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(v1.5.0.9008) Internal data sets to pkg, speed for auto col determination
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@ -82,7 +82,7 @@
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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">1.5.0.9006</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
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</span>
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</div>
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@ -346,14 +346,14 @@
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<p>The <code>as.rsi()</code> function works in four ways:</p><ol>
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<li><p>For <strong>cleaning raw / untransformed data</strong>. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with R/SI interpretations and MIC values such as <code>"<0.25; S"</code> will be coerced to <code>"S"</code>. Combined interpretations for multiple test methods (as seen in laboratory records) such as <code>"S; S"</code> will be coerced to <code>"S"</code>, but a value like <code>"S; I"</code> will return <code>NA</code> with a warning that the input is unclear.</p></li>
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<li><p>For <strong>interpreting minimum inhibitory concentration (MIC) values</strong> according to EUCAST or CLSI. You must clean your MIC values first using <code><a href='as.mic.html'>as.mic()</a></code>, that also gives your columns the new data class <code><a href='as.mic.html'>mic</a></code>. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the <code>mo</code> argument.</p><ul>
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<li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><pre><span class='va'>your_data</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='va'>is.mic</span>, <span class='va'>as.rsi</span><span class='op'>)</span> <span class='co'># until dplyr 1.0.0</span>
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<span class='va'>your_data</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='fu'>where</span><span class='op'>(</span><span class='va'>is.mic</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># since dplyr 1.0.0</span>
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<li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><pre><span class='va'>your_data</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='va'>is.mic</span>, <span class='va'>as.rsi</span><span class='op'>)</span> <span class='co'># until dplyr 1.0.0</span>
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<span class='va'>your_data</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='op'>(</span><span class='va'>is.mic</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># since dplyr 1.0.0</span>
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</pre></li>
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<li><p>Operators like "<=" will be stripped before interpretation. When using <code>conserve_capped_values = TRUE</code>, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (<code>conserve_capped_values = FALSE</code>) considers ">2" to be lower than ">=4" and might in this case return "S" or "I".</p></li>
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</ul></li>
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<li><p>For <strong>interpreting disk diffusion diameters</strong> according to EUCAST or CLSI. You must clean your disk zones first using <code><a href='as.disk.html'>as.disk()</a></code>, that also gives your columns the new data class <code><a href='as.disk.html'>disk</a></code>. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the <code>mo</code> argument.</p><ul>
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<li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><pre><span class='va'>your_data</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='va'>is.disk</span>, <span class='va'>as.rsi</span><span class='op'>)</span> <span class='co'># until dplyr 1.0.0</span>
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<span class='va'>your_data</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='fu'>where</span><span class='op'>(</span><span class='va'>is.disk</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># since dplyr 1.0.0</span>
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<li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><pre><span class='va'>your_data</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='va'>is.disk</span>, <span class='va'>as.rsi</span><span class='op'>)</span> <span class='co'># until dplyr 1.0.0</span>
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<span class='va'>your_data</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='op'>(</span><span class='va'>is.disk</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># since dplyr 1.0.0</span>
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</pre></li>
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</ul></li>
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<li><p>For <strong>interpreting a complete data set</strong>, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running <code>as.rsi(data)</code>.</p></li>
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@ -452,7 +452,7 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
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<span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://dplyr.tidyverse.org'>"dplyr"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
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<span class='va'>df</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='va'>is.mic</span>, <span class='va'>as.rsi</span><span class='op'>)</span>
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<span class='va'>df</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='fu'><a href='as.mic.html'>is.mic</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='op'>|</span> <span class='fu'><a href='as.disk.html'>is.disk</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span>
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<span class='va'>df</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='fu'>where</span><span class='op'>(</span><span class='va'>is.mic</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span>
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<span class='va'>df</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='op'>(</span><span class='va'>is.mic</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span>
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<span class='va'>df</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_at</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/vars.html'>vars</a></span><span class='op'>(</span><span class='va'>AMP</span><span class='op'>:</span><span class='va'>TOB</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span>
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<span class='va'>df</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='va'>AMP</span><span class='op'>:</span><span class='va'>TOB</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span>
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@ -497,7 +497,7 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
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<span class='co'># note: from dplyr 1.0.0 on, this will be: </span>
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<span class='co'># example_isolates %>%</span>
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<span class='co'># mutate(across(where(is.rsi.eligible), as.rsi))</span>
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<span class='co'># mutate(across((is.rsi.eligible), as.rsi))</span>
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<span class='op'>}</span>
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<span class='co'># }</span>
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</pre>
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