added mdr_tb()

vignettes/MDR.Rmd

@@ -0,0 +1,79 @@
+---
+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)
+```
+
+And review the result with a frequency table:
+
+```{r}
+freq(my_TB_data$mdr)
+```