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support groups for portion_df, update README

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dr. M.S. (Matthijs) Berends 2018-08-12 22:34:03 +02:00
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4
NAMESPACE
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@ -120,12 +120,14 @@ importFrom(dplyr,arrange)
importFrom(dplyr,arrange_at) importFrom(dplyr,arrange_at)
importFrom(dplyr,as_tibble) importFrom(dplyr,as_tibble)
importFrom(dplyr,between) importFrom(dplyr,between)
importFrom(dplyr,bind_cols) importFrom(dplyr,bind_rows)
importFrom(dplyr,case_when) importFrom(dplyr,case_when)
importFrom(dplyr,desc) importFrom(dplyr,desc)
importFrom(dplyr,everything)
importFrom(dplyr,filter) importFrom(dplyr,filter)
importFrom(dplyr,group_by) importFrom(dplyr,group_by)
importFrom(dplyr,group_by_at) importFrom(dplyr,group_by_at)
importFrom(dplyr,group_vars)
importFrom(dplyr,if_else) importFrom(dplyr,if_else)
importFrom(dplyr,lag) importFrom(dplyr,lag)
importFrom(dplyr,left_join) importFrom(dplyr,left_join)

4
R/data.R
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@ -279,11 +279,11 @@
#' Dataset with 2000 blood culture isolates of septic patients #' Dataset with 2000 blood culture isolates of septic patients
#' #'
#' An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis. For examples, press F1. #' An anonymised dataset containing 2000 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 \code{data.frame} can be used to practice AMR analysis. For examples, press F1.
#' @format A data.frame with 2000 observations and 49 variables: #' @format A data.frame with 2000 observations and 49 variables:
#' \describe{ #' \describe{
#' \item{\code{date}}{date of receipt at the laboratory} #' \item{\code{date}}{date of receipt at the laboratory}
#' \item{\code{hospital_id}}{ID of the hospital} #' \item{\code{hospital_id}}{ID of the hospital, from A to D}
#' \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit} #' \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
#' \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward} #' \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
#' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic} #' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}

2
R/first_isolate.R
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@ -55,7 +55,7 @@
#' @return A vector to add to table, see Examples. #' @return A vector to add to table, see Examples.
#' @source Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. #' @source Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
#' @examples #' @examples
#' # septic_patients is a dataset available in the AMR package. It is true data. #' # septic_patients is a dataset available in the AMR package. It is true, genuine data.
#' ?septic_patients #' ?septic_patients
#' #'
#' library(dplyr) #' library(dplyr)

29
R/ggplot_rsi.R
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@ -21,8 +21,9 @@
#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions. #' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
#' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}}) #' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})
#' @param position position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"} #' @param position position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}
#' @param x parameter to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} #' @param x variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"}
#' @param facet parameter to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} #' @param fill variable to categorise using the plots legend
#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"}
#' @details At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}. #' @details At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
#' #'
#' \strong{The functions}\cr #' \strong{The functions}\cr
@ -64,8 +65,18 @@
#' septic_patients %>% #' septic_patients %>%
#' select(amox, nitr, fosf, trim, cipr) %>% #' select(amox, nitr, fosf, trim, cipr) %>%
#' ggplot_rsi(x = "Interpretation", facet = "Antibiotic") #' ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
#'
#' # it also supports groups (don't forget to use facet on the group):
#' septic_patients %>%
#' select(hospital_id, amox, cipr) %>%
#' group_by(hospital_id) %>%
#' ggplot_rsi() +
#' facet_grid("hospital_id") +
#' labs(title = "AMR of Amoxicillin And Ciprofloxacine Per Hospital")
ggplot_rsi <- function(data, ggplot_rsi <- function(data,
position = "stack",
x = "Antibiotic", x = "Antibiotic",
fill = "Interpretation",
facet = NULL) { facet = NULL) {
if (!"ggplot2" %in% rownames(installed.packages())) { if (!"ggplot2" %in% rownames(installed.packages())) {
@ -73,11 +84,15 @@ ggplot_rsi <- function(data,
} }
p <- ggplot2::ggplot(data = data) + p <- ggplot2::ggplot(data = data) +
geom_rsi(x = x) + geom_rsi(position = position, x = x, fill = fill) +
scale_y_percent() + scale_y_percent() +
scale_rsi_colours() +
theme_rsi() theme_rsi()
if (fill == "Interpretation") {
# set RSI colours
p <- p + scale_rsi_colours()
}
if (!is.null(facet)) { if (!is.null(facet)) {
p <- p + facet_rsi(facet = facet) p <- p + facet_rsi(facet = facet)
} }
@ -87,7 +102,7 @@ ggplot_rsi <- function(data,
#' @rdname ggplot_rsi #' @rdname ggplot_rsi
#' @export #' @export
geom_rsi <- function(position = "stack", x = c("Antibiotic", "Interpretation")) { geom_rsi <- function(position = "stack", x = c("Antibiotic", "Interpretation"), fill = "Interpretation") {
x <- x[1] x <- x[1]
if (!x %in% c("Antibiotic", "Interpretation")) { if (!x %in% c("Antibiotic", "Interpretation")) {
@ -95,8 +110,8 @@ geom_rsi <- function(position = "stack", x = c("Antibiotic", "Interpretation"))
} }
ggplot2::layer(geom = "bar", stat = "identity", position = position, ggplot2::layer(geom = "bar", stat = "identity", position = position,
mapping = ggplot2::aes_string(x = x, y = "Percentage", fill = "Interpretation"), mapping = ggplot2::aes_string(x = x, y = "Percentage", fill = fill),
data = AMR::portion_df, params = list()) data = AMR::portion_df, params = list())
} }

55
R/portion.R
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@ -29,7 +29,7 @@
#' @param translate a logical value to indicate whether antibiotic abbreviations should be translated with \code{\link{abname}} #' @param translate a logical value to indicate whether antibiotic abbreviations should be translated with \code{\link{abname}}
#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set. #' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
#' #'
#' \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \code{data.frame} will have three rows (for R/I/S) and a column for each variable with class \code{"rsi"}. #' \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
#' #'
#' The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated. #' The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
#' \if{html}{ #' \if{html}{
@ -45,6 +45,8 @@
#' \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>} #' \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
#' } #' }
#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. #' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
#'
#' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
#' @seealso \code{\link{n_rsi}} to count cases with antimicrobial results. #' @seealso \code{\link{n_rsi}} to count cases with antimicrobial results.
#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates #' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
#' @return Double or, when \code{as_percent = TRUE}, a character. #' @return Double or, when \code{as_percent = TRUE}, a character.
@ -52,6 +54,9 @@
#' @name portion #' @name portion
#' @export #' @export
#' @examples #' @examples
#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
#' ?septic_patients
#'
#' # Calculate resistance #' # Calculate resistance
#' portion_R(septic_patients$amox) #' portion_R(septic_patients$amox)
#' portion_IR(septic_patients$amox) #' portion_IR(septic_patients$amox)
@ -100,6 +105,18 @@
#' combination_p = portion_S(cipr, gent, as_percent = TRUE), #' combination_p = portion_S(cipr, gent, as_percent = TRUE),
#' combination_n = n_rsi(cipr, gent)) #' combination_n = n_rsi(cipr, gent))
#' #'
#' # Get portions S/I/R immediately of all rsi columns
#' septic_patients %>%
#' select(amox, cipr) %>%
#' portion_df(translate = FALSE)
#'
#' # It also supports grouping variables
#' septic_patients %>%
#' select(hospital_id, amox, cipr) %>%
#' group_by(hospital_id) %>%
#' portion_df(translate = FALSE)
#'
#'
#' \dontrun{ #' \dontrun{
#' #'
#' # calculate current empiric combination therapy of Helicobacter gastritis: #' # calculate current empiric combination therapy of Helicobacter gastritis:
@ -177,25 +194,33 @@ portion_S <- function(ab1,
} }
#' @rdname portion #' @rdname portion
#' @importFrom dplyr bind_cols summarise_if mutate #' @importFrom dplyr bind_rows summarise_if mutate group_vars select everything
#' @export #' @export
portion_df <- function(data, translate = getOption("get_antibiotic_names", TRUE)) { portion_df <- function(data, translate = getOption("get_antibiotic_names", TRUE)) {
resS <- bind_cols(data.frame(Interpretation = "S", stringsAsFactors = FALSE),
summarise_if(.tbl = data, resS <- summarise_if(.tbl = data,
.predicate = is.rsi, .predicate = is.rsi,
.funs = portion_S)) .funs = portion_S) %>%
resI <- bind_cols(data.frame(Interpretation = "I", stringsAsFactors = FALSE), mutate(Interpretation = "S") %>%
summarise_if(.tbl = data, select(Interpretation, everything())
.predicate = is.rsi,
.funs = portion_I)) resI <- summarise_if(.tbl = data,
resR <- bind_cols(data.frame(Interpretation = "R", stringsAsFactors = FALSE), .predicate = is.rsi,
summarise_if(.tbl = data, .funs = portion_I) %>%
.predicate = is.rsi, mutate(Interpretation = "I") %>%
.funs = portion_R)) select(Interpretation, everything())
resR <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = portion_R) %>%
mutate(Interpretation = "R") %>%
select(Interpretation, everything())
data.groups <- group_vars(data)
res <- bind_rows(resS, resI, resR) %>% res <- bind_rows(resS, resI, resR) %>%
mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>% mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
tidyr::gather(Antibiotic, Percentage, -Interpretation) tidyr::gather(Antibiotic, Percentage, -Interpretation, -data.groups)
if (translate == TRUE) { if (translate == TRUE) {
res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = "official")) res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = "official"))
} }

163
README.md
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@ -24,6 +24,19 @@ Erwin E.A. Hassing<sup>2</sup>,
<a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_eh1h.png" height="60px"></a> <a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_eh1h.png" height="60px"></a>
<a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_interreg.png" height="60px"></a> <a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_interreg.png" height="60px"></a>
## Contents
* [Why this package?](#why-this-package)
* [How to get it?](#how-to-get-it)
* [Install from CRAN](#install-from-cran)
* [Install from GitHub](#install-from-github)
* [How to use it?](#how-to-use-it)
* [New classes](#new-classes)
* [Overwrite/force resistance based on EUCAST rules](#overwriteforce-resistance-based-on-eucast-rules)
* [Other (microbial) epidemiological functions](#other-microbial-epidemiological-functions)
* [Frequency tables](#frequency-tables)
* [Data sets included in package](#data-sets-included-in-package)
* [Copyright](#copyright)
## Why this package? ## Why this package?
This R package was intended to make microbial epidemiology easier. Most functions contain extensive help pages to get started. This R package was intended to make microbial epidemiology easier. Most functions contain extensive help pages to get started.
@ -50,12 +63,12 @@ And it contains:
With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E). With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).
#### Read all changes and new functions in [NEWS.md](NEWS.md). **Read all changes and new functions in [NEWS.md](NEWS.md).**
## How to get it? ## How to get it?
This package [is published on CRAN](http://cran.r-project.org/package=AMR), the official R network. This package [is published on CRAN](http://cran.r-project.org/package=AMR), the official R network.
### Install from CRAN (recommended) ### Install from CRAN
[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR) [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR) [![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR) [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
(Note: Downloads measured only by [cran.rstudio.com](https://cran.rstudio.com/package=AMR), this excludes e.g. the official [cran.r-project.org](https://cran.r-project.org/package=AMR)) (Note: Downloads measured only by [cran.rstudio.com](https://cran.rstudio.com/package=AMR), this excludes e.g. the official [cran.r-project.org](https://cran.r-project.org/package=AMR))
@ -67,7 +80,7 @@ This package [is published on CRAN](http://cran.r-project.org/package=AMR), the
- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> Install in R directly: - <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> Install in R directly:
- `install.packages("AMR")` - `install.packages("AMR")`
### Install from GitHub (latest development version) ### Install from GitHub
[![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR) [![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
[![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master) [![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)
[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg)](https://github.com/msberends/AMR/commits/master) [![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg)](https://github.com/msberends/AMR/commits/master)
@ -86,6 +99,85 @@ library(AMR)
# For a list of functions: # For a list of functions:
help(package = "AMR") help(package = "AMR")
``` ```
### New classes
This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`).
Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
These functions also try to coerce valid values.
#### RSI
The `septic_patients` data set comes with antimicrobial results of more than 40 different drugs. For example, columns `amox` and `cipr` contain results of amoxicillin and ciprofloxacin, respectively.
```r
summary(septic_patients[, c("amox", "cipr")])
# amox cipr
# Mode :rsi Mode :rsi
# <NA> :1002 <NA> :596
# Sum S :336 Sum S :1108
# Sum IR:662 Sum IR:296
# -Sum R:659 -Sum R:227
# -Sum I:3 -Sum I:69
```
You can use the `plot` function from base R:
```r
plot(septic_patients$cipr)
```
![example_1_rsi](man/figures/rsi_example1.png)
Or use the `ggplot2` and `dplyr` packages to create more appealing plots:
```r
library(dplyr)
library(ggplot2)
septic_patients %>%
select(amox, cipr) %>%
ggplot_rsi()
```
![example_2_rsi](man/figures/rsi_example2.png)
```r
septic_patients %>%
select(amox, cipr) %>%
ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
```
![example_3_rsi](man/figures/rsi_example3.png)
It also supports grouping variables. Let's say we want to compare resistance of these antibiotics between hospitals A to D (variable `hospital_id`):
```r
septic_patients %>%
select(hospital_id, amox, cipr) %>%
group_by(hospital_id) %>%
ggplot_rsi() + # start adding ggplot elements here with `+`
facet_grid("hospital_id") + # splitting the plots on our grouping variable
labs(title = "AMR of Amoxicillin And Ciprofloxacine Per Hospital")
```
![example_4_rsi](man/figures/rsi_example4.png)
You could use this to group on anything in your plots: Gram stain, age (group), genus, geographic location, et cetera.
#### MIC
```r
# Transform values to new class
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
summary(mic_data)
# Mode:mic
# <NA>:0
# Min.:<=0.128
# Max.:>=32
plot(mic_data)
```
![example_mic](man/figures/mic_example.png)
### Overwrite/force resistance based on EUCAST rules ### Overwrite/force resistance based on EUCAST rules
This is also called *interpretive reading*. This is also called *interpretive reading*.
```r ```r
@ -132,69 +224,12 @@ guess_bactid("VISA") # Vancomycin Intermediate S. aureus
guess_bactid("VRSA") # Vancomycin Resistant S. aureus guess_bactid("VRSA") # Vancomycin Resistant S. aureus
``` ```
### New classes ### Other (microbial) epidemiological functions
This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`).
Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
These functions also try to coerce valid values.
#### RSI
The `septic_patients` data set comes with antimicrobial results of more than 40 different drugs. For example, columns `amox` and `cipr` contain results of amoxicillin and ciprofloxacin, respectively.
```r
summary(septic_patients[, c("amox", "cipr")])
# amox cipr
# Mode :rsi Mode :rsi
# <NA> :1002 <NA> :596
# Sum S :336 Sum S :1108
# Sum IR:662 Sum IR:296
# -Sum R:659 -Sum R:227
# -Sum I:3 -Sum I:69
```
You can use the `plot` function from base R:
```r
plot(septic_patients$cipr)
```
![example_1_rsi](man/figures/rsi_example1.png)
Or use the `ggplot2` and `dplyr` packages to create more appealing plots:
```r
septic_patients %>%
select(amox, cipr) %>%
ggplot_rsi()
```
![example_2_rsi](man/figures/rsi_example2.png)
```r ```r
septic_patients %>% # G-test to replace Chi squared test
select(amox, cipr) %>% g.test(...)
ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
```
![example_3_rsi](man/figures/rsi_example3.png)
#### MIC
```r
# Transform values to new class
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
summary(mic_data)
# Mode:mic
# <NA>:0
# Min.:<=0.128
# Max.:>=32
plot(mic_data)
```
![example_mic](man/figures/mic_example.png)
Other epidemiological functions:
```r
# Determine key antibiotic based on bacteria ID # Determine key antibiotic based on bacteria ID
key_antibiotics(...) key_antibiotics(...)
@ -318,7 +353,7 @@ Learn more about this function with:
?freq ?freq
``` ```
### Databases included in package ### Data sets included in package
Datasets to work with antibiotics and bacteria properties. Datasets to work with antibiotics and bacteria properties.
```r ```r
# Dataset with 2000 random blood culture isolates from anonymised # Dataset with 2000 random blood culture isolates from anonymised

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man/first_isolate.Rd
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@ -76,7 +76,7 @@ Determine first (weighted) isolates of all microorganisms of every patient per e
} }
\examples{ \examples{
# septic_patients is a dataset available in the AMR package. It is true data. # septic_patients is a dataset available in the AMR package. It is true, genuine data.
?septic_patients ?septic_patients
library(dplyr) library(dplyr)

24
man/ggplot_rsi.Rd
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@ -9,9 +9,11 @@
\alias{theme_rsi} \alias{theme_rsi}
\title{AMR bar plots with \code{ggplot}} \title{AMR bar plots with \code{ggplot}}
\usage{ \usage{
ggplot_rsi(data, x = "Antibiotic", facet = NULL) ggplot_rsi(data, position = "stack", x = "Antibiotic",
fill = "Interpretation", facet = NULL)
geom_rsi(position = "stack", x = c("Antibiotic", "Interpretation")) geom_rsi(position = "stack", x = c("Antibiotic", "Interpretation"),
fill = "Interpretation")
facet_rsi(facet = c("Interpretation", "Antibiotic")) facet_rsi(facet = c("Interpretation", "Antibiotic"))
@ -24,11 +26,13 @@ theme_rsi()
\arguments{ \arguments{
\item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})} \item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})}
\item{x}{parameter to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"}}
\item{facet}{parameter to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"}}
\item{position}{position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}} \item{position}{position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}}
\item{x}{variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"}}
\item{fill}{variable to categorise using the plots legend}
\item{facet}{variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"}}
} }
\description{ \description{
Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions. Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
@ -74,4 +78,12 @@ septic_patients \%>\%
septic_patients \%>\% septic_patients \%>\%
select(amox, nitr, fosf, trim, cipr) \%>\% select(amox, nitr, fosf, trim, cipr) \%>\%
ggplot_rsi(x = "Interpretation", facet = "Antibiotic") ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
# it also supports groups (don't forget to use facet on the group):
septic_patients \%>\%
select(hospital_id, amox, cipr) \%>\%
group_by(hospital_id) \%>\%
ggplot_rsi() +
facet_grid("hospital_id") +
labs(title = "AMR of Amoxicillin And Ciprofloxacine Per Hospital")
} }

19
man/portion.Rd
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@ -11,6 +11,8 @@
\title{Calculate resistance of isolates} \title{Calculate resistance of isolates}
\source{ \source{
\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
} }
\usage{ \usage{
portion_R(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE) portion_R(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
@ -49,7 +51,7 @@ These functions can be used to calculate the (co-)resistance of microbial isolat
\details{ \details{
\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set. \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
\code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \code{data.frame} will have three rows (for R/I/S) and a column for each variable with class \code{"rsi"}. \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated. The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
\if{html}{ \if{html}{
@ -66,6 +68,9 @@ The old \code{\link{rsi}} function is still available for backwards compatibilit
} }
} }
\examples{ \examples{
# septic_patients is a data set available in the AMR package. It is true, genuine data.
?septic_patients
# Calculate resistance # Calculate resistance
portion_R(septic_patients$amox) portion_R(septic_patients$amox)
portion_IR(septic_patients$amox) portion_IR(septic_patients$amox)
@ -114,6 +119,18 @@ septic_patients \%>\%
combination_p = portion_S(cipr, gent, as_percent = TRUE), combination_p = portion_S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent)) combination_n = n_rsi(cipr, gent))
# Get portions S/I/R immediately of all rsi columns
septic_patients \%>\%
select(amox, cipr) \%>\%
portion_df(translate = FALSE)
# It also supports grouping variables
septic_patients \%>\%
select(hospital_id, amox, cipr) \%>\%
group_by(hospital_id) \%>\%
portion_df(translate = FALSE)
\dontrun{ \dontrun{
# calculate current empiric combination therapy of Helicobacter gastritis: # calculate current empiric combination therapy of Helicobacter gastritis:

4
man/septic_patients.Rd
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@ -7,7 +7,7 @@
\format{A data.frame with 2000 observations and 49 variables: \format{A data.frame with 2000 observations and 49 variables:
\describe{ \describe{
\item{\code{date}}{date of receipt at the laboratory} \item{\code{date}}{date of receipt at the laboratory}
\item{\code{hospital_id}}{ID of the hospital} \item{\code{hospital_id}}{ID of the hospital, from A to D}
\item{\code{ward_icu}}{logical to determine if ward is an intensive care unit} \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
\item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward} \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
\item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic} \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
@ -21,7 +21,7 @@
septic_patients septic_patients
} }
\description{ \description{
An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis. For examples, press F1. An anonymised dataset containing 2000 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 \code{data.frame} can be used to practice AMR analysis. For examples, press F1.
} }
\examples{ \examples{
# ----------- # # ----------- #

9
tests/testthat/test-ggplot_rsi.R
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@ -32,4 +32,13 @@ test_that("ggplot_rsi works", {
expect_error(geom_rsi(x = "test")) expect_error(geom_rsi(x = "test"))
expect_error(facet_rsi(facet = "test")) expect_error(facet_rsi(facet = "test"))
# support for groups
print(
septic_patients %>%
select(hospital_id, amox, cipr) %>%
group_by(hospital_id) %>%
ggplot_rsi() +
facet_grid("hospital_id")
)
}) })