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styled, unit test fix

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dr. M.S. (Matthijs) Berends
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vignettes/AMR.Rmd
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@ -48,13 +48,16 @@ For this tutorial, we will create fake demonstration data to work with.
You can skip to [Cleaning the data](#cleaning-the-data) if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:
```{r example table, echo = FALSE, results = 'asis'}
knitr::kable(data.frame(date = Sys.Date(),
patient_id = c("abcd", "abcd", "efgh"),
mo = "Escherichia coli",
AMX = c("S", "S", "R"),
CIP = c("S", "R", "S"),
stringsAsFactors = FALSE),
align = "c")
knitr::kable(data.frame(
date = Sys.Date(),
patient_id = c("abcd", "abcd", "efgh"),
mo = "Escherichia coli",
AMX = c("S", "S", "R"),
CIP = c("S", "R", "S"),
stringsAsFactors = FALSE
),
align = "c"
)
```
## Needed R packages
@ -87,9 +90,13 @@ patients <- unlist(lapply(LETTERS, paste0, 1:10))
The `LETTERS` object is available in R - it's a vector with 26 characters: `A` to `Z`. The `patients` object we just created is now a vector of length `r length(patients)`, with values (patient IDs) varying from ``r patients[1]`` to ``r patients[length(patients)]``. Now we we also set the gender of our patients, by putting the ID and the gender in a table:
```{r create gender}
patients_table <- data.frame(patient_id = patients,
gender = c(rep("M", 135),
rep("F", 125)))
patients_table <- data.frame(
patient_id = patients,
gender = c(
rep("M", 135),
rep("F", 125)
)
)
```
The first 135 patient IDs are now male, the other 125 are female.
@ -107,8 +114,10 @@ This `dates` object now contains all days in our date range.
For this tutorial, we will uses four different microorganisms: *Escherichia coli*, *Staphylococcus aureus*, *Streptococcus pneumoniae*, and *Klebsiella pneumoniae*:
```{r mo}
bacteria <- c("Escherichia coli", "Staphylococcus aureus",
"Streptococcus pneumoniae", "Klebsiella pneumoniae")
bacteria <- c(
"Escherichia coli", "Staphylococcus aureus",
"Streptococcus pneumoniae", "Klebsiella pneumoniae"
)
```
## Put everything together
@ -117,20 +126,27 @@ Using the `sample()` function, we can randomly select items from all objects we
```{r merge data}
sample_size <- 20000
data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
patient_id = sample(patients, size = sample_size, replace = TRUE),
hospital = sample(c("Hospital A",
"Hospital B",
"Hospital C",
"Hospital D"),
size = sample_size, replace = TRUE,
prob = c(0.30, 0.35, 0.15, 0.20)),
bacteria = sample(bacteria, size = sample_size, replace = TRUE,
prob = c(0.50, 0.25, 0.15, 0.10)),
AMX = random_rsi(sample_size, prob_RSI = c(0.35, 0.60, 0.05)),
AMC = random_rsi(sample_size, prob_RSI = c(0.15, 0.75, 0.10)),
CIP = random_rsi(sample_size, prob_RSI = c(0.20, 0.80, 0.00)),
GEN = random_rsi(sample_size, prob_RSI = c(0.08, 0.92, 0.00)))
data <- data.frame(
date = sample(dates, size = sample_size, replace = TRUE),
patient_id = sample(patients, size = sample_size, replace = TRUE),
hospital = sample(c(
"Hospital A",
"Hospital B",
"Hospital C",
"Hospital D"
),
size = sample_size, replace = TRUE,
prob = c(0.30, 0.35, 0.15, 0.20)
),
bacteria = sample(bacteria,
size = sample_size, replace = TRUE,
prob = c(0.50, 0.25, 0.15, 0.10)
),
AMX = random_rsi(sample_size, prob_RSI = c(0.35, 0.60, 0.05)),
AMC = random_rsi(sample_size, prob_RSI = c(0.15, 0.75, 0.10)),
CIP = random_rsi(sample_size, prob_RSI = c(0.20, 0.80, 0.00)),
GEN = random_rsi(sample_size, prob_RSI = c(0.08, 0.92, 0.00))
)
```
Using the `left_join()` function from the `dplyr` package, we can 'map' the gender to the patient ID using the `patients_table` object we created earlier:
@ -192,10 +208,12 @@ data <- eucast_rules(data, col_mo = "bacteria", rules = "all")
Now that we have the microbial ID, we can add some taxonomic properties:
```{r new taxo}
data <- data %>%
mutate(gramstain = mo_gramstain(bacteria),
genus = mo_genus(bacteria),
species = mo_species(bacteria))
data <- data %>%
mutate(
gramstain = mo_gramstain(bacteria),
genus = mo_genus(bacteria),
species = mo_species(bacteria)
)
```
## First isolates
@ -213,21 +231,21 @@ This `AMR` package includes this methodology with the `first_isolate()` function
The outcome of the function can easily be added to our data:
```{r 1st isolate}
data <- data %>%
data <- data %>%
mutate(first = first_isolate(info = TRUE))
```
So only `r percentage(sum(data$first) / nrow(data))` is suitable for resistance analysis! We can now filter on it with the `filter()` function, also from the `dplyr` package:
```{r 1st isolate filter}
data_1st <- data %>%
data_1st <- data %>%
filter(first == TRUE)
```
For future use, the above two syntaxes can be shortened:
```{r 1st isolate filter 2}
data_1st <- data %>%
data_1st <- data %>%
filter_first_isolate()
```
@ -261,7 +279,7 @@ Or can be used like the `dplyr` way, which is easier readable:
data_1st %>% freq(genus, species)
```
```{r freq 2b, results = 'asis', echo = FALSE}
data_1st %>%
data_1st %>%
freq(genus, species, header = TRUE)
```
@ -270,45 +288,48 @@ data_1st %>%
Using [tidyverse selections](https://tidyselect.r-lib.org/reference/language.html), you can also select or filter columns based on the antibiotic class they are in:
```{r bug_drg 2a, eval = FALSE}
data_1st %>%
data_1st %>%
filter(any(aminoglycosides() == "R"))
```
```{r bug_drg 2b, echo = FALSE, results = 'asis'}
knitr::kable(data_1st %>%
filter(any(aminoglycosides() == "R")) %>%
head(),
align = "c")
knitr::kable(data_1st %>%
filter(any(aminoglycosides() == "R")) %>%
head(),
align = "c"
)
```
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the `bug_drug_combinations()` function:
```{r bug_drg 1a, eval = FALSE}
data_1st %>%
bug_drug_combinations() %>%
data_1st %>%
bug_drug_combinations() %>%
head() # show first 6 rows
```
```{r bug_drg 1b, echo = FALSE, results = 'asis'}
knitr::kable(data_1st %>%
bug_drug_combinations() %>%
head(),
align = "c")
knitr::kable(data_1st %>%
bug_drug_combinations() %>%
head(),
align = "c"
)
```
```{r bug_drg 3a, eval = FALSE}
data_1st %>%
select(bacteria, aminoglycosides()) %>%
data_1st %>%
select(bacteria, aminoglycosides()) %>%
bug_drug_combinations()
```
```{r bug_drg 3b, echo = FALSE, results = 'asis'}
knitr::kable(data_1st %>%
select(bacteria, aminoglycosides()) %>%
bug_drug_combinations(),
align = "c")
knitr::kable(data_1st %>%
select(bacteria, aminoglycosides()) %>%
bug_drug_combinations(),
align = "c"
)
```
This will only give you the crude numbers in the data. To calculate antimicrobial resistance in a more sensible way, also by correcting for too few results, we use the `resistance()` and `susceptibility()` functions.
@ -328,86 +349,98 @@ data_1st %>% resistance(AMX)
Or can be used in conjunction with `group_by()` and `summarise()`, both from the `dplyr` package:
```{r, eval = FALSE}
data_1st %>%
group_by(hospital) %>%
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX)) %>%
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX)) %>%
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_rsi()` 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):
```{r, eval = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX),
available = n_rsi(AMX))
data_1st %>%
group_by(hospital) %>%
summarise(
amoxicillin = resistance(AMX),
available = n_rsi(AMX)
)
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX),
available = n_rsi(AMX)) %>%
data_1st %>%
group_by(hospital) %>%
summarise(
amoxicillin = resistance(AMX),
available = n_rsi(AMX)
) %>%
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:
```{r, eval = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxiclav = susceptibility(AMC),
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN))
data_1st %>%
group_by(genus) %>%
summarise(
amoxiclav = susceptibility(AMC),
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN)
)
```
```{r, echo = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxiclav = susceptibility(AMC),
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN)) %>%
data_1st %>%
group_by(genus) %>%
summarise(
amoxiclav = susceptibility(AMC),
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN)
) %>%
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:
```{r, eval = FALSE}
data_1st %>%
data_1st %>%
# group by hospital
group_by(hospital) %>%
group_by(hospital) %>%
# / -> select all penicillins in the data for calculation
# | / -> use resistance() for all peni's per hospital
# | | / -> print as percentages
summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%
summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%
# format the antibiotic column names, using so-called snake case,
# so 'Amoxicillin/clavulanic acid' becomes 'amoxicillin_clavulanic_acid'
rename_with(set_ab_names, penicillins())
```
```{r, echo = FALSE, message = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%
rename_with(set_ab_names, penicillins()) %>%
data_1st %>%
group_by(hospital) %>%
summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%
rename_with(set_ab_names, penicillins()) %>%
knitr::kable(align = "lrr")
```
To make a transition to the next part, let's see how differences in the previously calculated combination therapies could be plotted:
```{r plot 1}
data_1st %>%
group_by(genus) %>%
summarise("1. Amoxi/clav" = susceptibility(AMC),
"2. Gentamicin" = susceptibility(GEN),
"3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>%
data_1st %>%
group_by(genus) %>%
summarise(
"1. Amoxi/clav" = susceptibility(AMC),
"2. Gentamicin" = susceptibility(GEN),
"3. Amoxi/clav + genta" = susceptibility(AMC, GEN)
) %>%
# pivot_longer() from the tidyr package "lengthens" data:
tidyr::pivot_longer(-genus, names_to = "antibiotic") %>%
ggplot(aes(x = genus,
y = value,
fill = antibiotic)) +
tidyr::pivot_longer(-genus, names_to = "antibiotic") %>%
ggplot(aes(
x = genus,
y = value,
fill = antibiotic
)) +
geom_col(position = "dodge2")
```
@ -416,14 +449,20 @@ data_1st %>%
To show results in plots, most R users would nowadays use the `ggplot2` package. This package lets you create plots in layers. You can read more about it [on their website](https://ggplot2.tidyverse.org/). A quick example would look like these syntaxes:
```{r plot 2, eval = FALSE}
ggplot(data = a_data_set,
mapping = aes(x = year,
y = value)) +
ggplot(
data = a_data_set,
mapping = aes(
x = year,
y = value
)
) +
geom_col() +
labs(title = "A title",
subtitle = "A subtitle",
x = "My X axis",
y = "My Y axis")
labs(
title = "A title",
subtitle = "A subtitle",
x = "My X axis",
y = "My Y axis"
)
# or as short as:
ggplot(a_data_set) +
@ -443,11 +482,11 @@ If we group on e.g. the `genus` column and add some additional functions from ou
```{r plot 4}
# group the data on `genus`
ggplot(data_1st %>% group_by(genus)) +
ggplot(data_1st %>% group_by(genus)) +
# create bars with genus on x axis
# it looks for variables with class `rsi`,
# of which we have 4 (earlier created with `as.rsi`)
geom_rsi(x = "genus") +
geom_rsi(x = "genus") +
# split plots on antibiotic
facet_rsi(facet = "antibiotic") +
# set colours to the R/SI interpretations (colour-blind friendly)
@ -457,8 +496,10 @@ ggplot(data_1st %>% group_by(genus)) +
# turn 90 degrees, to make it bars instead of columns
coord_flip() +
# add labels
labs(title = "Resistance per genus and antibiotic",
subtitle = "(this is fake data)") +
labs(
title = "Resistance per genus and antibiotic",
subtitle = "(this is fake data)"
) +
# and print genus in italic to follow our convention
# (is now y axis because we turned the plot)
theme(axis.text.y = element_text(face = "italic"))
@ -467,12 +508,14 @@ ggplot(data_1st %>% group_by(genus)) +
To simplify this, we also created the `ggplot_rsi()` function, which combines almost all above functions:
```{r plot 5}
data_1st %>%
data_1st %>%
group_by(genus) %>%
ggplot_rsi(x = "genus",
facet = "antibiotic",
breaks = 0:4 * 25,
datalabels = FALSE) +
ggplot_rsi(
x = "genus",
facet = "antibiotic",
breaks = 0:4 * 25,
datalabels = FALSE
) +
coord_flip()
```
@ -527,9 +570,10 @@ And when using the `ggplot2` package, but now choosing the latest implemented CL
```{r disk_plots_mo_ab, message = FALSE, warning = FALSE}
autoplot(disk_values,
mo = "E. coli",
ab = "cipro",
guideline = "CLSI")
mo = "E. coli",
ab = "cipro",
guideline = "CLSI"
)
```
## Independence test
@ -544,13 +588,15 @@ 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
count_df(combine_SI = TRUE) %>% # count all isolates per group (ward)
pivot_wider(names_from = ward, # 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()
select(ward, FOS) %>% # select the hospitals and fosfomycin
group_by(ward) %>% # group on the hospitals
count_df(combine_SI = TRUE) %>% # count all isolates per group (ward)
pivot_wider(
names_from = ward, # 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
```
@ -559,7 +605,7 @@ We can apply the test now with:
```{r}
# do Fisher's Exact Test
fisher.test(check_FOS)
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.