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(v0.8.0.9027) adding susceptibility() and resistance()

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dr. M.S. (Matthijs) Berends
2019-11-10 12:16:56 +01:00
parent 228a4245cb
commit 59ededa8dc
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36
vignettes/AMR.Rmd
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@ -321,10 +321,12 @@ data_1st %>%
## Resistance percentages
The functions `portion_S()`, `portion_SI()`, `portion_I()`, `portion_IR()` and `portion_R()` can be used to determine the portion of a specific antimicrobial outcome. As per the EUCAST guideline of 2019, we calculate resistance as the portion of R (`portion_R()`) and susceptibility as the portion of S and I (`portion_SI()`). These functions can be used on their own:
The functions `resistance()` and `susceptibility()` can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions `proportion_S()`, `proportion_SI()`, `proportion_I()`, `proportion_IR()` and `proportion_R()` can be used to determine the proportion of a specific antimicrobial outcome.
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (`proportion_R()`, equal to `resistance()`) and susceptibility as the proportion of S and I (`proportion_SI()`, equal to `susceptibility()`). These functions can be used on their own:
```{r}
data_1st %>% portion_R(AMX)
data_1st %>% resistance(AMX)
```
Or can be used in conjuction with `group_by()` and `summarise()`, both from the `dplyr` package:
@ -332,12 +334,12 @@ Or can be used in conjuction with `group_by()` and `summarise()`, both from the
```{r, eval = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_R(AMX))
summarise(amoxicillin = resistance(AMX))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_R(AMX)) %>%
summarise(amoxicillin = resistance(AMX)) %>%
knitr::kable(align = "c", big.mark = ",")
```
@ -346,32 +348,32 @@ Of course it would be very convenient to know the number of isolates responsible
```{r, eval = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_R(AMX),
summarise(amoxicillin = resistance(AMX),
available = n_rsi(AMX))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_R(AMX),
summarise(amoxicillin = resistance(AMX),
available = n_rsi(AMX)) %>%
knitr::kable(align = "c", big.mark = ",")
```
These functions can also be used to get the portion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
```{r, eval = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxiclav = portion_SI(AMC),
gentamicin = portion_SI(GEN),
amoxiclav_genta = portion_SI(AMC, GEN))
summarise(amoxiclav = susceptibility(AMC),
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN))
```
```{r, echo = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxiclav = portion_SI(AMC),
gentamicin = portion_SI(GEN),
amoxiclav_genta = portion_SI(AMC, GEN)) %>%
summarise(amoxiclav = susceptibility(AMC),
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN)) %>%
knitr::kable(align = "c", big.mark = ",")
```
@ -380,9 +382,9 @@ 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. Amoxi/clav" = portion_SI(AMC),
"2. Gentamicin" = portion_SI(GEN),
"3. Amoxi/clav + genta" = portion_SI(AMC, GEN)) %>%
summarise("1. Amoxi/clav" = susceptibility(AMC),
"2. Gentamicin" = susceptibility(GEN),
"3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>%
tidyr::gather("antibiotic", "S", -genus) %>%
ggplot(aes(x = genus,
y = S,
@ -408,7 +410,7 @@ 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:
The `AMR` package contains functions to extend this `ggplot2` package, for example `geom_rsi()`. It automatically transforms data with `count_df()` or `proportion_df()` and show results in stacked bars. Its simplest and shortest example:
```{r plot 3}
ggplot(data_1st) +