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(v0.7.0.9010) mo_synonyms, plot/barplot fixes

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dr. M.S. (Matthijs) Berends
2019-06-16 21:42:40 +02:00
parent 980be2b22d
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vignettes/AMR.Rmd
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@ -78,7 +78,7 @@ patients_table <- data.frame(patient_id = patients,
The first 135 patient IDs are now male, the other 125 are female.
## Dates
Let's pretend that our data consists of blood cultures isolates from 1 January 2010 until 1 January 2018.
Let's pretend that our data consists of blood cultures isolates from between 1 January 2010 and 1 January 2018.
```{r create dates}
dates <- seq(as.Date("2010-01-01"), as.Date("2018-01-01"), by = "day")
@ -131,7 +131,7 @@ 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 5,000 blood culture isolates. With the `head()` function we can preview the first 6 values 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 values of this data set:
```{r preview data set 1, eval = FALSE}
head(data)
@ -154,7 +154,7 @@ data %>% freq(gender) # this would be the same: freq(data$gender)
data %>% freq(gender, markdown = FALSE, header = TRUE)
```
So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values `M` and `F`. From a researcher perspective: there are slightly more men. Nothing we didn't already know.
So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values `M` and `F`. From a researchers perspective: there are slightly more men. Nothing we didn't already know.
The data is already quite clean, but we still need to transform some variables. The `bacteria` column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The `mutate()` function of the `dplyr` package makes this really easy:
@ -219,7 +219,6 @@ data_1st <- data %>%
```
## First *weighted* isolates
We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Imagine this data, sorted on date:
```{r, echo = FALSE, message = FALSE, warning = FALSE, results = 'asis'}
weighted_df <- data %>%
@ -232,7 +231,11 @@ weighted_df <- data %>%
.[1:min(10, nrow(.)),] %>%
mutate(isolate = row_number()) %>%
select(isolate, everything())
```
We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient `r as.data.frame(weighted_df[1, 'patient_id'])`, sorted on date:
```{r, echo = FALSE, message = FALSE, warning = FALSE, results = 'asis'}
weighted_df %>%
knitr::kable(align = "c")
```
@ -378,10 +381,10 @@ data_1st %>%
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) %>%
tidyr::gather("antibiotic", "S", -genus) %>%
ggplot(aes(x = genus,
y = S,
fill = Antibiotic)) +
fill = antibiotic)) +
geom_col(position = "dodge2")
```
@ -410,7 +413,7 @@ ggplot(data_1st) +
geom_rsi(translate_ab = FALSE)
```
Omit the `translate_ab = FALSE` to have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin and betalactamase inhibitor, ciprofloxacin, gentamicin).
Omit the `translate_ab = FALSE` to have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin).
If we group on e.g. the `genus` column and add some additional functions from our package, we can create this:
@ -422,7 +425,7 @@ ggplot(data_1st %>% group_by(genus)) +
# of which we have 4 (earlier created with `as.rsi`)
geom_rsi(x = "genus") +
# split plots on antibiotic
facet_rsi(facet = "Antibiotic") +
facet_rsi(facet = "antibiotic") +
# make R red, I yellow and S green
scale_rsi_colours() +
# show percentages on y axis
@ -443,7 +446,7 @@ To simplify this, we also created the `ggplot_rsi()` function, which combines al
data_1st %>%
group_by(genus) %>%
ggplot_rsi(x = "genus",
facet = "Antibiotic",
facet = "antibiotic",
breaks = 0:4 * 25,
datalabels = FALSE) +
coord_flip()
@ -453,33 +456,26 @@ data_1st %>%
The next example uses the included `septic_patients`, which is an anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This `data.frame` can be used to practice AMR analysis.
We will compare the resistance to fosfomycin (column `FOS`) in hospital A and D. The input for the final `fisher.test()` will be this:
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, echo = FALSE, results = 'asis'}
septic_patients %>%
filter(hospital_id %in% c("A", "D")) %>%
select(hospital_id, FOS) %>%
group_by(hospital_id) %>%
count_df(combine_IR = TRUE) %>%
tidyr::spread(hospital_id, Value) %>%
select(A, D) %>%
bind_cols(tibble(" " = c("IR", "S")), .) %>%
as.matrix() %>%
knitr::kable()
```
We can transform the data and apply the test in only a couple of lines:
```{r}
septic_patients %>%
```{r, results = 'markup'}
check_FOS <- septic_patients %>%
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_IR = TRUE) %>% # count all isolates per group (hospital_id)
tidyr::spread(hospital_id, Value) %>% # transform output so A and D are columns
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()
fisher.test() # do Fisher's Exact Test
as.matrix() # transform to good old matrix for fisher.test()
check_FOS
```
As can be seen, the p value is 0.03, which means that the fosfomycin resistances found in hospital A and D are really different.
We can apply the test now with:
```{r}
# do Fisher's Exact Test
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.