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rlang dependency, new fungi

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dr. M.S. (Matthijs) Berends
2019-02-28 13:56:28 +01:00
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22
vignettes/AMR.Rmd
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@ -315,7 +315,7 @@ data_1st %>%
## Resistance percentages
The functions `portion_R`, `portion_RI`, `portion_I`, `portion_IS` and `portion_S` can be used to determine the portion of a specific antimicrobial outcome. They can be used on their own:
The functions `portion_R()`, `portion_RI()`, `portion_I()`, `portion_IS()` and `portion_S()` can be used to determine the portion of a specific antimicrobial outcome. They can be used on their own:
```{r}
data_1st %>% portion_IR(amox)
@ -351,21 +351,21 @@ data_1st %>%
knitr::kable(align = "c", big.mark = ",")
```
These functions can also be used to get the portion of multiple antibiotics, to calculate co-resistance very easily:
These functions can also be used to get the portion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
```{r, eval = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxicillin = portion_S(amcl),
summarise(amoxiclav = portion_S(amcl),
gentamicin = portion_S(gent),
"amox + gent" = portion_S(amcl, gent))
amoxiclav_genta = portion_S(amcl, gent))
```
```{r, echo = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxicillin = portion_S(amcl),
summarise(amoxiclav = portion_S(amcl),
gentamicin = portion_S(gent),
"amox + gent" = portion_S(amcl, gent)) %>%
amoxiclav_genta = portion_S(amcl, gent)) %>%
knitr::kable(align = "c", big.mark = ",")
```
@ -374,9 +374,9 @@ To make a transition to the next part, let's see how this difference could be pl
```{r plot 1}
data_1st %>%
group_by(genus) %>%
summarise("1. Amoxicillin" = portion_S(amcl),
summarise("1. Amoxi/clav" = portion_S(amcl),
"2. Gentamicin" = portion_S(gent),
"3. Amox + gent" = portion_S(amcl, gent)) %>%
"3. Amoxi/clav + gent" = portion_S(amcl, gent)) %>%
tidyr::gather("Antibiotic", "S", -genus) %>%
ggplot(aes(x = genus,
y = S,
@ -397,9 +397,9 @@ ggplot(data = a_data_set,
x = "My X axis",
y = "My Y axis")
ggplot(a_data_set,
aes(year, value) +
geom_bar()
# or as short as:
ggplot(a_data_set) +
geom_bar(aes(year))
```
The `AMR` package contains functions to extend this `ggplot2` package, for example `geom_rsi()`. It automatically transforms data with `count_df()` or `portion_df()` and show results in stacked bars. Its simplest and shortest example:

6
vignettes/SPSS.Rmd
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@ -38,17 +38,17 @@ As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major
* **R is extremely flexible.**
Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, gathering, grouping, summarising and drawing plots is endless - with SPSS, SAS or Stata you are bound to their algorithms and styles. It may be a bit flexible, but you can never create that very specific publication-ready plot without using other (paid) software.
Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, gathering, grouping, summarising and drawing plots is endless - with SPSS, SAS or Stata you are bound to their algorithms and styles. They may be a bit flexible, but you can probably never create that very specific publication-ready plot without using other (paid) software.
* **R can be easily automated.**
Over the last years, [R Markdown](https://rmarkdown.rstudio.com/) has really made an interesting development. With R Markdown, you can very easily reproduce your reports, whether it's to Word, Powerpoint, a website, a PDF document or just the raw data to Excel. I use this a lot to generate monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.
For an even more professional environment, you could create [Shiny apps](https://shiny.rstudio.com/): live manipulation of data using a custom made website. The webdesign knowledge needed (Javascript, CSS, HTML) is almost *zero*.
For an even more professional environment, you could create [Shiny apps](https://shiny.rstudio.com/): live manipulation of data using a custom made website. The webdesign knowledge needed (JavaScript, CSS, HTML) is almost *zero*.
* **R has a huge community.**
Many R users just ask questions on website like [stackoverflow.com](https://stackoverflow.com), the largest online community for programmers. At the time of writing, around [275,000 R questions](https://stackoverflow.com/questions/tagged/r?sort=votes) have been asked on this platform (which covers questions and answer for any programming language). In my own experience, most questions are answered within a couple of minutes.
Many R users just ask questions on websites like [StackOverflow.com](https://stackoverflow.com), the largest online community for programmers. At the time of writing, more than [275,000 R-related questions](https://stackoverflow.com/questions/tagged/r?sort=votes) have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
* **R understands any data type, including SPSS/SAS/Stata.**

2
vignettes/benchmarks.Rmd
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@ -89,7 +89,7 @@ Uncommon microorganisms take a lot more time than common microorganisms. To reli
### Repetitive results
Repetitive results mean that unique values are present more than once. Unique values will only be calculated once by `as.mo()`. We will use `mo_fullname()` for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses `as.mo()` internally.
Repetitive results are unique values that are present more than once. Unique values will only be calculated once by `as.mo()`. We will use `mo_fullname()` for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses `as.mo()` internally.
```{r, message = FALSE}
library(dplyr)