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(v1.1.0.9004) lose dependencies

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dr. M.S. (Matthijs) Berends
2020-05-16 13:05:47 +02:00
parent 9fce546901
commit 7f3da74b17
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vignettes/AMR.Rmd
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@ -32,7 +32,7 @@ Conducting antimicrobial resistance analysis unfortunately requires in-depth kno
* Good questions (always start with these!)
* A thorough understanding of (clinical) epidemiology, to understand the clinical and epidemiological relevance and possible bias of results
* A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment
* A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment, as well as understanding intrinsic and acquired microbial resistance
* Experience with data analysis with microbiological tests and their results, to understand the determination and limitations of MIC values and their interpretations to RSI values
* Availability of the biological taxonomy of microorganisms and probably normalisation factors for pharmaceuticals, such as defined daily doses (DDD)
* Available (inter-)national guidelines, and profound methods to apply them
@ -48,11 +48,12 @@ For this tutorial, we will create fake demonstration data to work with.
You can skip to [Cleaning the data](#cleaning-the-data) if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:
```{r example table, echo = FALSE, results = 'asis'}
knitr::kable(dplyr::tibble(date = Sys.Date(),
patient_id = c("abcd", "abcd", "efgh"),
mo = "Escherichia coli",
AMX = c("S", "S", "R"),
CIP = c("S", "R", "S")),
knitr::kable(data.frame(date = Sys.Date(),
patient_id = c("abcd", "abcd", "efgh"),
mo = "Escherichia coli",
AMX = c("S", "S", "R"),
CIP = c("S", "R", "S"),
stringsAsFactors = FALSE),
align = "c")
```
@ -61,13 +62,18 @@ As with many uses in R, we need some additional packages for AMR analysis. Our p
Our `AMR` package depends on these packages and even extends their use and functions.
```{r lib packages, message = FALSE}
```{r lib packages, eval = FALSE}
library(dplyr)
library(ggplot2)
library(AMR)
# (if not yet installed, install with:)
# install.packages(c("tidyverse", "AMR"))
# install.packages(c("dplyr", "ggplot2", "AMR"))
```
```{r lib packages 2, echo = FALSE, results = 'asis'}
library(AMR)
library(dplyr)
```
# Creation of data