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138 lines
4.9 KiB
Markdown
138 lines
4.9 KiB
Markdown
# Work with WHONET data
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### Import of data
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This tutorial assumes you already imported the WHONET data with e.g. the
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[`readxl` package](https://readxl.tidyverse.org/). In RStudio, this can
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be done using the menu button ‘Import Dataset’ in the tab ‘Environment’.
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Choose the option ‘From Excel’ and select your exported file. Make sure
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date fields are imported correctly.
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An example syntax could look like this:
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``` r
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library(readxl)
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data <- read_excel(path = "path/to/your/file.xlsx")
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```
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This package comes with an [example data set
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`WHONET`](https://amr-for-r.org/reference/WHONET.html). We will use it
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for this analysis.
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### Preparation
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First, load the relevant packages if you did not yet did this. I use the
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tidyverse for all of my analyses. All of them. If you don’t know it yet,
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I suggest you read about it on their website:
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<https://www.tidyverse.org/>.
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``` r
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library(dplyr) # part of tidyverse
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library(ggplot2) # part of tidyverse
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library(AMR) # this package
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library(cleaner) # to create frequency tables
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```
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We will have to transform some variables to simplify and automate the
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analysis:
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- Microorganisms should be transformed to our own microorganism codes
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(called an `mo`) using [our Catalogue of Life reference data
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set](https://amr-for-r.org/reference/catalogue_of_life), which
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contains all ~70,000 microorganisms from the taxonomic kingdoms
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Bacteria, Fungi and Protozoa. We do the tranformation with
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[`as.mo()`](https://amr-for-r.org/reference/as.mo.md). This function
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also recognises almost all WHONET abbreviations of microorganisms.
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- Antimicrobial results or interpretations have to be clean and valid.
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In other words, they should only contain values `"S"`, `"I"` or `"R"`.
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That is exactly where the
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[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) function is
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for.
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``` r
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# transform variables
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data <- WHONET %>%
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# get microbial ID based on given organism
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mutate(mo = as.mo(Organism)) %>%
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# transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class
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mutate_at(vars(AMP_ND10:CIP_EE), as.sir)
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```
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No errors or warnings, so all values are transformed succesfully.
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We also created a package dedicated to data cleaning and checking,
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called the `cleaner` package. Its
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[`freq()`](https://msberends.github.io/cleaner/reference/freq.html)
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function can be used to create frequency tables.
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So let’s check our data, with a couple of frequency tables:
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``` r
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# our newly created `mo` variable, put in the mo_name() function
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data %>% freq(mo_name(mo), nmax = 10)
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```
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**Frequency table**
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Class: character
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Length: 500
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Available: 500 (100%, NA: 0 = 0%)
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Unique: 38
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Shortest: 11
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Longest: 40
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| | Item | Count | Percent | Cum. Count | Cum. Percent |
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|:----|:-----------------------------------------|------:|--------:|-----------:|-------------:|
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| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
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| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
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| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
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| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
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| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
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| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
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| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
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| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
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| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
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| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
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(omitted 28 entries, n = 57 \[11.4%\])
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``` r
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# our transformed antibiotic columns
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# amoxicillin/clavulanic acid (J01CR02) as an example
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data %>% freq(AMC_ND2)
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```
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**Frequency table**
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Class: factor \> ordered \> sir (numeric)
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Length: 500
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Levels: 5: S \< SDD \< I \< R \< NI
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Available: 481 (96.2%, NA: 19 = 3.8%)
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Unique: 3
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Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)
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Drug group: Beta-lactams/penicillins
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%SI: 78.59%
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| | Item | Count | Percent | Cum. Count | Cum. Percent |
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|:----|:-----|------:|--------:|-----------:|-------------:|
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| 1 | S | 356 | 74.01% | 356 | 74.01% |
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| 2 | R | 103 | 21.41% | 459 | 95.43% |
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| 3 | I | 22 | 4.57% | 481 | 100.00% |
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### A first glimpse at results
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An easy `ggplot` will already give a lot of information, using the
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included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
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function:
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``` r
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data %>%
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group_by(Country) %>%
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select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
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ggplot_sir(translate_ab = "ab", facet = "Country", datalabels = FALSE)
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```
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