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support for old rsi arguments

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dr. M.S. (Matthijs) Berends
2023-03-11 14:24:34 +01:00
parent 4416394e10
commit 262598b8d7
21 changed files with 327 additions and 199 deletions
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46
vignettes/AMR.Rmd
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@ -140,11 +140,11 @@ For now, we will just clean the SIR columns in our data using dplyr:
```{r}
# method 1, be explicit about the columns:
our_data <- our_data %>%
our_data <- our_data %>%
mutate_at(vars(AMX:GEN), as.sir)
# method 2, let the AMR package determine the eligible columns
our_data <- our_data %>%
our_data <- our_data %>%
mutate_if(is_sir_eligible, as.sir)
# result:
@ -213,10 +213,10 @@ sapply(our_data_1st, n_distinct)
To just get an idea how the species are distributed, create a frequency table with `count()` based on the name of the microorganisms:
```{r freq 1}
our_data %>%
our_data %>%
count(mo_name(bacteria), sort = TRUE)
our_data_1st %>%
our_data_1st %>%
count(mo_name(bacteria), sort = TRUE)
```
@ -255,42 +255,48 @@ Below are some suggestions for how to generate the different antibiograms:
# traditional:
antibiogram(our_data_1st)
antibiogram(our_data_1st,
ab_transform = "name")
ab_transform = "name"
)
antibiogram(our_data_1st,
ab_transform = "name",
language = "es") # support for 20 languages
ab_transform = "name",
language = "es"
) # support for 20 languages
```
```{r}
# combined:
antibiogram(our_data_1st,
antibiotics = c("AMC", "AMC+CIP", "AMC+GEN"))
antibiotics = c("AMC", "AMC+CIP", "AMC+GEN")
)
```
```{r}
# for a syndromic antibiogram, we must fake some clinical conditions:
our_data_1st$condition <- sample(c("Cardial", "Respiratory", "Rheumatic"),
size = nrow(our_data_1st),
replace = TRUE)
size = nrow(our_data_1st),
replace = TRUE
)
# syndromic:
antibiogram(our_data_1st,
syndromic_group = "condition")
syndromic_group = "condition"
)
antibiogram(our_data_1st,
# you can use AB selectors here as well:
antibiotics = c(penicillins(), aminoglycosides()),
syndromic_group = "condition",
mo_transform = "gramstain")
# you can use AB selectors here as well:
antibiotics = c(penicillins(), aminoglycosides()),
syndromic_group = "condition",
mo_transform = "gramstain"
)
```
```{r}
# WISCA:
# WISCA:
# (we lack some details, but it could contain a filter on e.g. >65 year-old males)
wisca <- antibiogram(our_data_1st,
antibiotics = c("AMC", "AMC+CIP", "AMC+GEN"),
syndromic_group = "condition",
mo_transform = "gramstain")
antibiotics = c("AMC", "AMC+CIP", "AMC+GEN"),
syndromic_group = "condition",
mo_transform = "gramstain"
)
wisca
```