AMR/vignettes/MDR.Rmd

---
title: "How to determine multi-drug resistance (MDR)"
author: "Matthijs S. Berends"
date: '`r format(Sys.Date(), "%d %B %Y")`'
output: 
  rmarkdown::html_vignette:
    toc: true
vignette: >
  %\VignetteIndexEntry{How to determine multi-drug resistance (MDR)}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#"
)
library(AMR)
```

With the function `mdro()`, you can determine multi-drug resistant organisms (MDRO). It currently support these guidelines:

* "Intrinsic Resistance and Exceptional Phenotypes Tables", by EUCAST (European Committee on Antimicrobial Susceptibility Testing)
* "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis", by WHO (World Health Organization)
* "WIP-Richtlijn Bijzonder Resistente Micro-organismen (BRMO)", by RIVM (Rijksinstituut voor de Volksgezondheid, the Netherlands National Institute for Public Health and the Environment)

As an example, I will make a data set to determine multi-drug resistant TB:

```{r}
# a helper function to get a random vector with values S, I and R
# with the probabilities 50% - 10% - 40%
sample_rsi <- function() {
  sample(c("S", "I", "R"),
         size = 5000,
         prob = c(0.5, 0.1, 0.4),
         replace = TRUE)
}

my_TB_data <- data.frame(rifampicin = sample_rsi(),
                         isoniazid = sample_rsi(),
                         gatifloxacin = sample_rsi(),
                         ethambutol = sample_rsi(),
                         pyrazinamide = sample_rsi(),
                         moxifloxacin = sample_rsi(),
                         kanamycin = sample_rsi())
```

Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same:

```{r, eval = FALSE}
my_TB_data <- data.frame(RIF = sample_rsi(),
                         INH = sample_rsi(),
                         GAT = sample_rsi(),
                         ETH = sample_rsi(),
                         PZA = sample_rsi(),
                         MFX = sample_rsi(),
                         KAN = sample_rsi())
```

The data set looks like this now:

```{r}
head(my_TB_data)
```

We can now add the interpretation of MDR-TB to our data set:

```{r}
my_TB_data$mdr <- mdr_tb(my_TB_data)
```

We also created a package dedicated to data cleaning and checking, called the `clean` package. It gets automatically installed with the `AMR` package, so we only have to load it:

```{r lib clean, message = FALSE}
library(clean)
```

It contains the `freq()` function, to create a frequency table:

```{r, results = 'asis'}
freq(my_TB_data$mdr)
```