(v1.1.0.9009) lose dependencies

vignettes/AMR.Rmd

@@ -30,7 +30,7 @@ knitr::opts_chunk$set(
 
 Conducting antimicrobial resistance analysis unfortunately requires in-depth knowledge from different scientific fields, which makes it hard to do right. At least, it requires:
 
-* Good questions (always start with these!)
+* Good questions (always start with those!)
 * 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, 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

vignettes/benchmarks.Rmd

@@ -54,6 +54,7 @@ ggplot.bm <- function(df, title = NULL) {
 ```{r, message = FALSE}
 microbenchmark <- microbenchmark::microbenchmark
 library(AMR)
+library(dplyr)
 ```
 
 In the next test, we try to 'coerce' different input values into the microbial code of *Staphylococcus aureus*. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.