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dr. M.S. (Matthijs) Berends
2019-05-17 20:22:04 +02:00
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vignettes/AMR.Rmd
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@ -315,10 +315,10 @@ 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. They can be used on their own:
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:
```{r}
data_1st %>% portion_IR(AMX)
data_1st %>% portion_R(AMX)
```
Or can be used in conjuction with `group_by()` and `summarise()`, both from the `dplyr` package:
@ -326,12 +326,12 @@ 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_IR(AMX))
summarise(amoxicillin = portion_R(AMX))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_IR(AMX)) %>%
summarise(amoxicillin = portion_R(AMX)) %>%
knitr::kable(align = "c", big.mark = ",")
```
@ -340,13 +340,13 @@ 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_IR(AMX),
summarise(amoxicillin = portion_R(AMX),
available = n_rsi(AMX))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_IR(AMX),
summarise(amoxicillin = portion_R(AMX),
available = n_rsi(AMX)) %>%
knitr::kable(align = "c", big.mark = ",")
```
@ -356,16 +356,16 @@ These functions can also be used to get the portion of multiple antibiotics, to
```{r, eval = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxiclav = portion_S(AMC),
gentamicin = portion_S(GEN),
amoxiclav_genta = portion_S(AMC, GEN))
summarise(amoxiclav = portion_SI(AMC),
gentamicin = portion_SI(GEN),
amoxiclav_genta = portion_SI(AMC, GEN))
```
```{r, echo = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxiclav = portion_S(AMC),
gentamicin = portion_S(GEN),
amoxiclav_genta = portion_S(AMC, GEN)) %>%
summarise(amoxiclav = portion_SI(AMC),
gentamicin = portion_SI(GEN),
amoxiclav_genta = portion_SI(AMC, GEN)) %>%
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. Amoxi/clav" = portion_S(AMC),
"2. Gentamicin" = portion_S(GEN),
"3. Amoxi/clav + GEN" = portion_S(AMC, GEN)) %>%
summarise("1. Amoxi/clav" = portion_SI(AMC),
"2. Gentamicin" = portion_SI(GEN),
"3. Amoxi/clav + genta" = portion_SI(AMC, GEN)) %>%
tidyr::gather("Antibiotic", "S", -genus) %>%
ggplot(aes(x = genus,
y = S,
@ -426,7 +426,7 @@ ggplot(data_1st %>% group_by(genus)) +
scale_rsi_colours() +
# show percentages on y axis
scale_y_percent(breaks = 0:4 * 25) +
# turn 90 degrees, make it bars instead of columns
# turn 90 degrees, to make it bars instead of columns
coord_flip() +
# add labels
labs(title = "Resistance per genus and antibiotic",