From 13120f465f84a21af85ec60ed3071f8e617f6665 Mon Sep 17 00:00:00 2001 From: "Matthijs S. Berends" Date: Wed, 20 Feb 2019 13:57:23 +0100 Subject: [PATCH] website update --- DESCRIPTION | 4 +- R/catalogue_of_life.R | 4 +- README.md | 3 +- docs/articles/AMR.html | 1014 ++++++++--------- .../AMR_files/figure-html/plot 1-1.png | Bin 33724 -> 33736 bytes .../AMR_files/figure-html/plot 3-1.png | Bin 21109 -> 21123 bytes .../AMR_files/figure-html/plot 4-1.png | Bin 69620 -> 69624 bytes .../AMR_files/figure-html/plot 5-1.png | Bin 51370 -> 51370 bytes docs/articles/benchmarks.html | 175 +-- .../figure-html/unnamed-chunk-4-1.png | Bin 31454 -> 31518 bytes .../figure-html/unnamed-chunk-4-2.png | Bin 33495 -> 33318 bytes docs/index.html | 19 +- docs/reference/as.mo.html | 4 +- docs/reference/catalogue_of_life.html | 4 +- docs/reference/catalogue_of_life_version.html | 4 +- docs/reference/microorganisms.codes.html | 4 +- docs/reference/microorganisms.html | 4 +- docs/reference/microorganisms.old.html | 4 +- docs/reference/mo_property.html | 4 +- index.md | 18 +- man/as.mo.Rd | 4 +- man/catalogue_of_life.Rd | 4 +- man/catalogue_of_life_version.Rd | 4 +- man/microorganisms.Rd | 4 +- man/microorganisms.codes.Rd | 4 +- man/microorganisms.old.Rd | 4 +- man/mo_property.Rd | 4 +- vignettes/benchmarks.Rmd | 52 +- 28 files changed, 661 insertions(+), 684 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f7d59cd2..d82496bb 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -48,15 +48,15 @@ Imports: crayon (>= 1.3.0), data.table (>= 1.9.0), dplyr (>= 0.7.0), + ggplot2, hms, knitr (>= 1.0.0), + microbenchmark, rlang (>= 0.2.0), tidyr (>= 0.7.0) Suggests: covr (>= 3.0.1), curl, - ggplot2, - microbenchmark, readxl, rmarkdown, rstudioapi, diff --git a/R/catalogue_of_life.R b/R/catalogue_of_life.R index 30e9f626..480ab496 100755 --- a/R/catalogue_of_life.R +++ b/R/catalogue_of_life.R @@ -29,8 +29,8 @@ #' Included are: #' \itemize{ #' \item{All ~55,000 (sub)species from the kingdoms of Archaea, Bacteria, Protozoa and Viruses} -#' \item{All ~3,000 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales and Schizosaccharomycetales. The kingdom of Fungi is a very large taxon with almost 300,000 different species, of which most are not microbial. Including everything tremendously slows down our algortihms, and not all fungi fit the scope of this package. By only including the aforementioned taxonomic orders, the most relevant species are covered (like genera \emph{Aspergillus}, \emph{Candida}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).} -#' \item{All ~15,000 previously accepted names of inckuded (sub)species that have been taxonomically renamed} +#' \item{All ~3,000 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales and Schizosaccharomycetales. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant (sub)species are covered (like all species of \emph{Aspergillus}, \emph{Candida}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).} +#' \item{All ~15,000 previously accepted names of included (sub)species that have been taxonomically renamed} #' \item{The complete taxonomic tree of all included (sub)species: from kingdom to subspecies} #' \item{The responsible author(s) and year of scientific publication} #' } diff --git a/README.md b/README.md index 8c69270c..ad6a632e 100755 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ -# AMR (for R) +% AMR (for R) + ### Not a developer? Then please visit our website [https://msberends.gitlab.io/AMR](https://msberends.gitlab.io/AMR) to read about this package. diff --git a/docs/articles/AMR.html b/docs/articles/AMR.html index 1e382202..839a1db4 100644 --- a/docs/articles/AMR.html +++ b/docs/articles/AMR.html @@ -1,75 +1,35 @@ - - - - + + + + - How to conduct AMR analysis • AMR (for R) - - - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - + +
-
- + +
+ + + - - - -
+

How to conduct AMR analysis

@@ -245,19 +203,18 @@

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 RMarkdown. However, the methodology remains unchanged. This page was generated on 20 February 2019.

-

Introduction

+

+Introduction

For this tutorial, we will create fake demonstration data to work with.

You can skip to 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:

- - - +
+ - - + @@ -283,75 +240,81 @@
date patient_id mo amox cipr
2019-02-20
-

Needed R packages

+

+Needed R packages

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 Dr Hadley Wickham. 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.

Our AMR package depends on these packages and even extends their use and functions.

-
-

Creation of data

+

+Creation of data

We will create some fake example data to use for analysis. For antimicrobial resistance analysis, we need at least: a patient ID, name or code of a microorganism, a date and antimicrobial results (an antibiogram). It could also include a specimen type (e.g. to filter on blood or urine), the ward type (e.g. to filter on ICUs).

With additional columns (like a hospital name, the patients gender of even [well-defined] clinical properties) you can do a comparative analysis, as this tutorial will demonstrate too.

-

Patients

+

+Patients

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.

-

Dates

+

+Dates

Let’s pretend that our data consists of blood cultures isolates from 1 January 2010 until 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

+

+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")
-

Other variables

+

+Other variables

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.

+

+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)),
-                   amox = sample(ab_interpretations, size = sample_size, replace = TRUE,
-                                 prob = c(0.60, 0.05, 0.35)),
-                   amcl = sample(ab_interpretations, size = sample_size, replace = TRUE,
-                                 prob = c(0.75, 0.10, 0.15)),
-                   cipr = sample(ab_interpretations, size = sample_size, replace = TRUE,
-                                 prob = c(0.80, 0.00, 0.20)),
-                   gent = sample(ab_interpretations, size = sample_size, replace = TRUE,
-                                 prob = c(0.92, 0.00, 0.08))
+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)),
+                   amox = sample(ab_interpretations, size = sample_size, replace = TRUE,
+                                 prob = c(0.60, 0.05, 0.35)),
+                   amcl = sample(ab_interpretations, size = sample_size, replace = TRUE,
+                                 prob = c(0.75, 0.10, 0.15)),
+                   cipr = sample(ab_interpretations, size = sample_size, replace = TRUE,
+                                 prob = c(0.80, 0.00, 0.20)),
+                   gent = 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:

- -

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:

- - - - +

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:

+ +

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:

+
head(data)
+
+ @@ -361,73 +324,72 @@ - - + - - - - + + + + + + + + + + + + + + + + - + + + + + + + + + + + - - + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + - - - - - - - - - - - - + @@ -436,9 +398,10 @@
-

Cleaning the data

-

Use the frequency table function freq() to look specifically for unique values in any variable. For example, for the gender variable:

- +

+Cleaning the data

+

Use the frequency table function freq() to look specifically for unique values in any variable. For example, for the gender variable:

+
data %>% freq(gender) # this would be the same: freq(data$gender)
#> Frequency table of `gender` from a data.frame (20,000 x 9) 
 #> 
 #> Class:   factor (numeric)
@@ -448,18 +411,18 @@
 #> 
 #>      Item     Count   Percent   Cum. Count   Cum. Percent
 #> ---  -----  -------  --------  -----------  -------------
-#> 1    M       10,458     52.3%       10,458          52.3%
-#> 2    F        9,542     47.7%       20,000         100.0%
+#> 1 M 10,333 51.7% 10,333 51.7% +#> 2 F 9,667 48.3% 20,000 100.0%

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.

-

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:

+

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:

+ mutate(bacteria = as.mo(bacteria))
+

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 ampicillin = R when amoxicillin/clavulanic acid = R.

-

Because the amoxicillin (column amox) and amoxicillin/clavulanic acid (column amcl) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The eucast_rules() fixes this:

- +

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 ampicillin = R when amoxicillin/clavulanic acid = R.

+

Because the amoxicillin (column amox) and amoxicillin/clavulanic acid (column amcl) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The eucast_rules() fixes this:

+ +#> -> changed 4,118 test results (0 to S; 0 to I; 4,118 to R)
-

Adding new variables

+

+Adding new variables

Now that we have the microbial ID, we can add some taxonomic properties:

+ mutate(gramstain = mo_gramstain(bacteria), + genus = mo_genus(bacteria), + species = mo_species(bacteria))
-

First isolates

+

+First isolates

We also need to know which isolates we can actually use for analysis.

To conduct an analysis of antimicrobial resistance, you must only include the first isolate of every patient per episode (Hindler et al., Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be selection bias.

The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:

(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype). The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.
M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4

-

This 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:

+

This 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:

-

So only 28.2% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:

+#> => Found 5,690 first isolates (28.5% of total)
+

So only 28.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:

-

For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:

+ filter(first == TRUE) +

For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:

+ filter_first_isolate()
-

First weighted isolates

+

+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:

-
date patient_id hospitalcipr gent gender
2013-02-06O10Hospital BStreptococcus pneumoniae2014-07-28F8Hospital AEscherichia coliRRSSM
2017-04-01Y10Hospital BEscherichia coliR SI S S F
2015-04-18J2Hospital BKlebsiella pneumoniaeSIRSM
2010-01-25W12017-08-08Y6Hospital CEscherichia coliRSSSF
2010-12-27B3 Hospital B Klebsiella pneumoniae S I S SF
2011-02-16N10Hospital CEscherichia coliSSSSF
2010-04-01J4Hospital DEscherichia coliSSSS M
2015-02-27O2Hospital BEscherichia coliS
2016-12-01D4Hospital DStaphylococcus aureusR R SSF
2013-03-19E3Hospital BEscherichia coliSISSR M
- - +
+ @@ -547,37 +512,36 @@ - - + - - + + - - + + - - + + - - + + - - + + - + @@ -585,19 +549,19 @@ - - + + - + - - + + @@ -607,76 +571,75 @@ - - + + - + - - + + - - + + - - + + - - - - - - - - - - - - - - - + + + + - + + + + + + + + + + + +
isolate date patient_idcipr gent first
12010-01-19C62010-01-30J7 B_ESCHR_COLR SRSS S TRUE
22010-03-29C62010-01-30J7 B_ESCHR_COL SRRSS S FALSE
32010-05-27C62010-03-30J7 B_ESCHR_COLSR S S S
42010-06-25C62010-04-11J7 B_ESCHR_COL S SSR S FALSE
52010-06-28C62010-08-02J7 B_ESCHR_COL S R
62010-07-31C62010-10-14J7 B_ESCHR_COL S SSR S FALSE
72010-08-07C62010-11-02J7 B_ESCHR_COL RSSIR S FALSE
82010-11-13C62011-02-10J7 B_ESCHR_COLRI S SFALSE
92011-04-02C6B_ESCHR_COLRS S S TRUE
102011-06-26C6
92011-03-27J7 B_ESCHR_COLRS S S S FALSE
102011-04-21J7B_ESCHR_COLRSRSFALSE
-

Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.

-

If a column exists with a name like ‘key(…)ab’ the first_isolate() function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:

+

Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.

+

If a column exists with a name like ‘key(…)ab’ the first_isolate() function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:

- - - +#> => Found 15,784 first weighted isolates (78.9% of total) +
+ @@ -687,39 +650,38 @@ - - + - - + + - - + + - - + + - - + + - + - - + + - + @@ -728,20 +690,20 @@ - - + + - + - + - - + + @@ -752,79 +714,78 @@ - - + + - + - - + + - - + + - - + + - - - - + + + + - - + + - - - + + + - - + + - + - +
isolate date patient_idgent first first_weighted
12010-01-19C62010-01-30J7 B_ESCHR_COLR SRSS S TRUE TRUE
22010-03-29C62010-01-30J7 B_ESCHR_COL SRRSS S FALSETRUEFALSE
32010-05-27C62010-03-30J7 B_ESCHR_COLSR S S S
42010-06-25C62010-04-11J7 B_ESCHR_COL S SSR S FALSEFALSETRUE
52010-06-28C62010-08-02J7 B_ESCHR_COL S R
62010-07-31C62010-10-14J7 B_ESCHR_COL S SSR S FALSE TRUE
72010-08-07C62010-11-02J7 B_ESCHR_COL RSSIR S FALSE TRUE
82010-11-13C62011-02-10J7 B_ESCHR_COLRI S SFALSEFALSESSTRUETRUE
92011-04-02C62011-03-27J7 B_ESCHR_COLR S S STRUETRUESFALSEFALSE
102011-06-26C62011-04-21J7 B_ESCHR_COL R SSR S FALSEFALSETRUE
-

Instead of 2, now 7 isolates are flagged. In total, 79% of all isolates are marked ‘first weighted’ - 50.8% 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:

+

Instead of 2, now 8 isolates are flagged. In total, 78.9% of all isolates are marked ‘first weighted’ - 50.5% 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:

-

So we end up with 15,809 isolates for analysis.

+ filter_first_weighted_isolate()
+

So we end up with 15,784 isolates for analysis.

We can remove unneeded columns:

+ select(-c(first, keyab))

Now our data looks like:

- - - - +
head(data_1st)
+
+ @@ -839,32 +800,63 @@ - - + + + + + + + + + + + + + + + + + - - + + + + + + + + + + + + + + + + + + + - - + - - - + + + - + @@ -875,67 +867,35 @@ - - - + + + - - - - + + + + - - - + + + - - - - - - - - - - - - - - - - - - - - + + + + + - - - - - - - - - - - - - - - - - - - - + + + @@ -944,258 +904,253 @@
-

Analysing the data

+

+Analysing the data

You might want to start by getting an idea of how the data is distributed. It’s an important start, because it also decides how you will continue your analysis.

-

Dispersion of species

-

To 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:

- +

+Dispersion of species

+

To 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:

+
freq(paste(data_1st$genus, data_1st$species))

Or can be used like the dplyr way, which is easier readable:

- -

Frequency table of genus and species from a data.frame (15,809 x 13)

-

Columns: 2
-Length: 15,809 (of which NA: 0 = 0.00%)
+

data_1st %>% freq(genus, species)
+

Frequency table of genus and species from a data.frame (15,784 x 13)

+

Columns: 2
+Length: 15,784 (of which NA: 0 = 0.00%)
Unique: 4

-

Shortest: 16
+

Shortest: 16
Longest: 24

-
date patient_idgenus species first_weighted
12014-07-28F8Hospital AB_ESCHR_COLRRSSMGram negativeEscherichiacoliTRUE
22010-01-25W12017-04-01Y10Hospital BB_ESCHR_COLRSSSFGram negativeEscherichiacoliTRUE
32015-04-18J2 Hospital B B_KLBSL_PNE R IR SSFM Gram negative Klebsiella pneumoniae TRUE
32011-02-16N1042017-08-08Y6 Hospital C B_ESCHR_COLSR S S STRUE
42010-04-01J462016-12-01D4 Hospital DB_ESCHR_COLSSSB_STPHY_AURRR SR MGram negativeEscherichiacoliGram positiveStaphylococcusaureus TRUE
52015-02-27O2Hospital BB_ESCHR_COLSRSSFGram negativeEscherichiacoliTRUE
72011-08-19D6Hospital BB_STPHY_AUR2017-03-30N5Hospital AB_ESCHR_COLS SI S S MGram positiveStaphylococcusaureusTRUE
102017-01-12P10Hospital AB_STPHY_AURSRSSFGram positiveStaphylococcusaureusGram negativeEscherichiacoli TRUE
- - +
+ - - + - - - - + + + + - - - - + + + + - - - + + + - + - +
Item Count Percent Cum. Count Cum. Percent
1 Escherichia coli7,88949.9%7,88949.9%7,76849.2%7,76849.2%
2 Staphylococcus aureus3,87824.5%11,76774.4%3,95225.0%11,72074.3%
3 Streptococcus pneumoniae2,50115.8%14,2682,52916.0%14,249 90.3%
4 Klebsiella pneumoniae1,5411,535 9.7%15,80915,784 100.0%
-

Resistance percentages

+

+Resistance percentages

The functions portion_R, portion_RI, portion_I, portion_IS and portion_S can be used to determine the portion of a specific antimicrobial outcome. They can be used on their own:

- -

Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:

+ +

Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:

- - - + group_by(hospital) %>% + summarise(amoxicillin =portion_IR(amox)) +
+ - - + - + - + - + - +
hospital amoxicillin
Hospital A0.47356080.4792724
Hospital B0.47085040.4777657
Hospital C0.47797650.4674139
Hospital D0.47622010.4796148
-

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):

+

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):

- - - + group_by(hospital) %>% + summarise(amoxicillin =portion_IR(amox), + available = n_rsi(amox)) +
+ - - + - - + + - - + + - - + + - - + +
hospital amoxicillin available
Hospital A0.473560846900.47927244728
Hospital B0.470850456090.47776575532
Hospital C0.477976522930.46741392409
Hospital D0.476220132170.47961483115

These functions can also be used to get the portion of multiple antibiotics, to calculate co-resistance very easily:

- - - + group_by(genus) %>% + summarise(amoxicillin =portion_S(amcl), + gentamicin = portion_S(gent), + "amox + gent" = portion_S(amcl, gent)) +
+ - - + - - - + + + - - - + + + - - - + + + - + - +
genus amoxicillin gentamicin amox + gent
Escherichia0.73228550.90074790.97515530.73210610.90139030.9751545
Klebsiella0.73523690.89941600.97598960.73680780.90162870.9687296
Staphylococcus0.72408460.91877260.97447140.73785430.91649800.9792510
Streptococcus0.74170330.7406089 0.00000000.74170330.7406089

To make a transition to the next part, let’s see how this difference could be plotted:

-

+ geom_col(position = "dodge2")
+

-

Plots

+

+Plots

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,
+
-
The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or portion_df() and show results in stacked bars. Its simplest and shortest example:

-
-

+ggplot(a_data_set,
+       aes(year, value) +
+  geom_bar()
+

The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or portion_df() and show results in stacked bars. Its simplest and shortest example:

+
ggplot(data_1st) +
+  geom_rsi(translate_ab = FALSE)
+

Omit the translate_ab = FALSE to have the antibiotic codes (amox, amcl, cipr, gent) translated to official WHO names (amoxicillin, amoxicillin and betalactamase inhibitor, ciprofloxacin, gentamicin).

If we group on e.g. the genus column and add some additional functions from our package, we can create this:

-

-

To simplify this, we also created the ggplot_rsi() function, which combines almost all above functions:

+ theme(axis.text.y = element_text(face = "italic"))
+

+

To simplify this, we also created the ggplot_rsi() function, which combines almost all above functions:

-

+ coord_flip()
+

-

Independence test

+

+Independence test

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 fosf) in hospital A and D. The input for the final fisher.test() will be this:

- - - +

We will compare the resistance to fosfomycin (column fosf) in hospital A and D. The input for the final fisher.test() will be this:

+
+ - - + @@ -1211,16 +1166,16 @@ Longest: 24

A D
IR

We can transform the data and apply the test in only a couple of lines:

septic_patients %>%
-  filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
-  select(hospital_id, fosf) %>%            # 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
-  select(A, D) %>%                         # and select these only
-  as.matrix() %>%                          # transform to good old matrix for fisher.test()
-  fisher.test()                            # do Fisher's Exact Test
+  filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
+  select(hospital_id, fosf) %>%            # 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
+  select(A, D) %>%                         # and select these only
+  as.matrix() %>%                          # transform to good old matrix for fisher.test()
+  fisher.test()                            # do Fisher's Exact Test
 #> 
-#>  Fisher's Exact Test for Count Data
+#>  Fisher's Exact Test for Count Data
 #> 
 #> data:  .
 #> p-value = 0.03104
@@ -1237,49 +1192,56 @@ Longest: 24
-