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documentation for 'data.table' AB selectors
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Package: AMR
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Version: 1.8.2.9149
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Version: 1.8.2.9150
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Date: 2023-03-11
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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2
NEWS.md
2
NEWS.md
@ -1,4 +1,4 @@
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# AMR 1.8.2.9149
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# AMR 1.8.2.9150
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*(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
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@ -934,7 +934,7 @@ get_current_data <- function(arg_name, call) {
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}
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}
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# now go over all underlying environments looking for other dplyr and base R selection environments
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# now go over all underlying environments looking for other dplyr, data.table and base R selection environments
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with_generic <- vapply(FUN.VALUE = logical(1), frms, function(e) !is.null(e$`.Generic`))
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for (env in frms[which(with_generic)]) {
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if (valid_df(env$`.data`)) {
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@ -945,6 +945,7 @@ get_current_data <- function(arg_name, call) {
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return(env$xx)
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} else if (valid_df(env$x)) {
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# an element `x` will be in the environment for only cols in base R, e.g. `example_isolates[, carbapenems()]`
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# this element will also be present in data.table environments where there's a .Generic available
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return(env$x)
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}
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}
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@ -29,14 +29,16 @@
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#' Antibiotic Selectors
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#'
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#' These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group, without the need to define the columns or antibiotic abbreviations. In short, if you have a column name that resembles an antimicrobial drug, it will be picked up by any of these functions that matches its pharmaceutical class: "cefazolin", "CZO" and "J01DB04" will all be picked up by [cephalosporins()].
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#' @description These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group (according to the [antibiotics] data set), without the need to define the columns or antibiotic abbreviations.
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#'
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#' In short, if you have a column name that resembles an antimicrobial drug, it will be picked up by any of these functions that matches its pharmaceutical class: "cefazolin", "kefzol", "CZO" and "J01DB04" will all be picked up by [cephalosporins()].
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#' @param ab_class an antimicrobial class or a part of it, such as `"carba"` and `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
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#' @param filter an [expression] to be evaluated in the [antibiotics] data set, such as `name %like% "trim"`
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#' @param only_sir_columns a [logical] to indicate whether only columns of class `sir` must be selected (default is `FALSE`), see [as.sir()]
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#' @param only_treatable a [logical] to indicate whether antimicrobial drugs should be excluded that are only for laboratory tests (default is `TRUE`), such as gentamicin-high (`GEH`) and imipenem/EDTA (`IPE`)
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#' @param ... ignored, only in place to allow future extensions
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#' @details
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#' These functions can be used in data set calls for selecting columns and filtering rows. They are heavily inspired by the [Tidyverse selection helpers][tidyselect::language] such as [`everything()`][tidyselect::everything()], but also work in base \R and not only in `dplyr` verbs. Nonetheless, they are very convenient to use with `dplyr` functions such as [`select()`][dplyr::select()], [`filter()`][dplyr::filter()] and [`summarise()`][dplyr::summarise()], see *Examples*.
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#' These functions can be used in data set calls for selecting columns and filtering rows. They work with base \R, the Tidyverse, and `data.table`. They are heavily inspired by the [Tidyverse selection helpers][tidyselect::language] such as [`everything()`][tidyselect::everything()], but are not limited to `dplyr` verbs. Nonetheless, they are very convenient to use with `dplyr` functions such as [`select()`][dplyr::select()], [`filter()`][dplyr::filter()] and [`summarise()`][dplyr::summarise()], see *Examples*.
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#'
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#' All columns in the data in which these functions are called will be searched for known antibiotic names, abbreviations, brand names, and codes (ATC, EARS-Net, WHO, etc.) according to the [antibiotics] data set. This means that a selector such as [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
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#'
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@ -53,6 +55,10 @@
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#' # `example_isolates` is a data set available in the AMR package.
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#' # See ?example_isolates.
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#' example_isolates
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#'
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#'
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#' # Examples sections below are split into 'base R', 'dplyr', and 'data.table':
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#'
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#'
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#' # base R ------------------------------------------------------------------
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#'
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@ -76,7 +82,7 @@
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#' # filter with multiple antibiotic selectors using c()
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#' example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
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#'
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#' # filter + select in one go: get penicillins in carbapenems-resistant strains
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#' # filter + select in one go: get penicillins in carbapenem-resistant strains
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#' example_isolates[any(carbapenems() == "R"), penicillins()]
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#'
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#' # You can combine selectors with '&' to be more specific. For example,
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@ -86,13 +92,19 @@
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#' # and erythromycin is not a penicillin:
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#' example_isolates[, penicillins() & administrable_per_os()]
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#'
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#' # ab_selector() applies a filter in the `antibiotics` data set and is thus very
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#' # flexible. For instance, to select antibiotic columns with an oral DDD of at
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#' # least 1 gram:
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#' # ab_selector() applies a filter in the `antibiotics` data set and is thus
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#' # very flexible. For instance, to select antibiotic columns with an oral DDD
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#' # of at least 1 gram:
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#' example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
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#'
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#' # dplyr -------------------------------------------------------------------
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#'
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#' \donttest{
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#' # dplyr -------------------------------------------------------------------
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#'
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#' if (require("dplyr")) {
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#' tibble(kefzol = random_sir(5)) %>%
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#' select(cephalosporins())
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#' }
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#'
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#' if (require("dplyr")) {
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#' # get AMR for all aminoglycosides e.g., per ward:
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#' example_isolates %>%
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@ -173,6 +185,35 @@
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#' z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == "R"))
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#' identical(x, y) && identical(y, z)
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#' }
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#'
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#'
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#' # data.table --------------------------------------------------------------
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#'
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#' # data.table is supported as well, just use it in the same way as with
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#' # base R, but add `with = FALSE` if using a single AB selector:
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#'
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#' if (require("data.table")) {
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#' dt <- as.data.table(example_isolates)
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#'
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#' print(
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#' dt[, carbapenems()] # incorrect, returns column *names*
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#' )
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#' print(
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#' dt[, carbapenems(), with = FALSE] # so `with = FALSE` is required
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#' )
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#'
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#' # for multiple selections or AB selectors, `with = FALSE` is not needed:
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#' print(
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#' dt[, c("mo", aminoglycosides())]
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#' )
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#' print(
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#' dt[, c(carbapenems(), aminoglycosides())]
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#' )
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#'
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#' # row filters are also supported:
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#' print(dt[any(carbapenems() == "S"), ])
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#' print(dt[any(carbapenems() == "S"), penicillins(), with = FALSE])
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#' }
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#' }
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ab_class <- function(ab_class,
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only_sir_columns = FALSE,
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@ -214,7 +214,7 @@ is_new_episode <- function(x, episode_days = NULL, case_free_days = NULL, ...) {
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}
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exec_episode <- function(x, episode_days, case_free_days, ...) {
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stop_if_not(is.null(episode_days) || is.null(case_free_days),
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stop_ifnot(is.null(episode_days) || is.null(case_free_days),
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"either argument `episode_days` or argument `case_free_days` must be set.",
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call = -2
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)
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#' @param ab_result antibiotic results to test against, must be one or more values of "S", "I", or "R"
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#' @param confidence_level the confidence level for the returned confidence interval. For the calculation, the number of S or SI isolates, and R isolates are compared with the total number of available isolates with R, S, or I by using [binom.test()], i.e., the Clopper-Pearson method.
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#' @param side the side of the confidence interval to return. The default is `"both"` for a length 2 vector, but can also be (abbreviated as) `"min"`/`"left"`/`"lower"`/`"less"` or `"max"`/`"right"`/`"higher"`/`"greater"`.
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#' @param collapse a [logical] to indicate whether the output values should be 'collapsed', i.e. be merged together into one value, or a character value to use for collapsing
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#' @inheritSection as.sir Interpretation of SIR
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#' @details
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#' The function [resistance()] is equal to the function [proportion_R()]. The function [susceptibility()] is equal to the function [proportion_SI()].
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#' sir_confidence_interval(example_isolates$AMX,
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#' confidence_level = 0.975
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#' )
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#' sir_confidence_interval(example_isolates$AMX,
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#' confidence_level = 0.975,
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#' collapse = ", "
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#' )
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#'
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#' # determines %S+I:
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#' susceptibility(example_isolates$AMX)
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@ -260,10 +265,16 @@ sir_confidence_interval <- function(...,
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as_percent = FALSE,
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only_all_tested = FALSE,
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confidence_level = 0.95,
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side = "both") {
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side = "both",
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collapse = FALSE) {
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meet_criteria(ab_result, allow_class = c("character", "sir"), has_length = c(1, 2, 3), is_in = c("S", "I", "R"))
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meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
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meet_criteria(as_percent, allow_class = "logical", has_length = 1)
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meet_criteria(only_all_tested, allow_class = "logical", has_length = 1)
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meet_criteria(confidence_level, allow_class = "numeric", is_positive = TRUE, has_length = 1)
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meet_criteria(side, allow_class = "character", has_length = 1, is_in = c("both", "b", "left", "l", "lower", "lowest", "less", "min", "right", "r", "higher", "highest", "greater", "g", "max"))
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meet_criteria(collapse, allow_class = c("logical", "character"), has_length = 1)
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x <- tryCatch(
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sir_calc(...,
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ab_result = ab_result,
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@ -281,19 +292,7 @@ sir_confidence_interval <- function(...,
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error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
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)
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if (n < minimum) {
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warning_("Introducing NA: ",
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ifelse(n == 0, "no", paste("only", n)),
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" results available for `sir_confidence_interval()` (`minimum` = ", minimum, ").",
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call = FALSE
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)
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if (as_percent == TRUE) {
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return(NA_character_)
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} else {
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return(NA_real_)
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}
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}
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# this applies the Clopper-Pearson method
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out <- stats::binom.test(x = x, n = n, conf.level = confidence_level)$conf.int
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out <- set_clean_class(out, "double")
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@ -302,11 +301,29 @@ sir_confidence_interval <- function(...,
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} else if (side %in% c("right", "r", "higher", "highest", "greater", "g", "max")) {
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out <- out[2]
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}
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if (as_percent == TRUE) {
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percentage(out, digits = 1)
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if (isTRUE(as_percent)) {
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out <- percentage(out, digits = 1)
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} else {
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out
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out <- round(out, digits = 3)
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}
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if (!isFALSE(collapse) && length(out) > 1) {
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out <- paste(out, collapse = ifelse(isTRUE(collapse), "-", collapse))
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}
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if (n < minimum) {
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warning_("Introducing NA: ",
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ifelse(n == 0, "no", paste("only", n)),
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" results available for `sir_confidence_interval()` (`minimum` = ", minimum, ").",
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call = FALSE
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)
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if (is.character(out)) {
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return(NA_character_)
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} else {
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return(NA_real_)
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}
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}
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out
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}
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#' @rdname proportion
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33
index.md
33
index.md
@ -34,6 +34,8 @@ With the help of contributors from all corners of the world, the `AMR` package i
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#### Filtering and selecting data
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One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antibiotic columns. This can be done using the so-called [antibiotic class selectors](https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html) that work in base R, `dplyr` and `data.table`:
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```r
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# AMR works great with dplyr, but it's not required or neccesary
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library(AMR)
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example_isolates %>%
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mutate(bacteria = mo_fullname()) %>%
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# filtering functions for microorganisms:
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filter(mo_is_gram_negative(),
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mo_is_intrinsic_resistant(ab = "cefotax")) %>%
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# antibiotic selectors:
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select(bacteria,
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aminoglycosides(),
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carbapenems())
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@ -66,13 +70,18 @@ With only having defined a row filter on Gram-negative bacteria with intrinsic r
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A base R equivalent would be:
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```r
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library(AMR)
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example_isolates$bacteria <- mo_fullname(example_isolates$mo)
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example_isolates[which(mo_is_gram_negative() &
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mo_is_intrinsic_resistant(ab = "cefotax")),
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c("bacteria", aminoglycosides(), carbapenems())]
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```
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This base R snippet will work in any version of R since April 2013 (R-3.0).
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This base R code will work in any version of R since April 2013 (R-3.0). Moreover, this code works identically with the `data.table` package, only by starting with:
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```r
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example_isolates <- data.table::as.data.table(example_isolates)
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```
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#### Generating antibiograms
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@ -131,6 +140,25 @@ antibiogram(example_isolates,
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For a manual approach, you can use the `resistance` or `susceptibility()` function:
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```r
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example_isolates %>%
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# group by ward:
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group_by(ward) %>%
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# calculate AMR using resistance() for gentamicin and tobramycin
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# and get their 95% confidence intervals using sir_confidence_interval():
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summarise(across(c(GEN, TOB),
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list(total_R = resistance,
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conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
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```
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|ward | GEN_total_R|GEN_conf_int | TOB_total_R|TOB_conf_int |
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|:---------:|:----------:|:-----------:|:----------:|:-----------:|
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|Clinical | 0.229 |0.205-0.254 | 0.315 |0.284-0.347 |
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|ICU | 0.290 |0.253-0.330 | 0.400 |0.353-0.449 |
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|Outpatient | 0.200 |0.131-0.285 | 0.368 |0.254-0.493 |
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Or use [antibiotic class selectors](https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html) to select a series of antibiotic columns:
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```r
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library(AMR)
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library(dplyr)
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@ -138,8 +166,7 @@ library(dplyr)
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out <- example_isolates %>%
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# group by ward:
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group_by(ward) %>%
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# calculate AMR using resistance(), over all aminoglycosides
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# and polymyxins:
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# calculate AMR using resistance(), over all aminoglycosides and polymyxins:
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summarise(across(c(aminoglycosides(), polymyxins()),
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resistance))
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out
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@ -118,10 +118,12 @@ not_intrinsic_resistant(
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(internally) a \link{character} vector of column names, with additional class \code{"ab_selector"}
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}
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\description{
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These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group, without the need to define the columns or antibiotic abbreviations. In short, if you have a column name that resembles an antimicrobial drug, it will be picked up by any of these functions that matches its pharmaceutical class: "cefazolin", "CZO" and "J01DB04" will all be picked up by \code{\link[=cephalosporins]{cephalosporins()}}.
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These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group (according to the \link{antibiotics} data set), without the need to define the columns or antibiotic abbreviations.
|
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|
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In short, if you have a column name that resembles an antimicrobial drug, it will be picked up by any of these functions that matches its pharmaceutical class: "cefazolin", "kefzol", "CZO" and "J01DB04" will all be picked up by \code{\link[=cephalosporins]{cephalosporins()}}.
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}
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\details{
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These functions can be used in data set calls for selecting columns and filtering rows. They are heavily inspired by the \link[tidyselect:language]{Tidyverse selection helpers} such as \code{\link[tidyselect:everything]{everything()}}, but also work in base \R and not only in \code{dplyr} verbs. Nonetheless, they are very convenient to use with \code{dplyr} functions such as \code{\link[dplyr:select]{select()}}, \code{\link[dplyr:filter]{filter()}} and \code{\link[dplyr:summarise]{summarise()}}, see \emph{Examples}.
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These functions can be used in data set calls for selecting columns and filtering rows. They work with base \R, the Tidyverse, and \code{data.table}. They are heavily inspired by the \link[tidyselect:language]{Tidyverse selection helpers} such as \code{\link[tidyselect:everything]{everything()}}, but are not limited to \code{dplyr} verbs. Nonetheless, they are very convenient to use with \code{dplyr} functions such as \code{\link[dplyr:select]{select()}}, \code{\link[dplyr:filter]{filter()}} and \code{\link[dplyr:summarise]{summarise()}}, see \emph{Examples}.
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All columns in the data in which these functions are called will be searched for known antibiotic names, abbreviations, brand names, and codes (ATC, EARS-Net, WHO, etc.) according to the \link{antibiotics} data set. This means that a selector such as \code{\link[=aminoglycosides]{aminoglycosides()}} will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
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@ -174,6 +176,10 @@ All data sets in this \code{AMR} package (about microorganisms, antibiotics, SIR
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# See ?example_isolates.
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example_isolates
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# Examples sections below are split into 'base R', 'dplyr', and 'data.table':
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# base R ------------------------------------------------------------------
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# select columns 'IPM' (imipenem) and 'MEM' (meropenem)
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@ -196,7 +202,7 @@ example_isolates[all(carbapenems()), ]
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# filter with multiple antibiotic selectors using c()
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example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
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# filter + select in one go: get penicillins in carbapenems-resistant strains
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# filter + select in one go: get penicillins in carbapenem-resistant strains
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example_isolates[any(carbapenems() == "R"), penicillins()]
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# You can combine selectors with '&' to be more specific. For example,
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@ -206,13 +212,19 @@ example_isolates[any(carbapenems() == "R"), penicillins()]
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# and erythromycin is not a penicillin:
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example_isolates[, penicillins() & administrable_per_os()]
|
||||
|
||||
# ab_selector() applies a filter in the `antibiotics` data set and is thus very
|
||||
# flexible. For instance, to select antibiotic columns with an oral DDD of at
|
||||
# least 1 gram:
|
||||
# ab_selector() applies a filter in the `antibiotics` data set and is thus
|
||||
# very flexible. For instance, to select antibiotic columns with an oral DDD
|
||||
# of at least 1 gram:
|
||||
example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
|
||||
|
||||
# dplyr -------------------------------------------------------------------
|
||||
\donttest{
|
||||
# dplyr -------------------------------------------------------------------
|
||||
|
||||
if (require("dplyr")) {
|
||||
tibble(kefzol = random_sir(5)) \%>\%
|
||||
select(cephalosporins())
|
||||
}
|
||||
|
||||
if (require("dplyr")) {
|
||||
# get AMR for all aminoglycosides e.g., per ward:
|
||||
example_isolates \%>\%
|
||||
@ -293,5 +305,34 @@ if (require("dplyr")) {
|
||||
z <- example_isolates \%>\% filter(if_all(carbapenems(), ~ .x == "R"))
|
||||
identical(x, y) && identical(y, z)
|
||||
}
|
||||
|
||||
|
||||
# data.table --------------------------------------------------------------
|
||||
|
||||
# data.table is supported as well, just use it in the same way as with
|
||||
# base R, but add `with = FALSE` if using a single AB selector:
|
||||
|
||||
if (require("data.table")) {
|
||||
dt <- as.data.table(example_isolates)
|
||||
|
||||
print(
|
||||
dt[, carbapenems()] # incorrect, returns column *names*
|
||||
)
|
||||
print(
|
||||
dt[, carbapenems(), with = FALSE] # so `with = FALSE` is required
|
||||
)
|
||||
|
||||
# for multiple selections or AB selectors, `with = FALSE` is not needed:
|
||||
print(
|
||||
dt[, c("mo", aminoglycosides())]
|
||||
)
|
||||
print(
|
||||
dt[, c(carbapenems(), aminoglycosides())]
|
||||
)
|
||||
|
||||
# row filters are also supported:
|
||||
print(dt[any(carbapenems() == "S"), ])
|
||||
print(dt[any(carbapenems() == "S"), penicillins(), with = FALSE])
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -29,7 +29,8 @@ sir_confidence_interval(
|
||||
as_percent = FALSE,
|
||||
only_all_tested = FALSE,
|
||||
confidence_level = 0.95,
|
||||
side = "both"
|
||||
side = "both",
|
||||
collapse = FALSE
|
||||
)
|
||||
|
||||
proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
|
||||
@ -77,6 +78,8 @@ sir_df(
|
||||
|
||||
\item{side}{the side of the confidence interval to return. The default is \code{"both"} for a length 2 vector, but can also be (abbreviated as) \code{"min"}/\code{"left"}/\code{"lower"}/\code{"less"} or \code{"max"}/\code{"right"}/\code{"higher"}/\code{"greater"}.}
|
||||
|
||||
\item{collapse}{a \link{logical} to indicate whether the output values should be 'collapsed', i.e. be merged together into one value, or a character value to use for collapsing}
|
||||
|
||||
\item{data}{a \link{data.frame} containing columns with class \code{\link{sir}} (see \code{\link[=as.sir]{as.sir()}})}
|
||||
|
||||
\item{translate_ab}{a column name of the \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}}
|
||||
@ -172,6 +175,10 @@ sir_confidence_interval(example_isolates$AMX)
|
||||
sir_confidence_interval(example_isolates$AMX,
|
||||
confidence_level = 0.975
|
||||
)
|
||||
sir_confidence_interval(example_isolates$AMX,
|
||||
confidence_level = 0.975,
|
||||
collapse = ", "
|
||||
)
|
||||
|
||||
# determines \%S+I:
|
||||
susceptibility(example_isolates$AMX)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user