matrix) <code>For lists, subsetting is possible:</code>r my_list = list(age = septic_patients$age, gender = septic_patients$gender) my_list %>% freq(age) my_list %>% freq(gender) ```</p>
</div>
<divid="other-2"class="section level4">
<h4class="hasAnchor">
@ -525,21 +425,15 @@
<ahref="#new-3"class="anchor"></a>New</h4>
<ul>
<li>
<strong>BREAKING</strong>: <code>rsi_df</code> was removed in favour of new functions <code>portion_R</code>, <code>portion_IR</code>, <code>portion_I</code>, <code>portion_SI</code> and <code>portion_S</code> to selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old <code>rsi</code> function. The old function still works, but is deprecated.
<ul>
<strong>BREAKING</strong>: <code>rsi_df</code> was removed in favour of new functions <code>portion_R</code>, <code>portion_IR</code>, <code>portion_I</code>, <code>portion_SI</code> and <code>portion_S</code> to selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old <code>rsi</code> function. The old function still works, but is deprecated.</li>
<li>New function <code>portion_df</code> to get all portions of S, I and R of a data set with antibiotic columns, with support for grouped variables</li>
</ul>
</li>
<li>
<strong>BREAKING</strong>: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call <em>key antibiotics</em>) to include more first isolates (afterwards called first <em>weighted</em> isolates) are now as follows:
<ul>
<strong>BREAKING</strong>: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call <em>key antibiotics</em>) to include more first isolates (afterwards called first <em>weighted</em> isolates) are now as follows:</li>
<li>New functions <code>geom_rsi</code>, <code>facet_rsi</code>, <code>scale_y_percent</code>, <code>scale_rsi_colours</code> and <code>theme_rsi</code>
</li>
<li>New wrapper function <code>ggplot_rsi</code> to apply all above functions on a data set:
@ -550,32 +444,22 @@
</li>
</ul>
</li>
</ul>
</li>
<li>Determining bacterial ID:
<ul>
<li>Determining bacterial ID:</li>
<li>New functions <code>as.bactid</code> and <code>is.bactid</code> to transform/ look up microbial ID’s.</li>
<li>The existing function <code>guess_bactid</code> is now an alias of <code>as.bactid</code>
</li>
<li>New Becker classification for <em>Staphylococcus</em> to categorise them into Coagulase Negative <em>Staphylococci</em> (CoNS) and Coagulase Positve <em>Staphylococci</em> (CoPS)</li>
<li>New Lancefield classification for <em>Streptococcus</em> to categorise them into Lancefield groups</li>
</ul>
</li>
<li>For convience, new descriptive statistical functions <code>kurtosis</code> and <code>skewness</code> that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices</li>
<li>Function <code>g.test</code> to perform the Χ<sup>2</sup> distributed <ahref="https://en.wikipedia.org/wiki/G-test"><em>G</em>-test</a>, which use is the same as <code>chisq.test</code>
</li>
<li>
<del>Function <code>ratio</code> to transform a vector of values to a preset ratio</del>
<ul>
<li><del>Function <code>ratio</code> to transform a vector of values to a preset ratio</del></li>
<li><del>For example: <code><ahref="../reference/AMR-deprecated.html">ratio(c(10, 500, 10), ratio = "1:2:1")</a></code> would return <code>130, 260, 130</code></del></li>
</ul>
</li>
<li>Support for Addins menu in RStudio to quickly insert <code>%in%</code> or <code>%like%</code> (and give them keyboard shortcuts), or to view the datasets that come with this package</li>
<li>Function <code>p.symbol</code> to transform p values to their related symbols: <code>0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code>
</li>
<li>Functions <code>clipboard_import</code> and <code>clipboard_export</code> as helper functions to quickly copy and paste from/to software like Excel and SPSS. These functions use the <code>clipr</code> package, but are a little altered to also support headless Linux servers (so you can use it in RStudio Server)</li>
<li>New for frequency tables (function <code>freq</code>):
<ul>
<li>New for frequency tables (function <code>freq</code>):</li>
<li>A vignette to explain its usage</li>
<li>Support for <code>rsi</code> (antimicrobial resistance) to use as input</li>
<li>Support for <code>table</code> to use as input: <code><ahref="../reference/freq.html">freq(table(x, y))</a></code>
@ -590,8 +474,6 @@
<li>Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR)</li>
<li>Possibility to globally set the default for the amount of items to print, with <code><ahref="https://www.rdocumentation.org/packages/base/topics/options">options(max.print.freq = n)</a></code> where <em>n</em> is your preset value</li>
</ul>
</li>
</ul>
</div>
<divid="changed-3"class="section level4">
<h4class="hasAnchor">
@ -613,27 +495,21 @@
</li>
<li>Small improvements to the <code>microorganisms</code> dataset (especially for <em>Salmonella</em>) and the column <code>bactid</code> now has the new class <code>"bactid"</code>
</li>
<li>Combined MIC/RSI values will now be coerced by the <code>rsi</code> and <code>mic</code> functions:
<ul>
<li>Combined MIC/RSI values will now be coerced by the <code>rsi</code> and <code>mic</code> functions:</li>
<li>
<code><ahref="../reference/as.rsi.html">as.rsi("<=0.002; S")</a></code> will return <code>S</code>
</li>
<li>
<code><ahref="../reference/as.mic.html">as.mic("<=0.002; S")</a></code> will return <code><=0.002</code>
</li>
</ul>
</li>
<li>Now possible to coerce MIC values with a space between operator and value, i.e. <code><ahref="../reference/as.mic.html">as.mic("<= 0.002")</a></code> now works</li>
<li>Classes <code>rsi</code> and <code>mic</code> do not add the attribute <code>package.version</code> anymore</li>
<li>Added <code>"groups"</code> option for <code><ahref="../reference/atc_property.html">atc_property(..., property)</a></code>. It will return a vector of the ATC hierarchy as defined by the <ahref="https://www.whocc.no/atc/structure_and_principles/">WHO</a>. The new function <code>atc_groups</code> is a convenient wrapper around this.</li>
<li>Build-in host check for <code>atc_property</code> as it requires the host set by <code>url</code> to be responsive</li>
<li>Improved <code>first_isolate</code> algorithm to exclude isolates where bacteria ID or genus is unavailable</li>
<li>Fix for warning <em>hybrid evaluation forced for row_number</em> (<ahref="https://github.com/tidyverse/dplyr/commit/924b62"><code>924b62</code></a>) from the <code>dplyr</code> package v0.7.5 and above</li>
<li>Support for empty values and for 1 or 2 columns as input for <code>guess_bactid</code> (now called <code>as.bactid</code>)
<ul>
<li>Support for empty values and for 1 or 2 columns as input for <code>guess_bactid</code> (now called <code>as.bactid</code>)</li>
<li>So <code>yourdata %>% select(genus, species) %>% as.bactid()</code> now also works</li>
</ul>
</li>
<li>Other small fixes</li>
</ul>
</div>
@ -641,14 +517,11 @@
<h4class="hasAnchor">
<ahref="#other-3"class="anchor"></a>Other</h4>
<ul>
<li>Added integration tests (check if everything works as expected) for all releases of R 3.1 and higher
<ul>
<li>Added integration tests (check if everything works as expected) for all releases of R 3.1 and higher</li>
<li>Linux and macOS: <ahref="https://travis-ci.org/msberends/AMR"class="uri">https://travis-ci.org/msberends/AMR</a>
<li>Added thesis advisors to DESCRIPTION file</li>
</ul>
</div>
@ -667,15 +540,12 @@
<li>Function <code>guess_bactid</code> to <strong>determine the ID</strong> of a microorganism based on genus/species or known abbreviations like MRSA</li>
<li>Function <code>guess_atc</code> to <strong>determine the ATC</strong> of an antibiotic based on name, trade name, or known abbreviations</li>
<li>Function <code>freq</code> to create <strong>frequency tables</strong>, with additional info in a header</li>
<li>Function <code>MDRO</code> to <strong>determine Multi Drug Resistant Organisms (MDRO)</strong> with support for country-specific guidelines.
<ul>
<li>Function <code>MDRO</code> to <strong>determine Multi Drug Resistant Organisms (MDRO)</strong> with support for country-specific guidelines.</li>
<li>Suggest your own via <ahref="https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E">https://github.com/msberends/AMR/issues/new</a>
</li>
<li>
<ahref="http://www.eucast.org/expert_rules_and_intrinsic_resistance">Exceptional resistances defined by EUCAST</a> are also supported instead of countries alone</li>
<li>Functions <code>BRMO</code> and <code>MRGN</code> are wrappers for Dutch and German guidelines, respectively</li>
</ul>
</li>
<li>New algorithm to determine weighted isolates, can now be <code>"points"</code> or <code>"keyantibiotics"</code>, see <code><ahref="../reference/first_isolate.html">?first_isolate</a></code>
</li>
<li>New print format for <code>tibble</code>s and <code>data.table</code>s</li>
<metaproperty="og:description"content="Create a frequency table of a vector with items or a data frame. Supports quasiquotation and markdown for reports. top_freq can be used to get the top/bottom n items of a frequency table, with counts as names."/>
<metaproperty="og:description"content="Create a frequency table of a vector with items or a data frame. Supports quasiquotation and markdown for reports. The best practice is: data %&gt;% freq(var).
top_freq can be used to get the top/bottom n items of a frequency table, with counts as names."/>
<p>Create a frequency table of a vector with items or a data frame. Supports quasiquotation and markdown for reports. <code>top_freq</code> can be used to get the top/bottom <em>n</em> items of a frequency table, with counts as names.</p>
<p>Create a frequency table of a vector with items or a data frame. Supports quasiquotation and markdown for reports. The best practice is: <code>data %>% freq(var)</code>.<br/>
<code>top_freq</code> can be used to get the top/bottom <em>n</em> items of a frequency table, with counts as names.</p>
</div>
@ -240,7 +242,7 @@
</tr>
<tr>
<th>na</th>
<td><p>a character string to should be used to show empty (<code>NA</code>) values (only useful when <code>na.rm = FALSE</code>)</p></td>
<td><p>a character string that should be used to show empty (<code>NA</code>) values (only useful when <code>na.rm = FALSE</code>)</p></td>
The `LETTERS` object is available in R - it's a vector with 26 characters: `A` to `Z`. The `patients` object we just created is now a vector of length `r length(patients)`, with values (patient IDs) varying from ``r patients[1]`` to ``r patients[length(patients)]``. Now we we also set the gender of our patients, by putting the ID and the gender in a table:
The first 135 patient IDs are now male, the other 125 are female.
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