How to work with WHONET data

Matthijs S. Berends

08 February 2019

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:

• Microorganisms should be transformed to our own microorganism IDs (called an mo) using the ITIS reference data set, which contains all ~20,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with as.mo(). 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.rsi() function is for.

# 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. Let’s check it though, with a couple of frequency tables:

# our newly created `mo` variable
data %>% freq(mo, nmax = 10)

Frequency table of mo from a data.frame (500 x 54)
Class: mo (character)
Length: 500 (of which NA: 0 = 0.00%)
Unique: 56

Families: 14
Genera: 23
Species: 51

	Item	Count	Percent	Cum. Count	Cum. Percent
1	B_ESCHR_COL	127	25.4%	127	25.4%
2	B_STPHY_CNS	80	16.0%	207	41.4%
3	B_STPHY_AUR	50	10.0%	257	51.4%
4	B_STPHY_EPI	37	7.4%	294	58.8%
5	B_STRPTC_PNE	31	6.2%	325	65.0%
6	B_STPHY_HOM	23	4.6%	348	69.6%
7	B_PROTS_MIR	13	2.6%	361	72.2%
8	B_KLBSL_PNE	11	2.2%	372	74.4%
9	B_PDMNS_AER	8	1.6%	380	76.0%
10	B_STPHY_CAP	8	1.6%	388	77.6%

(omitted 46 entries, n = 112 [22.4%])

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

Frequency table of AMC_ND2 from a data.frame (500 x 54)
Class: factor > ordered > rsi (numeric)
Levels: S < I < R
Length: 500 (of which NA: 41 = 8.20%)
Unique: 3

%IR: 28.98% (ratio S : IR = 1.0 : 0.4)

	Item	Count	Percent	Cum. Count	Cum. Percent
1	S	326	71.0%	326	71.0%
2	R	111	24.2%	437	95.2%
3	I	22	4.8%	459	100.0%

Analysis

(more will be available soon)