<p>These functions can be used to predefine your own reference to be used in <code><ahref='as.mo.html'>as.mo</a></code> and consequently all <code>mo_*</code> functions like <code><ahref='mo_property.html'>mo_genus</a></code> and <code><ahref='mo_property.html'>mo_gramstain</a></code>.</p>
<p>The reference file can be a text file seperated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you need to have the <code>readxl</code> package installed.</p>
<p><code>set_mo_source</code> will check the file for validity: it must be a <code>data.frame</code>, must have a column named <code>"mo"</code> which contains values from <code>microorganisms$mo</code> and must have a reference column with your own defined values. If all tests pass, <code>set_mo_source</code> will read the file into R and export it to <code>"~/.mo_source.rds"</code>. This compressed data file will then be used at default for MO determination (function <code><ahref='as.mo.html'>as.mo</a></code> and consequently all <code>mo_*</code> functions like <code><ahref='mo_property.html'>mo_genus</a></code> and <code><ahref='mo_property.html'>mo_gramstain</a></code>). The location of the original file will be saved as option with <code><ahref='https://www.rdocumentation.org/packages/base/topics/options'>options</a>(mo_source = path)</code>. Its timestamp will be saved with <code><ahref='https://www.rdocumentation.org/packages/base/topics/options'>options</a>(mo_source_datetime = ...)</code>.</p>
<p><code>get_mo_source</code> will return the data set by reading <code>"~/.mo_source.rds"</code> with <code><ahref='https://www.rdocumentation.org/packages/base/topics/readRDS'>readRDS</a></code>. If the original file has changed (the file defined with <code>path</code>), it will call <code>set_mo_source</code> to update the data file automatically.</p>
<p>Reading an Excel file (<code>.xlsx</code>) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by <code>get_mo_source</code> in only a couple of microseconds (a millionth of a second).</p>
<h2class="hasAnchor"id="how-it-works"><aclass="anchor"href="#how-it-works"></a>How it works</h2>
<p>Imagine this data on a sheet of an Excel file (mo codes were looked up in the `microorganisms` data set). The first column contains the organisation specific codes, the second column contains an MO code from this package:</p><pre>
| A | B |
--|--------------------|-------------|
1 | Organisation XYZ | mo |
2 | lab_mo_ecoli | B_ESCHR_COL |
3 | lab_mo_kpneumoniae | B_KLBSL_PNE |
4 | | |
</pre>
<p>We save it as <code>'home/me/ourcodes.xlsx'</code>. Now we have to set it as a source:</p><pre>
set_mo_source("home/me/ourcodes.xlsx")
# Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
</pre>
<p>It has now created a file "~/.mo_source.rds" with the contents of our Excel file. It it an R specific format with great compression.</p>
<p>And now we can use it in our functions:</p><pre>
as.mo("lab_mo_ecoli")
# B_ESCHR_COL
mo_genus("lab_mo_kpneumoniae")
# "Klebsiella"
</pre>
<p>If we edit the Excel file to, let's say, this:</p><pre>
| A | B |
--|--------------------|-------------|
1 | Organisation XYZ | mo |
2 | lab_mo_ecoli | B_ESCHR_COL |
3 | lab_mo_kpneumoniae | B_KLBSL_PNE |
4 | lab_Staph_aureus | B_STPHY_AUR |
5 | | |
</pre>
<p>...any new usage of an MO function in this package will update your data:</p><pre>
as.mo("lab_mo_ecoli")
# Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
# B_ESCHR_COL
mo_genus("lab_Staph_aureus")
# "Staphylococcus"
</pre>
<p>To remove the reference completely, just use any of these:</p><pre>
<p>On our website <ahref='https://msberends.gitlab.io/AMR'>https://msberends.gitlab.io/AMR</a> you can find <ahref='https://msberends.gitlab.io/AMR/articles/AMR.html'>a comprehensive tutorial</a> about how to conduct AMR analysis, the <ahref='https://msberends.gitlab.io/AMR/reference'>complete documentation of all functions</a> (which reads a lot easier than here in R) and <ahref='https://msberends.gitlab.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>.</p>
<p>Developed by <ahref='https://www.rug.nl/staff/m.s.berends/'>Matthijs S. Berends</a>, <ahref='https://www.rug.nl/staff/c.f.luz/'>Christian F. Luz</a>, <ahref='https://www.rug.nl/staff/c.glasner/'>Corinna Glasner</a>, <ahref='https://www.rug.nl/staff/a.w.friedrich/'>Alex W. Friedrich</a>, <ahref='https://www.rug.nl/staff/b.sinha/'>Bhanu N. M. Sinha</a>.</p>
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