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fix AMR vignette

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dr. M.S. (Matthijs) Berends
2022-08-28 22:38:08 +02:00
parent e975a3043c
commit e7af5fc716
9 changed files with 20 additions and 17 deletions
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vignettes/AMR.Rmd
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@ -569,7 +569,8 @@ plot(disk_values, mo = "E. coli", ab = "cipro")
And when using the `ggplot2` package, but now choosing the latest implemented CLSI guideline (notice that the EUCAST-specific term "Susceptible, incr. exp." has changed to "Intermediate"):
```{r disk_plots_mo_ab, message = FALSE, warning = FALSE}
autoplot(disk_values,
autoplot(
disk_values,
mo = "E. coli",
ab = "cipro",
guideline = "CLSI"
@ -580,22 +581,22 @@ autoplot(disk_values,
The next example uses the `example_isolates` data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practise AMR data analysis.
We will compare the resistance to fosfomycin (column `FOS`) in hospital A and D. The input for the `fisher.test()` can be retrieved with a transformation like this:
We will compare the resistance to amoxicillin/clavulanic acid (column `FOS`) between an ICU and other clinical wards. The input for the `fisher.test()` can be retrieved with a transformation like this:
```{r, results = 'markup'}
# use package 'tidyr' to pivot data:
library(tidyr)
check_FOS <- example_isolates %>%
filter(ward %in% c("A", "D")) %>% # filter on only hospitals A and D
select(ward, FOS) %>% # select the hospitals and fosfomycin
group_by(ward) %>% # group on the hospitals
filter(ward %in% c("ICU", "Clinical")) %>% # filter on only these wards
select(ward, AMC) %>% # select the wards and amoxi/clav
group_by(ward) %>% # group on the wards
count_df(combine_SI = TRUE) %>% # count all isolates per group (ward)
pivot_wider(
names_from = ward, # transform output so A and D are columns
names_from = ward, # transform output so "ICU" and "Clinical" are columns
values_from = value
) %>%
select(A, D) %>% # and only select these columns
select(ICU, Clinical) %>% # and only select these columns
as.matrix() # transform to a good old matrix for fisher.test()
check_FOS
@ -608,4 +609,4 @@ We can apply the test now with:
fisher.test(check_FOS)
```
As can be seen, the p value is `r round(fisher.test(check_FOS)$p.value, 3)`, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.
As can be seen, the p value is practically zero (`r format(fisher.test(check_FOS)$p.value, scientific = FALSE)`), which means that the amoxicillin/clavulanic acid resistance found in isolates between patients in ICUs and other clinical wards are really different.