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dr. M.S. (Matthijs) Berends
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vignettes/WHONET.Rmd
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@ -32,7 +32,7 @@ library(readxl)
data <- read_excel(path = "path/to/your/file.xlsx")
```
This package comes with an [example data set `WHONET`](https://msberends.github.io/AMR/reference/WHONET.html). We will use it for this analysis.
This package comes with an [example data set `WHONET`](https://amr-for-r.org/reference/WHONET.html). We will use it for this analysis.
### Preparation
@ -47,7 +47,7 @@ library(cleaner) # to create frequency tables
We will have to transform some variables to simplify and automate the analysis:
* Microorganisms should be transformed to our own microorganism codes (called an `mo`) using [our Catalogue of Life reference data set](https://msberends.github.io/AMR/reference/catalogue_of_life), which contains all ~70,000 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.
* Microorganisms should be transformed to our own microorganism codes (called an `mo`) using [our Catalogue of Life reference data set](https://amr-for-r.org/reference/catalogue_of_life), which contains all ~70,000 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.sir()` function is for.
```{r}