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# ==================================================================== #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
# Center Groningen in The Netherlands, in collaboration with many #
# colleagues from around the world, see our website. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://amr-for-r.org #
# ==================================================================== #
#' Interpret MIC and Disk Diffusion as SIR, or Clean Existing SIR Data
#'
#' @description Clean up existing SIR values, or interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI. [as.sir()] transforms the input to a new class [`sir`], which is an ordered [factor] containing the levels `S`, `SDD`, `I`, `R`, `NI`.
#'
#' Breakpoints are currently implemented from EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`, see *Details*. All breakpoints used for interpretation are available in our [clinical_breakpoints] data set.
#' @rdname as.sir
#' @param x Vector of values (for class [`mic`]: MIC values in mg/L, for class [`disk`]: a disk diffusion radius in millimetres).
#' @param mo A vector (or column name) with [character]s that can be coerced to valid microorganism codes with [as.mo()], can be left empty to determine it automatically.
#' @param ab A vector (or column name) with [character]s that can be coerced to a valid antimicrobial drug code with [as.ab()].
#' @param uti (Urinary Tract Infection) a vector (or column name) with [logical]s (`TRUE` or `FALSE`) to specify whether a UTI specific interpretation from the guideline should be chosen. For using [as.sir()] on a [data.frame], this can also be a column containing [logical]s or when left blank, the data set will be searched for a column 'specimen', and rows within this column containing 'urin' (such as 'urine', 'urina') will be regarded isolates from a UTI. See *Examples*.
#' @inheritParams first_isolate
#' @param guideline A guideline name (or column name) to use for SIR interpretation. Defaults to `r AMR::clinical_breakpoints$guideline[1]` (the latest implemented EUCAST guideline in the [AMR::clinical_breakpoints] data set), but can be set with the package option [`AMR_guideline`][AMR-options]. Currently supports EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`), see *Details*. Using a column name allows for straightforward interpretation of historical data, which must be analysed in the context of, for example, different years.
#' @param capped_mic_handling A [character] string that controls how MIC values with a cap (i.e., starting with `<`, `<=`, `>`, or `>=`) are interpreted. Supports the following options:
#'
#' `"none"`
#' * `<=` and `>=` are treated as-is.
#' * `<` and `>` are treated as-is.
#'
#' `"conservative"`
#' * `<=` and `>=` return `"NI"` (non-interpretable) if the MIC is within the breakpoint guideline range.
#' * `<` always returns `"S"`, and `>` always returns `"R"`.
#'
#' `"standard"` (default)
#' * `<=` and `>=` return `"NI"` (non-interpretable) if the MIC is within the breakpoint guideline range.
#' * `<` and `>` are treated as-is.
#'
#' `"inverse"`
#' * `<=` and `>=` are treated as-is.
#' * `<` always returns `"S"`, and `>` always returns `"R"`.
#'
#' The default `"standard"` setting ensures cautious handling of uncertain values while preserving interpretability. This option can also be set with the package option [`AMR_capped_mic_handling`][AMR-options].
#' @param add_intrinsic_resistance *(only useful when using a EUCAST guideline)* a [logical] to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in *Klebsiella* species. Determination is based on the [intrinsic_resistant] data set, that itself is based on `r format_eucast_version_nr(3.3)`.
#' @param substitute_missing_r_breakpoint A [logical] to indicate that a missing clinical breakpoints for R (resistant) must be substituted with R - the default is `FALSE`. Some (especially CLSI) breakpoints only have a breakpoint for S, meaning that the outcome can only be `"S"` or `NA`. Setting this to `TRUE` will convert the `NA`s in these cases to `"R"`. Can also be set with the package option [`AMR_substitute_missing_r_breakpoint`][AMR-options].
#' @param include_screening A [logical] to indicate that clinical breakpoints for screening are allowed - the default is `FALSE`. Can also be set with the package option [`AMR_include_screening`][AMR-options].
#' @param include_PKPD A [logical] to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is `TRUE`. Can also be set with the package option [`AMR_include_PKPD`][AMR-options].
#' @param breakpoint_type The type of breakpoints to use, either `r vector_or(clinical_breakpoints$type)`. ECOFF stands for Epidemiological Cut-Off values. The default is `"human"`, which can also be set with the package option [`AMR_breakpoint_type`][AMR-options]. If `host` is set to values of veterinary species, this will automatically be set to `"animal"`.
#' @param host A vector (or column name) with [character]s to indicate the host. Only useful for veterinary breakpoints, as it requires `breakpoint_type = "animal"`. The values can be any text resembling the animal species, even in any of the `r length(LANGUAGES_SUPPORTED)` supported languages of this package. For foreign languages, be sure to set the language with [set_AMR_locale()] (though it will be automatically guessed based on the system language).
#' @param verbose A [logical] to indicate that all notes should be printed during interpretation of MIC values or disk diffusion values.
#' @param reference_data A [data.frame] to be used for interpretation, which defaults to the [clinical_breakpoints] data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the [clinical_breakpoints] data set (same column names and column types). Please note that the `guideline` argument will be ignored when `reference_data` is manually set.
#' @param threshold Maximum fraction of invalid antimicrobial interpretations of `x`, see *Examples*.
#' @param conserve_capped_values Deprecated, use `capped_mic_handling` instead.
#' @param ... For using on a [data.frame]: names of columns to apply [as.sir()] on (supports tidy selection such as `column1:column4`). Otherwise: arguments passed on to methods.
#' @details
#' *Note: The clinical breakpoints in this package were validated through, and imported from, [WHONET](https://whonet.org). The public use of this `AMR` package has been endorsed by both CLSI and EUCAST. See [clinical_breakpoints] for more information.*
#'
#' ### How it Works
#'
#' The [as.sir()] function can work in four ways:
#'
#' 1. For **cleaning raw / untransformed data**. The data will be cleaned to only contain valid values, namely: **S** for susceptible, **I** for intermediate or 'susceptible, increased exposure', **R** for resistant, **NI** for non-interpretable, and **SDD** for susceptible dose-dependent. Each of these can be set using a [regular expression][base::regex]. Furthermore, [as.sir()] will try its best to clean with some intelligence. For example, mixed values with SIR interpretations and MIC values such as `"<0.25; S"` will be coerced to `"S"`. Combined interpretations for multiple test methods (as seen in laboratory records) such as `"S; S"` will be coerced to `"S"`, but a value like `"S; I"` will return `NA` with a warning that the input is invalid.
#'
#' 2. For **interpreting minimum inhibitory concentration (MIC) values** according to EUCAST or CLSI. You must clean your MIC values first using [as.mic()], that also gives your columns the new data class [`mic`]. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` argument.
#' * Example to apply using `dplyr`:
#' ```r
#' your_data %>% mutate_if(is.mic, as.sir)
#' your_data %>% mutate(across(where(is.mic), as.sir))
#' your_data %>% mutate_if(is.mic, as.sir, ab = "column_with_antibiotics", mo = "column_with_microorganisms")
#' your_data %>% mutate_if(is.mic, as.sir, ab = c("cipro", "ampicillin", ...), mo = c("E. coli", "K. pneumoniae", ...))
#'
#' # for veterinary breakpoints, also set `host`:
#' your_data %>% mutate_if(is.mic, as.sir, host = "column_with_animal_species", guideline = "CLSI")
#'
#' # fast processing with parallel computing:
#' as.sir(your_data, ..., parallel = TRUE)
#' ```
#' * Operators like "<=" will be stripped before interpretation. When using `capped_mic_handling = "conservative"`, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (`capped_mic_handling = "standard"`) considers ">2" to be lower than ">=4" and might in this case return "S" or "I".
#' * **Note:** When using CLSI as the guideline, MIC values must be log2-based doubling dilutions. Values not in this format, will be automatically rounded up to the nearest log2 level as CLSI instructs, and a warning will be thrown.
#'
#' 3. For **interpreting disk diffusion diameters** according to EUCAST or CLSI. You must clean your disk zones first using [as.disk()], that also gives your columns the new data class [`disk`]. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` argument.
#' * Example to apply using `dplyr`:
#' ```r
#' your_data %>% mutate_if(is.disk, as.sir)
#' your_data %>% mutate(across(where(is.disk), as.sir))
#' your_data %>% mutate_if(is.disk, as.sir, ab = "column_with_antibiotics", mo = "column_with_microorganisms")
#' your_data %>% mutate_if(is.disk, as.sir, ab = c("cipro", "ampicillin", ...), mo = c("E. coli", "K. pneumoniae", ...))
#'
#' # for veterinary breakpoints, also set `host`:
#' your_data %>% mutate_if(is.disk, as.sir, host = "column_with_animal_species", guideline = "CLSI")
#'
#' # fast processing with parallel computing:
#' as.sir(your_data, ..., parallel = TRUE)
#' ```
#'
#' 4. For **interpreting a complete data set**, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running `as.sir(your_data)`.
#'
#' **For points 2, 3 and 4: Use [sir_interpretation_history()]** to retrieve a [data.frame] with all results of all previous [as.sir()] calls. It also contains notes about interpretation, and the exact input and output values.
#'
#' ### Supported Guidelines
#'
#' For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are:
#' - For **clinical microbiology**: EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "human")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "human")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "human")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "human")$guideline)))`;
#' - For **veterinary microbiology**: EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "animal")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`;
#' - For **ECOFFs** (Epidemiological Cut-off Values): EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "ECOFF")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "ECOFF")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "ECOFF")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "ECOFF")$guideline)))`.
#'
#' The `guideline` argument must be set to e.g., ``r paste0('"', subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline[1], '"')`` or ``r paste0('"', subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline[1], '"')``. By simply using `"EUCAST"` (the default) or `"CLSI"` as input, the latest included version of that guideline will automatically be selected. Importantly, using a column name of your data instead, allows for straightforward interpretation of historical data that must be analysed in the context of, for example, different years.
#'
#' You can set your own data set using the `reference_data` argument. The `guideline` argument will then be ignored.
#'
#' It is also possible to set the default guideline with the package option [`AMR_guideline`][AMR-options] (e.g. in your `.Rprofile` file), such as:
#'
#' ```
#' options(AMR_guideline = "CLSI")
#' options(AMR_guideline = "CLSI 2018")
#' options(AMR_guideline = "EUCAST 2020")
#' # or to reset:
#' options(AMR_guideline = NULL)
#' ```
#'
#' ### Working with Veterinary Breakpoints
#'
#' When using veterinary breakpoints (i.e., setting `breakpoint_type = "animal"`), a column with animal species must be available or set manually using the `host` argument. The column must contain names like "dogs", "cats", "cattle", "swine", "horses", "poultry", or "aquatic". Other animal names like "goats", "rabbits", or "monkeys" are also recognised but may not be available in all guidelines. Matching is case-insensitive and accepts Latin-based synonyms (e.g., "bovine" for cattle and "canine" for dogs).
#'
#' Regarding choice of veterinary guidelines, these might be the best options to set before analysis:
#'
#' ```
#' options(AMR_guideline = "CLSI")
#' options(AMR_breakpoint_type = "animal")
#' ```
#'
###### TODO #187 When applying veterinary breakpoints (by setting `host` or by setting `breakpoint_type = "animal"`), the [CLSI VET09 guideline](https://clsi.org/standards/products/veterinary-medicine/documents/vet09/) will be applied to cope with missing animal species-specific breakpoints.
#'
#' ### After Interpretation
#'
#' After using [as.sir()], you can use the [eucast_rules()] defined by EUCAST to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
#'
#' To determine which isolates are multi-drug resistant, be sure to run [mdro()] (which applies the MDR/PDR/XDR guideline from 2012 at default) on a data set that contains S/I/R values. Read more about [interpreting multidrug-resistant organisms here][mdro()].
#'
#' ### Other
#'
#' The function [is.sir()] detects if the input contains class `sir`. If the input is a [data.frame] or [list], it iterates over all columns/items and returns a [logical] vector.
#'
#' The base R function [as.double()] can be used to retrieve quantitative values from a `sir` object: `"S"` = 1, `"I"`/`"SDD"` = 2, `"R"` = 3. All other values are rendered `NA` . **Note:** Do not use `as.integer()`, since that (because of how R works internally) will return the factor level indices, and not these aforementioned quantitative values.
#'
#' The function [is_sir_eligible()] returns `TRUE` when a column contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R and/or NI and/or SDD), and `FALSE` otherwise. The threshold of 5% can be set with the `threshold` argument. If the input is a [data.frame], it iterates over all columns and returns a [logical] vector.
#' @section Interpretation of SIR:
#' In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (<https://www.eucast.org/newsiandr>).
#'
#' This AMR package follows insight; use [susceptibility()] (equal to [proportion_SI()]) to determine antimicrobial susceptibility and [count_susceptible()] (equal to [count_SI()]) to count susceptible isolates.
#' @return Ordered [factor] with new class `sir`
#' @aliases sir
#' @export
#' @seealso [as.mic()], [as.disk()], [as.mo()]
#' @source
#' For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:
#'
#' - **CLSI M39: Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' - **CLSI M100: Performance Standard for Antimicrobial Susceptibility Testing**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type != "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type != "animal")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/microbiology/documents/m100/>.
#' - **CLSI VET01: Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/veterinary-medicine/documents/vet01/>.
###### TODO - **CLSI VET09: Understanding Susceptibility Test Data as a Component of Antimicrobial Stewardship in Veterinary Settings**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/veterinary-medicine/documents/vet09/>.
#' - **EUCAST Breakpoint tables for interpretation of MICs and zone diameters**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`, *European Committee on Antimicrobial Susceptibility Testing* (EUCAST). <https://www.eucast.org/clinical_breakpoints>.
#' - **WHONET** as a source for machine-reading the clinical breakpoints ([read more here](https://amr-for-r.org/reference/clinical_breakpoints.html#imported-from-whonet)), 1989-`r max(as.integer(gsub("[^0-9]", "", AMR::clinical_breakpoints$guideline)))`, *WHO Collaborating Centre for Surveillance of Antimicrobial Resistance*. <https://whonet.org/>.
#'
#' @inheritSection AMR Download Our Reference Data
#' @examples
#' example_isolates
#'
#' summary(example_isolates[, 1:10]) # see all SIR results at a glance
#'
#' # create some example data sets, with combined MIC values and disk zones
#' df_wide <- data.frame(
#' microorganism = "Escherichia coli",
#' amoxicillin = as.mic(8),
#' cipro = as.mic(0.256),
#' tobra = as.disk(16),
#' genta = as.disk(18),
#' ERY = "R"
#' )
#' df_long <- data.frame(
#' bacteria = rep("Escherichia coli", 4),
#' antibiotic = c("amoxicillin", "cipro", "tobra", "genta"),
#' mics = as.mic(c(0.01, 1, 4, 8)),
#' disks = as.disk(c(6, 10, 14, 18)),
#' guideline = c("EUCAST 2021", "EUCAST 2022", "EUCAST 2023", "EUCAST 2024")
#' )
#' # and clean previous SIR interpretation logs
#' x <- sir_interpretation_history(clean = TRUE)
#'
#'
#' # For INTERPRETING disk diffusion and MIC values -----------------------
#'
#' # most basic application:
#' as.sir(df_wide)
#'
#' # return a 'logbook' about the results:
#' sir_interpretation_history()
#'
#' \donttest{
#' # using parallel computing, which is available in base R:
#' as.sir(df_wide, parallel = TRUE, info = TRUE)
#'
#'
#' ## Using dplyr -------------------------------------------------
#' if (require("dplyr")) {
#' # approaches that all work without additional arguments:
#' df_wide %>% mutate_if(is.mic, as.sir)
#' df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
#' df_wide %>% mutate(across(where(is.mic), as.sir))
#' df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)
#' df_wide %>% mutate(across(amoxicillin:tobra, as.sir))
#'
#' # approaches that all work with additional arguments:
#' df_long %>%
#' # given a certain data type, e.g. MIC values
#' mutate_if(is.mic, as.sir,
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "guideline"
#' )
#' df_long %>%
#' mutate(across(
#' where(is.mic),
#' function(x) {
#' as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "CLSI"
#' )
#' }
#' ))
#' df_wide %>%
#' # given certain columns, e.g. from 'cipro' to 'genta'
#' mutate_at(vars(cipro:genta), as.sir,
#' mo = "bacteria",
#' guideline = "CLSI"
#' )
#' df_wide %>%
#' mutate(across(
#' cipro:genta,
#' function(x) {
#' as.sir(x,
#' mo = "bacteria",
#' guideline = "CLSI"
#' )
#' }
#' ))
#'
#' # for veterinary breakpoints, add 'host':
#' df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
#' df_long %>%
#' # given a certain data type, e.g. MIC values
#' mutate_if(is.mic, as.sir,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI"
#' )
#' df_long %>%
#' mutate(across(
#' where(is.mic),
#' function(x) {
#' as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI"
#' )
#' }
#' ))
#' df_wide %>%
#' mutate_at(vars(cipro:genta), as.sir,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI"
#' )
#' df_wide %>%
#' mutate(across(
#' cipro:genta,
#' function(x) {
#' as.sir(x,
#' mo = "bacteria",
#' host = "animal_species",
#' guideline = "CLSI"
#' )
#' }
#' ))
#'
#' # to include information about urinary tract infections (UTI)
#' data.frame(
#' mo = "E. coli",
#' nitrofuratoin = c("<= 2", 32),
#' from_the_bladder = c(TRUE, FALSE)
#' ) %>%
#' as.sir(uti = "from_the_bladder")
#'
#' data.frame(
#' mo = "E. coli",
#' nitrofuratoin = c("<= 2", 32),
#' specimen = c("urine", "blood")
#' ) %>%
#' as.sir() # automatically determines urine isolates
#'
#' df_wide %>%
#' mutate_at(vars(cipro:genta), as.sir, mo = "E. coli", uti = TRUE)
#' }
#'
#'
#' ## Using base R ------------------------------------------------
#'
#'
#' # for single values
#' as.sir(
#' x = as.mic(2),
#' mo = as.mo("S. pneumoniae"),
#' ab = "AMP",
#' guideline = "EUCAST"
#' )
#'
#' as.sir(
#' x = as.disk(18),
#' mo = "Strep pneu", # `mo` will be coerced with as.mo()
#' ab = "ampicillin", # and `ab` with as.ab()
#' guideline = "EUCAST"
#' )
#'
#'
#' # For CLEANING existing SIR values -------------------------------------
#'
#' as.sir(c("S", "SDD", "I", "R", "NI", "A", "B", "C"))
#' as.sir("<= 0.002; S") # will return "S"
#' sir_data <- as.sir(c(rep("S", 474), rep("I", 36), rep("R", 370)))
#' is.sir(sir_data)
#' plot(sir_data) # for percentages
#' barplot(sir_data) # for frequencies
#'
#' # as common in R, you can use as.integer() to return factor indices:
#' as.integer(as.sir(c("S", "SDD", "I", "R", "NI", NA)))
#'
#' # but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R:
#' as.double(as.sir(c("S", "SDD", "I", "R", "NI", NA)))
#'
#' # the dplyr way
#' if (require("dplyr")) {
#' example_isolates %>%
#' mutate_at(vars(PEN:RIF), as.sir)
#' # same:
#' example_isolates %>%
#' as.sir(PEN:RIF)
#'
#' # fastest way to transform all columns with already valid AMR results to class `sir`:
#' example_isolates %>%
#' mutate_if(is_sir_eligible, as.sir)
#'
#' # since dplyr 1.0.0, this can also be the more impractical:
#' # example_isolates %>%
#' # mutate(across(where(is_sir_eligible), as.sir))
#' }
#' }
as.sir <- function(x, ...) {
UseMethod("as.sir")
}
as_sir_structure <- function(x,
guideline = NULL,
mo = NULL,
ab = NULL,
method = NULL,
ref_tbl = NULL,
ref_breakpoints = NULL) {
out <- structure(
factor(as.character(unlist(unname(x))),
levels = c("S", "SDD", "I", "R", "NI"),
ordered = TRUE
),
# TODO for #170
# guideline = guideline,
# mo = mo,
# ab = ab,
# method = method,
# ref_tbl = ref_tbl,
# ref_breakpoints = ref_breakpoints,
class = c("sir", "ordered", "factor")
)
}
#' @rdname as.sir
#' @details `NA_sir_` is a missing value of the new `sir` class, analogous to e.g. base \R's [`NA_character_`][base::NA].
#' @format NULL
#' @export
NA_sir_ <- as_sir_structure(NA_character_)
#' @rdname as.sir
#' @export
is.sir <- function(x) {
if (identical(typeof(x), "list")) {
unname(vapply(FUN.VALUE = logical(1), x, is.sir))
} else {
isTRUE(inherits(x, "sir"))
}
}
#' @rdname as.sir
#' @export
is_sir_eligible <- function(x, threshold = 0.05) {
meet_criteria(threshold, allow_class = "numeric", has_length = 1)
if (identical(typeof(x), "list")) {
# iterate this function over all columns
return(unname(vapply(FUN.VALUE = logical(1), x, is_sir_eligible)))
}
stop_if(NCOL(x) > 1, "`x` must be a one-dimensional vector.")
if (any(c(
"numeric",
"integer",
"mo",
"ab",
"Date",
"POSIXt",
"raw",
"hms",
"mic",
"disk"
)
%in% class(x))) {
# no transformation needed
return(FALSE)
} else if (all(x %in% c("S", "SDD", "I", "R", "NI", NA)) & !all(is.na(x))) {
return(TRUE)
} else if (!any(c("S", "SDD", "I", "R", "NI") %in% x, na.rm = TRUE) & !all(is.na(x))) {
return(FALSE)
} else {
x <- x[!is.na(x) & !is.null(x) & !x %in% c("", "-", "NULL")]
if (length(x) == 0) {
# no other values than empty
cur_col <- get_current_column()
if (!is.null(cur_col)) {
ab <- suppressWarnings(as.ab(cur_col, fast_mode = TRUE, info = FALSE))
if (!is.na(ab)) {
# this is a valid antibiotic drug code
message_(
"Column '", font_bold(cur_col), "' is SIR eligible (despite only having empty values), since it seems to be ",
ab_name(ab, language = NULL, tolower = TRUE), " (", ab, ")"
)
return(TRUE)
}
}
# all values empty and no antibiotic col name - return FALSE
return(FALSE)
}
# transform all values and see if it meets the set threshold
checked <- suppressWarnings(as.sir(x))
outcome <- sum(is.na(checked)) / length(x)
outcome <= threshold
}
}
#' @rdname as.sir
#' @export
#' @param S,I,R,NI,SDD A case-independent [regular expression][base::regex] to translate input to this result. This regular expression will be run *after* all non-letters and whitespaces are removed from the input.
#' @param info A [logical] to print information about the process, defaults to `TRUE` only in [interactive sessions][base::interactive()].
# extra param: warn (logical, to never throw a warning)
as.sir.default <- function(x,
S = "^(S|U)+$",
I = "^(I)+$",
R = "^(R)+$",
NI = "^(N|NI|V)+$",
SDD = "^(SDD|D|H)+$",
info = interactive(),
...) {
meet_criteria(S, allow_class = "character", has_length = 1)
meet_criteria(I, allow_class = "character", has_length = 1)
meet_criteria(R, allow_class = "character", has_length = 1)
meet_criteria(NI, allow_class = "character", has_length = 1)
meet_criteria(SDD, allow_class = "character", has_length = 1)
meet_criteria(info, allow_class = "logical", has_length = 1)
if (inherits(x, "sir")) {
return(as_sir_structure(x))
}
x.bak <- x
x <- as.character(x) # this is needed to prevent the vctrs pkg from throwing an error
if (inherits(x.bak, c("numeric", "integer")) && all(x %in% c(1:3, NA))) {
# support haven package for importing e.g., from SPSS - it adds the 'labels' attribute
lbls <- attributes(x.bak)$labels
if (!is.null(lbls) && all(c("S", "I", "R") %in% names(lbls)) && all(c(1:3) %in% lbls)) {
x[x.bak == 1] <- names(lbls[lbls == 1])
x[x.bak == 2] <- names(lbls[lbls == 2])
x[x.bak == 3] <- names(lbls[lbls == 3])
} else {
x[x.bak == 1] <- "S"
x[x.bak == 2] <- "I"
x[x.bak == 3] <- "R"
}
} else if (inherits(x.bak, "character") && all(x %in% c("1", "2", "3", "S", "I", "R", NA_character_))) {
x[x.bak == "1"] <- "S"
x[x.bak == "2"] <- "I"
x[x.bak == "3"] <- "R"
} else if (inherits(x.bak, "character") && all(x %in% c("1", "2", "3", "4", "5", "S", "SDD", "I", "R", "NI", NA_character_))) {
x[x.bak == "1"] <- "S"
x[x.bak == "2"] <- "SDD"
x[x.bak == "3"] <- "I"
x[x.bak == "4"] <- "R"
x[x.bak == "5"] <- "NI"
} else if (!all(is.na(x)) && !identical(levels(x), c("S", "SDD", "I", "R", "NI")) && !all(x %in% c("S", "SDD", "I", "R", "NI", NA))) {
if (all(x %unlike% "(S|I|R)", na.rm = TRUE)) {
# check if they are actually MICs or disks
if (all_valid_mics(x)) {
warning_("in `as.sir()`: input values were guessed to be MIC values - preferably transform them with `as.mic()` before running `as.sir()`.")
return(as.sir(as.mic(x), ...))
} else if (all_valid_disks(x)) {
warning_("in `as.sir()`: input values were guessed to be disk diffusion values - preferably transform them with `as.disk()` before running `as.sir()`.")
return(as.sir(as.disk(x), ...))
}
}
# trim leading and trailing spaces, new lines, etc.
x <- trimws2(as.character(unlist(x)))
x[x %in% c(NA, "", "-", "NULL")] <- NA_character_
x.bak <- x
na_before <- length(x[is.na(x)])
# correct for translations
trans_R <- unlist(TRANSLATIONS[
which(TRANSLATIONS$pattern == "Resistant"),
LANGUAGES_SUPPORTED[LANGUAGES_SUPPORTED %in% colnames(TRANSLATIONS)]
])
trans_S <- unlist(TRANSLATIONS[
which(TRANSLATIONS$pattern == "Susceptible"),
LANGUAGES_SUPPORTED[LANGUAGES_SUPPORTED %in% colnames(TRANSLATIONS)]
])
trans_I <- unlist(TRANSLATIONS[
which(TRANSLATIONS$pattern %in% c("Incr. exposure", "Susceptible, incr. exp.", "Intermediate")),
LANGUAGES_SUPPORTED[LANGUAGES_SUPPORTED %in% colnames(TRANSLATIONS)]
])
x <- gsub(paste0(unique(trans_R[!is.na(trans_R)]), collapse = "|"), "R", x, ignore.case = TRUE)
x <- gsub(paste0(unique(trans_S[!is.na(trans_S)]), collapse = "|"), "S", x, ignore.case = TRUE)
x <- gsub(paste0(unique(trans_I[!is.na(trans_I)]), collapse = "|"), "I", x, ignore.case = TRUE)
# replace all English textual input
x[x %like% "([^a-z]|^)res(is(tant)?)?"] <- "R"
x[x %like% "([^a-z]|^)sus(cep(tible)?)?"] <- "S"
x[x %like% "not|non"] <- "NI"
x[x %like% "([^a-z]|^)int(er(mediate)?)?|incr.*exp"] <- "I"
x[x %like% "dose"] <- "SDD"
x <- gsub("[^A-Z]+", "", x, perl = TRUE)
# apply regexes set by user
x[x %like% S] <- "S"
x[x %like% I] <- "I"
x[x %like% R] <- "R"
x[x %like% NI] <- "NI"
x[x %like% SDD] <- "SDD"
x[!x %in% c("S", "SDD", "I", "R", "NI")] <- NA_character_
na_after <- length(x[is.na(x) | x == ""])
if (!isFALSE(list(...)$warn)) { # so as.sir(..., warn = FALSE) will never throw a warning
if (na_before != na_after) {
list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ""] %pm>%
unique() %pm>%
sort() %pm>%
vector_and(quotes = TRUE)
cur_col <- get_current_column()
warning_("in `as.sir()`: ", na_after - na_before, " result",
ifelse(na_after - na_before > 1, "s", ""),
ifelse(is.null(cur_col), "", paste0(" in index '", cur_col, "'")),
" truncated (",
round(((na_after - na_before) / length(x)) * 100),
"%) that were invalid antimicrobial interpretations: ",
list_missing,
call = FALSE
)
}
}
}
as_sir_structure(x)
}
#' @rdname as.sir
#' @export
as.sir.mic <- function(x,
mo = NULL,
ab = deparse(substitute(x)),
guideline = getOption("AMR_guideline", "EUCAST"),
uti = NULL,
capped_mic_handling = getOption("AMR_capped_mic_handling", "standard"),
add_intrinsic_resistance = FALSE,
reference_data = AMR::clinical_breakpoints,
substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint", FALSE),
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
host = NULL,
verbose = FALSE,
info = interactive(),
conserve_capped_values = NULL,
...) {
as_sir_method(
method_short = "mic",
method_long = "MIC values",
x = x,
mo = mo,
ab = ab,
guideline = guideline,
uti = uti,
capped_mic_handling = capped_mic_handling,
add_intrinsic_resistance = add_intrinsic_resistance,
reference_data = reference_data,
substitute_missing_r_breakpoint = substitute_missing_r_breakpoint,
include_screening = include_screening,
include_PKPD = include_PKPD,
breakpoint_type = breakpoint_type,
host = host,
verbose = verbose,
info = info,
conserve_capped_values = conserve_capped_values,
...
)
}
#' @rdname as.sir
#' @export
as.sir.disk <- function(x,
mo = NULL,
ab = deparse(substitute(x)),
guideline = getOption("AMR_guideline", "EUCAST"),
uti = NULL,
add_intrinsic_resistance = FALSE,
reference_data = AMR::clinical_breakpoints,
substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint", FALSE),
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
host = NULL,
verbose = FALSE,
info = interactive(),
...) {
as_sir_method(
method_short = "disk",
method_long = "disk diffusion zones",
x = x,
mo = mo,
ab = ab,
guideline = guideline,
uti = uti,
capped_mic_handling = "standard", # will be ignored for non-MIC anyway
add_intrinsic_resistance = add_intrinsic_resistance,
reference_data = reference_data,
substitute_missing_r_breakpoint = substitute_missing_r_breakpoint,
include_screening = include_screening,
include_PKPD = include_PKPD,
breakpoint_type = breakpoint_type,
host = host,
verbose = verbose,
info = info,
...
)
}
#' @rdname as.sir
#' @param parallel A [logical] to indicate if parallel computing must be used, defaults to `FALSE`. This requires no additional packages, as the used `parallel` package is part of base \R. On Windows and on \R < 4.0.0 [parallel::parLapply()] will be used, in all other cases the more efficient [parallel::mclapply()] will be used.
#' @param max_cores Maximum number of cores to use if `parallel = TRUE`. Use a negative value to subtract that number from the available number of cores, e.g. a value of `-2` on an 8-core machine means that at most 6 cores will be used. Defaults to `-1`. There will never be used more cores than variables to analyse. The available number of cores are detected using [parallelly::availableCores()] if that package is installed, and base \R's [parallel::detectCores()] otherwise.
#' @export
as.sir.data.frame <- function(x,
...,
col_mo = NULL,
guideline = getOption("AMR_guideline", "EUCAST"),
uti = NULL,
capped_mic_handling = getOption("AMR_capped_mic_handling", "standard"),
add_intrinsic_resistance = FALSE,
reference_data = AMR::clinical_breakpoints,
substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint", FALSE),
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
host = NULL,
verbose = FALSE,
info = interactive(),
parallel = FALSE,
max_cores = -1,
conserve_capped_values = NULL) {
meet_criteria(x, allow_class = "data.frame") # will also check for dimensions > 0
meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character")
meet_criteria(uti, allow_class = c("logical", "character"), allow_NULL = TRUE, allow_NA = TRUE)
meet_criteria(capped_mic_handling, allow_class = "character", has_length = 1, is_in = c("standard", "conservative", "none", "inverse"))
meet_criteria(add_intrinsic_resistance, allow_class = "logical", has_length = 1)
meet_criteria(reference_data, allow_class = "data.frame")
meet_criteria(substitute_missing_r_breakpoint, allow_class = "logical", has_length = 1)
meet_criteria(include_screening, allow_class = "logical", has_length = 1)
meet_criteria(include_PKPD, allow_class = "logical", has_length = 1)
meet_criteria(breakpoint_type, allow_class = "character", is_in = reference_data$type, has_length = 1)
meet_criteria(host, allow_class = c("character", "factor"), allow_NULL = TRUE, allow_NA = TRUE)
meet_criteria(verbose, allow_class = "logical", has_length = 1)
meet_criteria(info, allow_class = "logical", has_length = 1)
meet_criteria(parallel, allow_class = "logical", has_length = 1)
meet_criteria(max_cores, allow_class = c("numeric", "integer"), has_length = 1)
x.bak <- x
for (i in seq_len(ncol(x))) {
# don't keep factors, overwriting them is hard
if (is.factor(x[, i, drop = TRUE])) {
x[, i] <- as.character(x[, i, drop = TRUE])
}
}
# -- MO
col_mo.bak <- col_mo
if (is.null(col_mo)) {
col_mo <- search_type_in_df(x = x, type = "mo", info = info)
}
# -- host
if (missing(breakpoint_type) && any(host %in% clinical_breakpoints$host[!clinical_breakpoints$host %in% c("human", "ECOFF")], na.rm = TRUE)) {
if (isTRUE(info)) message_("Assuming `breakpoint_type = \"animal\"` since `host` contains animal species.")
breakpoint_type <- "animal"
} else if (any(!suppressMessages(convert_host(host)) %in% c("human", "ECOFF"), na.rm = TRUE)) {
if (isTRUE(info)) message_("Assuming `breakpoint_type = \"animal\"`.")
breakpoint_type <- "animal"
}
if (breakpoint_type == "animal") {
if (is.null(host)) {
host <- search_type_in_df(x = x, type = "host", add_col_prefix = FALSE, info = info)
} else if (length(host) == 1 && as.character(host) %in% colnames(x)) {
host <- x[[as.character(host)]]
}
} else {
host <- breakpoint_type
}
# -- UTIs
col_uti <- uti
if (is.null(col_uti)) {
col_uti <- search_type_in_df(x = x, type = "uti", add_col_prefix = FALSE, info = info)
}
if (!is.null(col_uti)) {
if (is.logical(col_uti)) {
# already a logical vector as input
if (length(col_uti) == 1) {
uti <- rep(col_uti, NROW(x))
} else {
uti <- col_uti
}
} else {
# column found, transform to logical
stop_if(
length(col_uti) != 1 | !col_uti %in% colnames(x),
"argument `uti` must be a [logical] vector, of must be a single column name of `x`"
)
uti <- as.logical(x[, col_uti, drop = TRUE])
}
} else {
# col_uti is still NULL - look for specimen column and make logicals of the urines
col_specimen <- suppressMessages(search_type_in_df(x = x, type = "specimen", info = info))
if (!is.null(col_specimen)) {
uti <- x[, col_specimen, drop = TRUE] %like% "urin"
values <- sort(unique(x[uti, col_specimen, drop = TRUE]))
if (length(values) > 1) {
plural <- c("s", "", "")
} else {
plural <- c("", "s", "a ")
}
if (isTRUE(info)) {
message_(
"Assuming value", plural[1], " ",
vector_and(values, quotes = TRUE),
" in column '", font_bold(col_specimen),
"' reflect", plural[2], " ", plural[3], "urinary tract infection", plural[1],
".\n Use `as.sir(uti = FALSE)` to prevent this."
)
}
} else {
# no data about UTI's found
uti <- NULL
}
}
i <- 0
if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
sel <- colnames(pm_select(x, ...))
} else {
sel <- colnames(x)
}
if (!is.null(col_mo)) {
sel <- sel[sel != col_mo]
}
ab_cols <- colnames(x)[vapply(FUN.VALUE = logical(1), x, function(y) {
i <<- i + 1
check <- is.mic(y) | is.disk(y)
ab <- colnames(x)[i]
if (!is.null(col_mo) && ab == col_mo) {
return(FALSE)
}
if (!is.null(col_uti) && ab == col_uti) {
return(FALSE)
}
if (length(sel) == 0 || (length(sel) > 0 && ab %in% sel)) {
ab_coerced <- suppressWarnings(as.ab(ab, info = info))
if (is.na(ab_coerced) || (length(sel) > 0 & !ab %in% sel)) {
# not even a valid AB code
return(FALSE)
} else {
return(TRUE)
}
} else {
return(FALSE)
}
})]
stop_if(
length(ab_cols) == 0,
"no columns with MIC values, disk zones or antibiotic column names found in this data set. Use as.mic() or as.disk() to transform antimicrobial columns."
)
# set type per column
types <- character(length(ab_cols))
types[vapply(FUN.VALUE = logical(1), x.bak[, ab_cols, drop = FALSE], is.disk)] <- "disk"
types[vapply(FUN.VALUE = logical(1), x.bak[, ab_cols, drop = FALSE], is.mic)] <- "mic"
types[types == "" & vapply(FUN.VALUE = logical(1), x[, ab_cols, drop = FALSE], all_valid_disks)] <- "disk"
types[types == "" & vapply(FUN.VALUE = logical(1), x[, ab_cols, drop = FALSE], all_valid_mics)] <- "mic"
types[types == "" & !vapply(FUN.VALUE = logical(1), x.bak[, ab_cols, drop = FALSE], is.sir)] <- "sir"
if (any(types %in% c("mic", "disk"), na.rm = TRUE)) {
# now we need an mo column
stop_if(is.null(col_mo), "`col_mo` must be set")
# if not null, we already found it, now find again so a message will show
if (is.null(col_mo.bak)) {
col_mo <- search_type_in_df(x = x, type = "mo", info = info)
}
x_mo <- as.mo(x[, col_mo, drop = TRUE], info = info)
}
# set up parallel computing
n_cores <- get_n_cores(max_cores = max_cores)
n_cores <- min(n_cores, length(ab_cols)) # never more cores than variables required
if (isTRUE(parallel) && (.Platform$OS.type == "windows" || getRversion() < "4.0.0")) {
cl <- tryCatch(parallel::makeCluster(n_cores, type = "PSOCK"),
error = function(e) {
if (isTRUE(info)) {
message_("Could not create parallel cluster, using single-core computation. Error message: ", e$message, add_fn = font_red)
}
return(NULL)
}
)
if (is.null(cl)) {
n_cores <- 1
}
}
run_as_sir_column <- function(i) {
ab_col <- ab_cols[i]
out <- list(result = NULL, log = NULL)
if (types[i] == "mic") {
result <- x %pm>%
pm_pull(ab_col) %pm>%
as.character() %pm>%
as.mic() %pm>%
as.sir(
mo = x_mo,
mo.bak = x[, col_mo, drop = TRUE],
ab = ab_col,
guideline = guideline,
uti = uti,
capped_mic_handling = capped_mic_handling,
add_intrinsic_resistance = add_intrinsic_resistance,
reference_data = reference_data,
substitute_missing_r_breakpoint = substitute_missing_r_breakpoint,
include_screening = include_screening,
include_PKPD = include_PKPD,
breakpoint_type = breakpoint_type,
host = host,
verbose = verbose,
info = info,
conserve_capped_values = conserve_capped_values,
is_data.frame = TRUE
)
out$result <- result
out$log <- AMR_env$sir_interpretation_history
AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE] # reset log
return(out)
} else if (types[i] == "disk") {
result <- x %pm>%
pm_pull(ab_col) %pm>%
as.character() %pm>%
as.disk() %pm>%
as.sir(
mo = x_mo,
mo.bak = x[, col_mo, drop = TRUE],
ab = ab_col,
guideline = guideline,
uti = uti,
add_intrinsic_resistance = add_intrinsic_resistance,
reference_data = reference_data,
substitute_missing_r_breakpoint = substitute_missing_r_breakpoint,
include_screening = include_screening,
include_PKPD = include_PKPD,
breakpoint_type = breakpoint_type,
host = host,
verbose = verbose,
info = info,
is_data.frame = TRUE
)
out$result <- result
out$log <- AMR_env$sir_interpretation_history
AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
return(out)
} else if (types[i] == "sir") {
ab <- ab_col
ab_coerced <- suppressWarnings(as.ab(ab, info = info))
show_message <- FALSE
if (!all(x[, ab, drop = TRUE] %in% c("S", "SDD", "I", "R", "NI", NA), na.rm = TRUE)) {
show_message <- TRUE
if (isTRUE(info)) {
message_("Cleaning values in column '", font_bold(ab), "' (",
ifelse(ab_coerced != toupper(ab), paste0(ab_coerced, ", "), ""),
ab_name(ab_coerced, tolower = TRUE, info = info), ")... ",
appendLF = FALSE,
as_note = FALSE
)
}
} else if (!is.sir(x.bak[, ab, drop = TRUE])) {
show_message <- TRUE
if (isTRUE(info)) {
message_("Assigning class 'sir' to already clean column '", font_bold(ab), "' (",
ifelse(ab_coerced != toupper(ab), paste0(ab_coerced, ", "), ""),
ab_name(ab_coerced, tolower = TRUE, language = NULL, info = info), ")... ",
appendLF = FALSE,
as_note = FALSE
)
}
}
result <- as.sir.default(x = as.character(x[, ab, drop = TRUE]))
if (show_message == TRUE && isTRUE(info)) {
message(font_green_bg(" OK "))
}
out$result <- result
out$log <- NULL
return(out)
}
return(out)
}
if (isTRUE(parallel) && n_cores > 1 && length(ab_cols) > 1) {
if (isTRUE(info)) {
message()
message_("Running in parallel mode using ", n_cores, " out of ", get_n_cores(Inf), " cores, on columns ", vector_and(font_bold(ab_cols, collapse = NULL), quotes = "'", sort = FALSE), "...", as_note = FALSE, appendLF = FALSE)
}
if (.Platform$OS.type == "windows" || getRversion() < "4.0.0") {
# `cl` has been created in the part above before the `run_as_sir_column` function
on.exit(parallel::stopCluster(cl), add = TRUE)
parallel::clusterExport(cl, varlist = c(
"x", "x.bak", "x_mo", "ab_cols", "types",
"capped_mic_handling", "add_intrinsic_resistance",
"reference_data", "substitute_missing_r_breakpoint", "include_screening", "include_PKPD",
"breakpoint_type", "guideline", "host", "uti", "info", "verbose",
"col_mo", "AMR_env", "conserve_capped_values",
"run_as_sir_column"
), envir = environment())
result_list <- parallel::parLapply(cl, seq_along(ab_cols), run_as_sir_column)
} else {
# R>=4.0 on unix
result_list <- parallel::mclapply(seq_along(ab_cols), run_as_sir_column, mc.cores = n_cores)
}
if (isTRUE(info)) {
message_(font_green_bg(" DONE "), as_note = FALSE)
message()
message_("Run `sir_interpretation_history()` to retrieve a logbook with all details of the breakpoint interpretations.", add_fn = font_green)
}
} else {
# sequential mode (non-parallel)
if (isTRUE(info) && n_cores > 1 && NROW(x) * NCOL(x) > 10000) {
# give a note that parallel mode might be better
message()
message_("Running in sequential mode. Consider setting `parallel = TRUE` to speed up processing on multiple cores.\n", add_fn = font_red)
}
# this will contain a progress bar already
result_list <- lapply(seq_along(ab_cols), run_as_sir_column)
}
# bind results back to x
for (i in seq_along(ab_cols)) {
x[, ab_cols[i]] <- result_list[[i]]$result
}
# combine all sir_interpretation_history
sir_logs_all <- lapply(result_list, function(x) x$log)
sir_logs_all <- Filter(Negate(is.null), sir_logs_all) # remove NULLs early
if (length(sir_logs_all) > 0) {
sir_logs_all <- do.call(rbind_AMR, sir_logs_all)
AMR_env$sir_interpretation_history <- rbind_AMR(AMR_env$sir_interpretation_history, sir_logs_all)
}
x
}
get_guideline <- function(guideline, reference_data) {
if (!identical(reference_data, AMR::clinical_breakpoints)) {
return(guideline)
}
guideline_param <- trimws2(toupper(guideline))
latest_clsi <- rev(sort(subset(reference_data, guideline %like% "CLSI")$guideline))[1L]
latest_eucast <- rev(sort(subset(reference_data, guideline %like% "EUCAST")$guideline))[1L]
guideline_param[guideline_param == "CLSI"] <- latest_clsi
guideline_param[guideline_param == "EUCAST"] <- latest_eucast
# like 'EUCAST2020', should be 'EUCAST 2020'
guideline_param[guideline_param %unlike% " "] <- gsub("([a-z]+)([0-9]+)", "\\1 \\2", guideline_param[guideline_param %unlike% " "], ignore.case = TRUE)
stop_ifnot(guideline_param %in% reference_data$guideline,
"invalid guideline: '", guideline,
"'.\nValid guidelines are: ", vector_and(reference_data$guideline, quotes = TRUE, reverse = TRUE),
call = FALSE
)
guideline_param
}
convert_host <- function(x, lang = get_AMR_locale()) {
x <- gsub("[^a-zA-Z ]", "", trimws2(tolower(as.character(x))), perl = TRUE)
x_out <- rep(NA_character_, length(x))
x_out[trimws2(tolower(x)) == "human"] <- "human"
x_out[trimws2(tolower(x)) == "ecoff"] <- "ecoff"
# this order is based on: clinical_breakpoints |> filter(type == "animal") |> count(host, sort = TRUE)
x_out[is.na(x_out) & (x %like% "dog|canine|Canis lupus" | x %like% translate_AMR("dog|dogs|canine", lang))] <- "dogs"
x_out[is.na(x_out) & (x %like% "cattle|bovine|Bos taurus" | x %like% translate_AMR("cattle|bovine", lang))] <- "cattle"
x_out[is.na(x_out) & (x %like% "swine|suida(e)?|Sus scrofa" | x %like% translate_AMR("swine|swines", lang))] <- "swine"
x_out[is.na(x_out) & (x %like% "cat|feline|Felis catus" | x %like% translate_AMR("cat|cats|feline", lang))] <- "cats"
x_out[is.na(x_out) & (x %like% "horse|equine|Equus ferus" | x %like% translate_AMR("horse|horses|equine", lang))] <- "horse"
x_out[is.na(x_out) & (x %like% "aqua|fish|Pisces" | x %like% translate_AMR("aquatic|fish", lang))] <- "aquatic"
x_out[is.na(x_out) & (x %like% "bird|chicken|poultry|avia|Gallus gallus" | x %like% translate_AMR("bird|birds|poultry", lang))] <- "poultry"
# additional animals, not necessarily currently in breakpoint guidelines:
x_out[is.na(x_out) & (x %like% "camel|camelid|Camelus dromedarius" | x %like% translate_AMR("camel|camels|camelid", lang))] <- "camels"
x_out[is.na(x_out) & (x %like% "deer|cervine|Cervidae" | x %like% translate_AMR("deer|deers|cervine", lang))] <- "deer"
x_out[is.na(x_out) & (x %like% "donkey|asinine|Equus africanus" | x %like% translate_AMR("donkey|donkeys|asinine", lang))] <- "donkeys"
x_out[is.na(x_out) & (x %like% "ferret|musteline|Mustela putorius" | x %like% translate_AMR("ferret|ferrets|musteline", lang))] <- "ferrets"
x_out[is.na(x_out) & (x %like% "goat|caprine|Capra aegagrus" | x %like% translate_AMR("goat|goats|caprine", lang))] <- "goats"
x_out[is.na(x_out) & (x %like% "guinea pig|caviine|Cavia porcellus" | x %like% translate_AMR("guinea pig|guinea pigs|caviine", lang))] <- "guinea pigs"
x_out[is.na(x_out) & (x %like% "hamster|cricetine|Cricetinae" | x %like% translate_AMR("hamster|hamsters|cricetine", lang))] <- "hamsters"
x_out[is.na(x_out) & (x %like% "monkey|simian|Simia" | x %like% translate_AMR("monkey|monkeys|simian", lang))] <- "monkeys"
x_out[is.na(x_out) & (x %like% "mouse|murine|Mus musculus" | x %like% translate_AMR("mouse|mice|murine", lang))] <- "mice"
x_out[is.na(x_out) & (x %like% "pig|porcine|Sus scrofa" | x %like% translate_AMR("pig|pigs|porcine", lang))] <- "pigs"
x_out[is.na(x_out) & (x %like% "rabbit|leporine|Oryctolagus cuniculus" | x %like% translate_AMR("rabbit|rabbits|leporine", lang))] <- "rabbits"
x_out[is.na(x_out) & (x %like% "rat|ratine|Rattus" | x %like% translate_AMR("rat|rats|ratine", lang))] <- "rats"
x_out[is.na(x_out) & (x %like% "sheep|ovine|Ovis aries" | x %like% translate_AMR("sheep|sheeps|ovine", lang))] <- "sheep"
x_out[is.na(x_out) & (x %like% "snake|serpentine|Serpentes" | x %like% translate_AMR("snake|snakes|serpentine", lang))] <- "snakes"
x_out[is.na(x_out) & (x %like% "turkey|meleagrine|Meleagris gallopavo" | x %like% translate_AMR("turkey|turkeys|meleagrine", lang))] <- "turkey"
x_out[x_out == "ecoff"] <- "ECOFF"
x_out
}
as_sir_method <- function(method_short,
method_long,
x,
mo,
ab,
guideline,
uti,
capped_mic_handling,
add_intrinsic_resistance,
reference_data,
substitute_missing_r_breakpoint,
include_screening,
include_PKPD,
breakpoint_type,
host,
verbose,
info,
conserve_capped_values = NULL,
...) {
if (isTRUE(conserve_capped_values)) {
deprecation_warning(old = "conserve_capped_values", new = "capped_mic_handling", fn = "as.sir", is_argument = TRUE)
capped_mic_handling <- "conservative"
}
meet_criteria(x, allow_NA = TRUE, .call_depth = -2)
meet_criteria(mo, allow_class = c("mo", "character"), has_length = c(1, length(x)), allow_NULL = TRUE, .call_depth = -2)
meet_criteria(ab, allow_class = c("ab", "character"), has_length = c(1, length(x)), .call_depth = -2)
meet_criteria(guideline, allow_class = "character", has_length = c(1, length(x)), .call_depth = -2)
meet_criteria(uti, allow_class = c("logical", "character"), has_length = c(1, length(x)), allow_NULL = TRUE, allow_NA = TRUE, .call_depth = -2)
meet_criteria(capped_mic_handling, allow_class = "character", has_length = 1, is_in = c("standard", "conservative", "none", "inverse"), .call_depth = -2)
meet_criteria(add_intrinsic_resistance, allow_class = "logical", has_length = 1, .call_depth = -2)
meet_criteria(reference_data, allow_class = "data.frame", .call_depth = -2)
meet_criteria(substitute_missing_r_breakpoint, allow_class = "logical", has_length = 1, .call_depth = -2)
meet_criteria(include_screening, allow_class = "logical", has_length = 1, .call_depth = -2)
meet_criteria(include_PKPD, allow_class = "logical", has_length = 1, .call_depth = -2)
check_reference_data(reference_data, .call_depth = -2)
meet_criteria(breakpoint_type, allow_class = "character", is_in = reference_data$type, has_length = 1, .call_depth = -2)
meet_criteria(host, allow_class = c("character", "factor"), allow_NULL = TRUE, allow_NA = TRUE, .call_depth = -2)
meet_criteria(verbose, allow_class = "logical", has_length = 1, .call_depth = -2)
meet_criteria(info, allow_class = "logical", has_length = 1, .call_depth = -2)
# backward compatibilty
dots <- list(...)
dots <- dots[which(!names(dots) %in% c("warn", "mo.bak", "is_data.frame"))]
if (length(dots) != 0) {
warning_("These arguments in `as.sir()` are no longer used: ", vector_and(names(dots), quotes = "`"), ".", call = FALSE)
}
current_sir_interpretation_history <- NROW(AMR_env$sir_interpretation_history)
if (isTRUE(info) && message_not_thrown_before("as.sir", "sir_interpretation_history")) {
message()
message_("Run `sir_interpretation_history()` afterwards to retrieve a logbook with all details of the breakpoint interpretations.\n\n", add_fn = font_green)
}
current_df <- tryCatch(get_current_data(NA, 0), error = function(e) NULL)
# get guideline
if (!is.null(current_df) && length(guideline) == 1 && guideline %in% colnames(current_df) && any(current_df[[guideline]] %like% "CLSI|EUCAST", na.rm = TRUE)) {
guideline <- current_df[[guideline]]
}
guideline_coerced <- get_guideline(guideline, reference_data)
# get host
if (breakpoint_type == "animal") {
if (is.null(host)) {
host <- "dogs"
if (isTRUE(info) && message_not_thrown_before("as.sir", "host_missing")) {
message_("Animal hosts not set in `host`, assuming `host = \"dogs\"`, since these have the highest breakpoint availability.\n\n")
}
}
} else {
if (!is.null(host) && !all(toupper(as.character(host)) %in% c("HUMAN", "ECOFF"))) {
if (isTRUE(info) && message_not_thrown_before("as.sir", "assumed_breakpoint_animal")) {
message_("Assuming `breakpoint_type = \"animal\"`, since `host` is set.", ifelse(guideline_coerced %like% "EUCAST", " Do you also need to set `guideline = \"CLSI\"`?", ""), "\n\n")
}
breakpoint_type <- "animal"
} else {
host <- breakpoint_type
}
}
if (!is.null(host) && !all(toupper(as.character(host)) %in% c("HUMAN", "ECOFF"))) {
if (!is.null(current_df) && length(host) == 1 && host %in% colnames(current_df) && any(current_df[[host]] %like% "[A-Z]", na.rm = TRUE)) {
host <- current_df[[host]]
} else if (length(host) != length(x)) {
# for dplyr's across()
cur_column_dplyr <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
if (!is.null(cur_column_dplyr) && is.data.frame(current_df)) {
# try to get current column, which will only be available when in across()
host <- tryCatch(cur_column_dplyr(),
error = function(e) host
)
}
}
}
host.bak <- host
host <- convert_host(host)
if (any(is.na(host) & !is.na(host.bak)) && isTRUE(info) && message_not_thrown_before("as.sir", "missing_hosts")) {
warning_("The following animal host(s) could not be coerced: ", vector_and(host.bak[is.na(host) & !is.na(host.bak)]), immediate = TRUE)
message() # new line
}
# TODO add a switch to turn this off? In interactive sessions perhaps ask the user. Default should be On.
# if (breakpoint_type == "animal" && isTRUE(info) && message_not_thrown_before("as.sir", "host_missing_breakpoints")) {
# if (guideline_coerced %like% "CLSI") {
# message_("Please note that in the absence of specific veterinary breakpoints for certain animal hosts, the CLSI guideline VET09 will be applied where possible.\n\n")
# }
# }
# get ab
if (!is.null(current_df) && length(ab) == 1 && ab %in% colnames(current_df) && any(current_df[[ab]] %like% "[A-Z]", na.rm = TRUE)) {
ab <- current_df[[ab]]
} else if (length(ab) != length(x)) {
# for dplyr's across()
cur_column_dplyr <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
if (!is.null(cur_column_dplyr) && is.data.frame(current_df)) {
# try to get current column, which will only be available when in across()
ab <- tryCatch(cur_column_dplyr(),
error = function(e) ab
)
}
}
# get mo
if (!is.null(current_df) && length(mo) == 1 && mo %in% colnames(current_df)) {
mo_var_found <- paste0(" based on column '", font_bold(mo), "'")
mo <- current_df[[mo]]
} else if (length(mo) != length(x)) {
mo_var_found <- ""
if (is.null(mo)) {
tryCatch(
{
df <- get_current_data(arg_name = "mo", call = -3) # will return an error if not found
mo <- NULL
try(
{
mo <- suppressMessages(search_type_in_df(df, "mo", add_col_prefix = FALSE, info = info))
},
silent = TRUE
)
if (!is.null(df) && !is.null(mo) && is.data.frame(df)) {
mo_var_found <- paste0(" based on column '", font_bold(mo), "'")
mo <- df[, mo, drop = TRUE]
}
},
error = function(e) {
mo <- NULL
}
)
}
} else {
mo_var_found <- ""
}
if (is.null(mo)) {
stop_("No information was supplied about the microorganisms (missing argument `mo` and no column of class 'mo' found). See ?as.sir.\n\n",
"To transform certain columns with e.g. mutate(), use `data %>% mutate(across(..., as.sir, mo = x))`, where x is your column with microorganisms.\n",
"To transform all ", method_long, " in a data set, use `data %>% as.sir()` or `data %>% mutate_if(is.", method_short, ", as.sir)`.",
call = FALSE
)
}
# get uti
if (!is.null(current_df) && length(uti) == 1 && uti %in% colnames(current_df)) {
uti <- current_df[[uti]]
} else if (length(uti) != length(x)) {
if (is.null(uti)) {
tryCatch(
{
df <- get_current_data(arg_name = "uti", call = -3) # will return an error if not found
uti <- NULL
try(
{
uti <- suppressMessages(search_type_in_df(df, "uti", add_col_prefix = FALSE, info = info))
},
silent = TRUE
)
if (!is.null(df) && !is.null(uti) && is.data.frame(df)) {
uti <- df[, uti, drop = TRUE]
}
},
error = function(e) {
uti <- NULL
}
)
}
}
# TODO set uti to specimen column here
if (length(ab) == 1 && ab %like% paste0("as.", method_short)) {
stop_("No unambiguous name was supplied about the antibiotic (argument `ab`). See ?as.sir.", call = FALSE)
}
ab.bak <- trimws2(ab)
ab <- suppressWarnings(as.ab(ab, info = info))
if (!is.null(list(...)$mo.bak)) {
mo.bak <- list(...)$mo.bak
} else {
mo.bak <- mo
}
mo.bak <- trimws2(mo.bak)
# be sure to take current taxonomy, as the 'clinical_breakpoints' data set only contains current taxonomy
mo <- suppressWarnings(suppressMessages(as.mo(mo, keep_synonyms = FALSE, info = FALSE)))
if (all(is.na(ab))) {
if (isTRUE(info)) {
message_("Returning NAs for unknown antibiotic: ", vector_and(ab.bak, sort = FALSE, quotes = TRUE),
". Rename this column to a valid name or code, and check the output with `as.ab()`.",
add_fn = font_red,
as_note = FALSE
)
}
return(as.sir(rep(NA, length(x))))
}
if (length(mo) == 1) {
mo <- rep(mo, length(x))
}
if (length(ab) == 1) {
ab <- rep(ab, length(x))
ab.bak <- rep(ab.bak, length(ab))
}
if (length(host) == 1) {
host <- rep(host, length(x))
}
if (is.null(uti)) {
uti <- NA
}
if (length(uti) == 1) {
uti <- rep(uti, length(x))
}
if (isTRUE(add_intrinsic_resistance) && guideline_coerced %unlike% "EUCAST") {
if (isTRUE(info) && message_not_thrown_before("as.sir", "intrinsic")) {
message_("in `as.sir()`: using 'add_intrinsic_resistance' is only useful when using EUCAST guidelines, since the rules for intrinsic resistance are based on EUCAST.",
add_fn = font_red
)
}
}
# format agents ----
agent_formatted <- paste0("'", font_bold(ab.bak, collapse = NULL), "'")
agent_name <- ab_name(ab, tolower = TRUE, language = NULL, info = info)
same_ab <- generalise_antibiotic_name(ab) == generalise_antibiotic_name(agent_name)
same_ab.bak <- generalise_antibiotic_name(ab.bak) == generalise_antibiotic_name(agent_name)
agent_formatted[same_ab.bak] <- paste0(agent_formatted[same_ab.bak], " (", ab[same_ab.bak], ")")
agent_formatted[!same_ab.bak & !same_ab] <- paste0(
agent_formatted[!same_ab.bak & !same_ab],
" (", ifelse(ab.bak[!same_ab.bak & !same_ab] == ab[!same_ab.bak & !same_ab],
"",
paste0(ab[!same_ab.bak & !same_ab], ", ")
),
agent_name[!same_ab.bak & !same_ab],
")"
)
# this intro text will also be printed in the progress bar if the `progress` package is installed
intro_txt <- paste0(
"Interpreting ", method_long, ": ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""),
ifelse(length(unique(agent_formatted)) == 1, unique(agent_formatted), paste0(vector_and(agent_formatted, quotes = FALSE, sort = FALSE))),
mo_var_found,
ifelse(identical(reference_data, AMR::clinical_breakpoints),
paste0(", ", vector_and(font_bold(guideline_coerced, collapse = NULL), quotes = FALSE)),
""
),
"... "
)
# prepare used arguments ----
method <- method_short
metadata_mo <- get_mo_uncertainties()
rise_warning <- FALSE
rise_notes <- FALSE
method_coerced <- toupper(method)
ab_coerced <- as.ab(ab, info = info)
if (identical(reference_data, AMR::clinical_breakpoints)) {
breakpoints <- reference_data %pm>%
subset(guideline %in% guideline_coerced & method == method_coerced & ab %in% ab_coerced)
if (any(ab_coerced == "AMX") && nrow(breakpoints[breakpoints$ab == "AMX", , drop = FALSE]) == 0) {
ab_coerced[ab_coerced == "AMX"] <- "AMP"
breakpoints <- reference_data %pm>%
subset(guideline %in% guideline_coerced & method == method_coerced & ab %in% ab_coerced)
}
} else {
breakpoints <- reference_data %pm>%
subset(method == method_coerced & ab %in% ab_coerced)
}
# create the unique data frame to be filled to save time
df <- data.frame(
values = x,
values_bak = x,
guideline = guideline_coerced,
mo = mo,
ab = ab,
result = NA_sir_,
uti = uti,
host = host,
stringsAsFactors = FALSE
)
if (method == "mic") {
if (any(guideline_coerced %like% "CLSI")) {
# CLSI in log 2 ----
# CLSI says: if MIC is not a log2 value it must be rounded up to the nearest log2 value
log2_levels <- as.double(VALID_MIC_LEVELS[which(VALID_MIC_LEVELS %in% 2^c(-20:20))])
test_values <- df$values
test_values_dbl <- as.double(test_values)
test_values_dbl[test_values %like% "^>[0-9]"] <- test_values_dbl[test_values %like% "^>[0-9]"] + 0.0000001
test_values_dbl[test_values %like% "^<[0-9]"] <- test_values_dbl[test_values %like% "^<[0-9]"] - 0.0000001
test_outcome <- vapply(
FUN.VALUE = double(1),
test_values_dbl,
function(mic_val) {
if (is.na(mic_val)) {
return(NA_real_)
} else {
# find the smallest log2 level that is >= mic_val
log2_val <- log2_levels[which(log2_levels >= as.double(mic_val))][1]
if (!is.na(log2_val) && as.double(mic_val) != log2_val) {
if (message_not_thrown_before("as.sir", "CLSI", "MICupscaling")) {
warning_("Some MICs were converted to the nearest higher log2 level, following the CLSI interpretation guideline.")
}
return(as.double(log2_val)) # will be MIC later
} else {
return(as.double(mic_val))
}
}
}
)
df$values[which(df$guideline %like% "CLSI" & test_values != test_outcome)] <- test_outcome[which(df$guideline %like% "CLSI" & test_values != test_outcome)]
}
df$values <- as.mic(df$values)
} else if (method == "disk") {
# when as.sir.disk is called directly
df$values <- as.disk(df$values)
}
df_unique <- unique(df[, c("guideline", "mo", "ab", "uti", "host"), drop = FALSE])
mo_grams <- suppressWarnings(suppressMessages(mo_gramstain(df_unique$mo, language = NULL, keep_synonyms = FALSE)))
# get all breakpoints, use humans as backup for animals
breakpoint_type_lookup <- breakpoint_type
if (breakpoint_type == "animal") {
breakpoint_type_lookup <- c(breakpoint_type, "human")
}
breakpoints <- breakpoints %pm>%
subset(type %in% breakpoint_type_lookup)
if (isFALSE(include_screening)) {
# remove screening rules from the breakpoints table
breakpoints <- breakpoints %pm>%
subset(site %unlike% "screen" & ref_tbl %unlike% "screen")
}
if (isFALSE(include_PKPD)) {
# remove PKPD rules from the breakpoints table
breakpoints <- breakpoints %pm>%
subset(mo != "UNKNOWN" & ref_tbl %unlike% "PK.*PD")
}
notes <- character(0)
if (any(guideline_coerced %like% "EUCAST")) {
any_is_intrinsic_resistant <- FALSE
add_intrinsic_resistance_to_AMR_env()
}
if (isTRUE(info) && nrow(df_unique) < 10 || nrow(breakpoints) == 0) {
# only print intro under 10 items, otherwise progressbar will print this and then it will be printed double
message_(intro_txt, appendLF = FALSE, as_note = FALSE)
}
p <- progress_ticker(n = nrow(df_unique), n_min = 10, title = font_blue(intro_txt), only_bar_percent = TRUE)
has_progress_bar <- !is.null(import_fn("progress_bar", "progress", error_on_fail = FALSE)) && nrow(df_unique) >= 10
on.exit(close(p))
if (nrow(breakpoints) == 0) {
# apparently no breakpoints found
if (isTRUE(info)) {
message(font_grey_bg(font_black(" NO BREAKPOINTS ")))
}
load_mo_uncertainties(metadata_mo)
return(rep(NA_sir_, nrow(df)))
}
vectorise_log_entry <- function(x, len) {
if (length(x) == 1 && len > 1) {
rep(x, len)
} else {
x
}
}
# run the rules (df_unique is a row combination per mo/ab/uti/host) ----
for (i in seq_len(nrow(df_unique))) {
p$tick()
guideline_current <- df_unique[i, "guideline", drop = TRUE]
mo_current <- df_unique[i, "mo", drop = TRUE]
mo_gram_current <- mo_grams[i]
ab_current <- df_unique[i, "ab", drop = TRUE]
host_current <- df_unique[i, "host", drop = TRUE]
uti_current <- df_unique[i, "uti", drop = TRUE]
notes_current <- character(0)
rows <- which(as.character(df$mo) == mo_current & df$ab == ab_current & df$host == host_current & df$guideline == guideline_current)
if (!is.na(uti_current)) {
# also filter on UTIs
rows <- rows[df$uti[rows] == uti_current]
}
if (length(rows) == 0) {
# this can happen if a host is unavailable, just continue with the next one, since a note about hosts having NA are already given at this point
next
}
values <- df[rows, "values", drop = TRUE]
values_bak <- df[rows, "values_bak", drop = TRUE]
notes_current <- rep("", length(rows))
new_sir <- rep(NA_sir_, length(rows))
# find different mo properties, as fast as possible
# TODO in case of VET09, we need to keep E. coli, also when users have Proteus in their data set
# TODO look up which species, at least E. coli - also Staph or Strep?
mo_current_genus <- AMR_env$MO_lookup$mo[match(AMR_env$MO_lookup$genus[match(mo_current, AMR_env$MO_lookup$mo)], AMR_env$MO_lookup$fullname)]
mo_current_family <- AMR_env$MO_lookup$mo[match(AMR_env$MO_lookup$family[match(mo_current, AMR_env$MO_lookup$mo)], AMR_env$MO_lookup$fullname)]
mo_current_order <- AMR_env$MO_lookup$mo[match(AMR_env$MO_lookup$order[match(mo_current, AMR_env$MO_lookup$mo)], AMR_env$MO_lookup$fullname)]
mo_current_class <- AMR_env$MO_lookup$mo[match(AMR_env$MO_lookup$class[match(mo_current, AMR_env$MO_lookup$mo)], AMR_env$MO_lookup$fullname)]
mo_current_rank <- AMR_env$MO_lookup$rank[match(mo_current, AMR_env$MO_lookup$mo)]
mo_current_name <- AMR_env$MO_lookup$fullname[match(mo_current, AMR_env$MO_lookup$mo)]
mo_current_oxygen_tolerance <- AMR_env$MO_lookup$oxygen_tolerance[match(mo_current, AMR_env$MO_lookup$mo)]
if (mo_current %in% AMR::microorganisms.groups$mo) {
# get the species group (might be more than 1 entry)
mo_current_species_group <- AMR::microorganisms.groups$mo_group[which(AMR::microorganisms.groups$mo == mo_current)]
} else {
mo_current_species_group <- NULL
}
mo_current_gram <- structure(character(0), class = c("mo", "character"))
if (identical(mo_gram_current, "Gram-negative")) {
mo_current_gram <- c(mo_current_gram, "B_GRAMN")
if (identical(mo_current_oxygen_tolerance, "anaerobe")) {
mo_current_gram <- c(mo_current_gram, "B_ANAER", "B_ANAER-NEG")
}
} else if (identical(mo_gram_current, "Gram-positive")) {
mo_current_gram <- c(mo_current_gram, "B_GRAMP")
if (identical(mo_current_oxygen_tolerance, "anaerobe")) {
mo_current_gram <- c(mo_current_gram, "B_ANAER", "B_ANAER-POS")
}
}
mo_current_other <- structure("UNKNOWN", class = c("mo", "character"))
# formatted for notes
mo_formatted <- mo_current_name
if (!mo_current_rank %in% c("kingdom", "phylum", "class", "order")) {
mo_formatted <- font_italic(mo_formatted, collapse = NULL)
}
ab_formatted <- paste0(
suppressMessages(suppressWarnings(ab_name(ab_current, language = NULL, tolower = TRUE, info = info))),
" (", ab_current, ")"
)
# gather all available breakpoints for current MO
# TODO for VET09 do not filter out E. coli and such
breakpoints_current <- breakpoints %pm>%
subset(ab == ab_current & guideline == guideline_current) %pm>%
subset(mo %in% c(
mo_current, mo_current_genus, mo_current_family,
mo_current_order, mo_current_class,
mo_current_species_group,
mo_current_gram,
mo_current_other
))
if (breakpoint_type == "animal") {
# 2025-03-13 for now, only strictly follow guideline for current host, no extrapolation
breakpoints_current <- breakpoints_current[which(breakpoints_current$host == host_current), , drop = FALSE]
}
## fall-back methods for veterinary guidelines ----
## TODO actually implement this well
if (FALSE) {
# if (breakpoint_type == "animal" && !host_current %in% breakpoints_current$host) {
if (guideline_coerced %like% "CLSI") {
# VET09 says that staph/strep/enterococcus BP can be extrapolated to all Gr+ cocci except for intrinsic resistance, so take all Gr+ cocci:
gram_plus_cocci_vet09 <- microorganisms$mo[microorganisms$genus %in% c("Staphylococcus", "Streptococcus", "Peptostreptococcus", "Aerococcus", "Micrococcus") & microorganisms$rank == "genus"] # TODO should probably include genera that were either of these before
# HUMAN SUBSTITUTES
if (ab_current == "AZM" && mo_current_genus %in% gram_plus_cocci_vet09 && host_current %in% c("dogs", "cats", "horse")) {
# azithro can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " in Gram-positive cocci based on CLSI VET09."))
} else if (ab_current == "CTX" && mo_current_order == "B_[ORD]_ENTRBCTR" && host_current %in% c("dogs", "cats", "horse")) {
# cefotax can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " in Enterobacterales based on CLSI VET09."))
} else if (ab_current == "CAZ" && (mo_current_order == "B_[ORD]_ENTRBCTR" | mo_current == "B_PSDMN_AERG") && host_current %in% c("dogs", "cats", "horse")) {
# cefta can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " in Enterobacterales and ", font_italic("P. aeruginosa"), " based on CLSI VET09."))
} else if (ab_current == "ERY" && mo_current_genus %in% gram_plus_cocci_vet09 && host_current %in% c("dogs", "cats", "horse")) {
# erythro can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " in Gram-positive cocci based on CLSI VET09."))
} else if (ab_current == "IPM" && (mo_current_order == "B_[ORD]_ENTRBCTR" | mo_current == "B_PSDMN_AERG") && host_current %in% c("dogs", "cats", "horse")) {
# imipenem can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " in Enterobacterales and ", font_italic("P. aeruginosa"), " based on CLSI VET09."))
} else if (ab_current == "LNZ" && mo_current_genus %in% gram_plus_cocci_vet09 && host_current %in% c("dogs", "cats")) {
# linezolid can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " in staphylococci/enterococci based on CLSI VET09."))
} else if (ab_current == "NIT" && host_current %in% c("dogs", "cats")) {
# nitro can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09."))
} else if (ab_current == "PEN" && mo_current_genus %in% gram_plus_cocci_vet09 && host_current %in% c("dogs", "cats")) {
# penicillin can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " in Gram-positive cocci based on CLSI VET09."))
} else if (ab_current == "RIF" && mo_current_genus %in% gram_plus_cocci_vet09 && host_current %in% c("dogs", "cats")) {
# rifampicin can take human breakpoints for staphylococci
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " in staphylococci based on CLSI VET09."))
} else if (ab_current == "SXT" && host_current %in% c("dogs", "cats", "horse")) {
# trimethoprim-sulfamethoxazole (TMS) can take human breakpoints for these agents
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09."))
} else if (ab_current == "VAN" && host_current %in% c("dogs", "cats", "horse")) {
# vancomycin can take human breakpoints in these hosts
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09."))
} else if (host_current %in% c("dogs", "cats") && (mo_current_genus %in% c("B_AMYCS", "B_NOCRD", "B_CMPYL", "B_CRYNB", "B_ENTRC", "B_MYCBC", "B_PSDMN", "B_AERMN") | mo_current_class == "B_[CLS]_BTPRTBCT" | mo_current == "B_LISTR_MNCY")) {
# dog breakpoints if no canine/feline
# TODO do we still have dogs breakpoints at this point???
breakpoints_current <- breakpoints_current %pm>% subset(host == "human") # WRONG
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", mo_formatted, " based on CLSI VET09."))
} else {
# no specific CLSI solution for this, so only filter on current host (if no breakpoints available -> too bad)
breakpoints_current <- breakpoints_current %pm>%
subset(host == host_current)
}
}
}
if (NROW(breakpoints_current) == 0) {
out <- data.frame(
# recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added
datetime = vectorise_log_entry(Sys.time(), length(rows)),
index = rows,
method = vectorise_log_entry(method_coerced, length(rows)),
ab_given = vectorise_log_entry(ab.bak[match(ab_current, df$ab)][1], length(rows)),
mo_given = vectorise_log_entry(mo.bak[match(mo_current, df$mo)][1], length(rows)),
host_given = vectorise_log_entry(host.bak[match(host_current, df$host)][1], length(rows)),
input_given = vectorise_log_entry(as.character(values_bak), length(rows)),
ab = vectorise_log_entry(ab_current, length(rows)),
mo = vectorise_log_entry(mo_current, length(rows)),
host = vectorise_log_entry(host_current, length(rows)),
input = vectorise_log_entry(as.character(values), length(rows)),
outcome = vectorise_log_entry(NA_sir_, length(rows)),
notes = vectorise_log_entry("No breakpoint available", length(rows)),
guideline = vectorise_log_entry(guideline_current, length(rows)),
ref_table = vectorise_log_entry(NA_character_, length(rows)),
uti = vectorise_log_entry(uti_current, length(rows)),
breakpoint_S_R = vectorise_log_entry(NA_character_, length(rows)),
stringsAsFactors = FALSE
)
out <- subset(out, !is.na(input_given))
AMR_env$sir_interpretation_history <- rbind_AMR(AMR_env$sir_interpretation_history, out)
notes <- c(notes, notes_current)
next
}
# sort on host and taxonomic rank
# (this will e.g. prefer 'species' breakpoints over 'order' breakpoints)
if (is.na(uti_current)) {
breakpoints_current <- breakpoints_current %pm>%
# `uti` is a column in the data set
# this will put UTI = FALSE first, then UTI = NA, then UTI = TRUE
pm_mutate(uti_index = ifelse(!is.na(uti) & uti == FALSE, 1,
ifelse(is.na(uti), 2,
3
)
)) %pm>%
# be as specific as possible (i.e. prefer species over genus):
pm_arrange(rank_index, uti_index)
} else if (uti_current == TRUE) {
breakpoints_current <- breakpoints_current %pm>%
subset(uti == TRUE) %pm>%
# be as specific as possible (i.e. prefer species over genus):
pm_arrange(rank_index)
}
# throw messages for different body sites
site <- breakpoints_current[1L, "site", drop = FALSE] # this is the one we'll take
if (is.na(site)) {
site <- paste0("an unspecified body site")
} else {
site <- paste0("body site '", site, "'")
}
if (nrow(breakpoints_current) == 1 && all(breakpoints_current$uti == TRUE) && is.na(uti_current) && message_not_thrown_before("as.sir", "uti", ab_current)) {
# only UTI breakpoints available
notes_current <- paste0(
notes_current, "\n",
paste0("Breakpoints for ", font_bold(ab_formatted), " in ", mo_formatted, " are only available for (uncomplicated) urinary tract infections (UTI); assuming `uti = TRUE`.")
)
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && any(is.na(uti_current)) && all(c(TRUE, FALSE) %in% breakpoints_current$uti, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteUTI", mo_current, ab_current)) {
# both UTI and Non-UTI breakpoints available
breakpoints_current <- breakpoints_current %pm>%
pm_filter(uti == FALSE)
notes_current <- paste0(
notes_current, "\n",
paste0("Breakpoints for UTI ", font_bold("and"), " non-UTI available for ", ab_formatted, " in ", mo_formatted, " - assuming ", site, ". Use argument `uti` to set which isolates are from urine. See `?as.sir`.")
)
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && all(breakpoints_current$uti == FALSE, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteOther", mo_current, ab_current)) {
# breakpoints for multiple body sites available
notes_current <- paste0(
notes_current, "\n",
paste0("Multiple breakpoints available for ", font_bold(ab_formatted), " in ", mo_formatted, " - assuming ", site, ".")
)
}
# first check if mo is intrinsic resistant
if (isTRUE(add_intrinsic_resistance) && guideline_current %like% "EUCAST" && paste(mo_current, ab_current) %in% AMR_env$intrinsic_resistant) {
new_sir <- rep(as.sir("R"), length(rows))
notes_current <- paste0(
notes_current, "\n",
paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, "")
)
} else if (nrow(breakpoints_current) == 0) {
# no rules available
new_sir <- rep(NA_sir_, length(rows))
} else {
# then run the rules
breakpoints_current <- breakpoints_current[1L, , drop = FALSE]
notes_current <- paste0(
notes_current, "\n",
ifelse(breakpoints_current$mo == "UNKNOWN" | breakpoints_current$ref_tbl %like% "PK.*PD",
"Some PK/PD breakpoints were applied - use `include_PKPD = FALSE` to prevent this",
""
),
"\n",
ifelse(breakpoints_current$site %like% "screen" | breakpoints_current$ref_tbl %like% "screen",
"Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this",
""
),
"\n",
ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[<][0-9]",
paste0("MIC values with the operator '<' are all considered 'S' since capped_mic_handling = \"", capped_mic_handling, "\""),
""
),
"\n",
ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[>][0-9]",
paste0("MIC values with the operator '>' are all considered 'R' since capped_mic_handling = \"", capped_mic_handling, "\""),
""
),
"\n",
ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^[><]=[0-9]" & as.double(values) > breakpoints_current$breakpoint_S & as.double(values) < breakpoints_current$breakpoint_R,
paste0("MIC values within the breakpoint guideline range with the operator '<=' or '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
""
),
"\n",
ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^<=[0-9]" & as.double(values) == breakpoints_current$breakpoint_R,
paste0("MIC values at the R breakpoint with the operator '<=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
""
),
"\n",
ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^>=[0-9]" & as.double(values) == breakpoints_current$breakpoint_S,
paste0("MIC values at the S breakpoint with the operator '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
""
)
)
if (isTRUE(substitute_missing_r_breakpoint) && !is.na(breakpoints_current$breakpoint_S) && is.na(breakpoints_current$breakpoint_R)) {
# breakpoints_current only has 1 row at this moment
breakpoints_current$breakpoint_R <- breakpoints_current$breakpoint_S
notes_current <- paste0(
notes_current, "\n",
ifelse(!is.na(breakpoints_current$breakpoint_S) & is.na(breakpoints_current$breakpoint_R),
"NAs because of missing R breakpoints were substituted with R since substitute_missing_r_breakpoint = TRUE",
""
)
)
}
## actual interpretation ----
if (method == "mic") {
new_sir <- case_when_AMR(
is.na(values) ~ NA_sir_,
capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[<][0-9]" ~ as.sir("S"),
capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[>][0-9]" ~ as.sir("R"),
capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^[><]=[0-9]" & as.double(values) > breakpoints_current$breakpoint_S & as.double(values) < breakpoints_current$breakpoint_R ~ as.sir("NI"),
capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^<=[0-9]" & as.double(values) == breakpoints_current$breakpoint_R ~ as.sir("NI"),
capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^>=[0-9]" & as.double(values) == breakpoints_current$breakpoint_S ~ as.sir("NI"),
values <= breakpoints_current$breakpoint_S ~ as.sir("S"),
guideline_current %like% "EUCAST" & values > breakpoints_current$breakpoint_R ~ as.sir("R"),
guideline_current %like% "CLSI" & values >= breakpoints_current$breakpoint_R ~ as.sir("R"),
# return "I" or "SDD" when breakpoints are in the middle
!is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) & breakpoints_current$is_SDD == TRUE ~ as.sir("SDD"),
!is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) & breakpoints_current$is_SDD == FALSE ~ as.sir("I"),
# and NA otherwise
TRUE ~ NA_sir_
)
} else if (method == "disk") {
new_sir <- case_when_AMR(
is.na(values) ~ NA_sir_,
as.double(values) >= as.double(breakpoints_current$breakpoint_S) ~ as.sir("S"),
guideline_current %like% "EUCAST" & as.double(values) < as.double(breakpoints_current$breakpoint_R) ~ as.sir("R"),
guideline_current %like% "CLSI" & as.double(values) <= as.double(breakpoints_current$breakpoint_R) ~ as.sir("R"),
# return "I" or "SDD" when breakpoints are in the middle
!is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) & breakpoints_current$is_SDD == TRUE ~ as.sir("SDD"),
!is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) & breakpoints_current$is_SDD == FALSE ~ as.sir("I"),
# and NA otherwise
TRUE ~ NA_sir_
)
}
# write to verbose output
notes_current <- trimws2(notes_current)
notes_current[notes_current == ""] <- NA_character_
out <- data.frame(
# recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added
datetime = vectorise_log_entry(Sys.time(), length(rows)),
index = rows,
method = vectorise_log_entry(method_coerced, length(rows)),
ab_given = vectorise_log_entry(ab.bak[match(ab_current, df$ab)][1], length(rows)),
mo_given = vectorise_log_entry(mo.bak[match(mo_current, df$mo)][1], length(rows)),
host_given = vectorise_log_entry(host.bak[match(host_current, df$host)][1], length(rows)),
input_given = vectorise_log_entry(as.character(values_bak), length(rows)),
ab = vectorise_log_entry(breakpoints_current[, "ab", drop = TRUE], length(rows)),
mo = vectorise_log_entry(breakpoints_current[, "mo", drop = TRUE], length(rows)),
host = vectorise_log_entry(breakpoints_current[, "host", drop = TRUE], length(rows)),
input = vectorise_log_entry(as.character(values), length(rows)),
outcome = vectorise_log_entry(as.sir(new_sir), length(rows)),
notes = font_stripstyle(notes_current), # vectorise_log_entry(paste0(font_stripstyle(notes_current), collapse = "\n"), length(rows)),
guideline = vectorise_log_entry(guideline_current, length(rows)),
ref_table = vectorise_log_entry(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)),
uti = vectorise_log_entry(breakpoints_current[, "uti", drop = TRUE], length(rows)),
breakpoint_S_R = vectorise_log_entry(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
stringsAsFactors = FALSE
)
out <- subset(out, !is.na(input_given))
AMR_env$sir_interpretation_history <- rbind_AMR(AMR_env$sir_interpretation_history, out)
}
notes <- c(notes, notes_current)
df[rows, "result"] <- new_sir
}
close(p)
# printing messages
if (isTRUE(info)) {
if (has_progress_bar == TRUE) {
# the progress bar has overwritten the intro text, so:
message_(intro_txt, appendLF = FALSE, as_note = FALSE)
}
notes <- notes[!trimws2(notes) %in% c("", NA_character_)]
if (length(notes) > 0) {
if (isTRUE(rise_warning)) {
message(font_rose_bg(" WARNING "))
} else {
message(font_yellow_bg(" NOTE "))
}
notes <- unique(notes)
# if (isTRUE(verbose) || length(notes) == 1 || NROW(AMR_env$sir_interpretation_history) == 0) {
if (isTRUE(verbose)) {
for (i in seq_along(notes)) {
message(word_wrap(" ", AMR_env$bullet_icon, " ", notes[i], add_fn = font_black))
}
} else {
# message(word_wrap(" ", AMR_env$bullet_icon, " There were multiple notes. Print or View `sir_interpretation_history()` to examine them, or use `as.sir(..., verbose = TRUE)` next time to directly print them here.", add_fn = font_black))
}
} else {
message(font_green_bg(" OK "))
}
}
load_mo_uncertainties(metadata_mo)
# reorder AMR_env$sir_interpretation_history to get a clean ordering on index
old_part <- AMR_env$sir_interpretation_history[seq_len(current_sir_interpretation_history), , drop = FALSE]
new_part <- AMR_env$sir_interpretation_history[c((current_sir_interpretation_history + 1):NROW(AMR_env$sir_interpretation_history)), , drop = FALSE]
new_part <- new_part[order(new_part$index), , drop = FALSE]
AMR_env$sir_interpretation_history <- rbind_AMR(old_part, new_part)
df$result
}
#' @rdname as.sir
#' @param clean A [logical] to indicate whether previously stored results should be forgotten after returning the 'logbook' with results.
#' @export
sir_interpretation_history <- function(clean = FALSE) {
meet_criteria(clean, allow_class = "logical", has_length = 1)
out <- AMR_env$sir_interpretation_history
out <- out[which(!is.na(out$datetime)), , drop = FALSE]
out$outcome <- as.sir(out$outcome)
out$site <- as.character(out$site)
if (isTRUE(clean)) {
AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
}
if (pkg_is_available("tibble")) {
out <- import_fn("as_tibble", "tibble")(out)
}
as_original_data_class(out, class(out), extra_class = "sir_log")
}
#' @method print sir_log
#' @export
#' @noRd
print.sir_log <- function(x, ...) {
if (NROW(x) == 0) {
message_("No results to print. First run `as.sir()` on MIC values or disk diffusion zones (or on a `data.frame` containing any of these) to print a 'logbook' data set here.")
return(invisible(NULL))
}
class(x) <- class(x)[class(x) != "sir_log"]
print(x, ...)
}
# will be exported using s3_register() in R/zzz.R
pillar_shaft.sir <- function(x, ...) {
out <- trimws(format(x))
if (has_colour()) {
# colours will anyway not work when has_colour() == FALSE,
# but then the indentation should also not be applied
out[is.na(x)] <- font_grey(" NA")
out[x == "NI"] <- font_grey_bg(" NI ")
out[x == "S"] <- font_green_bg(" S ")
out[x == "I"] <- font_orange_bg(" I ")
out[x == "SDD"] <- font_orange_bg(" SDD ")
out[x == "R"] <- font_rose_bg(" R ")
}
create_pillar_column(out, align = "left", width = 5)
}
# will be exported using s3_register() in R/zzz.R
type_sum.sir <- function(x, ...) {
"sir"
}
# will be exported using s3_register() in R/zzz.R
freq.sir <- function(x, ...) {
x_name <- deparse(substitute(x))
x_name <- gsub(".*[$]", "", x_name)
if (x_name %in% c("x", ".")) {
# try again going through system calls
x_name <- stats::na.omit(vapply(
FUN.VALUE = character(1),
sys.calls(),
function(call) {
call_txt <- as.character(call)
ifelse(call_txt[1] %like% "freq$", call_txt[length(call_txt)], character(0))
}
))[1L]
}
ab <- suppressMessages(suppressWarnings(as.ab(x_name, info = FALSE)))
digits <- list(...)$digits
if (is.null(digits)) {
digits <- 2
}
if (!is.na(ab)) {
cleaner::freq.default(
x = x, ...,
.add_header = list(
Drug = paste0(ab_name(ab, language = NULL, info = FALSE), " (", ab, ", ", paste(ab_atc(ab, info = FALSE), collapse = "/"), ")"),
`Drug group` = ab_group(ab, language = NULL),
`%SI` = trimws(percentage(susceptibility(x, minimum = 0, as_percent = FALSE),
digits = digits
))
)
)
} else {
cleaner::freq.default(
x = x, ...,
.add_header = list(
`%SI` = trimws(percentage(susceptibility(x, minimum = 0, as_percent = FALSE),
digits = digits
))
)
)
}
}
# will be exported using s3_register() in R/zzz.R
get_skimmers.sir <- function(column) {
# get the variable name 'skim_variable'
name_call <- function(.data) {
calls <- sys.calls()
frms <- sys.frames()
calls_txt <- vapply(calls, function(x) paste(deparse(x), collapse = ""), FUN.VALUE = character(1))
if (any(calls_txt %like% "skim_variable", na.rm = TRUE)) {
ind <- which(calls_txt %like% "skim_variable")[1L]
vars <- tryCatch(eval(parse(text = ".data$skim_variable$sir"), envir = frms[[ind]]),
error = function(e) NULL
)
tryCatch(ab_name(as.character(calls[[length(calls)]][[2]]), language = NULL, info = FALSE),
error = function(e) NA_character_
)
} else {
NA_character_
}
}
skimr::sfl(
skim_type = "sir",
ab_name = name_call,
count_R = count_R,
count_S = count_susceptible,
count_I = count_I,
prop_R = ~ proportion_R(., minimum = 0),
prop_S = ~ susceptibility(., minimum = 0),
prop_I = ~ proportion_I(., minimum = 0)
)
}
#' @method print sir
#' @export
#' @noRd
print.sir <- function(x, ...) {
x_name <- deparse(substitute(x))
cat("Class 'sir'\n")
# TODO for #170
# if (!is.null(attributes(x)$guideline) && !all(is.na(attributes(x)$guideline))) {
# cat(font_blue(word_wrap("These values were interpreted using ",
# font_bold(vector_and(attributes(x)$guideline, quotes = FALSE)),
# " based on ",
# vector_and(attributes(x)$method, quotes = FALSE),
# " values. ",
# "Use `sir_interpretation_history(", x_name, ")` to return a full logbook.")))
# cat("\n")
# }
print(as.character(x), quote = FALSE)
}
#' @method as.double sir
#' @export
as.double.sir <- function(x, ...) {
dbls <- rep(NA_real_, length(x))
dbls[x == "S"] <- 1
dbls[x %in% c("SDD", "I")] <- 2
dbls[x == "R"] <- 3
dbls
}
#' @method droplevels sir
#' @export
#' @noRd
droplevels.sir <- function(x, exclude = if (any(is.na(levels(x)))) NULL else NA, ...) {
x <- droplevels.factor(x, exclude = exclude, ...)
class(x) <- c("sir", "ordered", "factor")
x
}
#' @method summary sir
#' @export
#' @noRd
summary.sir <- function(object, ...) {
x <- object
n <- sum(!is.na(x))
S <- sum(x == "S", na.rm = TRUE)
SDD <- sum(x == "SDD", na.rm = TRUE)
I <- sum(x == "I", na.rm = TRUE)
R <- sum(x == "R", na.rm = TRUE)
NI <- sum(x == "NI", na.rm = TRUE)
pad <- function(x) {
if (is.na(x)) {
return("??")
}
if (x == "0%") {
x <- " 0.0%"
}
if (nchar(x) < 5) {
x <- paste0(rep(" ", 5 - nchar(x)), x)
}
x
}
value <- c(
"Class" = "sir",
"%S" = paste0(pad(percentage(S / n, digits = 1)), " (n=", S, ")"),
"%SDD" = paste0(pad(percentage(SDD / n, digits = 1)), " (n=", SDD, ")"),
"%I" = paste0(pad(percentage(I / n, digits = 1)), " (n=", I, ")"),
"%R" = paste0(pad(percentage(R / n, digits = 1)), " (n=", R, ")"),
"%NI" = paste0(pad(percentage(NI / n, digits = 1)), " (n=", NI, ")")
)
class(value) <- c("summaryDefault", "table")
value
}
#' @method [<- sir
#' @export
#' @noRd
"[<-.sir" <- function(i, j, ..., value) {
value <- as.sir(value)
y <- NextMethod()
attributes(y) <- attributes(i)
y
}
#' @method [[<- sir
#' @export
#' @noRd
"[[<-.sir" <- function(i, j, ..., value) {
value <- as.sir(value)
y <- NextMethod()
attributes(y) <- attributes(i)
y
}
#' @method c sir
#' @export
#' @noRd
c.sir <- function(...) {
lst <- list(...)
# TODO for #170
# guideline <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$guideline %or% NA_character_)
# mo <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$mo %or% NA_character_)
# ab <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ab %or% NA_character_)
# method <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$method %or% NA_character_)
# ref_tbl <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_tbl %or% NA_character_)
# ref_breakpoints <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_breakpoints %or% NA_character_)
out <- as.sir(unlist(lapply(list(...), as.character)))
# TODO for #170
# if (!all(is.na(guideline))) {
# attributes(out)$guideline <- guideline
# attributes(out)$mo <- mo
# attributes(out)$ab <- ab
# attributes(out)$method <- method
# attributes(out)$ref_tbl <- ref_tbl
# attributes(out)$ref_breakpoints <- ref_breakpoints
# }
out
}
#' @method unique sir
#' @export
#' @noRd
unique.sir <- function(x, incomparables = FALSE, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method rep sir
#' @export
#' @noRd
rep.sir <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
check_reference_data <- function(reference_data, .call_depth) {
if (!identical(reference_data, AMR::clinical_breakpoints)) {
class_sir <- vapply(FUN.VALUE = character(1), AMR::clinical_breakpoints, function(x) paste0("<", class(x), ">", collapse = " and "))
class_ref <- vapply(FUN.VALUE = character(1), reference_data, function(x) paste0("<", class(x), ">", collapse = " and "))
if (!all(names(class_sir) == names(class_ref))) {
stop_("`reference_data` must have the same column names as the 'clinical_breakpoints' data set.", call = .call_depth)
}
if (!all(class_sir == class_ref)) {
stop_("`reference_data` must be the same structure as the 'clinical_breakpoints' data set. Column '", names(class_ref[class_sir != class_ref][1]), "' is of class ", class_ref[class_sir != class_ref][1], ", but should be of class ", class_sir[class_sir != class_ref][1], ".", call = .call_depth)
}
}
}
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