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< h1 > How to work with WHONET data< / h1 >
< h4 class = "author" > Matthijs S. Berends< / h4 >
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< h4 class = "date" > 03 June 2019< / h4 >
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< div class = "hidden name" > < code > WHONET.Rmd< / code > < / div >
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< div id = "import-of-data" class = "section level1" >
< h1 class = "hasAnchor" >
< a href = "#import-of-data" class = "anchor" > < / a > Import of data< / h1 >
< p > This tutorial assumes you already imported the WHONET data with e.g. < a href = "https://readxl.tidyverse.org/" > < code > readxl< / code > package< / a > . In RStudio, this can be done using the menu button ‘ ’ ‘ ’ ‘ ’ < / p >
< p > An example syntax could look like this:< / p >
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< div class = "sourceCode" id = "cb1" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb1-1" title = "1" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/library" > library< / a > < / span > (readxl)< / a >
< a class = "sourceLine" id = "cb1-2" title = "2" > data < -< span class = "st" > < / span > < span class = "kw" > < a href = "https://readxl.tidyverse.org/reference/read_excel.html" > read_excel< / a > < / span > (< span class = "dt" > path =< / span > < span class = "st" > "path/to/your/file.xlsx"< / span > )< / a > < / code > < / pre > < / div >
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< p > This package comes with an < a href = "https://msberends.gitlab.io/AMR/reference/WHONET.html" > example data set < code > WHONET< / code > < / a > . We will use it for this analysis.< / p >
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< / div >
< div id = "preparation" class = "section level1" >
< h1 class = "hasAnchor" >
< a href = "#preparation" class = "anchor" > < / a > Preparation< / h1 >
< p > First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’ < a href = "https://www.tidyverse.org/" class = "uri" > https://www.tidyverse.org/< / a > .< / p >
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< div class = "sourceCode" id = "cb2" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb2-1" title = "1" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/library" > library< / a > < / span > (dplyr) < span class = "co" > # part of tidyverse< / span > < / a >
< a class = "sourceLine" id = "cb2-2" title = "2" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/library" > library< / a > < / span > (ggplot2) < span class = "co" > # part of tidyverse< / span > < / a >
< a class = "sourceLine" id = "cb2-3" title = "3" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/library" > library< / a > < / span > (AMR) < span class = "co" > # this package< / span > < / a > < / code > < / pre > < / div >
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< p > We will have to transform some variables to simplify and automate the analysis:< / p >
< ul >
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< li > Microorganisms should be transformed to our own microorganism IDs (called an < code > mo< / code > ) using < a href = "https://msberends.gitlab.io/AMR/reference/ITIS.html" > the ITIS reference data set< / a > , which contains all ~20,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with < code > < a href = "../reference/as.mo.html" > as.mo()< / a > < / code > . This function also recognises almost all WHONET abbreviations of microorganisms.< / li >
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< li > Antimicrobial results or interpretations have to be clean and valid. In other words, they should only contain values < code > "S"< / code > , < code > "I"< / code > or < code > "R"< / code > . That is exactly where the < code > < a href = "../reference/as.rsi.html" > as.rsi()< / a > < / code > function is for.< / li >
< / ul >
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< div class = "sourceCode" id = "cb3" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb3-1" title = "1" > < span class = "co" > # transform variables< / span > < / a >
< a class = "sourceLine" id = "cb3-2" title = "2" > data < -< span class = "st" > < / span > WHONET < span class = "op" > %> %< / span > < / a >
< a class = "sourceLine" id = "cb3-3" title = "3" > < span class = "st" > < / span > < span class = "co" > # get microbial ID based on given organism< / span > < / a >
< a class = "sourceLine" id = "cb3-4" title = "4" > < span class = "st" > < / span > < span class = "kw" > < a href = "https://dplyr.tidyverse.org/reference/mutate.html" > mutate< / a > < / span > (< span class = "dt" > mo =< / span > < span class = "kw" > < a href = "../reference/as.mo.html" > as.mo< / a > < / span > (Organism)) < span class = "op" > %> %< / span > < span class = "st" > < / span > < / a >
< a class = "sourceLine" id = "cb3-5" title = "5" > < span class = "st" > < / span > < span class = "co" > # transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class< / span > < / a >
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< a class = "sourceLine" id = "cb3-6" title = "6" > < span class = "st" > < / span > < span class = "kw" > < a href = "https://dplyr.tidyverse.org/reference/mutate_all.html" > mutate_at< / a > < / span > (< span class = "kw" > < a href = "https://dplyr.tidyverse.org/reference/vars.html" > vars< / a > < / span > (AMP_ND10< span class = "op" > :< / span > CIP_EE), as.rsi)< / a > < / code > < / pre > < / div >
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< p > No errors or warnings, so all values are transformed succesfully. Let’ < / p >
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< div class = "sourceCode" id = "cb4" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb4-1" title = "1" > < span class = "co" > # our newly created `mo` variable< / span > < / a >
< a class = "sourceLine" id = "cb4-2" title = "2" > data < span class = "op" > %> %< / span > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/freq.html" > freq< / a > < / span > (mo, < span class = "dt" > nmax =< / span > < span class = "dv" > 10< / span > )< / a > < / code > < / pre > < / div >
< p > < strong > Frequency table of < code > mo< / code > from a < code > data.frame< / code > (500 x 54)< / strong > < / p >
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< p > Class: mo (character)< br >
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Length: 500 (of which NA: 0 = 0.00%)< br >
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Unique: 39< / p >
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< p > Families: 10< br >
Genera: 17< br >
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Species: 38< / p >
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< table class = "table" >
< thead > < tr class = "header" >
< th align = "left" > < / th >
< th align = "left" > Item< / th >
< th align = "right" > Count< / th >
< th align = "right" > Percent< / th >
< th align = "right" > Cum. Count< / th >
< th align = "right" > Cum. Percent< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td align = "left" > 1< / td >
< td align = "left" > B_ESCHR_COL< / td >
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< td align = "right" > 245< / td >
< td align = "right" > 49.0%< / td >
< td align = "right" > 245< / td >
< td align = "right" > 49.0%< / td >
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< / tr >
< tr class = "even" >
< td align = "left" > 2< / td >
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< td align = "left" > B_STPHY_CNS< / td >
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< td align = "right" > 74< / td >
< td align = "right" > 14.8%< / td >
< td align = "right" > 319< / td >
< td align = "right" > 63.8%< / td >
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< / tr >
< tr class = "odd" >
< td align = "left" > 3< / td >
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< td align = "left" > B_STPHY_EPI< / td >
< td align = "right" > 38< / td >
< td align = "right" > 7.6%< / td >
< td align = "right" > 357< / td >
< td align = "right" > 71.4%< / td >
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< / tr >
< tr class = "even" >
< td align = "left" > 4< / td >
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< td align = "left" > B_STRPT_PNE< / td >
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< td align = "right" > 31< / td >
< td align = "right" > 6.2%< / td >
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< td align = "right" > 388< / td >
< td align = "right" > 77.6%< / td >
< / tr >
< tr class = "odd" >
< td align = "left" > 5< / td >
< td align = "left" > B_STPHY_HOM< / td >
< td align = "right" > 21< / td >
< td align = "right" > 4.2%< / td >
< td align = "right" > 409< / td >
< td align = "right" > 81.8%< / td >
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< / tr >
< tr class = "even" >
< td align = "left" > 6< / td >
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< td align = "left" > B_PROTS_MIR< / td >
< td align = "right" > 9< / td >
< td align = "right" > 1.8%< / td >
< td align = "right" > 418< / td >
< td align = "right" > 83.6%< / td >
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< / tr >
< tr class = "odd" >
< td align = "left" > 7< / td >
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< td align = "left" > B_ENTRC_IUM< / td >
< td align = "right" > 8< / td >
< td align = "right" > 1.6%< / td >
< td align = "right" > 426< / td >
< td align = "right" > 85.2%< / td >
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< / tr >
< tr class = "even" >
< td align = "left" > 8< / td >
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< td align = "left" > B_STPHY_CAP< / td >
< td align = "right" > 8< / td >
< td align = "right" > 1.6%< / td >
< td align = "right" > 434< / td >
< td align = "right" > 86.8%< / td >
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< / tr >
< tr class = "odd" >
< td align = "left" > 9< / td >
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< td align = "left" > B_ENTRB_CLO< / td >
< td align = "right" > 5< / td >
< td align = "right" > 1.0%< / td >
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< td align = "right" > 439< / td >
< td align = "right" > 87.8%< / td >
< / tr >
< tr class = "even" >
< td align = "left" > 10< / td >
< td align = "left" > B_ENTRC_COL< / td >
< td align = "right" > 4< / td >
< td align = "right" > 0.8%< / td >
< td align = "right" > 443< / td >
< td align = "right" > 88.6%< / td >
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< / tr >
< / tbody >
< / table >
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< p > (omitted 29 entries, n = 57 [11.4%])< / p >
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< div class = "sourceCode" id = "cb5" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb5-1" title = "1" > < / a >
< a class = "sourceLine" id = "cb5-2" title = "2" > < span class = "co" > # our transformed antibiotic columns< / span > < / a >
< a class = "sourceLine" id = "cb5-3" title = "3" > < span class = "co" > # amoxicillin/clavulanic acid (J01CR02) as an example< / span > < / a >
< a class = "sourceLine" id = "cb5-4" title = "4" > data < span class = "op" > %> %< / span > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/freq.html" > freq< / a > < / span > (AMC_ND2)< / a > < / code > < / pre > < / div >
< p > < strong > Frequency table of < code > AMC_ND2< / code > from a < code > data.frame< / code > (500 x 54)< / strong > < / p >
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< p > Class: factor > ordered > rsi (numeric)< br >
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Length: 500 (of which NA: 19 = 3.80%)< br >
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Levels: 3: S < I < R< br >
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Unique: 3< / p >
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< p > Drug: Amoxicillin/clavulanic acid (AMC, J01CR02)< br >
Group: Beta-lactams/penicillins< br >
%SI: 78.59%< / p >
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< table class = "table" >
< thead > < tr class = "header" >
< th align = "left" > < / th >
< th align = "left" > Item< / th >
< th align = "right" > Count< / th >
< th align = "right" > Percent< / th >
< th align = "right" > Cum. Count< / th >
< th align = "right" > Cum. Percent< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td align = "left" > 1< / td >
< td align = "left" > S< / td >
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< td align = "right" > 356< / td >
< td align = "right" > 74.0%< / td >
< td align = "right" > 356< / td >
< td align = "right" > 74.0%< / td >
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< / tr >
< tr class = "even" >
< td align = "left" > 2< / td >
< td align = "left" > R< / td >
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< td align = "right" > 103< / td >
< td align = "right" > 21.4%< / td >
< td align = "right" > 459< / td >
< td align = "right" > 95.4%< / td >
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< / tr >
< tr class = "odd" >
< td align = "left" > 3< / td >
< td align = "left" > I< / td >
< td align = "right" > 22< / td >
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< td align = "right" > 4.6%< / td >
< td align = "right" > 481< / td >
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< td align = "right" > 100.0%< / td >
< / tr >
< / tbody >
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< a href = "#analysis" class = "anchor" > < / a > Analysis< / h1 >
< p > < em > (more will be available soon)< / em > < / p >
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< a href = "#tocnav" class = "anchor" > < / a > Contents< / h2 >
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< li > < a href = "#import-of-data" > Import of data< / a > < / li >
< li > < a href = "#preparation" > Preparation< / a > < / li >
< li > < a href = "#analysis" > Analysis< / a > < / li >
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< 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/c.glasner/" > Corinna Glasner< / a > , < a href = "https://www.rug.nl/staff/a.w.friedrich/" > Alex W. Friedrich< / a > , < a href = "https://www.rug.nl/staff/b.sinha/" > Bhanu N. M. Sinha< / a > .< / p >
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