How to work with WHONET data

Matthijs S. Berends

22 February 2020

Import of data

This tutorial assumes you already imported the WHONET data with e.g. the readxl package. 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:

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

This package comes with an example data set WHONET. 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/.

library(dplyr)   # part of tidyverse
library(ggplot2) # part of tidyverse
library(AMR)     # this package

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

# 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 `rsi` class
  mutate_at(vars(AMP_ND10:CIP_EE), as.rsi)

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. It gets automatically installed with the AMR package. For its freq() function to create frequency tables, you don’t even need to load it yourself as it is available through the AMR package as well.

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

# 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: 39

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 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 29 entries, n = 57 [11.40%])

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

Frequency table

Class: factor > ordered > rsi (numeric)
Length: 500
Levels: 3: S < I < R
Available: 481 (96.2%, NA: 19 = 3.8%)
Unique: 3

%SI: 78.6%

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_rsi() function:

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