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(v0.9.0.9013) Support for LOINC codes
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@ -41,7 +41,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.9.0.9007</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.9.0.9013</span>
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</span>
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</div>
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@ -187,7 +187,7 @@
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<h1>Benchmarks</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">27 December 2019</h4>
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<h4 class="date">26 January 2020</h4>
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<div class="hidden name"><code>benchmarks.Rmd</code></div>
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@ -221,21 +221,36 @@
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<a class="sourceLine" id="cb2-16" data-line-number="16"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(S.aureus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
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<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 9.2 9.3 20.0 10.0 34 39.0 10</span></a>
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<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 34.0 34.0 37.0 35.0 35 63.0 10</span></a>
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<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 33.0 34.0 39.0 35.0 36 58.0 10</span></a>
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<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 9.3 9.4 9.8 9.8 10 10.0 10</span></a>
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<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 9.3 9.4 16.0 9.7 11 42.0 10</span></a>
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<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 10.0 10.0 19.0 11.0 33 44.0 10</span></a>
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<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 10.0 10.0 18.0 11.0 34 36.0 10</span></a>
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<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 4.7 4.8 4.9 4.9 5 5.1 10</span></a>
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<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 650.0 700.0 760.0 740.0 820 940.0 10</span></a>
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<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 360.0 390.0 410.0 390.0 430 510.0 10</span></a>
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<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 9.1 9.4 17.0 9.7 34 35.0 10</span></a>
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<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 20.0 20.0 26.0 21.0 26 45.0 10</span></a>
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<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 20.0 21.0 34.0 33.0 46 53.0 10</span></a>
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<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 19.0 20.0 25.0 21.0 25 43.0 10</span></a></code></pre></div>
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<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max</span></a>
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<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 9.1 9.4 15.0 9.7 10.0 38.0</span></a>
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<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 33.0 34.0 43.0 36.0 58.0 68.0</span></a>
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<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 33.0 35.0 52.0 39.0 56.0 130.0</span></a>
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<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 9.0 9.5 13.0 9.9 11.0 36.0</span></a>
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<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 8.8 9.3 12.0 9.9 10.0 32.0</span></a>
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<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 9.9 10.0 11.0 11.0 11.0 13.0</span></a>
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<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 10.0 11.0 20.0 12.0 36.0 42.0</span></a>
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<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 4.4 4.7 4.9 5.0 5.1 5.3</span></a>
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<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 640.0 660.0 710.0 680.0 720.0 890.0</span></a>
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<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 360.0 370.0 390.0 380.0 410.0 460.0</span></a>
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<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 8.9 9.4 9.6 9.5 9.9 10.0</span></a>
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<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 21.0 22.0 28.0 24.0 27.0 50.0</span></a>
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<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 20.0 21.0 33.0 22.0 45.0 63.0</span></a>
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<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 19.0 20.0 26.0 21.0 27.0 45.0</span></a>
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<a class="sourceLine" id="cb2-34" data-line-number="34"><span class="co"># neval</span></a>
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<a class="sourceLine" id="cb2-35" data-line-number="35"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-36" data-line-number="36"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-37" data-line-number="37"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-38" data-line-number="38"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-39" data-line-number="39"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-40" data-line-number="40"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-41" data-line-number="41"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-42" data-line-number="42"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-43" data-line-number="43"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-44" data-line-number="44"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-45" data-line-number="45"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-46" data-line-number="46"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-47" data-line-number="47"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-48" data-line-number="48"><span class="co"># 10</span></a></code></pre></div>
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<p><img src="benchmarks_files/figure-html/unnamed-chunk-5-1.png" width="562.5"></p>
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<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.</p>
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<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
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@ -248,18 +263,18 @@
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<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(M.semesiae, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
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<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq</span></a>
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<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1437.000 1541.000 1599.000 1598.000 1657.000</span></a>
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<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1521.000 1539.000 1594.000 1606.000 1622.000</span></a>
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<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 10.840 11.110 19.690 12.620 35.530</span></a>
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<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 10.780 11.000 13.670 11.250 11.450</span></a>
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<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 5.385 5.563 6.217 5.784 6.134</span></a>
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<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1406.000 1502.000 1535.000 1531.000 1607.000</span></a>
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<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1492.000 1535.000 1575.000 1581.000 1613.000</span></a>
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<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 11.060 11.130 14.330 11.460 12.450</span></a>
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<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 10.810 10.860 18.920 11.270 35.510</span></a>
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<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 5.155 5.427 8.507 5.601 6.023</span></a>
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<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co"># max neval</span></a>
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<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1825.00 10</span></a>
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<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1690.00 10</span></a>
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<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 39.06 10</span></a>
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<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 35.94 10</span></a>
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<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 8.74 10</span></a></code></pre></div>
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<p>That takes 6.3 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>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>
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<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1616.00 10</span></a>
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<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1631.00 10</span></a>
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<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 37.45 10</span></a>
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<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 39.12 10</span></a>
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<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 32.03 10</span></a></code></pre></div>
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<p>That takes 6.5 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>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>
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<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>
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<p><img src="benchmarks_files/figure-html/unnamed-chunk-8-1.png" width="900"></p>
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<p>The highest outliers are the first times. All next determinations were done in only thousands of seconds, because the <code><a href="../reference/as.mo.html">as.mo()</a></code> function <strong>learns from its own output to speed up determinations for next times</strong>.</p>
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@ -296,8 +311,8 @@
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<a class="sourceLine" id="cb4-24" data-line-number="24"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
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<a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 550 600 656 627 702 954 100</span></a></code></pre></div>
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<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.63 seconds (627 ms). You only lose time on your unique input values.</p>
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<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 570 630 657 650 674 777 100</span></a></code></pre></div>
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<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.65 seconds (649 ms). You only lose time on your unique input values.</p>
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</div>
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<div id="precalculated-results" class="section level3">
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<h3 class="hasAnchor">
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@ -309,11 +324,11 @@
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<a class="sourceLine" id="cb5-4" data-line-number="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
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<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.680 7.01 7.730 7.710 8.160 9.36 10</span></a>
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<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 11.700 12.20 18.000 14.800 15.200 54.30 10</span></a>
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<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.723 0.87 0.924 0.945 0.973 1.06 10</span></a></code></pre></div>
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<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0009 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>
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<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.730 6.830 7.540 7.000 8.340 8.98 10</span></a>
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<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 11.600 11.700 15.500 11.900 13.200 44.90 10</span></a>
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<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.781 0.796 0.892 0.823 0.987 1.15 10</span></a></code></pre></div>
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<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0008 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>
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<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1">run_it <-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),</a>
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<a class="sourceLine" id="cb6-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</a>
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<a class="sourceLine" id="cb6-3" data-line-number="3"> <span class="dt">C =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"Staphylococcus aureus"</span>),</a>
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@ -326,14 +341,14 @@
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<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
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<a class="sourceLine" id="cb6-11" data-line-number="11"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb6-12" data-line-number="12"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.528 0.552 0.572 0.564 0.573 0.653 10</span></a>
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<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.583 0.602 0.623 0.618 0.647 0.665 10</span></a>
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<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.965 0.979 1.020 1.000 1.040 1.220 10</span></a>
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<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.582 0.621 0.632 0.627 0.636 0.696 10</span></a>
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<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.548 0.552 0.585 0.582 0.599 0.646 10</span></a>
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<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.538 0.540 0.555 0.548 0.567 0.588 10</span></a>
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<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.539 0.554 0.573 0.568 0.585 0.646 10</span></a>
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<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.512 0.545 0.554 0.551 0.556 0.628 10</span></a></code></pre></div>
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||||
<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.464 0.483 0.491 0.485 0.503 0.533 10</span></a>
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||||
<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.504 0.515 0.533 0.528 0.534 0.612 10</span></a>
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||||
<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.762 0.771 0.794 0.795 0.815 0.820 10</span></a>
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||||
<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.498 0.503 0.529 0.530 0.541 0.584 10</span></a>
|
||||
<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.478 0.481 0.494 0.495 0.498 0.534 10</span></a>
|
||||
<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.471 0.477 0.484 0.481 0.490 0.517 10</span></a>
|
||||
<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.468 0.474 0.483 0.480 0.482 0.530 10</span></a>
|
||||
<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.464 0.476 0.503 0.482 0.492 0.705 10</span></a></code></pre></div>
|
||||
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
|
||||
</div>
|
||||
<div id="results-in-other-languages" class="section level3">
|
||||
@ -360,13 +375,13 @@
|
||||
<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
|
||||
<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># Unit: milliseconds</span></a>
|
||||
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 20.88 22.82 27.81 23.63 24.78 78.11 100</span></a>
|
||||
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 22.56 24.77 31.69 25.54 31.31 150.90 100</span></a>
|
||||
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 28.06 30.72 37.50 32.17 41.06 76.13 100</span></a>
|
||||
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 22.64 24.58 30.66 25.43 32.62 72.82 100</span></a>
|
||||
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 22.44 24.34 30.07 25.23 32.15 60.99 100</span></a>
|
||||
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 22.57 24.85 32.13 25.72 34.49 79.05 100</span></a>
|
||||
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 22.54 24.52 30.13 25.29 30.88 80.61 100</span></a></code></pre></div>
|
||||
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 21.72 22.92 27.04 23.44 24.14 62.83 100</span></a>
|
||||
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 23.14 24.37 28.32 24.79 25.84 57.85 100</span></a>
|
||||
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 28.85 30.61 35.40 31.25 32.79 64.85 100</span></a>
|
||||
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 23.17 24.51 28.62 24.85 25.51 65.76 100</span></a>
|
||||
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 23.04 24.25 30.77 24.82 27.58 58.95 100</span></a>
|
||||
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 22.88 24.37 27.90 24.94 25.53 61.63 100</span></a>
|
||||
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 23.16 24.54 29.65 24.90 25.59 156.50 100</span></a></code></pre></div>
|
||||
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
|
||||
</div>
|
||||
</div>
|
||||
@ -380,7 +395,7 @@
|
||||
|
||||
|
||||
<footer><div class="copyright">
|
||||
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N M Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
|
||||
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown">
|
||||
|
||||
Reference in New Issue
Block a user