How to conduct AMR analysis
Matthijs S. Berends
-08 October 2020
+15 October 2020
Source:vignettes/AMR.Rmd
AMR.RmdNote: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 08 October 2020.
+Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 15 October 2020.
Introduction
@@ -233,21 +233,21 @@As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the tidyverse packages dplyr and ggplot2 by RStudio. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
We will also use the cleaner package, that can be used for cleaning data and creating frequency tables.
-library(dplyr) -library(ggplot2) -library(AMR) -library(cleaner) +library(dplyr) +library(ggplot2) +library(AMR) +library(cleaner) # (if not yet installed, install with:) -# install.packages(c("dplyr", "ggplot2", "AMR", "cleaner")) -
To start with patients, we need a unique list of patients.
+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 260, with values (patient IDs) varying from A1 to Z10. Now we we also set the gender of our patients, by putting the ID and the gender in a table:
-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.
Let’s pretend that our data consists of blood cultures isolates from between 1 January 2010 and 1 January 2018.
+dates <- seq(as.Date("2010-01-01"), as.Date("2018-01-01"), by = "day")This dates object now contains all days in our date range.
Microorganisms
For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:
-bacteria <- c("Escherichia coli", "Staphylococcus aureus", - "Streptococcus pneumoniae", "Klebsiella pneumoniae") -
For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:
-hospitals <- c("Hospital A", "Hospital B", "Hospital C", "Hospital D") -ab_interpretations <- c("S", "I", "R") -
Put everything together
Using the sample() function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the prob parameter.
-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(hospitals, 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 = sample(ab_interpretations, size = sample_size, replace = TRUE, - prob = c(0.60, 0.05, 0.35)), - AMC = sample(ab_interpretations, size = sample_size, replace = TRUE, - prob = c(0.75, 0.10, 0.15)), - CIP = sample(ab_interpretations, size = sample_size, replace = TRUE, - prob = c(0.80, 0.00, 0.20)), - GEN = sample(ab_interpretations, size = sample_size, replace = TRUE, - prob = c(0.92, 0.00, 0.08))) -
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:
-data <- data %>% left_join(patients_table) -
The resulting data set contains 20,000 blood culture isolates. With the head() function we can preview the first 6 rows of this data set:
-head(data) -
| date | @@ -361,71 +352,71 @@||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 2014-02-15 | -Y9 | -Hospital A | -Streptococcus pneumoniae | -R | -S | -S | -S | -F | -||
| 2012-05-11 | -J4 | +2011-04-04 | +A3 | Hospital C | -Klebsiella pneumoniae | -R | -S | -S | -S | -M | -
| 2013-06-10 | -J1 | -Hospital B | Staphylococcus aureus | R | -S | +I | S | S | M | |
| 2013-02-02 | -U4 | +2011-10-13 | +I4 | Hospital B | Escherichia coli | S | +S | +S | +S | +M | +
| 2015-11-20 | +J6 | +Hospital A | +Escherichia coli | +I | +S | R | S | +M | +||
| 2017-02-04 | +X3 | +Hospital B | +Klebsiella pneumoniae | +S | +I | +S | S | F | ||
| 2015-12-04 | -S10 | +2010-10-17 | +N9 | +Hospital D | +Staphylococcus aureus | +I | +R | +S | +R | +F | +
| 2014-06-04 | +X3 | Hospital A | -Streptococcus pneumoniae | +Escherichia coli | S | S | S | S | F | |
| 2016-09-17 | -G2 | -Hospital B | -Escherichia coli | -R | -S | -S | -S | -M | -
Now, let’s start the cleaning and the analysis!
@@ -437,8 +428,7 @@We also created a package dedicated to data cleaning and checking, called the cleaner package. It freq() function can be used to create frequency tables.
For example, for the gender variable:
-data %>% freq(gender) -
Frequency table
Class: character
Length: 20,000
@@ -459,16 +449,16 @@ Longest: 1
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:
We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The as.rsi() function ensures reliability and reproducibility in these kind of variables. The mutate_at() will run the as.rsi() function on defined variables:
Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the eucast_rules() function can also apply additional rules, like forcing
Because the amoxicillin (column AMX) and amoxicillin/clavulanic acid (column AMC) in our data were generated randomly, some rows will undoubtedly contain AMX = S and AMC = R, which is technically impossible. The eucast_rules() fixes this:
-data <- eucast_rules(data, col_mo = "bacteria", rules = "all") -# Set amoxicillin (AMX) = R where amoxicillin/clavulanic acid (AMC) = R -
Adding new variables
Now that we have the microbial ID, we can add some taxonomic properties:
-data <- data %>% - mutate(gramstain = mo_gramstain(bacteria), - genus = mo_genus(bacteria), - species = mo_species(bacteria)) -
First isolates
@@ -513,27 +499,24 @@ Longest: 1This AMR package includes this methodology with the first_isolate() function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:
-data <- data %>% - mutate(first = first_isolate(.)) +data <- data %>% + mutate(first = first_isolate(.)) # NOTE: Using column `bacteria` as input for `col_mo`. # NOTE: Using column `date` as input for `col_date`. -# NOTE: Using column `patient_id` as input for `col_patient_id`. -
So only 28.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 28.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
-data_1st <- data %>% - filter(first == TRUE) -
For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:
-data_1st <- data %>% - filter_first_isolate() -
First weighted isolates
-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 T8, sorted on date:
+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 Z7, sorted on date:
| isolate | @@ -549,19 +532,19 @@ Longest: 1|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-04-04 | -T8 | +2010-03-27 | +Z7 | B_ESCHR_COLI | +R | S | S | R | -S | TRUE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2 | -2010-10-03 | -T8 | +2010-06-10 | +Z7 | B_ESCHR_COLI | S | S | @@ -571,10 +554,10 @@ Longest: 1||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 3 | -2010-10-19 | -T8 | +2010-06-25 | +Z7 | B_ESCHR_COLI | -S | +I | S | S | S | @@ -582,19 +565,19 @@ Longest: 1|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 4 | -2011-01-12 | -T8 | +2010-11-05 | +Z7 | B_ESCHR_COLI | S | S | -S | +R | S | FALSE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 5 | -2011-02-03 | -T8 | +2010-11-22 | +Z7 | B_ESCHR_COLI | S | S | @@ -604,52 +587,52 @@ Longest: 1||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 6 | -2011-04-04 | -T8 | +2011-02-06 | +Z7 | B_ESCHR_COLI | -R | S | S | S | -TRUE | +S | +FALSE | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 7 | -2011-05-23 | -T8 | +2011-03-19 | +Z7 | B_ESCHR_COLI | R | S | -S | +R | S | FALSE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 8 | -2011-06-09 | -T8 | +2011-07-19 | +Z7 | B_ESCHR_COLI | +R | +I | S | S | -S | -S | -FALSE | +TRUE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 9 | -2011-07-02 | -T8 | +2011-08-15 | +Z7 | B_ESCHR_COLI | +R | S | S | -S | -S | +R | FALSE | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 10 | -2011-12-05 | -T8 | +2011-09-22 | +Z7 | B_ESCHR_COLI | S | S | @@ -662,15 +645,14 @@ Longest: 1
| isolate | @@ -687,20 +669,20 @@ Longest: 1||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-04-04 | -T8 | +2010-03-27 | +Z7 | B_ESCHR_COLI | +R | S | S | R | -S | TRUE | TRUE | ||
| 2 | -2010-10-03 | -T8 | +2010-06-10 | +Z7 | B_ESCHR_COLI | S | S | @@ -711,10 +693,10 @@ Longest: 1|||||||
| 3 | -2010-10-19 | -T8 | +2010-06-25 | +Z7 | B_ESCHR_COLI | -S | +I | S | S | S | @@ -723,106 +705,103 @@ Longest: 1||||
| 4 | -2011-01-12 | -T8 | +2010-11-05 | +Z7 | B_ESCHR_COLI | S | S | -S | +R | S | FALSE | -FALSE | +TRUE | |
| 5 | -2011-02-03 | -T8 | +2010-11-22 | +Z7 | B_ESCHR_COLI | S | S | S | S | FALSE | -FALSE | +TRUE | ||
| 6 | -2011-04-04 | -T8 | +2011-02-06 | +Z7 | B_ESCHR_COLI | -R | S | S | S | -TRUE | -TRUE | +S | +FALSE | +FALSE |
| 7 | -2011-05-23 | -T8 | +2011-03-19 | +Z7 | B_ESCHR_COLI | R | S | -S | +R | S | FALSE | -FALSE | +TRUE | |
| 8 | -2011-06-09 | -T8 | +2011-07-19 | +Z7 | B_ESCHR_COLI | +R | +I | S | S | -S | -S | -FALSE | +TRUE | TRUE |
| 9 | -2011-07-02 | -T8 | +2011-08-15 | +Z7 | B_ESCHR_COLI | +R | S | S | -S | -S | -FALSE | +R | FALSE | +TRUE |
| 10 | -2011-12-05 | -T8 | +2011-09-22 | +Z7 | B_ESCHR_COLI | S | S | S | S | FALSE | -FALSE | +TRUE |
Instead of 2, now 4 isolates are flagged. In total, 78.7% of all isolates are marked ‘first weighted’ - 50.2% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
+Instead of 2, now 8 isolates are flagged. In total, 78.6% of all isolates are marked ‘first weighted’ - 50.2% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
As with filter_first_isolate(), there’s a shortcut for this new algorithm too:
-data_1st <- data %>% - filter_first_weighted_isolate() -
So we end up with 15,741 isolates for analysis.
+data_1st <- data %>% + filter_first_weighted_isolate() +So we end up with 15,713 isolates for analysis.
We can remove unneeded columns:
+data_1st <- data_1st %>% + select(-c(first, keyab))Now our data looks like:
-head(data_1st) -
| 1 | -2014-02-15 | -Y9 | -Hospital A | -B_STRPT_PNMN | -R | +2011-04-04 | +A3 | +Hospital C | +B_STPHY_AURS | R | +I | S | -R | -F | +S | +M | Gram-positive | -Streptococcus | -pneumoniae | +Staphylococcus | +aureus | TRUE |
| 2 | -2012-05-11 | -J4 | -Hospital C | +2011-10-13 | +I4 | +Hospital B | +B_ESCHR_COLI | +S | +S | +S | +S | +M | +Gram-negative | +Escherichia | +coli | +TRUE | +||||||
| 3 | +2015-11-20 | +J6 | +Hospital A | +B_ESCHR_COLI | +I | +S | +R | +S | +M | +Gram-negative | +Escherichia | +coli | +TRUE | +|||||||||
| 4 | +2017-02-04 | +X3 | +Hospital B | B_KLBSL_PNMN | R | S | S | S | -M | +F | Gram-negative | Klebsiella | pneumoniae | TRUE | ||||||||
| 4 | -2013-02-02 | -U4 | -Hospital B | -B_ESCHR_COLI | +5 | +2010-10-17 | +N9 | +Hospital D | +B_STPHY_AURS | R | R | S | -S | +R | F | -Gram-negative | -Escherichia | -coli | +Gram-positive | +Staphylococcus | +aureus | TRUE |
| 5 | -2015-12-04 | -S10 | -Hospital A | +7 | +2011-06-10 | +P2 | +Hospital B | B_STRPT_PNMN | -S | -S | +I | +I | S | R | F | @@ -921,38 +932,6 @@ Longest: 1pneumoniae | TRUE | |||||
| 6 | -2016-09-17 | -G2 | -Hospital B | -B_ESCHR_COLI | -R | -S | -S | -S | -M | -Gram-negative | -Escherichia | -coli | -TRUE | -|||||||||
| 7 | -2012-06-30 | -V5 | -Hospital A | -B_ESCHR_COLI | -R | -R | -S | -S | -F | -Gram-negative | -Escherichia | -coli | -TRUE | -
Time for the analysis!
@@ -968,16 +947,14 @@ Longest: 1To just get an idea how the species are distributed, create a frequency table with our freq() function. We created the genus and species column earlier based on the microbial ID. With paste(), we can concatenate them together.
The freq() function can be used like the base R language was intended:
Or can be used like the dplyr way, which is easier readable:
-data_1st %>% freq(genus, species) -
Frequency table
Class: character
-Length: 15,741
-Available: 15,741 (100%, NA: 0 = 0%)
+Length: 15,713
+Available: 15,713 (100%, NA: 0 = 0%)
Unique: 4
Shortest: 16
Longest: 24
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:
-data_1st %>% - bug_drug_combinations() %>% - head() # show first 6 rows -
# NOTE: Using column `bacteria` as input for `col_mo`.| E. coli | AMX | -3823 | -237 | -3808 | -7868 | +3806 | +262 | +3799 | +7867 |
| E. coli | AMC | -6210 | -315 | -1343 | -7868 | +6223 | +321 | +1323 | +7867 |
| E. coli | CIP | -5984 | +6006 | 0 | -1884 | -7868 | +1861 | +7867 | |
| E. coli | GEN | -7078 | +7072 | 0 | -790 | -7868 | +795 | +7867 | |
| K. pneumoniae | AMX | 0 | 0 | -1552 | -1552 | +1658 | +1658 | ||
| K. pneumoniae | AMC | -1198 | -63 | -291 | -1552 | +1298 | +56 | +304 | +1658 |
Using Tidyverse selections, you can also select columns based on the antibiotic class they are in:
-data_1st %>% - select(bacteria, fluoroquinolones()) %>% - bug_drug_combinations() -
# Selecting fluoroquinolones: `CIP` (ciprofloxacin)
# NOTE: Using column `bacteria` as input for `col_mo`.| E. coli | CIP | -5984 | +6006 | 0 | -1884 | -7868 | +1861 | +7867 |
| K. pneumoniae | CIP | -1176 | +1257 | 0 | -376 | -1552 | +401 | +1658 |
| S. aureus | CIP | -3037 | +2963 | 0 | -890 | -3927 | +879 | +3842 |
| S. pneumoniae | CIP | -1852 | +1785 | 0 | -542 | -2394 | +561 | +2346 |
The functions resistance() and susceptibility() can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() and proportion_R() can be used to determine the proportion of a specific antimicrobial outcome.
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
-data_1st %>% resistance(AMX) -# [1] 0.531923 -
Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:
-data_1st %>% - group_by(hospital) %>% - summarise(amoxicillin = resistance(AMX)) -
# `summarise()` ungrouping output (override with `.groups` argument)| Hospital A | -0.5240989 | +0.5459117 |
| Hospital B | -0.5326633 | +0.5280457 |
| Hospital C | -0.5554161 | +0.5268007 |
| Hospital D | -0.5248050 | +0.5537583 |
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):
-data_1st %>% - group_by(hospital) %>% - summarise(amoxicillin = resistance(AMX), - available = n_rsi(AMX)) -
# `summarise()` ungrouping output (override with `.groups` argument)| Hospital A | -0.5240989 | -4772 | +0.5459117 | +4574 |
| Hospital B | -0.5326633 | -5373 | +0.5280457 | +5598 |
| Hospital C | -0.5554161 | -2391 | +0.5268007 | +2388 |
| Hospital D | -0.5248050 | -3205 | +0.5537583 | +3153 |
These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
-data_1st %>% - group_by(genus) %>% - summarise(amoxiclav = susceptibility(AMC), - gentamicin = susceptibility(GEN), - amoxiclav_genta = susceptibility(AMC, GEN)) -
# `summarise()` ungrouping output (override with `.groups` argument)| Escherichia | -0.8293086 | -0.8995933 | -0.9874174 | +0.8318292 | +0.8989450 | +0.9853820 |
| Klebsiella | -0.8125000 | -0.9085052 | -0.9832474 | +0.8166466 | +0.8908323 | +0.9782871 |
| Staphylococcus | -0.8245480 | -0.9238605 | -0.9870130 | +0.8232691 | +0.9190526 | +0.9880271 |
| Streptococcus | -0.5459482 | +0.5264280 | 0.0000000 | -0.5459482 | +0.5264280 |
To make a transition to the next part, let’s see how this difference could be plotted:
-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)) + - geom_col(position = "dodge2") -# `summarise()` ungrouping output (override with `.groups` argument) -
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. A quick example would look like these syntaxes:
-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") +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") # or as short as: -ggplot(a_data_set) + - geom_bar(aes(year)) -
The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or proportion_df() and show results in stacked bars. Its simplest and shortest example:
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:
# 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") + + facet_rsi(facet = "antibiotic") + # set colours to the R/SI interpretations - scale_rsi_colours() + + scale_rsi_colours() + # show percentages on y axis - scale_y_percent(breaks = 0:4 * 25) + + scale_y_percent(breaks = 0:4 * 25) + # turn 90 degrees, to make it bars instead of columns - coord_flip() + + 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")) -
To simplify this, we also created the ggplot_rsi() function, which combines almost all above functions:
-data_1st %>% - group_by(genus) %>% - ggplot_rsi(x = "genus", - facet = "antibiotic", - breaks = 0:4 * 25, - datalabels = FALSE) + - coord_flip() -
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:
# use package 'tidyr' to pivot data: -library(tidyr) +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) - 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 <- 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) + 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 +check_FOS # A D # [1,] 25 77 -# [2,] 24 33 -
We can apply the test now with:
# do Fisher's Exact Test -fisher.test(check_FOS) +fisher.test(check_FOS) # # Fisher's Exact Test for Count Data # @@ -1386,8 +1351,7 @@ Longest: 24 # 0.2111489 0.9485124 # sample estimates: # odds ratio -# 0.4488318 -
As can be seen, the p value is 0.031, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.
@@ -1408,7 +1372,7 @@ Longest: 24 diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png index 1aaf44b58..e78f6e21c 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 1-1.png and b/docs/articles/AMR_files/figure-html/plot 1-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 3-1.png b/docs/articles/AMR_files/figure-html/plot 3-1.png index a98b90e64..a6f2f57a0 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 3-1.png and b/docs/articles/AMR_files/figure-html/plot 3-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 4-1.png b/docs/articles/AMR_files/figure-html/plot 4-1.png index 5ef8a6ec7..8878ab97b 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 4-1.png and b/docs/articles/AMR_files/figure-html/plot 4-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 5-1.png b/docs/articles/AMR_files/figure-html/plot 5-1.png index c0919c0e8..dc78f9804 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 5-1.png and b/docs/articles/AMR_files/figure-html/plot 5-1.png differ diff --git a/docs/articles/AMR_files/header-attrs-2.4/header-attrs.js b/docs/articles/AMR_files/header-attrs-2.4/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/AMR_files/header-attrs-2.4/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/EUCAST.html b/docs/articles/EUCAST.html index 78e085256..d1c6cf950 100644 --- a/docs/articles/EUCAST.html +++ b/docs/articles/EUCAST.html @@ -39,7 +39,7 @@ @@ -187,7 +187,7 @@ -How to apply EUCAST rules
@@ -216,38 +216,35 @@These rules can be used to discard impossible bug-drug combinations in your data. For example, Klebsiella produces beta-lactamase that prevents ampicillin (or amoxicillin) from working against it. In other words, practically every strain of Klebsiella is resistant to ampicillin.
Sometimes, laboratory data can still contain such strains with ampicillin being susceptible to ampicillin. This could be because an antibiogram is available before an identification is available, and the antibiogram is then not re-interpreted based on the identification (namely, Klebsiella). EUCAST expert rules solve this, that can be applied using eucast_rules():
-oops <- data.frame(mo = c("Klebsiella", - "Escherichia"), - ampicillin = "S") -oops +oops <- data.frame(mo = c("Klebsiella", + "Escherichia"), + ampicillin = "S") +oops # mo ampicillin # 1 Klebsiella S # 2 Escherichia S -eucast_rules(oops, info = FALSE) +eucast_rules(oops, info = FALSE) # mo ampicillin # 1 Klebsiella R -# 2 Escherichia S -
EUCAST rules can not only be used for correction, they can also be used for filling in known resistance and susceptibility based on results of other antimicrobials drugs. This process is called interpretive reading and is part of the eucast_rules() function as well:
-data <- data.frame(mo = c("Staphylococcus aureus", +data <- data.frame(mo = c("Staphylococcus aureus", "Enterococcus faecalis", "Escherichia coli", "Klebsiella pneumoniae", - "Pseudomonas aeruginosa"), - VAN = "-", # Vancomycin - AMX = "-", # Amoxicillin - COL = "-", # Colistin - CAZ = "-", # Ceftazidime - CXM = "-", # Cefuroxime - PEN = "S", # Benzylenicillin - FOX = "S", # Cefoxitin - stringsAsFactors = FALSE) -
-data
-| mo | @@ -313,8 +310,7 @@
|---|
-eucast_rules(data) -
| mo | @@ -397,7 +393,7 @@ diff --git a/docs/articles/EUCAST_files/header-attrs-2.4/header-attrs.js b/docs/articles/EUCAST_files/header-attrs-2.4/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/EUCAST_files/header-attrs-2.4/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/MDR.html b/docs/articles/MDR.html index 07b19c454..1b5377b2d 100644 --- a/docs/articles/MDR.html +++ b/docs/articles/MDR.html @@ -39,7 +39,7 @@ @@ -187,7 +187,7 @@ -|||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | Mono-resistant | -3225 | -64.50% | -3225 | -64.50% | +3262 | +65.24% | +3262 | +65.24% |
| 2 | Negative | -698 | -13.96% | -3923 | -78.46% | +664 | +13.28% | +3926 | +78.52% |
| 3 | Multi-drug-resistant | -581 | -11.62% | -4504 | -90.08% | +609 | +12.18% | +4535 | +90.70% |
| 4 | Poly-resistant | -298 | -5.96% | -4802 | -96.04% | +283 | +5.66% | +4818 | +96.36% |
| 5 | Extensively drug-resistant | -198 | -3.96% | +182 | +3.64% | 5000 | 100.00% |
