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mirror of https://github.com/msberends/AMR.git synced 2024-12-25 19:26:13 +01:00

Remove RSI from package, add extra MIC scale functions

This commit is contained in:
dr. M.S. (Matthijs) Berends 2023-12-03 11:34:48 +01:00
parent 6f417d0ef2
commit c7461766ce
21 changed files with 260 additions and 580 deletions

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@ -1,5 +1,5 @@
Package: AMR
Version: 2.1.1.9001
Version: 2.1.1.9002
Date: 2023-12-03
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)

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@ -13,7 +13,6 @@ S3method("[<-",av)
S3method("[<-",disk)
S3method("[<-",mic)
S3method("[<-",mo)
S3method("[<-",rsi)
S3method("[<-",sir)
S3method("[[",ab)
S3method("[[",av)
@ -25,7 +24,6 @@ S3method("[[<-",av)
S3method("[[<-",disk)
S3method("[[<-",mic)
S3method("[[<-",mo)
S3method("[[<-",rsi)
S3method("[[<-",sir)
S3method("|",ab_selector)
S3method(Complex,mic)
@ -44,10 +42,6 @@ S3method(as.list,custom_eucast_rules)
S3method(as.list,custom_mdro_guideline)
S3method(as.matrix,mic)
S3method(as.numeric,mic)
S3method(as.rsi,data.frame)
S3method(as.rsi,default)
S3method(as.rsi,disk)
S3method(as.rsi,mic)
S3method(as.sir,data.frame)
S3method(as.sir,default)
S3method(as.sir,disk)
@ -55,7 +49,6 @@ S3method(as.sir,mic)
S3method(barplot,antibiogram)
S3method(barplot,disk)
S3method(barplot,mic)
S3method(barplot,rsi)
S3method(barplot,sir)
S3method(c,ab)
S3method(c,ab_selector)
@ -65,11 +58,9 @@ S3method(c,custom_mdro_guideline)
S3method(c,disk)
S3method(c,mic)
S3method(c,mo)
S3method(c,rsi)
S3method(c,sir)
S3method(close,progress_bar)
S3method(droplevels,mic)
S3method(droplevels,rsi)
S3method(droplevels,sir)
S3method(format,bug_drug_combinations)
S3method(hist,mic)
@ -87,7 +78,6 @@ S3method(plot,antibiogram)
S3method(plot,disk)
S3method(plot,mic)
S3method(plot,resistance_predict)
S3method(plot,rsi)
S3method(plot,sir)
S3method(print,ab)
S3method(print,av)
@ -100,7 +90,6 @@ S3method(print,mo)
S3method(print,mo_renamed)
S3method(print,mo_uncertainties)
S3method(print,pca)
S3method(print,rsi)
S3method(print,sir)
S3method(quantile,mic)
S3method(rep,ab)
@ -108,7 +97,6 @@ S3method(rep,av)
S3method(rep,disk)
S3method(rep,mic)
S3method(rep,mo)
S3method(rep,rsi)
S3method(rep,sir)
S3method(skewness,data.frame)
S3method(skewness,default)
@ -117,14 +105,12 @@ S3method(sort,mic)
S3method(summary,mic)
S3method(summary,mo)
S3method(summary,pca)
S3method(summary,rsi)
S3method(summary,sir)
S3method(unique,ab)
S3method(unique,av)
S3method(unique,disk)
S3method(unique,mic)
S3method(unique,mo)
S3method(unique,rsi)
S3method(unique,sir)
export("%like%")
export("%like_case%")
@ -132,7 +118,6 @@ export("%unlike%")
export("%unlike_case%")
export(NA_disk_)
export(NA_mic_)
export(NA_rsi_)
export(NA_sir_)
export(ab_atc)
export(ab_atc_group1)
@ -171,7 +156,6 @@ export(as.av)
export(as.disk)
export(as.mic)
export(as.mo)
export(as.rsi)
export(as.sir)
export(atc_online_ddd)
export(atc_online_ddd_units)
@ -217,21 +201,17 @@ export(custom_mdro_guideline)
export(eucast_dosage)
export(eucast_exceptional_phenotypes)
export(eucast_rules)
export(facet_rsi)
export(facet_sir)
export(filter_first_isolate)
export(first_isolate)
export(fluoroquinolones)
export(full_join_microorganisms)
export(g.test)
export(geom_rsi)
export(geom_sir)
export(get_AMR_locale)
export(get_episode)
export(get_mo_source)
export(ggplot_pca)
export(ggplot_rsi)
export(ggplot_rsi_predict)
export(ggplot_sir)
export(ggplot_sir_predict)
export(glycopeptides)
@ -242,8 +222,6 @@ export(is.av)
export(is.disk)
export(is.mic)
export(is.mo)
export(is.rsi)
export(is.rsi.eligible)
export(is.sir)
export(is_new_episode)
export(is_sir_eligible)
@ -251,7 +229,6 @@ export(italicise_taxonomy)
export(italicize_taxonomy)
export(key_antimicrobials)
export(kurtosis)
export(labels_rsi_count)
export(labels_sir_count)
export(left_join_microorganisms)
export(like)
@ -304,7 +281,6 @@ export(mo_uncertainties)
export(mo_url)
export(mo_year)
export(mrgn)
export(n_rsi)
export(n_sir)
export(not_intrinsic_resistant)
export(oxazolidinones)
@ -320,17 +296,17 @@ export(proportion_df)
export(quinolones)
export(random_disk)
export(random_mic)
export(random_rsi)
export(random_sir)
export(rescale_mic)
export(reset_AMR_locale)
export(resistance)
export(resistance_predict)
export(right_join_microorganisms)
export(rsi_df)
export(rsi_predict)
export(scale_rsi_colours)
export(scale_colour_mic)
export(scale_fill_mic)
export(scale_sir_colours)
export(scale_x_mic)
export(scale_y_mic)
export(scale_y_percent)
export(semi_join_microorganisms)
export(set_AMR_locale)
@ -344,7 +320,6 @@ export(skewness)
export(streptogramins)
export(susceptibility)
export(tetracyclines)
export(theme_rsi)
export(theme_sir)
export(translate_AMR)
export(trimethoprims)

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@ -1,4 +1,4 @@
# AMR 2.1.1.9001
# AMR 2.1.1.9002
## New
* Function `scale_x_mic()`, an advanced function to use in ggplot, to allow plotting of MIC values on the x axis. It allow for manual range definition and plotting missing intermediate log2 levels.

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@ -226,10 +226,6 @@ ab_class <- function(ab_class,
meet_criteria(ab_class, allow_class = "character", has_length = 1, allow_NULL = TRUE)
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec(NULL, only_sir_columns = only_sir_columns, ab_class_args = ab_class, only_treatable = only_treatable)
}
@ -242,10 +238,6 @@ ab_selector <- function(filter,
...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
# get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
# but it only takes a couple of milliseconds
@ -277,10 +269,6 @@ ab_selector <- function(filter,
aminoglycosides <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("aminoglycosides", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
@ -288,10 +276,6 @@ aminoglycosides <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...
#' @export
aminopenicillins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("aminopenicillins", only_sir_columns = only_sir_columns)
}
@ -299,10 +283,6 @@ aminopenicillins <- function(only_sir_columns = FALSE, ...) {
#' @export
antifungals <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("antifungals", only_sir_columns = only_sir_columns)
}
@ -310,10 +290,6 @@ antifungals <- function(only_sir_columns = FALSE, ...) {
#' @export
antimycobacterials <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("antimycobacterials", only_sir_columns = only_sir_columns)
}
@ -322,10 +298,6 @@ antimycobacterials <- function(only_sir_columns = FALSE, ...) {
betalactams <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("betalactams", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
@ -334,10 +306,6 @@ betalactams <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
carbapenems <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("carbapenems", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
@ -345,10 +313,6 @@ carbapenems <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
#' @export
cephalosporins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("cephalosporins", only_sir_columns = only_sir_columns)
}
@ -356,10 +320,6 @@ cephalosporins <- function(only_sir_columns = FALSE, ...) {
#' @export
cephalosporins_1st <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("cephalosporins_1st", only_sir_columns = only_sir_columns)
}
@ -367,10 +327,6 @@ cephalosporins_1st <- function(only_sir_columns = FALSE, ...) {
#' @export
cephalosporins_2nd <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("cephalosporins_2nd", only_sir_columns = only_sir_columns)
}
@ -378,10 +334,6 @@ cephalosporins_2nd <- function(only_sir_columns = FALSE, ...) {
#' @export
cephalosporins_3rd <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("cephalosporins_3rd", only_sir_columns = only_sir_columns)
}
@ -389,10 +341,6 @@ cephalosporins_3rd <- function(only_sir_columns = FALSE, ...) {
#' @export
cephalosporins_4th <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("cephalosporins_4th", only_sir_columns = only_sir_columns)
}
@ -400,10 +348,6 @@ cephalosporins_4th <- function(only_sir_columns = FALSE, ...) {
#' @export
cephalosporins_5th <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("cephalosporins_5th", only_sir_columns = only_sir_columns)
}
@ -411,10 +355,6 @@ cephalosporins_5th <- function(only_sir_columns = FALSE, ...) {
#' @export
fluoroquinolones <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("fluoroquinolones", only_sir_columns = only_sir_columns)
}
@ -422,10 +362,6 @@ fluoroquinolones <- function(only_sir_columns = FALSE, ...) {
#' @export
glycopeptides <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("glycopeptides", only_sir_columns = only_sir_columns)
}
@ -433,10 +369,6 @@ glycopeptides <- function(only_sir_columns = FALSE, ...) {
#' @export
lincosamides <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("lincosamides", only_sir_columns = only_sir_columns)
}
@ -444,10 +376,6 @@ lincosamides <- function(only_sir_columns = FALSE, ...) {
#' @export
lipoglycopeptides <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("lipoglycopeptides", only_sir_columns = only_sir_columns)
}
@ -455,10 +383,6 @@ lipoglycopeptides <- function(only_sir_columns = FALSE, ...) {
#' @export
macrolides <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("macrolides", only_sir_columns = only_sir_columns)
}
@ -466,10 +390,6 @@ macrolides <- function(only_sir_columns = FALSE, ...) {
#' @export
oxazolidinones <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("oxazolidinones", only_sir_columns = only_sir_columns)
}
@ -477,10 +397,6 @@ oxazolidinones <- function(only_sir_columns = FALSE, ...) {
#' @export
penicillins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("penicillins", only_sir_columns = only_sir_columns)
}
@ -489,10 +405,6 @@ penicillins <- function(only_sir_columns = FALSE, ...) {
polymyxins <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("polymyxins", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
@ -500,10 +412,6 @@ polymyxins <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
#' @export
streptogramins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("streptogramins", only_sir_columns = only_sir_columns)
}
@ -511,10 +419,6 @@ streptogramins <- function(only_sir_columns = FALSE, ...) {
#' @export
quinolones <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("quinolones", only_sir_columns = only_sir_columns)
}
@ -522,10 +426,6 @@ quinolones <- function(only_sir_columns = FALSE, ...) {
#' @export
tetracyclines <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("tetracyclines", only_sir_columns = only_sir_columns)
}
@ -533,10 +433,6 @@ tetracyclines <- function(only_sir_columns = FALSE, ...) {
#' @export
trimethoprims <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("trimethoprims", only_sir_columns = only_sir_columns)
}
@ -544,10 +440,6 @@ trimethoprims <- function(only_sir_columns = FALSE, ...) {
#' @export
ureidopenicillins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
ab_select_exec("ureidopenicillins", only_sir_columns = only_sir_columns)
}

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@ -184,7 +184,6 @@ eucast_rules <- function(x,
meet_criteria(ampc_cephalosporin_resistance, allow_class = c("logical", "character", "sir"), has_length = 1, allow_NA = TRUE, allow_NULL = TRUE)
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(custom_rules, allow_class = "custom_eucast_rules", allow_NULL = TRUE)
if ("only_rsi_columns" %in% names(list(...))) only_sir_columns <- list(...)$only_rsi_columns
add_MO_lookup_to_AMR_env()

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@ -227,10 +227,6 @@ first_isolate <- function(x = NULL,
meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(info, allow_class = "logical", has_length = 1)
meet_criteria(include_unknown, allow_class = "logical", has_length = 1)
if ("include_untested_rsi" %in% names(list(...))) {
deprecation_warning("include_untested_rsi", "include_untested_sir", is_function = FALSE)
include_untested_sir <- list(...)$include_untested_rsi
}
meet_criteria(include_untested_sir, allow_class = "logical", has_length = 1)
# remove data.table, grouping from tibbles, etc.

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@ -85,16 +85,18 @@
#' summary(pca_result)
#'
#' # old base R plotting method:
#' biplot(pca_result)
#' biplot(pca_result, main = "Base R biplot")
#'
#' # new ggplot2 plotting method using this package:
#' if (require("ggplot2")) {
#' ggplot_pca(pca_result)
#'
#' ggplot_pca(pca_result) +
#' labs(title = "ggplot2 biplot")
#' }
#' if (require("ggplot2")) {
#' # still extendible with any ggplot2 function
#' ggplot_pca(pca_result) +
#' scale_colour_viridis_d() +
#' labs(title = "Title here")
#' labs(title = "ggplot2 biplot")
#' }
#' }
#' }

View File

@ -149,10 +149,6 @@ key_antimicrobials <- function(x = NULL,
meet_criteria(gram_positive, allow_class = "character", allow_NULL = TRUE)
meet_criteria(antifungal, allow_class = "character", allow_NULL = TRUE)
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
# force regular data.frame, not a tibble or data.table
x <- as.data.frame(x, stringsAsFactors = FALSE)

View File

@ -192,10 +192,6 @@ mdro <- function(x = NULL,
meet_criteria(pct_required_classes, allow_class = "numeric", has_length = 1)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(verbose, allow_class = "logical", has_length = 1)
if ("only_rsi_columns" %in% names(list(...))) {
deprecation_warning("only_rsi_columns", "only_sir_columns", is_function = FALSE)
only_sir_columns <- list(...)$only_rsi_columns
}
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
if (!any(is_sir_eligible(x))) {

89
R/mic.R
View File

@ -29,7 +29,7 @@
# these are allowed MIC values and will become [factor] levels
operators <- c("<", "<=", "", ">=", ">")
valid_mic_levels <- c(
VALID_MIC_LEVELS <- c(
c(t(vapply(
FUN.VALUE = character(6), operators,
function(x) paste0(x, "0.000", c(1:4, 6, 8))
@ -121,6 +121,8 @@ valid_mic_levels <- c(
#' Using [as.double()] or [as.numeric()] on MIC values will remove the operators and return a numeric vector. Do **not** use [as.integer()] on MIC values as by the \R convention on [factor]s, it will return the index of the factor levels (which is often useless for regular users).
#'
#' Use [droplevels()] to drop unused levels. At default, it will return a plain factor. Use `droplevels(..., as.mic = TRUE)` to maintain the `mic` class.
#'
#' With [rescale_mic()], existing MIC ranges can be rescaled to a defined range of MIC values. This can be useful to better compare MIC distributions.
#' @return Ordered [factor] with additional class [`mic`], that in mathematical operations acts as decimal numbers. Bare in mind that the outcome of any mathematical operation on MICs will return a [numeric] value.
#' @aliases mic
#' @export
@ -137,6 +139,9 @@ valid_mic_levels <- c(
#' fivenum(mic_data)
#' quantile(mic_data)
#' all(mic_data < 512)
#'
#' # rescale MICs using rescale_mic()
#' rescale_mic(mic_data, mic_range = c(4, 16))
#'
#' # interpret MIC values
#' as.sir(
@ -231,7 +236,7 @@ as.mic <- function(x, na.rm = FALSE, keep_operators = "all") {
x[x.bak != "" & x == ""] <- "invalid"
na_before <- x[is.na(x) | x == ""] %pm>% length()
x[!x %in% valid_mic_levels] <- NA
x[!x %in% VALID_MIC_LEVELS] <- NA
na_after <- x[is.na(x) | x == ""] %pm>% length()
if (na_before != na_after) {
@ -261,34 +266,78 @@ as.mic <- function(x, na.rm = FALSE, keep_operators = "all") {
x[!x %in% keep] <- gsub("[>=<]", "", x[!x %in% keep])
}
set_clean_class(factor(x, levels = valid_mic_levels, ordered = TRUE),
set_clean_class(factor(x, levels = VALID_MIC_LEVELS, ordered = TRUE),
new_class = c("mic", "ordered", "factor")
)
}
}
all_valid_mics <- function(x) {
if (!inherits(x, c("mic", "character", "factor", "numeric", "integer"))) {
return(FALSE)
}
x_mic <- tryCatch(suppressWarnings(as.mic(x[!is.na(x)])),
error = function(e) NA
)
!any(is.na(x_mic)) && !all(is.na(x))
#' @rdname as.mic
#' @export
is.mic <- function(x) {
inherits(x, "mic")
}
#' @rdname as.mic
#' @details `NA_mic_` is a missing value of the new `mic` class, analogous to e.g. base \R's [`NA_character_`][base::NA].
#' @format NULL
#' @export
NA_mic_ <- set_clean_class(factor(NA, levels = valid_mic_levels, ordered = TRUE),
NA_mic_ <- set_clean_class(factor(NA, levels = VALID_MIC_LEVELS, ordered = TRUE),
new_class = c("mic", "ordered", "factor")
)
#' @rdname as.mic
#' @param mic_range a manual range to plot the MIC values, e.g., `mic_range = c(0.001, 32)`. Use `NA` to set no limit on one side, e.g., `mic_range = c(NA, 32)`.
#' @export
is.mic <- function(x) {
inherits(x, "mic")
rescale_mic <- function(x, mic_range, keep_operators = "edges", as.mic = TRUE) {
meet_criteria(mic_range, allow_class = c("numeric", "integer", "logical"), has_length = 2, allow_NA = TRUE, allow_NULL = TRUE)
stop_ifnot(all(mic_range %in% c(VALID_MIC_LEVELS, NA)),
"Values in `mic_range` must be valid MIC values. Unvalid: ", vector_and(mic_range[mic_range %in% c(levels(as.mic(1)), NA)]))
x <- as.mic(x)
if (is.null(mic_range)) {
mic_range <- c(NA, NA)
}
mic_range <- as.mic(mic_range)
min_mic <- mic_range[1]
max_mic <- mic_range[2]
if (!is.na(min_mic)) {
x[x < min_mic] <- min_mic
}
if (!is.na(max_mic)) {
x[x > max_mic] <- max_mic
}
x <- as.mic(x, keep_operators = ifelse(keep_operators == "edges", "none", keep_operators))
if (isTRUE(as.mic)) {
if (keep_operators == "edges") {
x[x == min(x, na.rm = TRUE)] <- paste0("<=", x[x == min(x, na.rm = TRUE)])
x[x == max(x, na.rm = TRUE)] <- paste0(">=", x[x == max(x, na.rm = TRUE)])
}
return(x)
}
# create a manual factor with levels only within desired range
expanded <- range_as_table(x,
expand = TRUE,
keep_operators = ifelse(keep_operators == "edges", "none", keep_operators),
mic_range = mic_range)
if (keep_operators == "edges") {
names(expanded)[1] <- paste0("<=", names(expanded)[1])
names(expanded)[length(expanded)] <- paste0(">=", names(expanded)[length(expanded)])
}
# MICs contain all MIC levels, so strip this to only existing levels and their intermediate values
out <- factor(names(expanded),
levels = names(expanded),
ordered = TRUE)
# and only keep the ones in the data
if (keep_operators == "edges") {
out <- out[match(x, as.double(as.mic(out, keep_operators = "all")))]
} else {
out <- out[match(x, out)]
}
out
}
#' @method as.double mic
@ -317,6 +366,16 @@ droplevels.mic <- function(x, as.mic = FALSE, ...) {
x
}
all_valid_mics <- function(x) {
if (!inherits(x, c("mic", "character", "factor", "numeric", "integer"))) {
return(FALSE)
}
x_mic <- tryCatch(suppressWarnings(as.mic(x[!is.na(x)])),
error = function(e) NA
)
!any(is.na(x_mic)) && !all(is.na(x))
}
# will be exported using s3_register() in R/zzz.R
pillar_shaft.mic <- function(x, ...) {
crude_numbers <- as.double(x)
@ -339,7 +398,7 @@ type_sum.mic <- function(x, ...) {
#' @noRd
print.mic <- function(x, ...) {
cat("Class 'mic'",
ifelse(length(levels(x)) < length(valid_mic_levels), font_red(" with dropped levels"), ""),
ifelse(length(levels(x)) < length(VALID_MIC_LEVELS), font_red(" with dropped levels"), ""),
"\n",
sep = ""
)

216
R/plot.R
View File

@ -32,7 +32,7 @@
#' @description
#' Functions to plot classes `sir`, `mic` and `disk`, with support for base \R and `ggplot2`.
#'
#' Especially [scale_x_mic()] is a relevant wrapper to plot MIC values for `ggplot2`. It allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.
#' Especially the [scale_*_mic()] functions are relevant wrappers to plot MIC values for `ggplot2`. They allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.
#' @param x,object values created with [as.mic()], [as.disk()] or [as.sir()] (or their `random_*` variants, such as [random_mic()])
#' @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 drug code with [as.ab()]
@ -68,9 +68,35 @@
#' plot(some_mic_values, mo = "S. aureus", ab = "ampicillin")
#' plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
#' plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "nl")
#'
#'
#'
#' # Plotting using scale_x_mic()
#' \donttest{
#' if (require("ggplot2")) {
#' mic_plot <- ggplot(data.frame(mics = as.mic(c(0.125, "<=4", 4, 8, 32, ">=32")),
#' counts = c(1, 1, 2, 2, 3, 3)),
#' aes(mics, counts)) +
#' geom_col()
#' mic_plot +
#' labs(title = "without scale_x_mic()")
#' }
#' if (require("ggplot2")) {
#' mic_plot +
#' scale_x_mic() +
#' labs(title = "with scale_x_mic()")
#' }
#' if (require("ggplot2")) {
#' mic_plot +
#' scale_x_mic(keep_operators = "all") +
#' labs(title = "with scale_x_mic() keeping all operators")
#' }
#' if (require("ggplot2")) {
#' mic_plot +
#' scale_x_mic(mic_range = c(1, 128)) +
#' labs(title = "with scale_x_mic() using a manual range")
#' }
#'
#' if (require("ggplot2")) {
#' autoplot(some_mic_values)
#' }
#' if (require("ggplot2")) {
@ -82,6 +108,54 @@
#' }
NULL
#' @export
#' @inheritParams as.mic
#' @rdname plot
scale_x_mic <- function(keep_operators = "edges", mic_range = NULL, ...) {
stop_ifnot_installed("ggplot2")
scale <- ggplot2::scale_x_discrete(...)
scale$transform <- function(x, keep_ops = keep_operators, mic_rng = mic_range) {
rescale_mic(x = x, keep_operators = keep_ops, mic_range = mic_rng, as.mic = FALSE)
}
scale
}
#' @export
#' @inheritParams as.mic
#' @rdname plot
scale_y_mic <- function(keep_operators = "edges", mic_range = NULL, ...) {
stop_ifnot_installed("ggplot2")
scale <- ggplot2::scale_y_discrete(...)
scale$transform <- function(x, keep_ops = keep_operators, mic_rng = mic_range) {
rescale_mic(x = x, keep_operators = keep_ops, mic_range = mic_rng, as.mic = FALSE)
}
scale
}
#' @export
#' @inheritParams as.mic
#' @rdname plot
scale_colour_mic <- function(keep_operators = "edges", mic_range = NULL, ...) {
stop_ifnot_installed("ggplot2")
scale <- ggplot2::scale_colour_discrete(...)
scale$transform <- function(x, keep_ops = keep_operators, mic_rng = mic_range) {
rescale_mic(x = x, keep_operators = keep_ops, mic_range = mic_rng, as.mic = FALSE)
}
scale
}
#' @export
#' @inheritParams as.mic
#' @rdname plot
scale_fill_mic <- function(keep_operators = "edges", mic_range = NULL, ...) {
stop_ifnot_installed("ggplot2")
scale <- ggplot2::scale_fill_discrete(...)
scale$transform <- function(x, keep_ops = keep_operators, mic_rng = mic_range) {
rescale_mic(x = x, keep_operators = keep_ops, mic_range = mic_rng, as.mic = FALSE)
}
scale
}
#' @method plot mic
#' @importFrom graphics barplot axis mtext legend
#' @export
@ -105,10 +179,6 @@ plot.mic <- function(x,
meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
if ("colours_RSI" %in% names(list(...))) {
deprecation_warning(extra_msg = "The 'colours_RSI' argument has been replaced with 'colours_SIR'.")
colours_SIR <- list(...)$colours_RSI
}
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
@ -118,8 +188,7 @@ plot.mic <- function(x,
}
main <- gsub(" +", " ", paste0(main, collapse = " "))
x <- plot_prepare_table(x, expand = expand)
x <- range_as_table(x, expand = expand)
cols_sub <- plot_colours_subtitle_guideline(
x = x,
mo = mo,
@ -195,10 +264,6 @@ barplot.mic <- function(height,
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
if ("colours_RSI" %in% names(list(...))) {
deprecation_warning(extra_msg = "The 'colours_RSI' argument has been replaced with 'colours_SIR'.")
colours_SIR <- list(...)$colours_RSI
}
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
@ -241,10 +306,6 @@ autoplot.mic <- function(object,
meet_criteria(title, allow_class = "character", allow_NULL = TRUE)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
if ("colours_RSI" %in% names(list(...))) {
deprecation_warning(extra_msg = "The 'colours_RSI' argument has been replaced with 'colours_SIR'.")
colours_SIR <- list(...)$colours_RSI
}
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
@ -256,7 +317,7 @@ autoplot.mic <- function(object,
title <- gsub(" +", " ", paste0(title, collapse = " "))
}
x <- plot_prepare_table(object, expand = expand)
x <- range_as_table(object, expand = expand)
cols_sub <- plot_colours_subtitle_guideline(
x = x,
mo = mo,
@ -319,93 +380,11 @@ autoplot.mic <- function(object,
# will be exported using s3_register() in R/zzz.R
fortify.mic <- function(object, ...) {
stats::setNames(
as.data.frame(plot_prepare_table(object, expand = FALSE)),
as.data.frame(range_as_table(object, expand = FALSE)),
c("x", "y")
)
}
#' @export
#' @inheritParams as.mic
#' @param mic_range a manual range to plot the MIC values, e.g., `mic_range = c(0.001, 32)`. Use `NA` to set no limit on one side, e.g., `mic_range = c(NA, 32)`.
#' @param drop,guide,position,na.translate arguments passed on to [ggplot2::scale_x_discrete()]
#' @rdname plot
#' @examples
#'
#' # Plotting using scale_x_mic()
#' \donttest{
#' if (require("ggplot2")) {
#' mic_plot <- ggplot(data.frame(mics = as.mic(c(0.125, "<=4", 4, 8, 32, ">=32")),
#' counts = c(1, 1, 2, 2, 3, 3)),
#' aes(mics, counts)) +
#' geom_col()
#' mic_plot +
#' labs(title = "without scale_x_mic()")
#' }
#' if (require("ggplot2")) {
#' mic_plot +
#' scale_x_mic() +
#' labs(title = "with scale_x_mic()")
#' }
#' if (require("ggplot2")) {
#' mic_plot +
#' scale_x_mic(keep_operators = "all") +
#' labs(title = "with scale_x_mic() keeping all operators")
#' }
#' if (require("ggplot2")) {
#' mic_plot +
#' scale_x_mic(mic_range = c(1, 128)) +
#' labs(title = "with scale_x_mic() using a manual range")
#' }
#' }
scale_x_mic <- function(keep_operators = "edges", mic_range = NULL, ..., drop = FALSE, guide = waiver(), position = "bottom", na.translate = TRUE) {
meet_criteria(keep_operators, allow_class = c("character", "logical"), is_in = c("all", "none", "edges", FALSE, TRUE), has_length = 1)
meet_criteria(mic_range, allow_class = c("numeric", "integer", "logical"), has_length = 2, allow_NA = TRUE, allow_NULL = TRUE)
stop_ifnot(all(mic_range %in% c(levels(as.mic(1)), NA)),
"Values in `mic_range` must be valid MIC values")
stop_ifnot_installed("ggplot2")
ggplot2::ggproto(NULL, ggplot2::ScaleDiscretePosition,
aesthetics = c("x", "xmin", "xmax", "xend"),
na.translate = na.translate,
drop = drop,
guide = guide,
position = position,
transform = function(x, keep_ops = keep_operators, mic_rng = mic_range) {
if (!is.null(mic_rng)) {
min_mic <- mic_rng[1]
max_mic <- mic_rng[2]
if (!is.na(min_mic)) {
x[x < as.mic(min_mic)] <- as.mic(min_mic)
}
if (!is.na(max_mic)) {
x[x > as.mic(max_mic)] <- as.mic(max_mic)
}
}
# transform MICs to only keep required operators
x <- as.mic(x, keep_operators = ifelse(keep_ops == "edges", "none", keep_ops))
# get range betwen min and max of MICs
expanded <- plot_prepare_table(x,
expand = TRUE,
keep_operators = ifelse(keep_ops == "edges", "none", keep_ops),
mic_range = mic_rng)
if (keep_ops == "edges") {
names(expanded)[1] <- paste0("<=", names(expanded)[1])
names(expanded)[length(expanded)] <- paste0(">=", names(expanded)[length(expanded)])
}
# MICs contain all MIC levels, so strip this to only existing levels and their intermediate values
out <- factor(names(expanded),
levels = names(expanded),
ordered = TRUE)
# and only keep the ones in the data
if (keep_ops == "edges") {
out <- out[match(x, as.double(as.mic(out, keep_operators = "all")))]
} else {
out <- out[match(x, out)]
}
out
},
...)
}
#' @method plot disk
#' @export
@ -430,10 +409,6 @@ plot.disk <- function(x,
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)
if ("colours_RSI" %in% names(list(...))) {
deprecation_warning(extra_msg = "The 'colours_RSI' argument has been replaced with 'colours_SIR'.")
colours_SIR <- list(...)$colours_RSI
}
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
@ -443,8 +418,7 @@ plot.disk <- function(x,
}
main <- gsub(" +", " ", paste0(main, collapse = " "))
x <- plot_prepare_table(x, expand = expand)
x <- range_as_table(x, expand = expand)
cols_sub <- plot_colours_subtitle_guideline(
x = x,
mo = mo,
@ -520,10 +494,6 @@ barplot.disk <- function(height,
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)
if ("colours_RSI" %in% names(list(...))) {
deprecation_warning(extra_msg = "The 'colours_RSI' argument has been replaced with 'colours_SIR'.")
colours_SIR <- list(...)$colours_RSI
}
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
@ -566,10 +536,6 @@ autoplot.disk <- function(object,
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
if ("colours_RSI" %in% names(list(...))) {
deprecation_warning(extra_msg = "The 'colours_RSI' argument has been replaced with 'colours_SIR'.")
colours_SIR <- list(...)$colours_RSI
}
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
@ -581,7 +547,7 @@ autoplot.disk <- function(object,
title <- gsub(" +", " ", paste0(title, collapse = " "))
}
x <- plot_prepare_table(object, expand = expand)
x <- range_as_table(object, expand = expand)
cols_sub <- plot_colours_subtitle_guideline(
x = x,
mo = mo,
@ -645,7 +611,7 @@ autoplot.disk <- function(object,
# will be exported using s3_register() in R/zzz.R
fortify.disk <- function(object, ...) {
stats::setNames(
as.data.frame(plot_prepare_table(object, expand = FALSE)),
as.data.frame(range_as_table(object, expand = FALSE)),
c("x", "y")
)
}
@ -720,10 +686,6 @@ barplot.sir <- function(height,
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
meet_criteria(ylab, allow_class = "character", has_length = 1)
if ("colours_RSI" %in% names(list(...))) {
deprecation_warning(extra_msg = "The 'colours_RSI' argument has been replaced with 'colours_SIR'.")
colours_SIR <- list(...)$colours_RSI
}
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
@ -799,9 +761,8 @@ fortify.sir <- function(object, ...) {
)
}
plot_prepare_table <- function(x, expand, keep_operators = "all", mic_range = NULL) {
range_as_table <- function(x, expand, keep_operators = "all", mic_range = NULL) {
x <- x[!is.na(x)]
stop_if(length(x) == 0, "no observations to plot", call = FALSE)
if (is.mic(x)) {
x <- as.mic(x, keep_operators = keep_operators)
if (expand == TRUE) {
@ -847,6 +808,15 @@ plot_prepare_table <- function(x, expand, keep_operators = "all", mic_range = NU
as.table(x)
}
ggplot2_get_from_dots <- function(arg, default, ...) {
dots <- list(...)
if (!arg %in% names(dots)) {
default
} else {
dots[[arg]]
}
}
plot_name_of_I <- function(guideline) {
if (guideline %unlike% "CLSI" && as.double(gsub("[^0-9]+", "", guideline)) >= 2019) {
# interpretation since 2019
@ -858,6 +828,8 @@ plot_name_of_I <- function(guideline) {
}
plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_SIR, fn, language, method, breakpoint_type, include_PKPD, ...) {
stop_if(length(x) == 0, "no observations to plot", call = FALSE)
guideline <- get_guideline(guideline, AMR::clinical_breakpoints)
# store previous interpretations to backup

View File

@ -83,10 +83,6 @@ random_disk <- function(size = NULL, mo = NULL, ab = NULL, ...) {
#' @export
random_sir <- function(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...) {
meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
if ("prob_RSI" %in% names(list(...))) {
deprecation_warning("prob_RSI", "prob_SIR", is_function = FALSE)
prob_SIR <- list(...)$prob_RSI
}
meet_criteria(prob_SIR, allow_class = c("numeric", "integer"), has_length = 3)
if (is.null(size)) {
size <- NROW(get_current_data(arg_name = "size", call = -3))

View File

@ -237,12 +237,7 @@ is.sir <- function(x) {
if (inherits(x, "data.frame")) {
unname(vapply(FUN.VALUE = logical(1), x, is.sir))
} else {
rsi <- inherits(x, "rsi")
sir <- inherits(x, "sir")
if (isTRUE(rsi) && message_not_thrown_before("is.sir-rsi")) {
deprecation_warning(extra_msg = "The 'rsi' class has been replaced with 'sir'. Transform your 'rsi' columns to 'sir' with `as.sir()`, e.g.:\n your_data %>% mutate_if(is.rsi, as.sir)")
}
isTRUE(rsi) || isTRUE(sir)
isTRUE(inherits(x, "sir"))
}
}

View File

@ -223,7 +223,7 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
combine_SI = TRUE,
confidence_level = 0.95) {
meet_criteria(type, is_in = c("proportion", "count", "both"), has_length = 1)
meet_criteria(data, allow_class = "data.frame", contains_column_class = c("sir", "rsi"))
meet_criteria(data, allow_class = "data.frame", contains_column_class = "sir")
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
@ -373,5 +373,5 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
rownames(out) <- NULL
out <- as_original_data_class(out, class(data.bak)) # will remove tibble groups
structure(out, class = c("sir_df", "rsi_df", class(out)))
structure(out, class = c("sir_df", class(out)))
}

View File

@ -27,171 +27,14 @@
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Deprecated Functions
#'
#' These functions are so-called '[Deprecated]'. **They will be removed in a future release.** Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
#' @keywords internal
#' @name AMR-deprecated
#' @rdname AMR-deprecated
#' @export
NA_rsi_ <- set_clean_class(factor(NA_character_, levels = c("S", "I", "R"), ordered = TRUE),
new_class = c("rsi", "ordered", "factor")
)
#' @rdname AMR-deprecated
#' @export
as.rsi <- function(x, ...) {
deprecation_warning("as.rsi", "as.sir")
UseMethod("as.rsi")
}
#' @noRd
#' @export
as.rsi.default <- function(...) {
as.sir.default(...)
}
#' @noRd
#' @export
as.rsi.mic <- function(...) {
as.sir.mic(...)
}
#' @noRd
#' @export
as.rsi.disk <- function(...) {
as.sir.disk(...)
}
#' @noRd
#' @export
as.rsi.data.frame <- function(...) {
as.sir.data.frame(...)
}
#' @rdname AMR-deprecated
#' @export
facet_rsi <- function(...) {
deprecation_warning("facet_rsi", "facet_sir")
facet_sir(...)
}
#' @rdname AMR-deprecated
#' @export
geom_rsi <- function(...) {
deprecation_warning("geom_rsi", "geom_sir")
geom_sir(...)
}
#' @rdname AMR-deprecated
#' @export
ggplot_rsi <- function(...) {
deprecation_warning("ggplot_rsi", "ggplot_sir")
ggplot_sir(...)
}
#' @rdname AMR-deprecated
#' @export
ggplot_rsi_predict <- function(...) {
deprecation_warning("ggplot_rsi_predict", "ggplot_sir_predict")
ggplot_sir_predict(...)
}
#' @rdname AMR-deprecated
#' @export
is.rsi <- function(...) {
# REMINDER: change as.sir() to remove the deprecation warning there
suppressWarnings(is.sir(...))
}
#' @rdname AMR-deprecated
#' @export
is.rsi.eligible <- function(...) {
deprecation_warning("is.rsi.eligible", "is_sir_eligible")
is_sir_eligible(...)
}
#' @rdname AMR-deprecated
#' @export
labels_rsi_count <- function(...) {
deprecation_warning("labels_rsi_count", "labels_sir_count")
labels_sir_count(...)
}
#' @rdname AMR-deprecated
#' @export
n_rsi <- function(...) {
deprecation_warning("n_rsi", "n_sir")
n_sir(...)
}
#' @rdname AMR-deprecated
#' @export
random_rsi <- function(...) {
deprecation_warning("random_rsi", "random_sir")
random_sir(...)
}
#' @rdname AMR-deprecated
#' @export
rsi_df <- function(...) {
deprecation_warning("rsi_df", "sir_df")
sir_df(...)
}
#' @rdname AMR-deprecated
#' @export
rsi_predict <- function(...) {
deprecation_warning("rsi_predict", "sir_predict")
sir_predict(...)
}
#' @rdname AMR-deprecated
#' @export
scale_rsi_colours <- function(...) {
deprecation_warning("scale_rsi_colours", "scale_sir_colours")
scale_sir_colours(...)
}
#' @rdname AMR-deprecated
#' @export
theme_rsi <- function(...) {
deprecation_warning("theme_rsi", "theme_sir")
theme_sir(...)
}
# will be exported using s3_register() in R/zzz.R
pillar_shaft.rsi <- pillar_shaft.sir
type_sum.rsi <- function(x, ...) {
if (message_not_thrown_before("type_sum.rsi")) {
deprecation_warning(extra_msg = "The 'rsi' class has been replaced with 'sir'. Transform your 'rsi' columns to 'sir' with `as.sir()`, e.g.:\n your_data %>% mutate_if(is.rsi, as.sir)")
}
"rsi"
}
#' @method print rsi
#' @export
#' @noRd
print.rsi <- function(x, ...) {
deprecation_warning(extra_msg = "The 'rsi' class has been replaced with 'sir' - transform your 'rsi' data with `as.sir()`")
cat("Class 'rsi'", font_bold(font_red("[!]\n")))
print(as.character(x), quote = FALSE)
}
#' @noRd
#' @export
`[<-.rsi` <- `[<-.sir`
#' @noRd
#' @export
`[[<-.rsi` <- `[[<-.sir`
#' @noRd
#' @export
barplot.rsi <- barplot.sir
#' @noRd
#' @export
c.rsi <- c.sir
#' @noRd
#' @export
droplevels.rsi <- droplevels.sir
#' @noRd
#' @export
plot.rsi <- plot.sir
#' @noRd
#' @export
rep.rsi <- rep.sir
#' @noRd
#' @export
summary.rsi <- summary.sir
#' @noRd
#' @export
unique.rsi <- unique.sir
# WHEN REMOVING RSI, DON'T FORGET TO REMOVE :
# - THE "rsi_df" CLASS FROM R/sir_calc.R
# - CODE CONTAINING only_rsi_columns, colours_RSI, include_untested_rsi, prob_RSI
# #' Deprecated Functions
# #'
# #' These functions are so-called '[Deprecated]'. **They will be removed in a future release.** Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
# #' @keywords internal
# #' @name AMR-deprecated
# #' @rdname AMR-deprecated
# #' @export
# NULL
deprecation_warning <- function(old = NULL, new = NULL, extra_msg = NULL, is_function = TRUE) {
if (is.null(old)) {

View File

@ -98,14 +98,12 @@ if (pkg_is_available("cli")) {
s3_register("pillar::pillar_shaft", "av")
s3_register("pillar::pillar_shaft", "mo")
s3_register("pillar::pillar_shaft", "sir")
s3_register("pillar::pillar_shaft", "rsi") # remove in a later version
s3_register("pillar::pillar_shaft", "mic")
s3_register("pillar::pillar_shaft", "disk")
s3_register("pillar::type_sum", "ab")
s3_register("pillar::type_sum", "av")
s3_register("pillar::type_sum", "mo")
s3_register("pillar::type_sum", "sir")
s3_register("pillar::type_sum", "rsi") # remove in a later version
s3_register("pillar::type_sum", "mic")
s3_register("pillar::type_sum", "disk")
# Support for frequency tables from the cleaner package

View File

@ -174,7 +174,7 @@ reference:
- "`atc_online_property`"
- "`add_custom_antimicrobials`"
- title: "Preparing data: antimicrobial resistance"
- title: "Preparing data"
desc: >
With `as.mic()` and `as.disk()` you can transform your raw input to valid MIC or disk diffusion values.
Use `as.sir()` for cleaning raw data to let it only contain "R", "I" and "S", or to interpret MIC or disk diffusion values as SIR based on the lastest EUCAST and CLSI guidelines.
@ -186,7 +186,7 @@ reference:
- "`eucast_rules`"
- "`custom_eucast_rules`"
- title: "Analysing data: antimicrobial resistance"
- title: "Analysing data"
desc: >
Use these function for the analysis part. You can use `susceptibility()` or `resistance()` on any antibiotic column.
With `antibiogram()`, you can generate a traditional, combined, syndromic, or weighted-incidence syndromic combination
@ -202,14 +202,25 @@ reference:
- "`key_antimicrobials`"
- "`mdro`"
- "`count`"
- "`plot`"
- "`ggplot_sir`"
- "`bug_drug_combinations`"
- "`antibiotic_class_selectors`"
- "`mean_amr_distance`"
- "`resistance_predict`"
- "`guess_ab_col`"
- title: "Plotting data"
desc: >
Use these functions for the plotting part. The `scale_*_mic()` functions extend the ggplot2 package to allow plotting of
MIC values, even within a manually set range.
If using `plot()` (base R) or `autoplot()` (ggplot2) on MIC values or disk diffusion values, the user can
set the interpretation guideline to give the bars the right SIR colours.
The `ggplot_sir()` function is a short wrapper for users not much accustomed to ggplot2 yet.
The `ggplot_pca()` function is a specific function to plot so-called biplots for PCA (principal component analysis).
contents:
- "`plot`"
- "`ggplot_sir`"
- "`ggplot_pca`"
- title: "Other: AMR-specific options"
desc: >
The AMR package is customisable, by providing settings that can be set per user or per team. For
@ -257,7 +268,6 @@ reference:
- "`age`"
- "`availability`"
- "`get_AMR_locale`"
- "`ggplot_pca`"
- "`italicise_taxonomy`"
- "`join`"
- "`like`"
@ -273,10 +283,10 @@ reference:
- "`kurtosis`"
- "`skewness`"
- title: "Other: deprecated functions"
desc: >
These functions are deprecated, meaning that they will still
work but show a warning with every use and will be removed
in a future version.
contents:
- "`AMR-deprecated`"
# - title: "Other: deprecated functions"
# desc: >
# These functions are deprecated, meaning that they will still
# work but show a warning with every use and will be removed
# in a future version.
# contents:
# - "`AMR-deprecated`"

View File

@ -1,59 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zz_deprecated.R
\docType{data}
\name{AMR-deprecated}
\alias{AMR-deprecated}
\alias{NA_rsi_}
\alias{as.rsi}
\alias{facet_rsi}
\alias{geom_rsi}
\alias{ggplot_rsi}
\alias{ggplot_rsi_predict}
\alias{is.rsi}
\alias{is.rsi.eligible}
\alias{labels_rsi_count}
\alias{n_rsi}
\alias{random_rsi}
\alias{rsi_df}
\alias{rsi_predict}
\alias{scale_rsi_colours}
\alias{theme_rsi}
\title{Deprecated Functions}
\format{
An object of class \code{rsi} (inherits from \code{ordered}, \code{factor}) of length 1.
}
\usage{
NA_rsi_
as.rsi(x, ...)
facet_rsi(...)
geom_rsi(...)
ggplot_rsi(...)
ggplot_rsi_predict(...)
is.rsi(...)
is.rsi.eligible(...)
labels_rsi_count(...)
n_rsi(...)
random_rsi(...)
rsi_df(...)
rsi_predict(...)
scale_rsi_colours(...)
theme_rsi(...)
}
\description{
These functions are so-called '\link{Deprecated}'. \strong{They will be removed in a future release.} Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
}
\keyword{internal}

View File

@ -4,16 +4,19 @@
\name{as.mic}
\alias{as.mic}
\alias{mic}
\alias{NA_mic_}
\alias{is.mic}
\alias{NA_mic_}
\alias{rescale_mic}
\alias{droplevels.mic}
\title{Transform Input to Minimum Inhibitory Concentrations (MIC)}
\usage{
as.mic(x, na.rm = FALSE, keep_operators = "all")
is.mic(x)
NA_mic_
is.mic(x)
rescale_mic(x, mic_range, keep_operators = "edges", as.mic = TRUE)
\method{droplevels}{mic}(x, as.mic = FALSE, ...)
}
@ -24,6 +27,8 @@ is.mic(x)
\item{keep_operators}{a \link{character} specifying how to handle operators (such as \code{>} and \code{<=}) in the input. Accepts one of three values: \code{"all"} (or \code{TRUE}) to keep all operators, \code{"none"} (or \code{FALSE}) to remove all operators, or \code{"edges"} to keep operators only at both ends of the range.}
\item{mic_range}{a manual range to plot the MIC values, e.g., \code{mic_range = c(0.001, 32)}. Use \code{NA} to set no limit on one side, e.g., \code{mic_range = c(NA, 32)}.}
\item{as.mic}{a \link{logical} to indicate whether the \code{mic} class should be kept - the default is \code{FALSE}}
\item{...}{arguments passed on to methods}
@ -77,6 +82,8 @@ Using \code{\link[=as.double]{as.double()}} or \code{\link[=as.numeric]{as.numer
Use \code{\link[=droplevels]{droplevels()}} to drop unused levels. At default, it will return a plain factor. Use \code{droplevels(..., as.mic = TRUE)} to maintain the \code{mic} class.
With \code{\link[=rescale_mic]{rescale_mic()}}, existing MIC ranges can be rescaled to a defined range of MIC values. This can be useful to better compare MIC distributions.
\code{NA_mic_} is a missing value of the new \code{mic} class, analogous to e.g. base \R's \code{\link[base:NA]{NA_character_}}.
}
\examples{
@ -92,6 +99,9 @@ fivenum(mic_data)
quantile(mic_data)
all(mic_data < 512)
# rescale MICs using rescale_mic()
rescale_mic(mic_data, mic_range = c(4, 16))
# interpret MIC values
as.sir(
x = as.mic(2),

View File

@ -130,16 +130,18 @@ if (require("dplyr")) {
summary(pca_result)
# old base R plotting method:
biplot(pca_result)
biplot(pca_result, main = "Base R biplot")
# new ggplot2 plotting method using this package:
if (require("ggplot2")) {
ggplot_pca(pca_result)
ggplot_pca(pca_result) +
labs(title = "ggplot2 biplot")
}
if (require("ggplot2")) {
# still extendible with any ggplot2 function
ggplot_pca(pca_result) +
scale_colour_viridis_d() +
labs(title = "Title here")
labs(title = "ggplot2 biplot")
}
}
}

View File

@ -2,10 +2,13 @@
% Please edit documentation in R/plot.R
\name{plot}
\alias{plot}
\alias{scale_x_mic}
\alias{scale_y_mic}
\alias{scale_colour_mic}
\alias{scale_fill_mic}
\alias{plot.mic}
\alias{autoplot.mic}
\alias{fortify.mic}
\alias{scale_x_mic}
\alias{plot.disk}
\alias{autoplot.disk}
\alias{fortify.disk}
@ -14,6 +17,14 @@
\alias{fortify.sir}
\title{Plotting for Classes \code{sir}, \code{mic} and \code{disk}}
\usage{
scale_x_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_y_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_colour_mic(keep_operators = "edges", mic_range = NULL, ...)
scale_fill_mic(keep_operators = "edges", mic_range = NULL, ...)
\method{plot}{mic}(
x,
mo = NULL,
@ -48,16 +59,6 @@
\method{fortify}{mic}(object, ...)
scale_x_mic(
keep_operators = "edges",
mic_range = NULL,
...,
drop = FALSE,
guide = waiver(),
position = "bottom",
na.translate = TRUE
)
\method{plot}{disk}(
x,
main = deparse(substitute(x)),
@ -114,6 +115,12 @@ scale_x_mic(
\method{fortify}{sir}(object, ...)
}
\arguments{
\item{keep_operators}{a \link{character} specifying how to handle operators (such as \code{>} and \code{<=}) in the input. Accepts one of three values: \code{"all"} (or \code{TRUE}) to keep all operators, \code{"none"} (or \code{FALSE}) to remove all operators, or \code{"edges"} to keep operators only at both ends of the range.}
\item{mic_range}{a manual range to plot the MIC values, e.g., \code{mic_range = c(0.001, 32)}. Use \code{NA} to set no limit on one side, e.g., \code{mic_range = c(NA, 32)}.}
\item{...}{arguments passed on to methods}
\item{x, object}{values created with \code{\link[=as.mic]{as.mic()}}, \code{\link[=as.disk]{as.disk()}} or \code{\link[=as.sir]{as.sir()}} (or their \verb{random_*} variants, such as \code{\link[=random_mic]{random_mic()}})}
\item{mo}{any (vector of) text that can be coerced to a valid microorganism code with \code{\link[=as.mo]{as.mo()}}}
@ -135,14 +142,6 @@ scale_x_mic(
\item{include_PKPD}{a \link{logical} to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is \code{TRUE}. Can also be set with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_include_PKPD}}.}
\item{breakpoint_type}{the type of breakpoints to use, either "ECOFF", "animal", or "human". ECOFF stands for Epidemiological Cut-Off values. The default is \code{"human"}, which can also be set with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_breakpoint_type}}.}
\item{...}{arguments passed on to methods}
\item{keep_operators}{a \link{character} specifying how to handle operators (such as \code{>} and \code{<=}) in the input. Accepts one of three values: \code{"all"} (or \code{TRUE}) to keep all operators, \code{"none"} (or \code{FALSE}) to remove all operators, or \code{"edges"} to keep operators only at both ends of the range.}
\item{mic_range}{a manual range to plot the MIC values, e.g., \code{mic_range = c(0.001, 32)}. Use \code{NA} to set no limit on one side, e.g., \code{mic_range = c(NA, 32)}.}
\item{drop, guide, position, na.translate}{arguments passed on to \code{\link[ggplot2:scale_discrete]{ggplot2::scale_x_discrete()}}}
}
\value{
The \code{autoplot()} functions return a \code{\link[ggplot2:ggplot]{ggplot}} model that is extendible with any \code{ggplot2} function.
@ -152,7 +151,7 @@ The \code{fortify()} functions return a \link{data.frame} as an extension for us
\description{
Functions to plot classes \code{sir}, \code{mic} and \code{disk}, with support for base \R and \code{ggplot2}.
Especially \code{\link[=scale_x_mic]{scale_x_mic()}} is a relevant wrapper to plot MIC values for \code{ggplot2}. It allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.
Especially the \code{\link[=scale_*_mic]{scale_*_mic()}} functions are relevant wrappers to plot MIC values for \code{ggplot2}. They allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.
}
\details{
The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
@ -175,17 +174,6 @@ plot(some_mic_values, mo = "S. aureus", ab = "ampicillin")
plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "nl")
\donttest{
if (require("ggplot2")) {
autoplot(some_mic_values)
}
if (require("ggplot2")) {
autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
}
if (require("ggplot2")) {
autoplot(some_sir_values)
}
}
# Plotting using scale_x_mic()
\donttest{
@ -212,5 +200,15 @@ if (require("ggplot2")) {
scale_x_mic(mic_range = c(1, 128)) +
labs(title = "with scale_x_mic() using a manual range")
}
if (require("ggplot2")) {
autoplot(some_mic_values)
}
if (require("ggplot2")) {
autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
}
if (require("ggplot2")) {
autoplot(some_sir_values)
}
}
}