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().
  • 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.

No errors or warnings, so all values are transformed succesfully. Let’s check it though, with a couple of frequency tables:

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%])

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)