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Work with WHONET data
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< h1 data-toc-skip > How to work with WHONET data< / h1 >
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< small class = "dont-index" > Source: < a href = "https://github.com/msberends/AMR/blob/HEAD/vignettes/WHONET.Rmd" class = "external-link" > < code > vignettes/WHONET.Rmd< / code > < / a > < / small >
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< div class = "hidden name" > < code > WHONET.Rmd< / code > < / div >
< / div >
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< div class = "section level3" >
< h3 id = "import-of-data" > Import of data< a class = "anchor" aria-label = "anchor" href = "#import-of-data" > < / a >
< / h3 >
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< p > This tutorial assumes you already imported the WHONET data with
e.g. < a href = "https://readxl.tidyverse.org/" class = "external-link" > < code > readxl< / code >
package< / a > . In RStudio, this can be done using the menu button ‘
Dataset’ ‘ ’ ‘ ’
select your exported file. Make sure date fields are imported
correctly.< / p >
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< p > An example syntax could look like this:< / p >
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< div class = "sourceCode" id = "cb1" > < pre class = "downlit sourceCode r" >
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< code class = "sourceCode R" > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://readxl.tidyverse.org" class = "external-link" > readxl< / a > < / span > < span class = "op" > )< / span >
< span class = "va" > data< / span > < span class = "op" > < -< / span > < span class = "fu" > < a href = "https://readxl.tidyverse.org/reference/read_excel.html" class = "external-link" > read_excel< / a > < / span > < span class = "op" > (< / span > path < span class = "op" > =< / span > < span class = "st" > "path/to/your/file.xlsx"< / span > < span class = "op" > )< / span > < / code > < / pre > < / div >
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< p > This package comes with an < a href = "https://msberends.github.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 >
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< div class = "section level3" >
< h3 id = "preparation" > Preparation< a class = "anchor" aria-label = "anchor" href = "#preparation" > < / a >
< / h3 >
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< 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’
yet, I suggest you read about it on their website: < a href = "https://www.tidyverse.org/" class = "external-link uri" > https://www.tidyverse.org/< / a > .< / p >
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< div class = "sourceCode" id = "cb2" > < pre class = "downlit sourceCode r" >
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< code class = "sourceCode R" > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://dplyr.tidyverse.org" class = "external-link" > dplyr< / a > < / span > < span class = "op" > )< / span > < span class = "co" > # part of tidyverse< / span >
< span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://ggplot2.tidyverse.org" class = "external-link" > ggplot2< / a > < / span > < span class = "op" > )< / span > < span class = "co" > # part of tidyverse< / span >
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< span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://msberends.github.io/AMR/" > AMR< / a > < / span > < span class = "op" > )< / span > < span class = "co" > # this package< / span >
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< span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://github.com/msberends/cleaner" class = "external-link" > cleaner< / a > < / span > < span class = "op" > )< / span > < span class = "co" > # to create frequency tables< / span > < / code > < / pre > < / div >
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< p > We will have to transform some variables to simplify and automate the
analysis:< / p >
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< ul >
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< li > Microorganisms should be transformed to our own microorganism codes
(called an < code > mo< / code > ) using < a href = "https://msberends.github.io/AMR/reference/catalogue_of_life" > our
Catalogue of Life reference data set< / a > , which contains all ~70,000
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 >
< 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 >
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< / ul >
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< div class = "sourceCode" id = "cb3" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "co" > # transform variables< / span >
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< span class = "va" > data< / span > < span class = "op" > < -< / span > < span class = "va" > WHONET< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span >
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< span class = "co" > # get microbial ID based on given organism< / span >
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< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/mutate.html" class = "external-link" > mutate< / a > < / span > < span class = "op" > (< / span > mo < span class = "op" > =< / span > < span class = "fu" > < a href = "../reference/as.mo.html" > as.mo< / a > < / span > < span class = "op" > (< / span > < span class = "va" > Organism< / span > < span class = "op" > )< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span >
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< span class = "co" > # transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class< / span >
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< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/mutate_all.html" class = "external-link" > mutate_at< / a > < / span > < span class = "op" > (< / span > < span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/vars.html" class = "external-link" > vars< / a > < / span > < span class = "op" > (< / span > < span class = "va" > AMP_ND10< / span > < span class = "op" > :< / span > < span class = "va" > CIP_EE< / span > < span class = "op" > )< / span > , < span class = "va" > as.rsi< / span > < span class = "op" > )< / span > < / code > < / pre > < / div >
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< p > No errors or warnings, so all values are transformed succesfully.< / p >
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< p > We also created a package dedicated to data cleaning and checking,
called the < code > cleaner< / code > package. Its < code > < a href = "https://rdrr.io/pkg/cleaner/man/freq.html" class = "external-link" > freq()< / a > < / code >
function can be used to create frequency tables.< / p >
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< p > So let’ < / p >
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< div class = "sourceCode" id = "cb4" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "co" > # our newly created `mo` variable, put in the mo_name() function< / span >
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< span class = "va" > data< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < span class = "fu" > < a href = "https://rdrr.io/pkg/cleaner/man/freq.html" class = "external-link" > freq< / a > < / span > < span class = "op" > (< / span > < span class = "fu" > < a href = "../reference/mo_property.html" > mo_name< / a > < / span > < span class = "op" > (< / span > < span class = "va" > mo< / span > < span class = "op" > )< / span > , nmax < span class = "op" > =< / span > < span class = "fl" > 10< / span > < span class = "op" > )< / span > < / code > < / pre > < / div >
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< p > < strong > Frequency table< / strong > < / p >
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< p > Class: character< br >
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Length: 500< br >
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Available: 500 (100%, NA: 0 = 0%)< br >
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Unique: 37< / p >
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< p > Shortest: 11< br >
Longest: 40< / p >
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< table class = "table" >
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< colgroup >
< col width = "4%" >
< col width = "47%" >
< col width = "7%" >
< col width = "10%" >
< col width = "13%" >
< col width = "15%" >
< / colgroup >
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< 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 >
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< td align = "left" > Escherichia coli< / 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 >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "even" >
< td align = "left" > 2< / td >
2019-11-29 19:43:23 +01:00
< td align = "left" > Coagulase-negative Staphylococcus (CoNS)< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 74< / td >
< td align = "right" > 14.8%< / td >
< td align = "right" > 319< / td >
< td align = "right" > 63.8%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "odd" >
< td align = "left" > 3< / td >
2019-11-29 19:43:23 +01:00
< td align = "left" > Staphylococcus epidermidis< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 38< / td >
< td align = "right" > 7.6%< / td >
< td align = "right" > 357< / td >
< td align = "right" > 71.4%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "even" >
< td align = "left" > 4< / td >
2019-11-29 19:43:23 +01:00
< td align = "left" > Streptococcus pneumoniae< / td >
2019-01-29 20:20:09 +01:00
< td align = "right" > 31< / td >
< td align = "right" > 6.2%< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 388< / td >
< td align = "right" > 77.6%< / td >
< / tr >
< tr class = "odd" >
< td align = "left" > 5< / td >
2019-11-29 19:43:23 +01:00
< td align = "left" > Staphylococcus hominis< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 21< / td >
< td align = "right" > 4.2%< / td >
< td align = "right" > 409< / td >
< td align = "right" > 81.8%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "even" >
< td align = "left" > 6< / td >
2019-11-29 19:43:23 +01:00
< td align = "left" > Proteus mirabilis< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 9< / td >
< td align = "right" > 1.8%< / td >
< td align = "right" > 418< / td >
< td align = "right" > 83.6%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "odd" >
< td align = "left" > 7< / td >
2019-11-29 19:43:23 +01:00
< td align = "left" > Enterococcus faecium< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 8< / td >
< td align = "right" > 1.6%< / td >
< td align = "right" > 426< / td >
< td align = "right" > 85.2%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "even" >
< td align = "left" > 8< / td >
2019-11-29 19:43:23 +01:00
< td align = "left" > Staphylococcus capitis< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 8< / td >
< td align = "right" > 1.6%< / td >
< td align = "right" > 434< / td >
< td align = "right" > 86.8%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "odd" >
< td align = "left" > 9< / td >
2019-11-29 19:43:23 +01:00
< td align = "left" > Enterobacter cloacae< / td >
2019-03-15 13:57:25 +01:00
< td align = "right" > 5< / td >
< td align = "right" > 1.0%< / td >
2019-03-27 11:22:36 +01:00
< td align = "right" > 439< / td >
< td align = "right" > 87.8%< / td >
< / tr >
< tr class = "even" >
< td align = "left" > 10< / td >
2020-05-28 16:48:55 +02:00
< td align = "left" > Streptococcus anginosus< / td >
< td align = "right" > 5< / td >
< td align = "right" > 1.0%< / td >
< td align = "right" > 444< / td >
< td align = "right" > 88.8%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< / tbody >
< / table >
2022-03-14 16:36:10 +01:00
< p > (omitted 27 entries, n = 56 [11.2%])< / p >
2021-05-24 15:29:17 +02:00
< div class = "sourceCode" id = "cb5" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "co" > # our transformed antibiotic columns< / span >
2020-04-17 19:16:30 +02:00
< span class = "co" > # amoxicillin/clavulanic acid (J01CR02) as an example< / span >
2021-12-06 11:12:30 +01:00
< span class = "va" > data< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < span class = "fu" > < a href = "https://rdrr.io/pkg/cleaner/man/freq.html" class = "external-link" > freq< / a > < / span > < span class = "op" > (< / span > < span class = "va" > AMC_ND2< / span > < span class = "op" > )< / span > < / code > < / pre > < / div >
2019-07-29 13:33:48 +02:00
< p > < strong > Frequency table< / strong > < / p >
2019-05-28 16:50:40 +02:00
< p > Class: factor > ordered > rsi (numeric)< br >
2020-02-23 20:56:11 +01:00
Length: 500< br >
2019-05-28 16:50:40 +02:00
Levels: 3: S < I < R< br >
2020-02-23 20:56:11 +01:00
Available: 481 (96.2%, NA: 19 = 3.8%)< br >
2019-01-30 16:00:55 +01:00
Unique: 3< / p >
2020-10-26 12:23:03 +01:00
< p > Drug: Amoxicillin/clavulanic acid (AMC, J01CR02)< br >
Drug group: Beta-lactams/penicillins< br >
%SI: 78.59%< / p >
2019-01-29 20:20:09 +01:00
< 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 >
2019-02-09 22:16:24 +01:00
< td align = "right" > 356< / td >
2019-10-13 09:31:58 +02:00
< td align = "right" > 74.01%< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 356< / td >
2019-10-13 09:31:58 +02:00
< td align = "right" > 74.01%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "even" >
< td align = "left" > 2< / td >
< td align = "left" > R< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 103< / td >
2019-10-13 09:31:58 +02:00
< td align = "right" > 21.41%< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 459< / td >
2019-10-13 09:31:58 +02:00
< td align = "right" > 95.43%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< tr class = "odd" >
< td align = "left" > 3< / td >
< td align = "left" > I< / td >
< td align = "right" > 22< / td >
2019-10-13 09:31:58 +02:00
< td align = "right" > 4.57%< / td >
2019-02-09 22:16:24 +01:00
< td align = "right" > 481< / td >
2019-10-13 09:31:58 +02:00
< td align = "right" > 100.00%< / td >
2019-01-29 20:20:09 +01:00
< / tr >
< / tbody >
< / table >
< / div >
2021-12-06 11:12:30 +01:00
< div class = "section level3" >
< h3 id = "a-first-glimpse-at-results" > A first glimpse at results< a class = "anchor" aria-label = "anchor" href = "#a-first-glimpse-at-results" > < / a >
< / h3 >
2022-05-11 10:26:58 +02:00
< p > An easy < code > ggplot< / code > will already give a lot of information,
using the included < code > < a href = "../reference/ggplot_rsi.html" > ggplot_rsi()< / a > < / code > function:< / p >
2021-05-24 15:29:17 +02:00
< div class = "sourceCode" id = "cb6" > < pre class = "downlit sourceCode r" >
2021-12-06 11:12:30 +01:00
< code class = "sourceCode R" > < span class = "va" > data< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span >
< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/group_by.html" class = "external-link" > group_by< / a > < / span > < span class = "op" > (< / span > < span class = "va" > Country< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span >
< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/select.html" class = "external-link" > select< / a > < / span > < span class = "op" > (< / span > < span class = "va" > Country< / span > , < span class = "va" > AMP_ND2< / span > , < span class = "va" > AMC_ED20< / span > , < span class = "va" > CAZ_ED10< / span > , < span class = "va" > CIP_ED5< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span >
2021-05-24 15:29:17 +02:00
< span class = "fu" > < a href = "../reference/ggplot_rsi.html" > ggplot_rsi< / a > < / span > < span class = "op" > (< / span > translate_ab < span class = "op" > =< / span > < span class = "st" > 'ab'< / span > , facet < span class = "op" > =< / span > < span class = "st" > "Country"< / span > , datalabels < span class = "op" > =< / span > < span class = "cn" > FALSE< / span > < span class = "op" > )< / span > < / code > < / pre > < / div >
2019-11-29 19:43:23 +01:00
< p > < img src = "WHONET_files/figure-html/unnamed-chunk-7-1.png" width = "720" > < / p >
2019-01-29 20:20:09 +01:00
< / div >
2019-01-29 00:06:50 +01:00
< / div >
2020-04-13 21:09:56 +02:00
< div class = "col-md-3 hidden-xs hidden-sm" id = "pkgdown-sidebar" >
2019-10-13 09:31:58 +02:00
2019-01-29 00:06:50 +01:00
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2019-10-13 09:31:58 +02:00
2019-01-29 00:06:50 +01:00
< footer > < div class = "copyright" >
2021-07-23 21:42:11 +02:00
< p > < / p >
2022-03-27 09:37:55 +02:00
< p > Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.< / p >
2019-01-29 00:06:50 +01:00
< / div >
< div class = "pkgdown" >
2021-07-23 21:42:11 +02:00
< p > < / p >
2022-05-11 10:26:58 +02:00
< p > Site built with < a href = "https://pkgdown.r-lib.org/" class = "external-link" > pkgdown< / a > 2.0.3.< / p >
2019-01-29 00:06:50 +01:00
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2019-10-13 09:31:58 +02:00
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< / footer >
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2020-02-23 20:56:11 +01:00
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