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(v0.8.0.9030) depend on tidyr >= 1.0.0

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dr. M.S. (Matthijs) Berends
2019-11-11 10:46:39 +01:00
parent d22834c5b8
commit 248b45da71
28 changed files with 580 additions and 515 deletions
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18
vignettes/AMR.Rmd
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@ -385,9 +385,10 @@ data_1st %>%
summarise("1. Amoxi/clav" = susceptibility(AMC),
"2. Gentamicin" = susceptibility(GEN),
"3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>%
tidyr::gather("antibiotic", "S", -genus) %>%
# pivot_longer() from the tidyr package "lengthens" data:
tidyr::pivot_longer(-genus, names_to = "antibiotic") %>%
ggplot(aes(x = genus,
y = S,
y = value,
fill = antibiotic)) +
geom_col(position = "dodge2")
```
@ -463,14 +464,19 @@ The next example uses the included `example_isolates`, which is an anonymised da
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:
```{r, results = 'markup'}
# use package 'tidyr' to pivot data;
# it gets installed with this 'AMR' package
library(tidyr)
check_FOS <- example_isolates %>%
filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
select(hospital_id, FOS) %>% # select the hospitals and fosfomycin
group_by(hospital_id) %>% # group on the hospitals
count_df(combine_SI = TRUE) %>% # count all isolates per group (hospital_id)
tidyr::spread(hospital_id, value) %>% # transform output so A and D are columns
select(A, D) %>% # and select these only
as.matrix() # transform to good old matrix for fisher.test()
pivot_wider(names_from = hospital_id, # transform output so A and D are columns
values_from = value) %>%
select(A, D) %>% # and only select these columns
as.matrix() # transform to a good old matrix for fisher.test()
check_FOS
```
@ -482,4 +488,4 @@ 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)$p.value, 3)`, which means that the fosfomycin resistances found in hospital A and D are really different.
As can be seen, the p value is `r round(fisher.test(check_FOS)$p.value, 3)`, which means that the fosfomycin resistance found in hospital A and D are really different.