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(v1.5.0.9025) big plot and ggplot generics update

This commit is contained in:
dr. M.S. (Matthijs) Berends 2021-02-25 10:33:08 +01:00
parent 31ceba5441
commit a673407904
42 changed files with 1058 additions and 842 deletions
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 1.5.0.9024
Date: 2021-02-22
Version: 1.5.0.9025
Date: 2021-02-25
Title: Antimicrobial Resistance Data Analysis
Authors@R: c(
person(role = c("aut", "cre"),

3
NAMESPACE
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@ -32,6 +32,7 @@ S3method(as.rsi,data.frame)
S3method(as.rsi,default)
S3method(as.rsi,disk)
S3method(as.rsi,mic)
S3method(barplot,disk)
S3method(barplot,mic)
S3method(barplot,rsi)
S3method(c,ab)
@ -242,7 +243,7 @@ export(theme_rsi)
importFrom(graphics,arrows)
importFrom(graphics,axis)
importFrom(graphics,barplot)
importFrom(graphics,par)
importFrom(graphics,mtext)
importFrom(graphics,plot)
importFrom(graphics,points)
importFrom(graphics,text)

9
NEWS.md
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@ -1,5 +1,5 @@
# AMR 1.5.0.9024
## <small>Last updated: 22 February 2021</small>
# AMR 1.5.0.9025
## <small>Last updated: 25 February 2021</small>
### New
* Support for EUCAST Clinical Breakpoints v11.0 (2021), effective in the `eucast_rules()` function and in `as.rsi()` to interpret MIC and disk diffusion values. This is now the default guideline in this package.
@ -23,6 +23,7 @@
```
* Support for custom MDRO guidelines, using the new `custom_mdro_guideline()` function, please see `mdro()` for additional info
* Function `isolate_identifier()`, which will paste a microorganism code with all antimicrobial results of a data set into one string for each row. This is useful to compare isolates, e.g. between institutions or regions, when there is no genotyping available.
* `ggplot()` generics for classes `<mic>` and `<disk>`
* Function `mo_is_yeast()`, which determines whether a microorganism is a member of the taxonomic class Saccharomycetes or the taxonomic order Saccharomycetales:
```r
mo_kingdom(c("Aspergillus", "Candida"))
@ -54,12 +55,14 @@
* `is.rsi.eligible()` now detects if the column name resembles an antibiotic name or code and now returns `TRUE` immediately if the input contains any of the values "R", "S" or "I". This drastically improves speed, also for a lot of other functions that rely on automatic determination of antibiotic columns.
* Functions `get_episode()` and `is_new_episode()` now support less than a day as value for argument `episode_days` (e.g., to include one patient/test per hour)
* Argument `ampc_cephalosporin_resistance` in `eucast_rules()` now also applies to value "I" (not only "S")
* Updated colours of values R, S and I in tibble printing
* Updated `plot()` functions for classes `<mic>`, `<disk>` and `<rsi>` - the former two now support colouring if you supply the microorganism and antimicrobial agent
* Updated colours to colour-blind friendly version for values R, S and I in tibble printing and for all plot methods (`ggplot_rsi()` and using `plot()` on classes `<mic>`, `<disk>` and `<rsi>`)
* Functions `print()` and `summary()` on a Principal Components Analysis object (`pca()`) now print additional group info if the original data was grouped using `dplyr::group_by()`
* Improved speed and reliability of `guess_ab_col()`. As this also internally improves the reliability of `first_isolate()` and `mdro()`, this might have a slight impact on the results of those functions.
* Fix for `mo_name()` when used in other languages than English
* The `like()` function (and its fast alias `%like%`) now always use Perl compatibility, improving speed for many functions in this package (e.g., `as.mo()` is now up to 4 times faster)
* *Staphylococcus cornubiensis* is now correctly categorised as coagulase-positive
* `random_disk()` and `random_mic()` now have an expanded range in their randomisation
### Other
* Big documentation updates

9
R/aa_helper_functions.R
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@ -879,13 +879,16 @@ font_green_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[42m", after = "\033[49m", collapse = collapse)
}
font_rsi_R_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[48;5;210m", after = "\033[49m", collapse = collapse)
#ED553B
try_colour(..., before = "\033[48;5;203m", after = "\033[49m", collapse = collapse)
}
font_rsi_S_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[48;5;113m", after = "\033[49m", collapse = collapse)
#3CAEA3
try_colour(..., before = "\033[48;5;79m", after = "\033[49m", collapse = collapse)
}
font_rsi_I_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[48;5;185m", after = "\033[49m", collapse = collapse)
#F6D55C
try_colour(..., before = "\033[48;5;222m", after = "\033[49m", collapse = collapse)
}
font_red_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[41m", after = "\033[49m", collapse = collapse)

13
R/amr.R
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@ -73,16 +73,3 @@
#' @name AMR
#' @rdname AMR
NULL
#' Plotting for Classes `rsi`, `mic` and `disk`
#'
#' Functions to print classes of the `AMR` package.
#' @inheritSection lifecycle Stable Lifecycle
#' @inheritSection AMR Read more on Our Website!
#' @param ... Arguments passed on to functions
#' @inheritParams base::plot
#' @inheritParams graphics::barplot
#' @name plot
#' @rdname plot
#' @keywords internal
NULL

24
R/disk.R
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@ -145,30 +145,6 @@ print.disk <- function(x, ...) {
print(as.integer(x), quote = FALSE)
}
#' @method plot disk
#' @export
#' @importFrom graphics barplot axis
#' @rdname plot
plot.disk <- function(x,
main = paste("Disk zones values of", deparse(substitute(x))),
ylab = "Frequency",
xlab = "Disk diffusion (mm)",
axes = FALSE,
...) {
meet_criteria(main, allow_class = "character", has_length = 1)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(axes, allow_class = "logical", has_length = 1)
barplot(table(x),
ylab = ylab,
xlab = xlab,
axes = axes,
main = main,
...)
axis(2, seq(0, max(table(x))))
}
#' @method [ disk
#' @export
#' @noRd

33
R/ggplot_rsi.R
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@ -36,7 +36,8 @@
#' @param facet variable to split plots by, either `"interpretation"` (default) or `"antibiotic"` or a grouping variable
#' @inheritParams proportion
#' @param nrow (when using `facet`) number of rows
#' @param colours a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be `FALSE` to use default [ggplot2][ggplot2::ggplot()] colours.
#' @param colours a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be `FALSE` for standard [ggplot2][ggplot2::ggplot()] colours. The default colours are colour-blind friendly.
#' @param aesthetics aesthetics to apply the colours to, defaults to "fill" but can also be "colour" or "both"
#' @param datalabels show datalabels using [labels_rsi_count()]
#' @param datalabels.size size of the datalabels
#' @param datalabels.colour colour of the datalabels
@ -364,25 +365,27 @@ scale_y_percent <- function(breaks = seq(0, 1, 0.1), limits = NULL) {
#' @rdname ggplot_rsi
#' @export
scale_rsi_colours <- function(colours = c(S = "#61a8ff",
SI = "#61a8ff",
I = "#61f7ff",
IR = "#ff6961",
R = "#ff6961")) {
scale_rsi_colours <- function(colours = c(S = "#3CAEA3",
SI = "#3CAEA3",
I = "#F6D55C",
IR = "#ED553B",
R = "#ED553B"),
aesthetics = "fill") {
stop_ifnot_installed("ggplot2")
meet_criteria(colours, allow_class = c("character", "logical"))
# previous colour: palette = "RdYlGn"
# previous colours: values = c("#b22222", "#ae9c20", "#7cfc00")
meet_criteria(aesthetics, allow_class = c("character"), has_length = c(1, 2), is_in = c("colour", "color", "fill", "both"))
if (!identical(colours, FALSE)) {
original_cols <- c(S = "#61a8ff",
SI = "#61a8ff",
I = "#61f7ff",
IR = "#ff6961",
R = "#ff6961")
if ("both" %in% aesthetics) {
aesthetics <- c("colour", "fill")
}
original_cols <- c(S = "#3CAEA3",
SI = "#3CAEA3",
I = "#F6D55C",
IR = "#ED553B",
R = "#ED553B")
colours <- replace(original_cols, names(colours), colours)
ggplot2::scale_fill_manual(values = colours)
ggplot2::scale_fill_manual(values = colours, aesthetics = aesthetics)
}
}

83
R/like.R
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@ -25,7 +25,7 @@
#' Pattern Matching with Keyboard Shortcut
#'
#' Convenient wrapper around [grep()] to match a pattern: `x %like% pattern`. It always returns a [`logical`] vector and is always case-insensitive (use `x %like_case% pattern` for case-sensitive matching). Also, `pattern` can be as long as `x` to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.
#' Convenient wrapper around [grepl()] to match a pattern: `x %like% pattern`. It always returns a [`logical`] vector and is always case-insensitive (use `x %like_case% pattern` for case-sensitive matching). Also, `pattern` can be as long as `x` to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a character vector where matches are sought, or an object which can be coerced by [as.character()] to a character vector.
#' @param pattern a character string containing a regular expression (or [character] string for `fixed = TRUE`) to be matched in the given character vector. Coerced by [as.character()] to a character string if possible. If a [character] vector of length 2 or more is supplied, the first element is used with a warning.
@ -43,7 +43,7 @@
#'
#' Using RStudio? The text `%like%` can also be directly inserted in your code from the Addins menu and can have its own Keyboard Shortcut like `Ctrl+Shift+L` or `Cmd+Shift+L` (see `Tools` > `Modify Keyboard Shortcuts...`).
#' @source Idea from the [`like` function from the `data.table` package](https://github.com/Rdatatable/data.table/blob/master/R/like.R)
#' @seealso [grep()]
#' @seealso [grepl()]
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # simple test
@ -53,13 +53,17 @@
#' #> TRUE
#' b %like% a
#' #> FALSE
#'
#' # also supports multiple patterns, length must be equal to x
#'
#' # also supports multiple patterns
#' a <- c("Test case", "Something different", "Yet another thing")
#' b <- c( "case", "diff", "yet")
#' a %like% b
#' #> TRUE TRUE TRUE
#'
#' a[1] %like% b
#' #> TRUE FALSE FALSE
#' a %like% b[1]
#' #> TRUE FALSE FALSE
#'
#' # get isolates whose name start with 'Ent' or 'ent'
#' \donttest{
#' if (require("dplyr")) {
@ -71,7 +75,11 @@ like <- function(x, pattern, ignore.case = TRUE) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(pattern, allow_NA = FALSE)
meet_criteria(ignore.case, allow_class = "logical", has_length = 1)
if (all(is.na(x))) {
return(rep(FALSE, length(x)))
}
# set to fixed if no regex found
fixed <- !any(is_possibly_regex(pattern))
if (ignore.case == TRUE) {
@ -79,53 +87,26 @@ like <- function(x, pattern, ignore.case = TRUE) {
x <- tolower(x)
pattern <- tolower(pattern)
}
if (length(pattern) > 1 & length(x) == 1) {
x <- rep(x, length(pattern))
}
if (all(is.na(x))) {
return(rep(FALSE, length(x)))
}
if (length(pattern) > 1) {
res <- vector(length = length(pattern))
if (length(x) != length(pattern)) {
if (length(x) == 1) {
x <- rep(x, length(pattern))
}
# return TRUE for every 'x' that matches any 'pattern', FALSE otherwise
for (i in seq_len(length(res))) {
if (is.factor(x[i])) {
res[i] <- as.integer(x[i]) %in% grep(pattern[i], levels(x[i]), ignore.case = FALSE, fixed = fixed)
} else {
res[i] <- grepl(pattern[i], x[i], ignore.case = FALSE, fixed = fixed, perl = !fixed)
}
}
res <- vapply(FUN.VALUE = logical(1), pattern, function(pttrn) grepl(pttrn, x, ignore.case = FALSE, fixed = fixed))
res2 <- as.logical(rowSums(res))
# get only first item of every hit in pattern
res2[duplicated(res)] <- FALSE
res2[rowSums(res) == 0] <- NA
return(res2)
} else {
# x and pattern are of same length, so items with each other
for (i in seq_len(length(res))) {
if (is.factor(x[i])) {
res[i] <- as.integer(x[i]) %in% grep(pattern[i], levels(x[i]), ignore.case = FALSE, fixed = fixed, perl = !fixed)
} else {
res[i] <- grepl(pattern[i], x[i], ignore.case = FALSE, fixed = fixed, perl = !fixed)
}
}
return(res)
}
}
# the regular way how grepl works; just one pattern against one or more x
if (is.factor(x)) {
as.integer(x) %in% grep(pattern, levels(x), ignore.case = FALSE, fixed = fixed, perl = !fixed)
} else {
x <- as.character(x)
}
if (length(pattern) == 1) {
grepl(pattern, x, ignore.case = FALSE, fixed = fixed, perl = !fixed)
} else {
if (length(x) == 1) {
x <- rep(x, length(pattern))
} else if (length(pattern) != length(x)) {
stop_("arguments `x` and `pattern` must be of same length, or either one must be 1")
}
mapply(FUN = grepl,
pattern,
x,
MoreArgs = list(ignore.case = FALSE, fixed = fixed, perl = !fixed),
SIMPLIFY = TRUE,
USE.NAMES = FALSE)
}
}

57
R/mic.R
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@ -53,8 +53,9 @@
#' ab = "AMX",
#' guideline = "EUCAST")
#'
#' # plot MIC values, see ?plot
#' plot(mic_data)
#' barplot(mic_data)
#' plot(mic_data, mo = "E. coli", ab = "cipro")
as.mic <- function(x, na.rm = FALSE) {
meet_criteria(x, allow_class = c("mic", "character", "numeric", "integer"), allow_NA = TRUE)
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
@ -175,9 +176,11 @@ as.numeric.mic <- function(x, ...) {
#' @method droplevels mic
#' @export
#' @noRd
droplevels.mic <- function(x, exclude = if (any(is.na(levels(x)))) NULL else NA, ...) {
droplevels.mic <- function(x, exclude = if (any(is.na(levels(x)))) NULL else NA, as.mic = TRUE, ...) {
x <- droplevels.factor(x, exclude = exclude, ...)
class(x) <- c("mic", "ordered", "factor")
if (as.mic == TRUE) {
class(x) <- c("mic", "ordered", "factor")
}
x
}
@ -221,54 +224,6 @@ summary.mic <- function(object, ...) {
value
}
#' @method plot mic
#' @export
#' @importFrom graphics barplot axis
#' @rdname plot
plot.mic <- function(x,
main = paste("MIC values of", deparse(substitute(x))),
ylab = "Frequency",
xlab = "MIC value",
axes = FALSE,
...) {
meet_criteria(main, allow_class = "character", has_length = 1)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(axes, allow_class = "logical", has_length = 1)
barplot(table(as.double(x)),
ylab = ylab,
xlab = xlab,
axes = axes,
main = main,
...)
axis(2, seq(0, max(table(as.double(x)))))
}
#' @method barplot mic
#' @export
#' @importFrom graphics barplot axis
#' @rdname plot
barplot.mic <- function(height,
main = paste("MIC values of", deparse(substitute(height))),
ylab = "Frequency",
xlab = "MIC value",
axes = FALSE,
...) {
meet_criteria(main, allow_class = "character", has_length = 1)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(axes, allow_class = "logical", has_length = 1)
barplot(table(as.double(height)),
ylab = ylab,
xlab = xlab,
axes = axes,
main = main,
...)
axis(2, seq(0, max(table(as.double(height)))))
}
#' @method [ mic
#' @export
#' @noRd

552
R/plot.R Normal file
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@ -0,0 +1,552 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2021 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Plotting for Classes `rsi`, `mic` and `disk`
#'
#' Functions to plot classes `rsi`, `mic` and `disk`, with support for base R and `ggplot2`.
#' @inheritSection lifecycle Stable Lifecycle
#' @inheritSection AMR Read more on Our Website!
#' @param x MIC values created with [as.mic()] or disk diffusion values created with [as.disk()]
#' @param mapping aesthetic mappings to use for [`ggplot()`][ggplot2::ggplot()]
#' @param main,title title of the plot
#' @param xlab,ylab axis title
#' @param mo any (vector of) text that can be coerced to a valid microorganism code with [as.mo()]
#' @param ab any (vector of) text that can be coerced to a valid antimicrobial code with [as.ab()]
#' @param guideline interpretation guideline to use, defaults to the latest included EUCAST guideline, see *Details*
#' @param colours_RSI colours to use for filling in the bars, must be a vector of three values (in the order R, S and I). The default colours are colour-blind friendly.
#' @param expand logical to indicate whether the range on the x axis should be expanded between the lowest and highest value. For MIC values, intermediate values will be factors of 2 starting from the highest MIC value. For disk diameters, the whole diameter range will be filled.
#' @details For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the `guideline` argument are: `r vector_and(AMR::rsi_translation$guideline, quotes = TRUE, reverse = TRUE)`.
#'
#' Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline.
#' @name plot
#' @rdname plot
#' @return The `ggplot` functions return a [`ggplot`][ggplot2::ggplot()] model that is extendible with any `ggplot2` function.
#' @param ... arguments passed on to [as.rsi()]
#' @examples
#' some_mic_values <- random_mic(size = 100)
#' some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
#'
#' plot(some_mic_values)
#' plot(some_disk_values)
#'
#' # when providing the microorganism and antibiotic, colours will show interpretations:
#' plot(some_mic_values, mo = "S. aureus", ab = "ampicillin")
#' plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
#'
#' if (require("ggplot2")) {
#' ggplot(some_mic_values)
#' ggplot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
#' }
NULL
#' @method plot mic
#' @importFrom graphics barplot axis mtext
#' @export
#' @rdname plot
plot.mic <- function(x,
main = paste("MIC values of", deparse(substitute(x))),
ylab = "Frequency",
xlab = "Minimum Inhibitory Concentration (mg/L)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...) {
meet_criteria(main, allow_class = "character")
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
if (length(colours_RSI) == 1) {
colours_RSI <- rep(colours_RSI, 3)
}
main <- gsub(" +", " ", paste0(main, collapse = " "))
x <- plot_prepare_table(x, expand = expand)
cols_sub <- plot_colours_and_sub(x = x,
mo = mo,
ab = ab,
guideline = guideline,
colours_RSI = colours_RSI,
fn = as.mic,
...)
barplot(x,
col = cols_sub$cols,
main = main,
ylim = c(0, max(x) * ifelse(any(colours_RSI %in% cols_sub$cols), 1.1, 1)),
ylab = ylab,
xlab = xlab,
axes = FALSE)
axis(2, seq(0, max(as.double(x))))
if (!is.null(cols_sub$sub)) {
mtext(side = 3, line = 0.5, adj = 0.5, cex = 0.75, cols_sub$sub)
}
if (any(colours_RSI %in% cols_sub$cols)) {
legend_txt <- character(0)
legend_col <- character(0)
if (colours_RSI[2] %in% cols_sub$cols) {
legend_txt <- "Susceptible"
legend_col <- colours_RSI[2]
}
if (colours_RSI[3] %in% cols_sub$cols) {
legend_txt <- c(legend_txt, "Incr. exposure")
legend_col <- c(legend_col, colours_RSI[3])
}
if (colours_RSI[1] %in% cols_sub$cols) {
legend_txt <- c(legend_txt, "Resistant")
legend_col <- c(legend_col, colours_RSI[1])
}
legend("top",
x.intersp = 0.5,
legend = legend_txt,
fill = legend_col,
horiz = TRUE,
cex = 0.75,
box.lwd = 0,
bg = "#FFFFFF55")
}
}
#' @method barplot mic
#' @export
#' @noRd
barplot.mic <- function(height,
main = paste("MIC values of", deparse(substitute(height))),
ylab = "Frequency",
xlab = "Minimum Inhibitory Concentration (mg/L)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...) {
meet_criteria(main, allow_class = "character")
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
main <- gsub(" +", " ", paste0(main, collapse = " "))
plot(x = height,
main = main,
ylab = ylab,
xlab = xlab,
mo = mo,
ab = ab,
guideline = guideline,
colours_RSI = colours_RSI,
...)
}
#' @method ggplot mic
#' @rdname plot
# will be exported using s3_register() in R/zzz.R
ggplot.mic <- function(data,
mapping = NULL,
title = paste("MIC values of", deparse(substitute(data))),
ylab = "Frequency",
xlab = "Minimum Inhibitory Concentration (mg/L)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...) {
stop_ifnot_installed("ggplot2")
meet_criteria(title, allow_class = "character")
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
title <- gsub(" +", " ", paste0(title, collapse = " "))
x <- plot_prepare_table(data, expand = expand)
cols_sub <- plot_colours_and_sub(x = x,
mo = mo,
ab = ab,
guideline = guideline,
colours_RSI = colours_RSI,
fn = as.mic,
...)
df <- as.data.frame(x, stringsAsFactors = TRUE)
colnames(df) <- c("mic", "count")
df$cols <- cols_sub$cols
df$cols[df$cols == colours_RSI[1]] <- "Resistant"
df$cols[df$cols == colours_RSI[2]] <- "Susceptible"
df$cols[df$cols == colours_RSI[3]] <- "Incr. exposure"
df$cols <- factor(df$cols,
levels = c("Susceptible", "Incr. exposure", "Resistant"),
ordered = TRUE)
if (!is.null(mapping)) {
p <- ggplot2::ggplot(df, mapping = mapping)
} else {
p <- ggplot2::ggplot(df)
}
if (any(colours_RSI %in% cols_sub$cols)) {
p <- p +
ggplot2::geom_col(aes(x = mic, y = count, fill = cols)) +
ggplot2::scale_fill_manual(values = c("Resistant" = colours_RSI[1],
"Susceptible" = colours_RSI[2],
"Incr. exposure" = colours_RSI[3]),,
name = NULL)
} else {
p <- p +
ggplot2::geom_col(aes(x = mic, y = count))
}
p +
ggplot2::labs(title = title, x = xlab, y = ylab, subtitle = cols_sub$sub)
}
#' @method plot disk
#' @export
#' @importFrom graphics barplot axis mtext
#' @rdname plot
plot.disk <- function(x,
main = paste("Disk zones values of", deparse(substitute(x))),
ylab = "Frequency",
xlab = "Disk diffusion diameter (mm)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...) {
meet_criteria(main, allow_class = "character")
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
if (length(colours_RSI) == 1) {
colours_RSI <- rep(colours_RSI, 3)
}
main <- gsub(" +", " ", paste0(main, collapse = " "))
x <- plot_prepare_table(x, expand = expand)
cols_sub <- plot_colours_and_sub(x = x,
mo = mo,
ab = ab,
guideline = guideline,
colours_RSI = colours_RSI,
fn = as.disk,
...)
barplot(x,
col = cols_sub$cols,
main = main,
ylim = c(0, max(x) * ifelse(any(colours_RSI %in% cols_sub$cols), 1.1, 1)),
ylab = ylab,
xlab = xlab,
axes = FALSE)
axis(2, seq(0, max(x)))
if (!is.null(cols_sub$sub)) {
mtext(side = 3, line = 0.5, adj = 0.5, cex = 0.75, cols_sub$sub)
}
if (any(colours_RSI %in% cols_sub$cols)) {
legend_txt <- character(0)
legend_col <- character(0)
if (colours_RSI[1] %in% cols_sub$cols) {
legend_txt <- "Resistant"
legend_col <- colours_RSI[1]
}
if (colours_RSI[3] %in% cols_sub$cols) {
legend_txt <- c(legend_txt, "Incr. exposure")
legend_col <- c(legend_col, colours_RSI[3])
}
if (colours_RSI[2] %in% cols_sub$cols) {
legend_txt <- c(legend_txt, "Susceptible")
legend_col <- c(legend_col, colours_RSI[2])
}
legend("top",
x.intersp = 0.5,
legend = legend_txt,
fill = legend_col,
horiz = TRUE,
cex = 0.75,
box.lwd = 0,
bg = "#FFFFFF55")
}
}
#' @method barplot disk
#' @export
#' @noRd
barplot.disk <- function(height,
main = paste("Disk zones values of", deparse(substitute(height))),
ylab = "Frequency",
xlab = "Disk diffusion diameter (mm)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...) {
meet_criteria(main, allow_class = "character")
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
main <- gsub(" +", " ", paste0(main, collapse = " "))
plot(x = height,
main = main,
ylab = ylab,
xlab = xlab,
mo = mo,
ab = ab,
guideline = guideline,
colours_RSI = colours_RSI,
...)
}
#' @method ggplot disk
#' @rdname plot
# will be exported using s3_register() in R/zzz.R
ggplot.disk <- function(data,
mapping = NULL,
title = paste("Disk zones values of", deparse(substitute(data))),
ylab = "Frequency",
xlab = "Disk diffusion diameter (mm)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...) {
stop_ifnot_installed("ggplot2")
meet_criteria(title, allow_class = "character")
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
title <- gsub(" +", " ", paste0(title, collapse = " "))
x <- plot_prepare_table(data, expand = expand)
cols_sub <- plot_colours_and_sub(x = x,
mo = mo,
ab = ab,
guideline = guideline,
colours_RSI = colours_RSI,
fn = as.disk,
...)
df <- as.data.frame(x, stringsAsFactors = TRUE)
colnames(df) <- c("disk", "count")
df$cols <- cols_sub$cols
df$cols[df$cols == colours_RSI[1]] <- "Resistant"
df$cols[df$cols == colours_RSI[2]] <- "Susceptible"
df$cols[df$cols == colours_RSI[3]] <- "Incr. exposure"
df$cols <- factor(df$cols,
levels = c("Resistant", "Incr. exposure", "Susceptible"),
ordered = TRUE)
if (!is.null(mapping)) {
p <- ggplot2::ggplot(df, mapping = mapping)
} else {
p <- ggplot2::ggplot(df)
}
if (any(colours_RSI %in% cols_sub$cols)) {
p <- p +
ggplot2::geom_col(aes(x = disk, y = count, fill = cols)) +
ggplot2::scale_fill_manual(values = c("Resistant" = colours_RSI[1],
"Susceptible" = colours_RSI[2],
"Incr. exposure" = colours_RSI[3]),
name = NULL)
} else {
p <- p +
ggplot2::geom_col(aes(x = disk, y = count))
}
p +
ggplot2::labs(title = title, x = xlab, y = ylab, sub = cols_sub$sub)
}
plot_prepare_table <- function(x, expand) {
if (is.mic(x)) {
if (expand == TRUE) {
# expand range for MIC by adding factors of 2 from lowest to highest so all MICs in between also print
extra_range <- max(as.double(x)) / 2
while (min(extra_range) / 2 > min(as.double(x))) {
extra_range <- c(min(extra_range) / 2, extra_range)
}
extra_range <- setNames(rep(0, length(extra_range)), extra_range)
x <- table(droplevels(x, as.mic = FALSE))
extra_range <- extra_range[!names(extra_range) %in% names(x)]
x <- as.table(c(x, extra_range))
} else {
x <- table(droplevels(x, as.mic = FALSE))
}
x <- x[order(as.double(as.mic(names(x))))]
} else if (is.disk(x)) {
if (expand == TRUE) {
# expand range for disks from lowest to highest so all mm's in between also print
extra_range <- rep(0, max(x) - min(x) - 1)
names(extra_range) <- seq(min(x) + 1, max(x) - 1)
x <- table(x)
extra_range <- extra_range[!names(extra_range) %in% names(x)]
x <- as.table(c(x, extra_range))
} else {
x <- table(x)
}
x <- x[order(as.double(names(x)))]
}
as.table(x)
}
plot_colours_and_sub <- function(x, mo, ab, guideline, colours_RSI, fn, ...) {
if (!is.null(mo) && !is.null(ab)) {
# interpret and give colour based on MIC values
mo <- as.mo(mo)
ab <- as.ab(ab)
guideline <- get_guideline(guideline, AMR::rsi_translation)
rsi <- suppressWarnings(suppressMessages(as.rsi(fn(names(x)), mo = mo, ab = ab, guideline = guideline, ...)))
cols <- character(length = length(rsi))
cols[is.na(rsi)] <- "#BEBEBE"
cols[rsi == "R"] <- colours_RSI[1]
cols[rsi == "S"] <- colours_RSI[2]
cols[rsi == "I"] <- colours_RSI[3]
moname <- mo_name(mo, language = NULL)
abname <- ab_name(ab, language = NULL)
if (all(cols == "#BEBEBE")) {
message_("No ", guideline, " interpretations found for ",
ab_name(ab, language = NULL, tolower = TRUE), " in ", moname)
guideline <- ""
} else {
guideline <- paste0("(following ", guideline, ")")
}
sub <- bquote(.(abname)~"in"~italic(.(moname))~.(guideline))
} else {
cols <- "#BEBEBE"
sub <- NULL
}
list(cols = cols, sub = sub)
}
#' @method plot rsi
#' @export
#' @importFrom graphics plot text axis
#' @rdname plot
plot.rsi <- function(x,
ylab = "Percentage",
xlab = "Antimicrobial Interpretation",
main = paste("Resistance Overview of", deparse(substitute(x))),
...) {
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(main, allow_class = "character", has_length = 1)
data <- as.data.frame(table(x), stringsAsFactors = FALSE)
colnames(data) <- c("x", "n")
data$s <- round((data$n / sum(data$n)) * 100, 1)
if (!"S" %in% data$x) {
data <- rbind(data, data.frame(x = "S", n = 0, s = 0, stringsAsFactors = FALSE),
stringsAsFactors = FALSE)
}
if (!"I" %in% data$x) {
data <- rbind(data, data.frame(x = "I", n = 0, s = 0, stringsAsFactors = FALSE),
stringsAsFactors = FALSE)
}
if (!"R" %in% data$x) {
data <- rbind(data, data.frame(x = "R", n = 0, s = 0, stringsAsFactors = FALSE),
stringsAsFactors = FALSE)
}
data$x <- factor(data$x, levels = c("R", "S", "I"), ordered = TRUE)
ymax <- pm_if_else(max(data$s) > 95, 105, 100)
plot(x = data$x,
y = data$s,
lwd = 2,
ylim = c(0, ymax),
ylab = ylab,
xlab = xlab,
main = main,
axes = FALSE)
# x axis
axis(side = 1, at = 1:pm_n_distinct(data$x), labels = levels(data$x), lwd = 0)
# y axis, 0-100%
axis(side = 2, at = seq(0, 100, 5))
text(x = data$x,
y = data$s + 4,
labels = paste0(data$s, "% (n = ", data$n, ")"))
}
#' @method barplot rsi
#' @importFrom graphics barplot axis
#' @export
#' @noRd
barplot.rsi <- function(height,
main = paste("Resistance Overview of", deparse(substitute(height))),
xlab = "Antimicrobial Interpretation",
ylab = "Frequency",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...) {
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(main, allow_class = "character", has_length = 1)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
if (length(colours_RSI) == 1) {
colours_RSI <- rep(colours_RSI, 3)
}
main <- gsub(" +", " ", paste0(main, collapse = " "))
x <- table(height)
x <- x[c(3, 1, 2)]
barplot(x,
col = colours_RSI,
xlab = xlab,
main = main,
ylab = ylab,
axes = FALSE)
axis(2, seq(0, max(x)))
}

10
R/random.R
View File

@ -25,7 +25,7 @@
#' Random MIC Values/Disk Zones/RSI Generation
#'
#' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice.
#' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial agent, the generated results will reflect reality as much as possible.
#' @inheritSection lifecycle Maturing Lifecycle
#' @param size desired size of the returned vector
#' @param mo any character that can be coerced to a valid microorganism code with [as.mo()]
@ -111,8 +111,8 @@ random_exec <- function(type, size, mo = NULL, ab = NULL) {
if (log(set_range_max, 2) %% 1 == 0) {
# return powers of 2
valid_range <- unique(as.double(valid_range))
# add one higher MIC level to set_range_max
set_range_max <- 2 ^ (log(set_range_max, 2) + 1)
# add 1-3 higher MIC levels to set_range_max
set_range_max <- 2 ^ (log(set_range_max, 2) + sample(c(1:3), 1))
set_range <- as.mic(valid_range[log(valid_range, 2) %% 1 == 0 & valid_range <= set_range_max])
} else {
# no power of 2, return factors of 2 to left and right side
@ -121,8 +121,8 @@ random_exec <- function(type, size, mo = NULL, ab = NULL) {
}
return(as.mic(sample(set_range, size = size, replace = TRUE)))
} else if (type == "DISK") {
set_range <- seq(from = as.integer(min(df$breakpoint_R)),
to = as.integer(max(df$breakpoint_S)),
set_range <- seq(from = as.integer(min(df$breakpoint_R) / 1.25),
to = as.integer(max(df$breakpoint_S) * 1.25),
by = 1)
out <- sample(set_range, size = size, replace = TRUE)
out[out < 6] <- sample(c(6:10), length(out[out < 6]), replace = TRUE)

110
R/rsi.R
View File

@ -252,12 +252,13 @@ is.rsi.eligible <- function(x, threshold = 0.05) {
}
#' @export
# extra param: warn (never throw warning)
as.rsi.default <- function(x, ...) {
if (is.rsi(x)) {
return(x)
}
if (inherits(x, "integer") & all(x %in% c(1:3, NA))) {
if (inherits(x, c("integer", "numeric", "double")) && all(x %in% c(1:3, NA))) {
x[x == 1] <- "S"
x[x == 2] <- "I"
x[x == 3] <- "R"
@ -265,11 +266,11 @@ as.rsi.default <- function(x, ...) {
} else if (!all(is.na(x)) && !identical(levels(x), c("S", "I", "R"))) {
if (!any(x %like% "(R|S|I)", na.rm = TRUE)) {
# check if they are actually MICs or disks now that the antibiotic name is valid
# check if they are actually MICs or disks
if (all_valid_mics(x)) {
warning_("The input seems to be MIC values. Transform them with as.mic() before running as.rsi() to interpret them.")
warning_("The input seems to be MIC values. Transform them with `as.mic()` before running `as.rsi()` to interpret them.")
} else if (all_valid_disks(x)) {
warning_("The input seems to be disk diffusion values. Transform them with as.disk() before running as.rsi() to interpret them.")
warning_("The input seems to be disk diffusion values. Transform them with `as.disk()` before running `as.rsi()` to interpret them.")
}
}
@ -1010,107 +1011,6 @@ summary.rsi <- function(object, ...) {
value
}
#' @method plot rsi
#' @export
#' @importFrom graphics plot text axis
#' @rdname plot
plot.rsi <- function(x,
lwd = 2,
ylim = NULL,
ylab = "Percentage",
xlab = "Antimicrobial Interpretation",
main = paste("Resistance Overview of", deparse(substitute(x))),
axes = FALSE,
...) {
meet_criteria(lwd, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(ylim, allow_class = c("numeric", "integer"), allow_NULL = TRUE)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(main, allow_class = "character", has_length = 1)
meet_criteria(axes, allow_class = "logical", has_length = 1)
data <- as.data.frame(table(x), stringsAsFactors = FALSE)
colnames(data) <- c("x", "n")
data$s <- round((data$n / sum(data$n)) * 100, 1)
if (!"S" %in% data$x) {
data <- rbind(data, data.frame(x = "S", n = 0, s = 0, stringsAsFactors = FALSE),
stringsAsFactors = FALSE)
}
if (!"I" %in% data$x) {
data <- rbind(data, data.frame(x = "I", n = 0, s = 0, stringsAsFactors = FALSE),
stringsAsFactors = FALSE)
}
if (!"R" %in% data$x) {
data <- rbind(data, data.frame(x = "R", n = 0, s = 0, stringsAsFactors = FALSE),
stringsAsFactors = FALSE)
}
# don't use as.rsi() here, it will confuse plot()
data$x <- factor(data$x, levels = c("S", "I", "R"), ordered = TRUE)
ymax <- pm_if_else(max(data$s) > 95, 105, 100)
plot(x = data$x,
y = data$s,
lwd = lwd,
ylim = c(0, ymax),
ylab = ylab,
xlab = xlab,
main = main,
axes = axes,
...)
# x axis
axis(side = 1, at = 1:pm_n_distinct(data$x), labels = levels(data$x), lwd = 0)
# y axis, 0-100%
axis(side = 2, at = seq(0, 100, 5))
text(x = data$x,
y = data$s + 4,
labels = paste0(data$s, "% (n = ", data$n, ")"))
}
#' @method barplot rsi
#' @export
#' @importFrom graphics barplot axis par
#' @rdname plot
barplot.rsi <- function(height,
col = c("chartreuse4", "chartreuse3", "brown3"),
xlab = ifelse(beside, "Antimicrobial Interpretation", ""),
main = paste("Resistance Overview of", deparse(substitute(height))),
ylab = "Frequency",
beside = TRUE,
axes = beside,
...) {
meet_criteria(col, allow_class = "character", has_length = 3)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(main, allow_class = "character", has_length = 1)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(beside, allow_class = "logical", has_length = 1)
meet_criteria(axes, allow_class = "logical", has_length = 1)
if (axes == TRUE) {
par(mar = c(5, 4, 4, 2) + 0.1)
} else {
par(mar = c(2, 4, 4, 2) + 0.1)
}
barplot(as.matrix(table(height)),
col = col,
xlab = xlab,
main = main,
ylab = ylab,
beside = beside,
axes = FALSE,
...)
# y axis, 0-100%
axis(side = 2, at = seq(0, max(table(height)) + max(table(height)) * 1.1, by = 25))
if (axes == TRUE && beside == TRUE) {
axis(side = 1, labels = levels(height), at = c(1, 2, 3) + 0.5, lwd = 0)
}
}
#' @method [<- rsi
#' @export
#' @noRd

2
R/zzz.R
View File

@ -50,6 +50,8 @@ pkg_env$mo_failed <- character(0)
s3_register("skimr::get_skimmers", "rsi")
s3_register("skimr::get_skimmers", "mic")
s3_register("skimr::get_skimmers", "disk")
s3_register("ggplot2::ggplot", "mic")
s3_register("ggplot2::ggplot", "disk")
# if mo source exists, fire it up (see mo_source())
try({

BIN
data-raw/AMR_latest.tar.gz
View File

Binary file not shown.

2
docs/404.html
View File

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="https://msberends.github.io/AMR//index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>

2
docs/LICENSE-text.html
View File

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>

76
docs/articles/benchmarks.html
View File

@ -39,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>
@ -226,19 +226,19 @@
times <span class="op">=</span> <span class="fl">25</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 9.3 10 11.0 10 11.0 13.0 25</span>
<span class="co"># as.mo("stau") 52.0 55 73.0 58 92.0 100.0 25</span>
<span class="co"># as.mo("STAU") 50.0 54 73.0 58 96.0 110.0 25</span>
<span class="co"># as.mo("staaur") 9.7 10 14.0 11 12.0 57.0 25</span>
<span class="co"># as.mo("STAAUR") 8.9 10 14.0 10 11.0 52.0 25</span>
<span class="co"># as.mo("S. aureus") 26.0 28 41.0 29 67.0 76.0 25</span>
<span class="co"># as.mo("S aureus") 27.0 28 41.0 30 65.0 76.0 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 2.6 3 3.2 3 3.3 4.6 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 240.0 260 270.0 260 270.0 380.0 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 160.0 190 200.0 200 200.0 300.0 25</span>
<span class="co"># as.mo("MRSA") 9.3 10 15.0 10 12.0 49.0 25</span>
<span class="co"># as.mo("VISA") 18.0 19 31.0 21 54.0 67.0 25</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 10 11.0 15 11.0 13.0 47 25</span>
<span class="co"># as.mo("stau") 56 57.0 75 62.0 95.0 100 25</span>
<span class="co"># as.mo("STAU") 54 56.0 67 58.0 66.0 110 25</span>
<span class="co"># as.mo("staaur") 10 11.0 12 11.0 12.0 13 25</span>
<span class="co"># as.mo("STAAUR") 10 11.0 16 11.0 12.0 50 25</span>
<span class="co"># as.mo("S. aureus") 28 31.0 46 33.0 65.0 71 25</span>
<span class="co"># as.mo("S aureus") 29 30.0 42 33.0 64.0 67 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 3 3.2 5 3.3 3.7 40 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 240 260.0 270 270.0 280.0 290 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 170 200.0 210 200.0 210.0 280 25</span>
<span class="co"># as.mo("MRSA") 10 11.0 17 11.0 13.0 51 25</span>
<span class="co"># as.mo("VISA") 19 20.0 36 21.0 50.0 150 25</span></code></pre></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="750"></p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 200 times slower to determine.</p>
<p>To improve performance, we implemented two important algorithms to save unnecessary calculations: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
@ -260,8 +260,8 @@
<span class="co"># what do these values look like? They are of class &lt;mo&gt;:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># Class &lt;mo&gt;</span>
<span class="co"># [1] B_STPHY_AURS B_STRPT_GRPC B_STPHY_CONS B_STPHY_EPDR B_STRPT_PNMN</span>
<span class="co"># [6] B_PROTS_VLGR</span>
<span class="co"># [1] B_ESCHR_COLI B_PROTS_MRBL B_PROTS_MRBL B_PROTS_MRBL B_STPHY_CONS</span>
<span class="co"># [6] B_ENTRC</span>
<span class="co"># as the example_isolates data set has 2,000 rows, we should have 2 million items</span>
<span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
@ -277,8 +277,8 @@
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># mo_name(x) 157 187 222 206 224 372 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.206 seconds. That is 2.471 milliseconds per unique item on average. You only lose time on your unique input values.</p>
<span class="co"># mo_name(x) 160 189 224 201 228 356 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.201 seconds. That is 101 nanoseconds on average. You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -291,10 +291,10 @@
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 6.97 7.10 7.55 7.21 8.18 8.32 10</span>
<span class="co"># B 23.70 24.20 36.30 26.30 29.00 89.70 10</span>
<span class="co"># C 1.53 1.65 1.80 1.76 2.00 2.19 10</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 6.80 7.17 7.46 7.54 7.81 8.0 10</span>
<span class="co"># B 24.30 25.80 31.60 26.20 28.80 75.6 10</span>
<span class="co"># C 1.59 1.70 1.89 1.84 2.02 2.5 10</span></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0018 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
@ -309,14 +309,14 @@
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 1.44 1.72 1.69 1.74 1.75 1.78 10</span>
<span class="co"># B 1.47 1.55 1.67 1.73 1.74 1.89 10</span>
<span class="co"># C 1.52 1.59 1.73 1.74 1.89 1.91 10</span>
<span class="co"># D 1.46 1.51 1.64 1.64 1.75 1.89 10</span>
<span class="co"># E 1.42 1.47 1.60 1.52 1.71 1.97 10</span>
<span class="co"># F 1.44 1.71 1.69 1.72 1.75 1.88 10</span>
<span class="co"># G 1.42 1.50 1.68 1.61 1.73 2.36 10</span>
<span class="co"># H 1.52 1.54 1.66 1.71 1.73 1.87 10</span></code></pre></div>
<span class="co"># A 1.25 1.28 1.39 1.39 1.50 1.56 10</span>
<span class="co"># B 1.17 1.19 1.41 1.43 1.48 1.92 10</span>
<span class="co"># C 1.20 1.33 1.43 1.46 1.50 1.61 10</span>
<span class="co"># D 1.43 1.45 1.50 1.49 1.53 1.61 10</span>
<span class="co"># E 1.26 1.40 1.45 1.43 1.49 1.81 10</span>
<span class="co"># F 1.15 1.17 1.32 1.26 1.44 1.69 10</span>
<span class="co"># G 1.19 1.25 1.37 1.35 1.47 1.58 10</span>
<span class="co"># H 1.20 1.25 1.46 1.31 1.53 2.33 10</span></code></pre></div>
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package contains all phyla of all known bacteria, it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
@ -343,14 +343,14 @@
times <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 17.12 17.40 22.65 17.54 18.39 78.39 100</span>
<span class="co"># de 19.93 20.26 22.60 20.48 20.97 81.27 100</span>
<span class="co"># nl 24.87 25.24 30.93 25.50 26.90 87.17 100</span>
<span class="co"># es 20.00 20.26 24.39 20.58 21.13 82.01 100</span>
<span class="co"># it 19.92 20.26 26.54 20.66 21.38 79.15 100</span>
<span class="co"># fr 19.62 19.90 24.74 20.10 21.04 77.20 100</span>
<span class="co"># pt 19.74 20.02 26.76 20.41 22.68 82.61 100</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 17.05 17.38 21.26 17.74 18.50 97.23 100</span>
<span class="co"># de 19.90 20.33 24.80 20.67 21.06 93.34 100</span>
<span class="co"># nl 24.86 25.30 31.21 25.65 26.34 102.20 100</span>
<span class="co"># es 19.83 20.22 26.56 20.49 21.20 97.59 100</span>
<span class="co"># it 19.79 20.20 26.82 20.63 21.31 94.85 100</span>
<span class="co"># fr 19.61 19.87 24.26 20.21 20.68 92.42 100</span>
<span class="co"># pt 19.64 20.06 23.78 20.40 21.05 92.91 100</span></code></pre></div>
<p>Currently supported non-English languages are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>

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<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
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<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
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<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>
@ -236,13 +236,13 @@
<small>Source: <a href='https://github.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
</div>
<div id="amr-1509024" class="section level1">
<h1 class="page-header" data-toc-text="1.5.0.9024">
<a href="#amr-1509024" class="anchor"></a>AMR 1.5.0.9024<small> Unreleased </small>
<div id="amr-1509025" class="section level1">
<h1 class="page-header" data-toc-text="1.5.0.9025">
<a href="#amr-1509025" class="anchor"></a>AMR 1.5.0.9025<small> Unreleased </small>
</h1>
<div id="last-updated-22-february-2021" class="section level2">
<div id="last-updated-25-february-2021" class="section level2">
<h2 class="hasAnchor">
<a href="#last-updated-22-february-2021" class="anchor"></a><small>Last updated: 22 February 2021</small>
<a href="#last-updated-25-february-2021" class="anchor"></a><small>Last updated: 25 February 2021</small>
</h2>
<div id="new" class="section level3">
<h3 class="hasAnchor">
@ -280,6 +280,7 @@
</li>
<li><p>Support for custom MDRO guidelines, using the new <code><a href="../reference/mdro.html">custom_mdro_guideline()</a></code> function, please see <code><a href="../reference/mdro.html">mdro()</a></code> for additional info</p></li>
<li><p>Function <code><a href="../reference/isolate_identifier.html">isolate_identifier()</a></code>, which will paste a microorganism code with all antimicrobial results of a data set into one string for each row. This is useful to compare isolates, e.g. between institutions or regions, when there is no genotyping available.</p></li>
<li><p><code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot()</a></code> generics for classes <code>&lt;mic&gt;</code> and <code>&lt;disk&gt;</code></p></li>
<li>
<p>Function <code><a href="../reference/mo_property.html">mo_is_yeast()</a></code>, which determines whether a microorganism is a member of the taxonomic class Saccharomycetes or the taxonomic order Saccharomycetales:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
@ -321,7 +322,8 @@
<code><a href="../reference/as.rsi.html">is.rsi.eligible()</a></code> now detects if the column name resembles an antibiotic name or code and now returns <code>TRUE</code> immediately if the input contains any of the values “R”, “S” or “I”. This drastically improves speed, also for a lot of other functions that rely on automatic determination of antibiotic columns.</li>
<li>Functions <code><a href="../reference/get_episode.html">get_episode()</a></code> and <code><a href="../reference/get_episode.html">is_new_episode()</a></code> now support less than a day as value for argument <code>episode_days</code> (e.g., to include one patient/test per hour)</li>
<li>Argument <code>ampc_cephalosporin_resistance</code> in <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> now also applies to value “I” (not only “S”)</li>
<li>Updated colours of values R, S and I in tibble printing</li>
<li>Updated <code><a href="../reference/plot.html">plot()</a></code> functions for classes <code>&lt;mic&gt;</code>, <code>&lt;disk&gt;</code> and <code>&lt;rsi&gt;</code> - the former two now support colouring if you supply the microorganism and antimicrobial agent</li>
<li>Updated colours to colour-blind friendly version for values R, S and I in tibble printing and for all plot methods (<code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> and using <code><a href="../reference/plot.html">plot()</a></code> on classes <code>&lt;mic&gt;</code>, <code>&lt;disk&gt;</code> and <code>&lt;rsi&gt;</code>)</li>
<li>Functions <code><a href="https://rdrr.io/r/base/print.html">print()</a></code> and <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> on a Principal Components Analysis object (<code><a href="../reference/pca.html">pca()</a></code>) now print additional group info if the original data was grouped using <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">dplyr::group_by()</a></code>
</li>
<li>Improved speed and reliability of <code><a href="../reference/guess_ab_col.html">guess_ab_col()</a></code>. As this also internally improves the reliability of <code><a href="../reference/first_isolate.html">first_isolate()</a></code> and <code><a href="../reference/mdro.html">mdro()</a></code>, this might have a slight impact on the results of those functions.</li>
@ -329,6 +331,8 @@
<li>The <code><a href="../reference/like.html">like()</a></code> function (and its fast alias <code><a href="../reference/like.html">%like%</a></code>) now always use Perl compatibility, improving speed for many functions in this package (e.g., <code><a href="../reference/as.mo.html">as.mo()</a></code> is now up to 4 times faster)</li>
<li>
<em>Staphylococcus cornubiensis</em> is now correctly categorised as coagulase-positive</li>
<li>
<code><a href="../reference/random.html">random_disk()</a></code> and <code><a href="../reference/random.html">random_mic()</a></code> now have an expanded range in their randomisation</li>
</ul>
</div>
<div id="other" class="section level3">

2
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@ -12,7 +12,7 @@ articles:
datasets: datasets.html
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
last_built: 2021-02-22T19:20Z
last_built: 2021-02-25T09:21Z
urls:
reference: https://msberends.github.io/AMR//reference
article: https://msberends.github.io/AMR//articles

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@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9016</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>
@ -298,8 +298,9 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
ab <span class='op'>=</span> <span class='st'>"AMX"</span>,
guideline <span class='op'>=</span> <span class='st'>"EUCAST"</span><span class='op'>)</span>
<span class='co'># plot MIC values, see ?plot</span>
<span class='fu'><a href='plot.html'>plot</a></span><span class='op'>(</span><span class='va'>mic_data</span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/graphics/barplot.html'>barplot</a></span><span class='op'>(</span><span class='va'>mic_data</span><span class='op'>)</span>
<span class='fu'><a href='plot.html'>plot</a></span><span class='op'>(</span><span class='va'>mic_data</span>, mo <span class='op'>=</span> <span class='st'>"E. coli"</span>, ab <span class='op'>=</span> <span class='st'>"cipro"</span><span class='op'>)</span>
</pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">

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13
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@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9016</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>
@ -286,8 +286,9 @@
<span class='fu'>scale_y_percent</span><span class='op'>(</span>breaks <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0.1</span><span class='op'>)</span>, limits <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span>
<span class='fu'>scale_rsi_colours</span><span class='op'>(</span>
colours <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>S <span class='op'>=</span> <span class='st'>"#61a8ff"</span>, SI <span class='op'>=</span> <span class='st'>"#61a8ff"</span>, I <span class='op'>=</span> <span class='st'>"#61f7ff"</span>, IR <span class='op'>=</span> <span class='st'>"#ff6961"</span>, R <span class='op'>=</span>
<span class='st'>"#ff6961"</span><span class='op'>)</span>
colours <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>S <span class='op'>=</span> <span class='st'>"#3CAEA3"</span>, SI <span class='op'>=</span> <span class='st'>"#3CAEA3"</span>, I <span class='op'>=</span> <span class='st'>"#F6D55C"</span>, IR <span class='op'>=</span> <span class='st'>"#ED553B"</span>, R <span class='op'>=</span>
<span class='st'>"#ED553B"</span><span class='op'>)</span>,
aesthetics <span class='op'>=</span> <span class='st'>"fill"</span>
<span class='op'>)</span>
<span class='fu'>theme_rsi</span><span class='op'>(</span><span class='op'>)</span>
@ -361,7 +362,7 @@
</tr>
<tr>
<th>colours</th>
<td><p>a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be <code>FALSE</code> to use default <a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot2</a> colours.</p></td>
<td><p>a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be <code>FALSE</code> for standard <a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot2</a> colours. The default colours are colour-blind friendly.</p></td>
</tr>
<tr>
<th>datalabels</th>
@ -399,6 +400,10 @@
<th>...</th>
<td><p>other arguments passed on to <code>geom_rsi()</code></p></td>
</tr>
<tr>
<th>aesthetics</th>
<td><p>aesthetics to apply the colours to, defaults to "fill" but can also be "colour" or "both"</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

4
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@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>
@ -453,7 +453,7 @@
</tr><tr>
<td>
<p><code><a href="plot.html">plot(<i>&lt;disk&gt;</i>)</a></code> <code><a href="plot.html">plot(<i>&lt;mic&gt;</i>)</a></code> <code><a href="plot.html">barplot(<i>&lt;mic&gt;</i>)</a></code> <code><a href="plot.html">plot(<i>&lt;rsi&gt;</i>)</a></code> <code><a href="plot.html">barplot(<i>&lt;rsi&gt;</i>)</a></code> </p>
<p><code><a href="plot.html">plot(<i>&lt;mic&gt;</i>)</a></code> <code><a href="plot.html">ggplot(<i>&lt;mic&gt;</i>)</a></code> <code><a href="plot.html">plot(<i>&lt;disk&gt;</i>)</a></code> <code><a href="plot.html">ggplot(<i>&lt;disk&gt;</i>)</a></code> <code><a href="plot.html">plot(<i>&lt;rsi&gt;</i>)</a></code> </p>
</td>
<td><p>Plotting for Classes <code>rsi</code>, <code>mic</code> and <code>disk</code></p></td>
</tr><tr>

14
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@ -49,7 +49,7 @@
<script src="../extra.js"></script>
<meta property="og:title" content="Pattern Matching with Keyboard Shortcut — like" />
<meta property="og:description" content="Convenient wrapper around grep() to match a pattern: x %like% pattern. It always returns a logical vector and is always case-insensitive (use x %like_case% pattern for case-sensitive matching). Also, pattern can be as long as x to compare items of each index in both vectors, or they both can have the same length to iterate over all cases." />
<meta property="og:description" content="Convenient wrapper around grepl() to match a pattern: x %like% pattern. It always returns a logical vector and is always case-insensitive (use x %like_case% pattern for case-sensitive matching). Also, pattern can be as long as x to compare items of each index in both vectors, or they both can have the same length to iterate over all cases." />
<meta property="og:image" content="https://msberends.github.io/AMR/logo.png" />
@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>
@ -239,7 +239,7 @@
</div>
<div class="ref-description">
<p>Convenient wrapper around <code><a href='https://rdrr.io/r/base/grep.html'>grep()</a></code> to match a pattern: <code>x %like% pattern</code>. It always returns a <code><a href='https://rdrr.io/r/base/logical.html'>logical</a></code> vector and is always case-insensitive (use <code>x %like_case% pattern</code> for case-sensitive matching). Also, <code>pattern</code> can be as long as <code>x</code> to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.</p>
<p>Convenient wrapper around <code><a href='https://rdrr.io/r/base/grep.html'>grepl()</a></code> to match a pattern: <code>x %like% pattern</code>. It always returns a <code><a href='https://rdrr.io/r/base/logical.html'>logical</a></code> vector and is always case-insensitive (use <code>x %like_case% pattern</code> for case-sensitive matching). Also, <code>pattern</code> can be as long as <code>x</code> to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.</p>
</div>
<pre class="usage"><span class='fu'>like</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>pattern</span>, ignore.case <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
@ -295,7 +295,7 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
<p>On our website <a href='https://msberends.github.io/AMR/'>https://msberends.github.io/AMR/</a> you can find <a href='https://msberends.github.io/AMR/articles/AMR.html'>a comprehensive tutorial</a> about how to conduct AMR data analysis, the <a href='https://msberends.github.io/AMR/reference/'>complete documentation of all functions</a> and <a href='https://msberends.github.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>. As we would like to better understand the backgrounds and needs of our users, please <a href='https://msberends.github.io/AMR/survey.html'>participate in our survey</a>!</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p><code><a href='https://rdrr.io/r/base/grep.html'>grep()</a></code></p></div>
<div class='dont-index'><p><code><a href='https://rdrr.io/r/base/grep.html'>grepl()</a></code></p></div>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'># simple test</span>
@ -306,11 +306,15 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
<span class='va'>b</span> <span class='op'>%like%</span> <span class='va'>a</span>
<span class='co'>#&gt; FALSE</span>
<span class='co'># also supports multiple patterns, length must be equal to x</span>
<span class='co'># also supports multiple patterns</span>
<span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"Test case"</span>, <span class='st'>"Something different"</span>, <span class='st'>"Yet another thing"</span><span class='op'>)</span>
<span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span> <span class='st'>"case"</span>, <span class='st'>"diff"</span>, <span class='st'>"yet"</span><span class='op'>)</span>
<span class='va'>a</span> <span class='op'>%like%</span> <span class='va'>b</span>
<span class='co'>#&gt; TRUE TRUE TRUE</span>
<span class='va'>a</span><span class='op'>[</span><span class='fl'>1</span><span class='op'>]</span> <span class='op'>%like%</span> <span class='va'>b</span>
<span class='co'>#&gt; TRUE FALSE FALSE</span>
<span class='va'>a</span> <span class='op'>%like%</span> <span class='va'>b</span><span class='op'>[</span><span class='fl'>1</span><span class='op'>]</span>
<span class='co'>#&gt; TRUE FALSE FALSE</span>
<span class='co'># get isolates whose name start with 'Ent' or 'ent'</span>
<span class='co'># \donttest{</span>

174
docs/reference/plot.html
View File

@ -49,7 +49,7 @@
<script src="../extra.js"></script>
<meta property="og:title" content="Plotting for Classes rsi, mic and disk — plot" />
<meta property="og:description" content="Functions to print classes of the AMR package." />
<meta property="og:description" content="Functions to plot classes rsi, mic and disk, with support for base R and ggplot2." />
<meta property="og:image" content="https://msberends.github.io/AMR/logo.png" />
@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9016</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>
@ -234,65 +234,78 @@
<div class="col-md-9 contents">
<div class="page-header">
<h1>Plotting for Classes <code>rsi</code>, <code>mic</code> and <code>disk</code></h1>
<small class="dont-index">Source: <a href='https://github.com/msberends/AMR/blob/master/R/amr.R'><code>R/amr.R</code></a>, <a href='https://github.com/msberends/AMR/blob/master/R/disk.R'><code>R/disk.R</code></a>, <a href='https://github.com/msberends/AMR/blob/master/R/mic.R'><code>R/mic.R</code></a>, and 1 more</small>
<small class="dont-index">Source: <a href='https://github.com/msberends/AMR/blob/master/R/plot.R'><code>R/plot.R</code></a></small>
<div class="hidden name"><code>plot.Rd</code></div>
</div>
<div class="ref-description">
<p>Functions to print classes of the <code>AMR</code> package.</p>
<p>Functions to plot classes <code>rsi</code>, <code>mic</code> and <code>disk</code>, with support for base R and <code>ggplot2</code>.</p>
</div>
<pre class="usage"><span class='co'># S3 method for disk</span>
<span class='fu'>plot</span><span class='op'>(</span>
<span class='va'>x</span>,
main <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Disk zones values of"</span>, <span class='fu'><a href='https://rdrr.io/r/base/deparse.html'>deparse</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/substitute.html'>substitute</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
ylab <span class='op'>=</span> <span class='st'>"Frequency"</span>,
xlab <span class='op'>=</span> <span class='st'>"Disk diffusion (mm)"</span>,
axes <span class='op'>=</span> <span class='cn'>FALSE</span>,
<span class='va'>...</span>
<span class='op'>)</span>
<span class='co'># S3 method for mic</span>
<pre class="usage"><span class='co'># S3 method for mic</span>
<span class='fu'>plot</span><span class='op'>(</span>
<span class='va'>x</span>,
main <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"MIC values of"</span>, <span class='fu'><a href='https://rdrr.io/r/base/deparse.html'>deparse</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/substitute.html'>substitute</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
ylab <span class='op'>=</span> <span class='st'>"Frequency"</span>,
xlab <span class='op'>=</span> <span class='st'>"MIC value"</span>,
axes <span class='op'>=</span> <span class='cn'>FALSE</span>,
xlab <span class='op'>=</span> <span class='st'>"Minimum Inhibitory Concentration (mg/L)"</span>,
mo <span class='op'>=</span> <span class='cn'>NULL</span>,
ab <span class='op'>=</span> <span class='cn'>NULL</span>,
guideline <span class='op'>=</span> <span class='st'>"EUCAST"</span>,
colours_RSI <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"#ED553B"</span>, <span class='st'>"#3CAEA3"</span>, <span class='st'>"#F6D55C"</span><span class='op'>)</span>,
expand <span class='op'>=</span> <span class='cn'>TRUE</span>,
<span class='va'>...</span>
<span class='op'>)</span>
<span class='co'># S3 method for mic</span>
<span class='fu'><a href='https://rdrr.io/r/graphics/barplot.html'>barplot</a></span><span class='op'>(</span>
<span class='va'>height</span>,
main <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"MIC values of"</span>, <span class='fu'><a href='https://rdrr.io/r/base/deparse.html'>deparse</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/substitute.html'>substitute</a></span><span class='op'>(</span><span class='va'>height</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></span><span class='op'>(</span>
<span class='va'>data</span>,
mapping <span class='op'>=</span> <span class='cn'>NULL</span>,
title <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"MIC values of"</span>, <span class='fu'><a href='https://rdrr.io/r/base/deparse.html'>deparse</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/substitute.html'>substitute</a></span><span class='op'>(</span><span class='va'>data</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
ylab <span class='op'>=</span> <span class='st'>"Frequency"</span>,
xlab <span class='op'>=</span> <span class='st'>"MIC value"</span>,
axes <span class='op'>=</span> <span class='cn'>FALSE</span>,
xlab <span class='op'>=</span> <span class='st'>"Minimum Inhibitory Concentration (mg/L)"</span>,
mo <span class='op'>=</span> <span class='cn'>NULL</span>,
ab <span class='op'>=</span> <span class='cn'>NULL</span>,
guideline <span class='op'>=</span> <span class='st'>"EUCAST"</span>,
colours_RSI <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"#ED553B"</span>, <span class='st'>"#3CAEA3"</span>, <span class='st'>"#F6D55C"</span><span class='op'>)</span>,
expand <span class='op'>=</span> <span class='cn'>TRUE</span>,
<span class='va'>...</span>
<span class='op'>)</span>
<span class='co'># S3 method for disk</span>
<span class='fu'>plot</span><span class='op'>(</span>
<span class='va'>x</span>,
main <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Disk zones values of"</span>, <span class='fu'><a href='https://rdrr.io/r/base/deparse.html'>deparse</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/substitute.html'>substitute</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
ylab <span class='op'>=</span> <span class='st'>"Frequency"</span>,
xlab <span class='op'>=</span> <span class='st'>"Disk diffusion diameter (mm)"</span>,
mo <span class='op'>=</span> <span class='cn'>NULL</span>,
ab <span class='op'>=</span> <span class='cn'>NULL</span>,
guideline <span class='op'>=</span> <span class='st'>"EUCAST"</span>,
colours_RSI <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"#ED553B"</span>, <span class='st'>"#3CAEA3"</span>, <span class='st'>"#F6D55C"</span><span class='op'>)</span>,
expand <span class='op'>=</span> <span class='cn'>TRUE</span>,
<span class='va'>...</span>
<span class='op'>)</span>
<span class='co'># S3 method for disk</span>
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></span><span class='op'>(</span>
<span class='va'>data</span>,
mapping <span class='op'>=</span> <span class='cn'>NULL</span>,
title <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Disk zones values of"</span>, <span class='fu'><a href='https://rdrr.io/r/base/deparse.html'>deparse</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/substitute.html'>substitute</a></span><span class='op'>(</span><span class='va'>data</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
ylab <span class='op'>=</span> <span class='st'>"Frequency"</span>,
xlab <span class='op'>=</span> <span class='st'>"Disk diffusion diameter (mm)"</span>,
mo <span class='op'>=</span> <span class='cn'>NULL</span>,
ab <span class='op'>=</span> <span class='cn'>NULL</span>,
guideline <span class='op'>=</span> <span class='st'>"EUCAST"</span>,
colours_RSI <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"#ED553B"</span>, <span class='st'>"#3CAEA3"</span>, <span class='st'>"#F6D55C"</span><span class='op'>)</span>,
expand <span class='op'>=</span> <span class='cn'>TRUE</span>,
<span class='va'>...</span>
<span class='op'>)</span>
<span class='co'># S3 method for rsi</span>
<span class='fu'>plot</span><span class='op'>(</span>
<span class='va'>x</span>,
lwd <span class='op'>=</span> <span class='fl'>2</span>,
ylim <span class='op'>=</span> <span class='cn'>NULL</span>,
ylab <span class='op'>=</span> <span class='st'>"Percentage"</span>,
xlab <span class='op'>=</span> <span class='st'>"Antimicrobial Interpretation"</span>,
main <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Resistance Overview of"</span>, <span class='fu'><a href='https://rdrr.io/r/base/deparse.html'>deparse</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/substitute.html'>substitute</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
axes <span class='op'>=</span> <span class='cn'>FALSE</span>,
<span class='va'>...</span>
<span class='op'>)</span>
<span class='co'># S3 method for rsi</span>
<span class='fu'><a href='https://rdrr.io/r/graphics/barplot.html'>barplot</a></span><span class='op'>(</span>
<span class='va'>height</span>,
col <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"chartreuse4"</span>, <span class='st'>"chartreuse3"</span>, <span class='st'>"brown3"</span><span class='op'>)</span>,
xlab <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/ifelse.html'>ifelse</a></span><span class='op'>(</span><span class='va'>beside</span>, <span class='st'>"Antimicrobial Interpretation"</span>, <span class='st'>""</span><span class='op'>)</span>,
main <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Resistance Overview of"</span>, <span class='fu'><a href='https://rdrr.io/r/base/deparse.html'>deparse</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/substitute.html'>substitute</a></span><span class='op'>(</span><span class='va'>height</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
ylab <span class='op'>=</span> <span class='st'>"Frequency"</span>,
beside <span class='op'>=</span> <span class='cn'>TRUE</span>,
axes <span class='op'>=</span> <span class='va'>beside</span>,
<span class='va'>...</span>
<span class='op'>)</span></pre>
@ -301,62 +314,53 @@
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>x</th>
<td><p>the coordinates of points in the plot. Alternatively, a
single plotting structure, function or <em>any <span style="R">R</span> object with a
<code>plot</code> method</em> can be provided.</p></td>
<td><p>MIC values created with <code><a href='as.mic.html'>as.mic()</a></code> or disk diffusion values created with <code><a href='as.disk.html'>as.disk()</a></code></p></td>
</tr>
<tr>
<th>main</th>
<td><p>overall and sub title for the plot.</p></td>
<th>main, title</th>
<td><p>title of the plot</p></td>
</tr>
<tr>
<th>ylab</th>
<td><p>a label for the y axis.</p></td>
<th>xlab, ylab</th>
<td><p>axis title</p></td>
</tr>
<tr>
<th>xlab</th>
<td><p>a label for the x axis.</p></td>
<th>mo</th>
<td><p>any (vector of) text that can be coerced to a valid microorganism code with <code><a href='as.mo.html'>as.mo()</a></code></p></td>
</tr>
<tr>
<th>axes</th>
<td><p>logical. If <code>TRUE</code>, a vertical (or horizontal, if
<code>horiz</code> is true) axis is drawn.</p></td>
<th>ab</th>
<td><p>any (vector of) text that can be coerced to a valid antimicrobial code with <code><a href='as.ab.html'>as.ab()</a></code></p></td>
</tr>
<tr>
<th>guideline</th>
<td><p>interpretation guideline to use, defaults to the latest included EUCAST guideline, see <em>Details</em></p></td>
</tr>
<tr>
<th>colours_RSI</th>
<td><p>colours to use for filling in the bars, must be a vector of three values (in the order R, S and I). The default colours are colour-blind friendly.</p></td>
</tr>
<tr>
<th>expand</th>
<td><p>logical to indicate whether the range on the x axis should be expanded between the lowest and highest value. For MIC values, intermediate values will be factors of 2 starting from the highest MIC value. For disk diameters, the whole diameter range will be filled.</p></td>
</tr>
<tr>
<th>...</th>
<td><p>Arguments passed on to functions</p></td>
<td><p>arguments passed on to <code><a href='as.rsi.html'>as.rsi()</a></code></p></td>
</tr>
<tr>
<th>height</th>
<td><p>either a vector or matrix of values describing the
bars which make up the plot. If <code>height</code> is a vector, the
plot consists of a sequence of rectangular bars with heights
given by the values in the vector. If <code>height</code> is a matrix
and <code>beside</code> is <code>FALSE</code> then each bar of the plot
corresponds to a column of <code>height</code>, with the values in the
column giving the heights of stacked sub-bars making up the
bar. If <code>height</code> is a matrix and <code>beside</code> is
<code>TRUE</code>, then the values in each column are juxtaposed
rather than stacked.</p></td>
</tr>
<tr>
<th>ylim</th>
<td><p>limits for the y axis.</p></td>
</tr>
<tr>
<th>col</th>
<td><p>a vector of colors for the bars or bar components.
By default, grey is used if <code>height</code> is a vector, and a
gamma-corrected grey palette if <code>height</code> is a matrix.</p></td>
</tr>
<tr>
<th>beside</th>
<td><p>a logical value. If <code>FALSE</code>, the columns of
<code>height</code> are portrayed as stacked bars, and if <code>TRUE</code>
the columns are portrayed as juxtaposed bars.</p></td>
<th>mapping</th>
<td><p>aesthetic mappings to use for <code><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot()</a></code></p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>The <code>ggplot</code> functions return a <code><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></code> model that is extendible with any <code>ggplot2</code> function.</p>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the <code>guideline</code> argument are: "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", "CLSI 2011" and "CLSI 2010".</p>
<p>Simply using <code>"CLSI"</code> or <code>"EUCAST"</code> as input will automatically select the latest version of that guideline.</p>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
@ -370,6 +374,22 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
<p>On our website <a href='https://msberends.github.io/AMR/'>https://msberends.github.io/AMR/</a> you can find <a href='https://msberends.github.io/AMR/articles/AMR.html'>a comprehensive tutorial</a> about how to conduct AMR data analysis, the <a href='https://msberends.github.io/AMR/reference/'>complete documentation of all functions</a> and <a href='https://msberends.github.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>. As we would like to better understand the backgrounds and needs of our users, please <a href='https://msberends.github.io/AMR/survey.html'>participate in our survey</a>!</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='va'>some_mic_values</span> <span class='op'>&lt;-</span> <span class='fu'><a href='random.html'>random_mic</a></span><span class='op'>(</span>size <span class='op'>=</span> <span class='fl'>100</span><span class='op'>)</span>
<span class='va'>some_disk_values</span> <span class='op'>&lt;-</span> <span class='fu'><a href='random.html'>random_disk</a></span><span class='op'>(</span>size <span class='op'>=</span> <span class='fl'>100</span>, mo <span class='op'>=</span> <span class='st'>"Escherichia coli"</span>, ab <span class='op'>=</span> <span class='st'>"cipro"</span><span class='op'>)</span>
<span class='fu'>plot</span><span class='op'>(</span><span class='va'>some_mic_values</span><span class='op'>)</span>
<span class='fu'>plot</span><span class='op'>(</span><span class='va'>some_disk_values</span><span class='op'>)</span>
<span class='co'># when providing the microorganism and antibiotic, colours will show interpretations:</span>
<span class='fu'>plot</span><span class='op'>(</span><span class='va'>some_mic_values</span>, mo <span class='op'>=</span> <span class='st'>"S. aureus"</span>, ab <span class='op'>=</span> <span class='st'>"ampicillin"</span><span class='op'>)</span>
<span class='fu'>plot</span><span class='op'>(</span><span class='va'>some_disk_values</span>, mo <span class='op'>=</span> <span class='st'>"Escherichia coli"</span>, ab <span class='op'>=</span> <span class='st'>"cipro"</span><span class='op'>)</span>
<span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='http://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></span><span class='op'>(</span><span class='va'>some_mic_values</span><span class='op'>)</span>
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></span><span class='op'>(</span><span class='va'>some_disk_values</span>, mo <span class='op'>=</span> <span class='st'>"Escherichia coli"</span>, ab <span class='op'>=</span> <span class='st'>"cipro"</span><span class='op'>)</span>
<span class='op'>}</span>
</pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
<nav id="toc" data-toggle="toc" class="sticky-top">

6
docs/reference/random.html
View File

@ -49,7 +49,7 @@
<script src="../extra.js"></script>
<meta property="og:title" content="Random MIC Values/Disk Zones/RSI Generation — random" />
<meta property="og:description" content="These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice." />
<meta property="og:description" content="These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial agent, the generated results will reflect reality as much as possible." />
<meta property="og:image" content="https://msberends.github.io/AMR/logo.png" />
@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9016</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>
@ -239,7 +239,7 @@
</div>
<div class="ref-description">
<p>These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice.</p>
<p>These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial agent, the generated results will reflect reality as much as possible.</p>
</div>
<pre class="usage"><span class='fu'>random_mic</span><span class='op'>(</span><span class='va'>size</span>, mo <span class='op'>=</span> <span class='cn'>NULL</span>, ab <span class='op'>=</span> <span class='cn'>NULL</span>, <span class='va'>...</span><span class='op'>)</span>

2
docs/survey.html
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@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9024</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9025</span>
</span>
</div>

3
man/as.mic.Rd
View File

@ -54,8 +54,9 @@ as.rsi(x = as.mic(4),
ab = "AMX",
guideline = "EUCAST")
# plot MIC values, see ?plot
plot(mic_data)
barplot(mic_data)
plot(mic_data, mo = "E. coli", ab = "cipro")
}
\seealso{
\code{\link[=as.rsi]{as.rsi()}}

120
man/figures/lifecycle_tidyverse.svg
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9
man/ggplot_rsi.Rd
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@ -54,8 +54,9 @@ facet_rsi(facet = c("interpretation", "antibiotic"), nrow = NULL)
scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)
scale_rsi_colours(
colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR = "#ff6961", R =
"#ff6961")
colours = c(S = "#3CAEA3", SI = "#3CAEA3", I = "#F6D55C", IR = "#ED553B", R =
"#ED553B"),
aesthetics = "fill"
)
theme_rsi()
@ -99,7 +100,7 @@ labels_rsi_count(
\item{nrow}{(when using \code{facet}) number of rows}
\item{colours}{a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be \code{FALSE} to use default \link[ggplot2:ggplot]{ggplot2} colours.}
\item{colours}{a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be \code{FALSE} for standard \link[ggplot2:ggplot]{ggplot2} colours. The default colours are colour-blind friendly.}
\item{datalabels}{show datalabels using \code{\link[=labels_rsi_count]{labels_rsi_count()}}}
@ -118,6 +119,8 @@ labels_rsi_count(
\item{y.title}{text to show as y axis description}
\item{...}{other arguments passed on to \code{\link[=geom_rsi]{geom_rsi()}}}
\item{aesthetics}{aesthetics to apply the colours to, defaults to "fill" but can also be "colour" or "both"}
}
\description{
Use these functions to create bar plots for AMR data analysis. All functions rely on \link[ggplot2:ggplot]{ggplot2} functions.

10
man/like.Rd
View File

@ -26,7 +26,7 @@ x \%like_case\% pattern
A \code{\link{logical}} vector
}
\description{
Convenient wrapper around \code{\link[=grep]{grep()}} to match a pattern: \code{x \%like\% pattern}. It always returns a \code{\link{logical}} vector and is always case-insensitive (use \code{x \%like_case\% pattern} for case-sensitive matching). Also, \code{pattern} can be as long as \code{x} to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.
Convenient wrapper around \code{\link[=grepl]{grepl()}} to match a pattern: \code{x \%like\% pattern}. It always returns a \code{\link{logical}} vector and is always case-insensitive (use \code{x \%like_case\% pattern} for case-sensitive matching). Also, \code{pattern} can be as long as \code{x} to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.
}
\details{
The \verb{\%like\%} function:
@ -61,11 +61,15 @@ a \%like\% b
b \%like\% a
#> FALSE
# also supports multiple patterns, length must be equal to x
# also supports multiple patterns
a <- c("Test case", "Something different", "Yet another thing")
b <- c( "case", "diff", "yet")
a \%like\% b
#> TRUE TRUE TRUE
a[1] \%like\% b
#> TRUE FALSE FALSE
a \%like\% b[1]
#> TRUE FALSE FALSE
# get isolates whose name start with 'Ent' or 'ent'
\donttest{
@ -76,5 +80,5 @@ if (require("dplyr")) {
}
}
\seealso{
\code{\link[=grep]{grep()}}
\code{\link[=grepl]{grepl()}}
}

144
man/plot.Rd
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@ -1,102 +1,107 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/amr.R, R/disk.R, R/mic.R, R/rsi.R
% Please edit documentation in R/plot.R
\name{plot}
\alias{plot}
\alias{plot.disk}
\alias{plot.mic}
\alias{barplot.mic}
\alias{ggplot.mic}
\alias{plot.disk}
\alias{ggplot.disk}
\alias{plot.rsi}
\alias{barplot.rsi}
\title{Plotting for Classes \code{rsi}, \code{mic} and \code{disk}}
\usage{
\method{plot}{disk}(
x,
main = paste("Disk zones values of", deparse(substitute(x))),
ylab = "Frequency",
xlab = "Disk diffusion (mm)",
axes = FALSE,
...
)
\method{plot}{mic}(
x,
main = paste("MIC values of", deparse(substitute(x))),
ylab = "Frequency",
xlab = "MIC value",
axes = FALSE,
xlab = "Minimum Inhibitory Concentration (mg/L)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...
)
\method{barplot}{mic}(
height,
main = paste("MIC values of", deparse(substitute(height))),
\method{ggplot}{mic}(
data,
mapping = NULL,
title = paste("MIC values of", deparse(substitute(data))),
ylab = "Frequency",
xlab = "MIC value",
axes = FALSE,
xlab = "Minimum Inhibitory Concentration (mg/L)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...
)
\method{plot}{disk}(
x,
main = paste("Disk zones values of", deparse(substitute(x))),
ylab = "Frequency",
xlab = "Disk diffusion diameter (mm)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...
)
\method{ggplot}{disk}(
data,
mapping = NULL,
title = paste("Disk zones values of", deparse(substitute(data))),
ylab = "Frequency",
xlab = "Disk diffusion diameter (mm)",
mo = NULL,
ab = NULL,
guideline = "EUCAST",
colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
expand = TRUE,
...
)
\method{plot}{rsi}(
x,
lwd = 2,
ylim = NULL,
ylab = "Percentage",
xlab = "Antimicrobial Interpretation",
main = paste("Resistance Overview of", deparse(substitute(x))),
axes = FALSE,
...
)
\method{barplot}{rsi}(
height,
col = c("chartreuse4", "chartreuse3", "brown3"),
xlab = ifelse(beside, "Antimicrobial Interpretation", ""),
main = paste("Resistance Overview of", deparse(substitute(height))),
ylab = "Frequency",
beside = TRUE,
axes = beside,
...
)
}
\arguments{
\item{x}{the coordinates of points in the plot. Alternatively, a
single plotting structure, function or \emph{any \R object with a
\code{plot} method} can be provided.}
\item{x}{MIC values created with \code{\link[=as.mic]{as.mic()}} or disk diffusion values created with \code{\link[=as.disk]{as.disk()}}}
\item{main}{overall and sub title for the plot.}
\item{main, title}{title of the plot}
\item{ylab}{a label for the y axis.}
\item{xlab, ylab}{axis title}
\item{xlab}{a label for the x axis.}
\item{mo}{any (vector of) text that can be coerced to a valid microorganism code with \code{\link[=as.mo]{as.mo()}}}
\item{axes}{logical. If \code{TRUE}, a vertical (or horizontal, if
\code{horiz} is true) axis is drawn.}
\item{ab}{any (vector of) text that can be coerced to a valid antimicrobial code with \code{\link[=as.ab]{as.ab()}}}
\item{...}{Arguments passed on to functions}
\item{guideline}{interpretation guideline to use, defaults to the latest included EUCAST guideline, see \emph{Details}}
\item{height}{either a vector or matrix of values describing the
bars which make up the plot. If \code{height} is a vector, the
plot consists of a sequence of rectangular bars with heights
given by the values in the vector. If \code{height} is a matrix
and \code{beside} is \code{FALSE} then each bar of the plot
corresponds to a column of \code{height}, with the values in the
column giving the heights of stacked sub-bars making up the
bar. If \code{height} is a matrix and \code{beside} is
\code{TRUE}, then the values in each column are juxtaposed
rather than stacked.}
\item{colours_RSI}{colours to use for filling in the bars, must be a vector of three values (in the order R, S and I). The default colours are colour-blind friendly.}
\item{ylim}{limits for the y axis.}
\item{expand}{logical to indicate whether the range on the x axis should be expanded between the lowest and highest value. For MIC values, intermediate values will be factors of 2 starting from the highest MIC value. For disk diameters, the whole diameter range will be filled.}
\item{col}{a vector of colors for the bars or bar components.
By default, grey is used if \code{height} is a vector, and a
gamma-corrected grey palette if \code{height} is a matrix.}
\item{...}{arguments passed on to \code{\link[=as.rsi]{as.rsi()}}}
\item{beside}{a logical value. If \code{FALSE}, the columns of
\code{height} are portrayed as stacked bars, and if \code{TRUE}
the columns are portrayed as juxtaposed bars.}
\item{mapping}{aesthetic mappings to use for \code{\link[ggplot2:ggplot]{ggplot()}}}
}
\value{
The \code{ggplot} functions return a \code{\link[ggplot2:ggplot]{ggplot}} model that is extendible with any \code{ggplot2} function.
}
\description{
Functions to print classes of the \code{AMR} package.
Functions to plot classes \code{rsi}, \code{mic} and \code{disk}, with support for base R and \code{ggplot2}.
}
\details{
For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", "CLSI 2011" and "CLSI 2010".
Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline.
}
\section{Stable Lifecycle}{
@ -111,4 +116,19 @@ If the unlying code needs breaking changes, they will occur gradually. For examp
On our website \url{https://msberends.github.io/AMR/} you can find \href{https://msberends.github.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR data analysis, the \href{https://msberends.github.io/AMR/reference/}{complete documentation of all functions} and \href{https://msberends.github.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}. As we would like to better understand the backgrounds and needs of our users, please \href{https://msberends.github.io/AMR/survey.html}{participate in our survey}!
}
\keyword{internal}
\examples{
some_mic_values <- random_mic(size = 100)
some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
plot(some_mic_values)
plot(some_disk_values)
# when providing the microorganism and antibiotic, colours will show interpretations:
plot(some_mic_values, mo = "S. aureus", ab = "ampicillin")
plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
if (require("ggplot2")) {
ggplot(some_mic_values)
ggplot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
}
}

2
man/random.Rd
View File

@ -28,7 +28,7 @@ random_rsi(size, prob_RSI = c(0.33, 0.33, 0.33), ...)
class \verb{<mic>} for \code{\link[=random_mic]{random_mic()}} (see \code{\link[=as.mic]{as.mic()}}) and class \verb{<disk>} for \code{\link[=random_disk]{random_disk()}} (see \code{\link[=as.disk]{as.disk()}})
}
\description{
These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice.
These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial agent, the generated results will reflect reality as much as possible.
}
\details{
The base R function \code{\link[=sample]{sample()}} is used for generating values.

120
pkgdown/logos/lifecycle_tidyverse.svg
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10
tests/testthat/test-disk.R
View File

@ -43,9 +43,17 @@ test_that("disk works", {
expect_s3_class(x, "disk")
pdf(NULL) # prevent Rplots.pdf being created
expect_silent(barplot(as.disk(c(10, 20, 40))))
expect_silent(plot(as.disk(c(10, 20, 40))))
expect_silent(plot(as.disk(c(10, 20, 40)), expand = FALSE))
expect_silent(plot(as.disk(c(10, 20, 40)), mo = "esco", ab = "cipr"))
if (require("ggplot2")) {
expect_s3_class(ggplot(as.disk(c(10, 20, 40))), "gg")
expect_s3_class(ggplot(as.disk(c(10, 20, 40)), expand = FALSE), "gg")
expect_s3_class(ggplot(as.disk(c(10, 20, 40)), mo = "esco", ab = "cipr"), "gg")
}
expect_output(print(as.disk(12)))
library(dplyr, warn.conflicts = FALSE)
expect_output(print(tibble(d = as.disk(12))))

7
tests/testthat/test-mic.R
View File

@ -51,6 +51,13 @@ test_that("mic works", {
pdf(NULL) # prevent Rplots.pdf being created
expect_silent(barplot(as.mic(c(1, 2, 4, 8))))
expect_silent(plot(as.mic(c(1, 2, 4, 8))))
expect_silent(plot(as.mic(c(1, 2, 4, 8)), expand = FALSE))
expect_silent(plot(as.mic(c(1, 2, 4, 8)), mo = "esco", ab = "cipr"))
if (require("ggplot2")) {
expect_s3_class(ggplot(as.mic(c(1, 2, 4, 8))), "gg")
expect_s3_class(ggplot(as.mic(c(1, 2, 4, 8)), expand = FALSE), "gg")
expect_s3_class(ggplot(as.mic(c(1, 2, 4, 8, 32)), mo = "esco", ab = "cipr"), "gg")
}
expect_output(print(as.mic(c(1, 2, 4, 8))))
expect_equal(summary(as.mic(c(2, 8))),

2
vignettes/benchmarks.Rmd
View File

@ -117,7 +117,7 @@ run_it <- microbenchmark(mo_name(x),
print(run_it, unit = "ms", signif = 3)
```
So getting official taxonomic names of `r format(length(x), big.mark = ",")` (!!) items consisting of `r n_distinct(x)` unique values only takes `r round(median(run_it$time, na.rm = TRUE) / 1e9, 3)` seconds. That is `r round(mean(run_it$time, na.rm = TRUE) / 1e6 / n_distinct(x), 3)` milliseconds per unique item on average. You only lose time on your unique input values.
So getting official taxonomic names of `r format(length(x), big.mark = ",")` (!!) items consisting of `r n_distinct(x)` unique values only takes `r round(median(run_it$time, na.rm = TRUE) / 1e9, 3)` seconds. That is `r round(median(run_it$time, na.rm = TRUE) / length(x), 0)` nanoseconds on average. You only lose time on your unique input values.
### Precalculated results