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count_* functions

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dr. M.S. (Matthijs) Berends 2018-08-22 00:02:26 +02:00
parent e47e77febc
commit 3e87c8f409
16 changed files with 504 additions and 59 deletions
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 0.3.0
Date: 2018-08-14
Version: 0.3.0.9001
Date: 2018-08-21
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(

8
NAMESPACE
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@ -43,6 +43,12 @@ export(atc_groups)
export(atc_property)
export(clipboard_export)
export(clipboard_import)
export(count_I)
export(count_IR)
export(count_R)
export(count_S)
export(count_SI)
export(count_df)
export(facet_rsi)
export(first_isolate)
export(freq)
@ -58,6 +64,7 @@ export(interpretive_reading)
export(is.bactid)
export(is.mic)
export(is.rsi)
export(is.rsi.eligible)
export(key_antibiotics)
export(key_antibiotics_equal)
export(kurtosis)
@ -137,6 +144,7 @@ importFrom(dplyr,progress_estimated)
importFrom(dplyr,pull)
importFrom(dplyr,row_number)
importFrom(dplyr,select)
importFrom(dplyr,select_if)
importFrom(dplyr,slice)
importFrom(dplyr,summarise)
importFrom(dplyr,summarise_if)

13
NEWS.md
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@ -1,4 +1,15 @@
# 0.3.0
# 0.3.0.90xx (latest development version)
#### New
* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptibile isolates
* Function `is.rsi.eligible` to check for columns that have valid antimicrobial results, but do not have the `rsi` class yet. Transform the columns of your raw data with: `data %>% mutate_at(is.rsi.eligible, as.rsi)`
#### Changed
* Added parameters `minimum` and `as_percent` to `portion_df`
* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` parameter has value `portion_df` at default, but can be set to `count_df`.
* Fix for `ggplot_rsi` when the `ggplot2` was not loaded
# 0.3.0 (latest stable version)
**Published on CRAN: 2018-08-14**
#### New

19
R/classes.R
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@ -21,6 +21,7 @@
#' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
#' @rdname as.rsi
#' @param x vector
#' @details The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
#' @return Ordered factor with new class \code{rsi} and new attribute \code{package}
#' @keywords rsi
#' @export
@ -37,6 +38,12 @@
#' plot(rsi_data) # for percentages
#' barplot(rsi_data) # for frequencies
#' freq(rsi_data) # frequency table with informative header
#'
#' # fastest way to transform all columns with already valid AB results to class `rsi`:
#' library(dplyr)
#' septic_patients %>%
#' mutate_if(is.rsi.eligible,
#' as.rsi)
as.rsi <- function(x) {
if (is.rsi(x)) {
x
@ -88,6 +95,18 @@ is.rsi <- function(x) {
class(x) %>% identical(c('rsi', 'ordered', 'factor'))
}
#' @rdname as.rsi
#' @export
#' @importFrom dplyr %>%
is.rsi.eligible <- function(x) {
distinct_val <- x %>% unique() %>% sort() %>% as.character()
distinct_val <- distinct_val[!is.na(distinct_val) & trimws(distinct_val) != ""]
distinct_val_rsi <- as.character(suppressWarnings(as.rsi(distinct_val)))
length(distinct_val) > 0 &
identical(distinct_val, distinct_val_rsi)
}
#' @exportMethod print.rsi
#' @export
#' @importFrom dplyr %>%

199
R/count.R Normal file
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@ -0,0 +1,199 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# AUTHORS #
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# LICENCE #
# This program is free software; you can redistribute it and/or modify #
# it under the terms of the GNU General Public License version 2.0, #
# as published by the Free Software Foundation. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# ==================================================================== #
#' Count isolates
#'
#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
#'
#' \code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
#' @inheritParams portion
#' @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.
#'
#' These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
#'
#' \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of 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"}.
#' @source 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{portion}_*} to calculate microbial resistance and susceptibility.\cr
#' \code{\link{n_rsi}} to count all cases where antimicrobial results are available.
#' @keywords resistance susceptibility rsi antibiotics isolate isolates
#' @return Integer
#' @rdname count
#' @name count
#' @export
#' @examples
#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
#' ?septic_patients
#'
#' # Count resistant isolates
#' count_R(septic_patients$amox)
#' count_IR(septic_patients$amox)
#'
#' # Or susceptibile isolates
#' count_S(septic_patients$amox)
#' count_SI(septic_patients$amox)
#'
#' # Since n_rsi counts available isolates, you can
#' # calculate back to count e.g. non-susceptible isolates.
#' # This results in the same:
#' count_IR(septic_patients$amox)
#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
#'
#' library(dplyr)
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(R = count_R(cipr),
#' I = count_I(cipr),
#' S = count_S(cipr),
#' n = n_rsi(cipr), # the actual total; sum of all three
#' total = n()) # NOT the amount of tested isolates!
#'
#' # Count co-resistance between amoxicillin/clav acid and gentamicin,
#' # so we can see that combination therapy does a lot more than mono therapy.
#' # Please mind that `portion_S` calculates percentages right away instead.
#' count_S(septic_patients$amcl) # S = 1056 (67.3%)
#' n_rsi(septic_patients$amcl) # n = 1570
#'
#' count_S(septic_patients$gent) # S = 1363 (74.0%)
#' n_rsi(septic_patients$gent) # n = 1842
#'
#' with(septic_patients,
#' count_S(amcl, gent)) # S = 1385 (92.1%)
#' with(septic_patients, # n = 1504
#' n_rsi(amcl, gent))
#'
#' # Get portions S/I/R immediately of all rsi columns
#' septic_patients %>%
#' select(amox, cipr) %>%
#' count_df(translate = FALSE)
#'
#' # It also supports grouping variables
#' septic_patients %>%
#' select(hospital_id, amox, cipr) %>%
#' group_by(hospital_id) %>%
#' count_df(translate = FALSE)
#'
count_R <- function(ab1,
ab2 = NULL) {
rsi_calc(type = "R",
ab1 = ab1,
ab2 = ab2,
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_IR <- function(ab1,
ab2 = NULL) {
rsi_calc(type = "R",
ab1 = ab1,
ab2 = ab2,
include_I = TRUE,
minimum = 0,
as_percent = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_I <- function(ab1) {
rsi_calc(type = "I",
ab1 = ab1,
ab2 = NULL,
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_SI <- function(ab1,
ab2 = NULL) {
rsi_calc(type = "S",
ab1 = ab1,
ab2 = ab2,
include_I = TRUE,
minimum = 0,
as_percent = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_S <- function(ab1,
ab2 = NULL) {
rsi_calc(type = "S",
ab1 = ab1,
ab2 = ab2,
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
#' @export
count_df <- function(data,
translate_ab = getOption("get_antibiotic_names", "official")) {
if (data %>% select_if(is.rsi) %>% ncol() == 0) {
stop("No columns with class 'rsi' found. See ?as.rsi.")
}
if (as.character(translate_ab) == "TRUE") {
translate_ab <- "official"
}
options(get_antibiotic_names = translate_ab)
resS <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = count_S) %>%
mutate(Interpretation = "S") %>%
select(Interpretation, everything())
resI <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = count_I) %>%
mutate(Interpretation = "I") %>%
select(Interpretation, everything())
resR <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = count_R) %>%
mutate(Interpretation = "R") %>%
select(Interpretation, everything())
data.groups <- group_vars(data)
res <- bind_rows(resS, resI, resR) %>%
mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
tidyr::gather(Antibiotic, Count, -Interpretation, -data.groups)
if (!translate_ab == FALSE) {
if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
}
res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
}
res
}

63
R/ggplot_rsi.R
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@ -20,16 +20,17 @@
#'
#' 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 position position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}
#' @param position position adjustment of bars, either \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) or \code{"dodge"} (default when \code{fun} is \code{\link{count_df}})
#' @param x variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
#' @param fill variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable
#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.
#' @param fun function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}
#' @param ... other parameters passed on to \code{\link[ggplot2]{facet_wrap}}
#' @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
#' \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{\link{portion_df}} and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
#' \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{fun} (\code{\link{portion_df}} at default, could also be \code{\link{count_df}}) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
#'
#' \code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
#'
@ -63,6 +64,11 @@
#' septic_patients %>%
#' select(amox, nitr, fosf, trim, cipr) %>%
#' ggplot_rsi()
#'
#' # get counts instead of percentages:
#' septic_patients %>%
#' select(amox, nitr, fosf, trim, cipr) %>%
#' ggplot_rsi(fun = count_df)
#' \donttest{
#' # it also supports groups (don't forget to use the group on `x` or `facet`):
#' septic_patients %>%
@ -102,26 +108,35 @@
#' x = "Microorganisms")
#' }
ggplot_rsi <- function(data,
position = "stack",
position = NULL,
x = "Antibiotic",
fill = "Interpretation",
facet = NULL,
translate_ab = "official",
fun = portion_df,
...) {
if (!"ggplot2" %in% rownames(installed.packages())) {
stop('this function requires the ggplot2 package.', call. = FALSE)
}
fun_name <- deparse(substitute(fun))
if (!fun_name %in% c("portion_df", "count_df")) {
stop("`fun` must be portion_df or count_df")
}
p <- ggplot2::ggplot(data = data) +
geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab) +
scale_y_percent() +
geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, fun = fun) +
theme_rsi()
if (fill == "Interpretation") {
# set RSI colours
p <- p + scale_rsi_colours()
}
if (fun_name == "portion_df") {
# portions, so use y scale with percentage
p <- p + scale_y_percent()
}
if (!is.null(facet)) {
p <- p + facet_rsi(facet = facet, ...)
@ -132,10 +147,27 @@ ggplot_rsi <- function(data,
#' @rdname ggplot_rsi
#' @export
geom_rsi <- function(position = "stack",
geom_rsi <- function(position = NULL,
x = c("Antibiotic", "Interpretation"),
fill = "Interpretation",
translate_ab = "official") {
translate_ab = "official",
fun = portion_df) {
fun_name <- deparse(substitute(fun))
if (!fun_name %in% c("portion_df", "count_df", "fun")) {
stop("`fun` must be portion_df or count_df")
}
if (identical(fun, count_df)) {
y <- "Count"
if (missing(position) | is.null(position)) {
position <- "dodge"
}
} else {
y <- "Percentage"
if (missing(position) | is.null(position)) {
position <- "stack"
}
}
x <- x[1]
if (x %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
@ -147,8 +179,8 @@ geom_rsi <- function(position = "stack",
options(get_antibiotic_names = translate_ab)
ggplot2::layer(geom = "bar", stat = "identity", position = position,
mapping = ggplot2::aes_string(x = x, y = "Percentage", fill = fill),
data = AMR::portion_df, params = list())
mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
data = fun, params = list())
}
@ -163,14 +195,13 @@ facet_rsi <- function(facet = c("Interpretation", "Antibiotic"), ...) {
facet <- "Antibiotic"
}
ggplot2::facet_wrap(facets = facet, scales = "free", ...)
ggplot2::facet_wrap(facets = facet, scales = "free_x", ...)
}
#' @rdname ggplot_rsi
#' @export
scale_y_percent <- function() {
ggplot2::scale_y_continuous(name = "Percentage",
breaks = seq(0, 1, 0.1),
ggplot2::scale_y_continuous(breaks = seq(0, 1, 0.1),
limits = c(0, 1),
labels = percent(seq(0, 1, 0.1)))
}
@ -184,8 +215,8 @@ scale_rsi_colours <- function() {
#' @rdname ggplot_rsi
#' @export
theme_rsi <- function() {
theme_minimal() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(colour = "grey75"))
ggplot2::theme_minimal() +
ggplot2::theme(panel.grid.major.x = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_line(colour = "grey75"))
}

1
R/globals.R
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@ -32,6 +32,7 @@ globalVariables(c('abname',
'C_chisq_sim',
'cnt',
'count',
'Count',
'counts',
'cum_count',
'cum_percent',

13
R/misc.R
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@ -138,21 +138,18 @@ tbl_parse_guess <- function(tbl,
asciify = FALSE))
}
if (any(tbl %>% pull(i) %>% class() %in% c('factor', 'character'))) {
# get values
distinct_val <- tbl %>% pull(i) %>% unique() %>% sort()
if (remove_ASCII_escape_char == TRUE) {
# remove ASCII escape character: https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
tbl[, i] <- tbl %>% pull(i) %>% gsub('\033', ' ', ., fixed = TRUE)
}
# look for RSI, shouldn't all be "" and must be valid antibiotic interpretations
if (!all(distinct_val[!is.na(distinct_val)] == '')
& all(distinct_val[!is.na(distinct_val)] %in% c('', 'I', 'I;I', 'R', 'R;R', 'S', 'S;S'))) {
tbl[, i] <- tbl %>% pull(i) %>% as.rsi()
if (tbl %>% pull(i) %>% is.rsi.eligible()) {
# look for RSI
tbl[, i] <- as.rsi(tbl[, i])
}
}
# convert to MIC class
# convert to MIC class when ends on `_mic`
if (colnames(tbl)[i] %like% '_mic$') {
tbl[, i] <- tbl %>% pull(i) %>% as.mic()
tbl[, i] <- as.mic(tbl[, i])
}
}
tbl

64
R/portion.R
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@ -24,11 +24,13 @@
#' @param ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
#' @param ab2 like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.
#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
#' @param data a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
#' @param as_percent logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.
#' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
#' @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.
#'
#' These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. If a column has been transformed with \code{\link{as.rsi}}, just use e.g. \code{isolates[isolates == "R"]} to get the resistant ones. You could then calculate the \code{\link{length}} of it.
#'
#' \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.
@ -47,7 +49,8 @@
#' @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[AMR]{count}_*} to count resistant and susceptibile isolates.\cr
#' \code{\link{n_rsi}} to count all cases where antimicrobial results are available.
#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
#' @return Double or, when \code{as_percent = TRUE}, a character.
#' @rdname portion
@ -135,7 +138,8 @@ portion_R <- function(ab1,
ab2 = ab2,
include_I = FALSE,
minimum = minimum,
as_percent = as_percent)
as_percent = as_percent,
only_count = FALSE)
}
#' @rdname portion
@ -149,7 +153,8 @@ portion_IR <- function(ab1,
ab2 = ab2,
include_I = TRUE,
minimum = minimum,
as_percent = as_percent)
as_percent = as_percent,
only_count = FALSE)
}
#' @rdname portion
@ -162,7 +167,8 @@ portion_I <- function(ab1,
ab2 = NULL,
include_I = FALSE,
minimum = minimum,
as_percent = as_percent)
as_percent = as_percent,
only_count = FALSE)
}
#' @rdname portion
@ -176,7 +182,8 @@ portion_SI <- function(ab1,
ab2 = ab2,
include_I = TRUE,
minimum = minimum,
as_percent = as_percent)
as_percent = as_percent,
only_count = FALSE)
}
#' @rdname portion
@ -190,13 +197,21 @@ portion_S <- function(ab1,
ab2 = ab2,
include_I = FALSE,
minimum = minimum,
as_percent = as_percent)
as_percent = as_percent,
only_count = FALSE)
}
#' @rdname portion
#' @importFrom dplyr bind_rows summarise_if mutate group_vars select everything
#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
#' @export
portion_df <- function(data, translate_ab = getOption("get_antibiotic_names", "official")) {
portion_df <- function(data,
translate_ab = getOption("get_antibiotic_names", "official"),
minimum = 30,
as_percent = FALSE) {
if (data %>% select_if(is.rsi) %>% ncol() == 0) {
stop("No columns with class 'rsi' found. See ?as.rsi.")
}
if (as.character(translate_ab) == "TRUE") {
translate_ab <- "official"
@ -205,19 +220,25 @@ portion_df <- function(data, translate_ab = getOption("get_antibiotic_names", "o
resS <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = portion_S) %>%
.funs = portion_S,
minimum = minimum,
as_percent = as_percent) %>%
mutate(Interpretation = "S") %>%
select(Interpretation, everything())
resI <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = portion_I) %>%
.funs = portion_I,
minimum = minimum,
as_percent = as_percent) %>%
mutate(Interpretation = "I") %>%
select(Interpretation, everything())
resR <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = portion_R) %>%
.funs = portion_R,
minimum = minimum,
as_percent = as_percent) %>%
mutate(Interpretation = "R") %>%
select(Interpretation, everything())
@ -242,7 +263,8 @@ rsi_calc <- function(type,
ab2,
include_I,
minimum,
as_percent) {
as_percent,
only_count) {
if (NCOL(ab1) > 1) {
stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
@ -284,11 +306,6 @@ rsi_calc <- function(type,
warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
}
total <- length(x) - sum(is.na(x))
if (total < minimum) {
return(NA)
}
if (type == "S") {
found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
} else if (type == "I") {
@ -299,6 +316,15 @@ rsi_calc <- function(type,
stop("invalid type")
}
if (only_count == TRUE) {
return(found)
}
total <- length(x) - sum(is.na(x))
if (total < minimum) {
return(NA)
}
if (as_percent == TRUE) {
percent(found / total, force_zero = TRUE)
} else {

10
README.md
View File

@ -67,7 +67,7 @@ With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you c
**Read all changes and new functions in [NEWS.md](NEWS.md).**
## How to get it?
This package [is published on CRAN](http://cran.r-project.org/package=AMR), the official R network.
All versions of this package [are published on CRAN](http://cran.r-project.org/package=AMR), the official R network with a peer-reviewed submission process.
### Install from CRAN
[![CRAN_Badge](https://www.r-pkg.org/badges/version/AMR)](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)
@ -87,10 +87,16 @@ This package [is published on CRAN](http://cran.r-project.org/package=AMR), the
[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg)](https://github.com/msberends/AMR/commits/master)
[![Code_Coverage](https://codecov.io/gh/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gh/msberends/AMR)
```r install.packages("devtools")
```r
install.packages("devtools")
devtools::install_github("msberends/AMR")
```
### Install from Zenodo
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1305355.svg)](https://doi.org/10.5281/zenodo.1305355)
This package was also published on Zenodo: https://doi.org/10.5281/zenodo.1305355
## How to use it?
```r
# Call it with:

12
man/as.rsi.Rd
View File

@ -3,11 +3,14 @@
\name{as.rsi}
\alias{as.rsi}
\alias{is.rsi}
\alias{is.rsi.eligible}
\title{Class 'rsi'}
\usage{
as.rsi(x)
is.rsi(x)
is.rsi.eligible(x)
}
\arguments{
\item{x}{vector}
@ -18,6 +21,9 @@ Ordered factor with new class \code{rsi} and new attribute \code{package}
\description{
This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
}
\details{
The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
}
\examples{
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
@ -29,6 +35,12 @@ as.rsi("<= 0.002; S") # will return S
plot(rsi_data) # for percentages
barplot(rsi_data) # for frequencies
freq(rsi_data) # frequency table with informative header
# fastest way to transform all columns with already valid AB results to class `rsi`:
library(dplyr)
septic_patients \%>\%
mutate_if(is.rsi.eligible,
as.rsi)
}
\seealso{
\code{\link{as.mic}}

115
man/count.Rd Normal file
View File

@ -0,0 +1,115 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/count.R
\name{count}
\alias{count}
\alias{count_R}
\alias{count_IR}
\alias{count_I}
\alias{count_SI}
\alias{count_S}
\alias{count_df}
\title{Count isolates}
\source{
Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
}
\usage{
count_R(ab1, ab2 = NULL)
count_IR(ab1, ab2 = NULL)
count_I(ab1)
count_SI(ab1, ab2 = NULL)
count_S(ab1, ab2 = NULL)
count_df(data, translate_ab = getOption("get_antibiotic_names",
"official"))
}
\arguments{
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.}
}
\value{
Integer
}
\description{
These functions can be used to count resistant/susceptible microbial isolates. All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
\code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
}
\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.
These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
\code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of 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"}.
}
\examples{
# septic_patients is a data set available in the AMR package. It is true, genuine data.
?septic_patients
# Count resistant isolates
count_R(septic_patients$amox)
count_IR(septic_patients$amox)
# Or susceptibile isolates
count_S(septic_patients$amox)
count_SI(septic_patients$amox)
# Since n_rsi counts available isolates, you can
# calculate back to count e.g. non-susceptible isolates.
# This results in the same:
count_IR(septic_patients$amox)
portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(R = count_R(cipr),
I = count_I(cipr),
S = count_S(cipr),
n = n_rsi(cipr), # the actual total; sum of all three
total = n()) # NOT the amount of tested isolates!
# Count co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy.
# Please mind that `portion_S` calculates percentages right away instead.
count_S(septic_patients$amcl) # S = 1056 (67.3\%)
n_rsi(septic_patients$amcl) # n = 1570
count_S(septic_patients$gent) # S = 1363 (74.0\%)
n_rsi(septic_patients$gent) # n = 1842
with(septic_patients,
count_S(amcl, gent)) # S = 1385 (92.1\%)
with(septic_patients, # n = 1504
n_rsi(amcl, gent))
# Get portions S/I/R immediately of all rsi columns
septic_patients \%>\%
select(amox, cipr) \%>\%
count_df(translate = FALSE)
# It also supports grouping variables
septic_patients \%>\%
select(hospital_id, amox, cipr) \%>\%
group_by(hospital_id) \%>\%
count_df(translate = FALSE)
}
\seealso{
\code{\link{portion}_*} to calculate microbial resistance and susceptibility.\cr
\code{\link{n_rsi}} to count all cases where antimicrobial results are available.
}
\keyword{antibiotics}
\keyword{isolate}
\keyword{isolates}
\keyword{resistance}
\keyword{rsi}
\keyword{susceptibility}

20
man/ggplot_rsi.Rd
View File

@ -9,12 +9,13 @@
\alias{theme_rsi}
\title{AMR bar plots with \code{ggplot}}
\usage{
ggplot_rsi(data, position = "stack", x = "Antibiotic",
ggplot_rsi(data, position = NULL, x = "Antibiotic",
fill = "Interpretation", facet = NULL, translate_ab = "official",
...)
fun = portion_df, ...)
geom_rsi(position = "stack", x = c("Antibiotic", "Interpretation"),
fill = "Interpretation", translate_ab = "official")
geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
fill = "Interpretation", translate_ab = "official",
fun = portion_df)
facet_rsi(facet = c("Interpretation", "Antibiotic"), ...)
@ -27,7 +28,7 @@ theme_rsi()
\arguments{
\item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})}
\item{position}{position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}}
\item{position}{position adjustment of bars, either \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) or \code{"dodge"} (default when \code{fun} is \code{\link{count_df}})}
\item{x}{variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable}
@ -37,6 +38,8 @@ theme_rsi()
\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.}
\item{fun}{function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}}
\item{...}{other parameters passed on to \code{\link[ggplot2]{facet_wrap}}}
}
\description{
@ -46,7 +49,7 @@ Use these functions to create bar plots for antimicrobial resistance analysis. A
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
\code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{\link{portion_df}} and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
\code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{fun} (\code{\link{portion_df}} at default, could also be \code{\link{count_df}}) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
\code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
@ -79,6 +82,11 @@ ggplot(df) +
septic_patients \%>\%
select(amox, nitr, fosf, trim, cipr) \%>\%
ggplot_rsi()
# get counts instead of percentages:
septic_patients \%>\%
select(amox, nitr, fosf, trim, cipr) \%>\%
ggplot_rsi(fun = count_df)
\donttest{
# it also supports groups (don't forget to use the group on `x` or `facet`):
septic_patients \%>\%

11
man/portion.Rd
View File

@ -26,7 +26,7 @@ portion_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
portion_df(data, translate_ab = getOption("get_antibiotic_names",
"official"))
"official"), minimum = 30, as_percent = FALSE)
}
\arguments{
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
@ -35,9 +35,9 @@ portion_df(data, translate_ab = getOption("get_antibiotic_names",
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{as_percent}{logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.}
\item{data}{a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.}
}
@ -52,6 +52,8 @@ These functions can be used to calculate the (co-)resistance of microbial isolat
\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.
These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. If a column has been transformed with \code{\link{as.rsi}}, just use e.g. \code{isolates[isolates == "R"]} to get the resistant ones. You could then calculate the \code{\link{length}} of it.
\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.
@ -143,7 +145,8 @@ my_table \%>\%
}
}
\seealso{
\code{\link{n_rsi}} to count cases with antimicrobial results.
\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.\cr
\code{\link{n_rsi}} to count all cases where antimicrobial results are available.
}
\keyword{antibiotics}
\keyword{isolate}

2
man/rsi.Rd
View File

@ -16,7 +16,7 @@ rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{as_percent}{logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.}
\item{...}{deprecated parameters to support usage on older versions}
}

9
tests/testthat/test-ggplot_rsi.R
View File

@ -29,4 +29,13 @@ test_that("ggplot_rsi works", {
summarise_all(portion_IR) %>% as.double()
)
expect_equal(
(septic_patients %>% select(amcl, cipr) %>% ggplot_rsi(x = "Antibiotic",
facet = "Interpretation",
fun = count_df))$data %>%
summarise_all(count_IR) %>% as.double(),
septic_patients %>% select(amcl, cipr) %>%
summarise_all(count_IR) %>% as.double()
)
})