# Work with WHONET data

### Import of data

This tutorial assumes you already imported the WHONET data with e.g. the
[`readxl` package](https://readxl.tidyverse.org/). In RStudio, this can
be done using the menu button ‘Import Dataset’ in the tab ‘Environment’.
Choose the option ‘From Excel’ and select your exported file. Make sure
date fields are imported correctly.

An example syntax could look like this:

``` r

library(readxl)
data <- read_excel(path = "path/to/your/file.xlsx")
```

This package comes with an [example data set
`WHONET`](https://amr-for-r.org/reference/WHONET.html). We will use it
for this analysis.

### Preparation

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’t know it yet,
I suggest you read about it on their website:
<https://www.tidyverse.org/>.

``` r

library(dplyr) # part of tidyverse
library(ggplot2) # part of tidyverse
library(AMR) # this package
library(cleaner) # to create frequency tables
```

We will have to transform some variables to simplify and automate the
analysis:

- Microorganisms should be transformed to our own microorganism codes
  (called an `mo`) using [our Catalogue of Life reference data
  set](https://amr-for-r.org/reference/catalogue_of_life), which
  contains all ~70,000 microorganisms from the taxonomic kingdoms
  Bacteria, Fungi and Protozoa. We do the tranformation with
  [`as.mo()`](https://amr-for-r.org/reference/as.mo.md). This function
  also recognises almost all WHONET abbreviations of microorganisms.
- Antimicrobial results or interpretations have to be clean and valid.
  In other words, they should only contain values `"S"`, `"I"` or `"R"`.
  That is exactly where the
  [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) function is
  for.

``` r

# transform variables
data <- WHONET %>%
  # get microbial ID based on given organism
  mutate(mo = as.mo(Organism)) %>%
  # transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class
  mutate_at(vars(AMP_ND10:CIP_EE), as.sir)
```

No errors or warnings, so all values are transformed succesfully.

We also created a package dedicated to data cleaning and checking,
called the `cleaner` package. Its
[`freq()`](https://msberends.github.io/cleaner/reference/freq.html)
function can be used to create frequency tables.

So let’s check our data, with a couple of frequency tables:

``` r

# our newly created `mo` variable, put in the mo_name() function
data %>% freq(mo_name(mo), nmax = 10)
```

**Frequency table**

Class: character  
Length: 500  
Available: 500 (100%, NA: 0 = 0%)  
Unique: 38

Shortest: 11  
Longest: 40

|  | Item | Count | Percent | Cum. Count | Cum. Percent |
|:---|:---|---:|---:|---:|---:|
| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |

(omitted 28 entries, n = 57 \[11.4%\])

``` r

# our transformed antibiotic columns
# amoxicillin/clavulanic acid (J01CR02) as an example
data %>% freq(AMC_ND2)
```

**Frequency table**

Class: factor \> ordered \> sir (numeric)  
Length: 500  
Levels: 8: S \< SDD \< I \< R \< NI \< WT \< NWT \< NS  
Available: 481 (96.2%, NA: 19 = 3.8%)  
Unique: 3

Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)  
Drug group: Aminopenicillins  
%SI: 78.59%

|     | Item | Count | Percent | Cum. Count | Cum. Percent |
|:----|:-----|------:|--------:|-----------:|-------------:|
| 1   | S    |   356 |  74.01% |        356 |       74.01% |
| 2   | R    |   103 |  21.41% |        459 |       95.43% |
| 3   | I    |    22 |   4.57% |        481 |      100.00% |

### A first glimpse at results

An easy `ggplot` will already give a lot of information, using the
included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
function:

``` r

data %>%
  group_by(Country) %>%
  select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
  ggplot_sir(translate_ab = "ab", facet = "Country", datalabels = FALSE)
```

![](WHONET_files/figure-html/unnamed-chunk-7-1.png)
