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bring back antibiogram(), without deps

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dr. M.S. (Matthijs) Berends
2023-02-10 16:18:00 +01:00
parent 70a7ba0206
commit bc434db835
42 changed files with 11307 additions and 4734 deletions
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10
vignettes/AMR.Rmd
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@ -157,7 +157,7 @@ Using the `left_join()` function from the `dplyr` package, we can 'map' the gend
data <- data %>% left_join(patients_table)
```
The resulting data set contains `r format(nrow(data), big.mark = ",")` blood culture isolates. With the `head()` function we can preview the first 6 rows of this data set:
The resulting data set contains `r format(nrow(data), big.mark = " ")` blood culture isolates. With the `head()` function we can preview the first 6 rows of this data set:
```{r preview data set 1, eval = FALSE}
head(data)
@ -251,7 +251,7 @@ data_1st <- data %>%
filter_first_isolate()
```
So we end up with `r format(nrow(data_1st), big.mark = ",")` isolates for analysis. Now our data looks like:
So we end up with `r format(nrow(data_1st), big.mark = " ")` isolates for analysis. Now our data looks like:
```{r preview data set 3, eval = FALSE}
head(data_1st)
@ -362,7 +362,7 @@ data_1st %>%
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX)) %>%
knitr::kable(align = "c", big.mark = ",")
knitr::kable(align = "c", big.mark = " ")
```
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the `n_sir()` can be used, which works exactly like `n_distinct()` from the `dplyr` package. It counts all isolates available for every group (i.e. values S, I or R):
@ -382,7 +382,7 @@ data_1st %>%
amoxicillin = resistance(AMX),
available = n_sir(AMX)
) %>%
knitr::kable(align = "c", big.mark = ",")
knitr::kable(align = "c", big.mark = " ")
```
These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
@ -404,7 +404,7 @@ data_1st %>%
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN)
) %>%
knitr::kable(align = "c", big.mark = ",")
knitr::kable(align = "c", big.mark = " ")
```
Or if you are curious for the resistance within certain antibiotic classes, use a antibiotic class selector such as `penicillins()`, which automatically will include the columns `AMX` and `AMC` of our data: