diff --git a/index.md b/index.md
index c2352576..e0991cce 100644
--- a/index.md
+++ b/index.md
@@ -2,13 +2,13 @@
> *18 October 2019*
> **METHODS PAPER PREPRINTED**
-> A methods paper about this package has been preprinted at bioRxiv. It was updated on 8 November 2019. Please click [here for the publishers page](https://doi.org/10.1101/810622).
+> A methods paper about this package has been preprinted at bioRxiv (DOI: 10.1101/810622). It was **updated on 18 December 2019** and in parallel sent to a journal. Please click [here for the paper on bioRxiv's publishers page](https://doi.org/10.1101/810622).
### What is `AMR` (for R)?
*(
TLDR - to find out how to conduct AMR analysis, please [continue reading here to get started](./articles/AMR.html).*
-`AMR` is a free and open-source [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to supply a standard** for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
+`AMR` is a free and open-source [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
After installing this package, R knows [**~70,000 distinct microbial species**](./reference/microorganisms.html) and all [**~550 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code, and knows all about valid RSI and MIC values. It supports any data format, including WHONET/EARS-Net data.
@@ -16,9 +16,9 @@ We created this package for both routine analysis and academic research (as part
- 
- Used in over 70 countries
- Since its first public release in early 2018, this package has been downloaded over 25,000 times from 78 countries (as of December 2019, CRAN logs). Click the map to enlarge.
+
+
Used in almost 80 countries
+ Since its first public release in early 2018, this package has been downloaded over 25,000 times from 79 countries
(as of December 2019, CRAN logs). Click the map to enlarge.
#### Partners
@@ -106,7 +106,7 @@ To find out how to conduct AMR analysis, please [continue reading here to get st
#### Microbial (taxonomic) reference data
-This package contains the complete taxonomic tree of almost all 70,000 microorganisms from the authoritative and comprehensive Catalogue of Life (CoL, [www.catalogueoflife.org](http://www.catalogueoflife.org)). With `catalogue_of_life_version()` can be checked which version of the CoL is included in this package.
+This package contains the complete taxonomic tree of almost all ~70,000 microorganisms from the authoritative and comprehensive Catalogue of Life (CoL, [www.catalogueoflife.org](http://www.catalogueoflife.org)). With `catalogue_of_life_version()` can be checked which version of the CoL is included in this package.
Read more about which data from the Catalogue of Life [in our manual](./reference/catalogue_of_life.html).
diff --git a/pkgdown/logos/countries.png b/pkgdown/logos/countries.png
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diff --git a/vignettes/benchmarks.Rmd b/vignettes/benchmarks.Rmd
index f48f3ead..d14c7865 100755
--- a/vignettes/benchmarks.Rmd
+++ b/vignettes/benchmarks.Rmd
@@ -22,14 +22,15 @@ knitr::opts_chunk$set(
fig.height = 4.5,
dpi = 75
)
+options(AMR_disable_mo_history = FALSE)
```
-
Source: https://gitlab.com/msberends/AMR/blob/master/vignettes/benchmarks.Rmd
-
One of the most important features of this package is the complete microbial taxonomic database, supplied by the [Catalogue of Life](http://catalogueoflife.org). We created a function `as.mo()` that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.
Using the `microbenchmark` package, we can review the calculation performance of this function. Its function `microbenchmark()` runs different input expressions independently of each other and measures their time-to-result.
+`r interactive()`
+
```{r, message = FALSE, echo = FALSE}
library(dplyr)
library(ggplot2)
@@ -64,7 +65,9 @@ library(microbenchmark)
library(AMR)
```
-In the next test, we try to 'coerce' different input values for *Staphylococcus aureus*. The actual result is the same every time: it returns its microorganism code `B_STPHY_AURS` (*B* stands for *Bacteria*, the taxonomic kingdom).
+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.
+
+The actual result is the same every time: it returns its microorganism code `r as.character(as.mo("Staphylococcus aureus"))` (*B* stands for *Bacteria*, the taxonomic kingdom).
But the calculation time differs a lot:
@@ -103,43 +106,20 @@ clear_mo_history()
```
```{r, warning=FALSE}
M.semesiae <- microbenchmark(as.mo("metsem"),
- as.mo("METSEM"),
- as.mo("M. semesiae"),
- as.mo("M. semesiae"),
- as.mo("Methanosarcina semesiae"),
- times = 10)
+ as.mo("METSEM"),
+ as.mo("M. semesiae"),
+ as.mo("M. semesiae"),
+ as.mo("Methanosarcina semesiae"),
+ times = 10)
print(M.semesiae, unit = "ms", signif = 4)
```
-That takes `r round(mean(M.semesiae$time, na.rm = TRUE) / mean(S.aureus$time, na.rm = TRUE), 1)` times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like *Methanosarcina semesiae*) are almost fast - these are the most probable input from most data sets.
+That takes `r round(mean(M.semesiae$time, na.rm = TRUE) / mean(S.aureus$time, na.rm = TRUE), 1)` times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like *Methanosarcina semesiae*) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.
In the figure below, we compare *Escherichia coli* (which is very common) with *Prevotella brevis* (which is moderately common) and with *Methanosarcina semesiae* (which is uncommon):
-```{r, echo = FALSE}
+```{r, echo = FALSE, fig.width=12}
clear_mo_history()
-```
-```{r, echo = FALSE}
-par(mar = c(5, 16, 4, 2))
-boxplot(microbenchmark(
- 'as.mo("Methanosarcina semesiae")' = as.mo("Methanosarcina semesiae"),
- 'as.mo("Prevotella brevis")' = as.mo("Prevotella brevis"),
- 'as.mo("Escherichia coli")' = as.mo("Escherichia coli"),
- 'as.mo("M. semesiae")' = as.mo("M. semesiae"),
- 'as.mo("P. brevis")' = as.mo("P. brevis"),
- 'as.mo("E. coli")' = as.mo("E. coli"),
- times = 10),
- horizontal = TRUE, las = 1, unit = "s", log = TRUE,
- xlab = "", ylab = "Time in seconds (log)",
- main = "Benchmarks per prevalence")
-```
-
-In reality, the `as.mo()` functions **learns from its own output to speed up determinations for next times**. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use `as.mo()` yourself:
-
-```{r, echo = FALSE}
-clear_mo_history()
-```
-
-```{r, echo = FALSE}
par(mar = c(5, 16, 4, 2))
boxplot(microbenchmark(
'as.mo("Methanosarcina semesiae")' = as.mo("Methanosarcina semesiae", force_mo_history = TRUE),
@@ -154,6 +134,26 @@ boxplot(microbenchmark(
main = "Benchmarks per prevalence")
```
+In reality, the `as.mo()` functions **learns from its own output to speed up determinations for next times**. In below figure, this effect was disabled to show the difference with the boxplot above:
+
+```{r, echo = FALSE, fig.width=12}
+clear_mo_history()
+options(AMR_disable_mo_history = TRUE)
+par(mar = c(5, 16, 4, 2))
+boxplot(microbenchmark(
+ 'as.mo("Methanosarcina semesiae")' = as.mo("Methanosarcina semesiae"),
+ 'as.mo("Prevotella brevis")' = as.mo("Prevotella brevis"),
+ 'as.mo("Escherichia coli")' = as.mo("Escherichia coli"),
+ 'as.mo("M. semesiae")' = as.mo("M. semesiae"),
+ 'as.mo("P. brevis")' = as.mo("P. brevis"),
+ 'as.mo("E. coli")' = as.mo("E. coli"),
+ times = 10),
+ horizontal = TRUE, las = 1, unit = "s", log = TRUE,
+ xlab = "", ylab = "Time in seconds (log)",
+ main = "Benchmarks per prevalence")
+options(AMR_disable_mo_history = FALSE)
+```
+
The highest outliers are the first times. All next determinations were done in only thousands of seconds.
Uncommon microorganisms take a lot more time than common microorganisms. To relieve this pitfall and further improve performance, two important calculations take almost no time at all: **repetitive results** and **already precalculated results**.