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(v2.1.1.9050) vctrs fix for sir, small documentation fixes
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 2.1.1.9049
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Date: 2024-06-14
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Version: 2.1.1.9050
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Date: 2024-06-15
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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data analysis and to work with microbial and antimicrobial properties by
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@ -84,6 +84,7 @@ S3method(plot,mic)
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S3method(plot,resistance_predict)
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S3method(plot,sir)
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S3method(print,ab)
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S3method(print,ab_selector)
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S3method(print,av)
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S3method(print,bug_drug_combinations)
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S3method(print,custom_eucast_rules)
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2
NEWS.md
2
NEWS.md
@ -1,4 +1,4 @@
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# AMR 2.1.1.9049
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# AMR 2.1.1.9050
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*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support!)*
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@ -524,6 +524,9 @@ word_wrap <- function(...,
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# otherwise, give a 'click to run' popup
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parts[cmds & parts %unlike% "[.]"] <- font_url(url = paste0("ide:run:AMR::", parts[cmds & parts %unlike% "[.]"]),
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txt = parts[cmds & parts %unlike% "[.]"])
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# text starting with `?` must also lead to the help page
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parts[parts %like% "^[?]"] <- font_url(url = paste0("ide:help:AMR::", gsub("()", "", gsub("^[?]", "", parts[parts %like% "^[?]"]), fixed = TRUE)),
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txt = parts[parts %like% "^[?]"])
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msg <- paste0(parts, collapse = "`")
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}
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msg <- gsub("`(.+?)`", font_grey_bg("\\1"), msg)
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109
R/ab_selectors.R
109
R/ab_selectors.R
@ -57,59 +57,31 @@
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#' example_isolates
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#'
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#'
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#' # Examples sections below are split into 'base R', 'dplyr', and 'data.table':
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#'
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#'
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#' # base R ------------------------------------------------------------------
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#'
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#' # select columns 'IPM' (imipenem) and 'MEM' (meropenem)
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#' example_isolates[, carbapenems()]
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#'
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#' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
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#' example_isolates[, c("mo", aminoglycosides())]
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#'
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#' # select only antibiotic columns with DDDs for oral treatment
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#' example_isolates[, administrable_per_os()]
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#'
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#' # filter using any() or all()
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#' example_isolates[any(carbapenems() == "R"), ]
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#' subset(example_isolates, any(carbapenems() == "R"))
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#'
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#' # filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
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#' example_isolates[any(carbapenems()), ]
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#' example_isolates[all(carbapenems()), ]
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#'
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#' # filter with multiple antibiotic selectors using c()
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#' example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
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#'
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#' # filter + select in one go: get penicillins in carbapenem-resistant strains
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#' example_isolates[any(carbapenems() == "R"), penicillins()]
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#'
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#' # You can combine selectors with '&' to be more specific. For example,
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#' # penicillins() would select benzylpenicillin ('peni G') and
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#' # administrable_per_os() would select erythromycin. Yet, when combined these
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#' # drugs are both omitted since benzylpenicillin is not administrable per os
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#' # and erythromycin is not a penicillin:
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#' example_isolates[, penicillins() & administrable_per_os()]
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#'
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#' # ab_selector() applies a filter in the `antibiotics` data set and is thus
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#' # very flexible. For instance, to select antibiotic columns with an oral DDD
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#' # of at least 1 gram:
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#' example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
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#'
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#' # Examples sections below are split into 'dplyr', 'base R', and 'data.table':
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#'
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#' \donttest{
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#' # dplyr -------------------------------------------------------------------
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#'
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#' if (require("dplyr")) {
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#'. example_isolates %>% select(carbapenems())
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#' }
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#'
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#' if (require("dplyr")) {
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#' tibble(kefzol = random_sir(5)) %>%
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#' select(cephalosporins())
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#' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
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#' example_isolates %>% select(mo, aminoglycosides())
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#' }
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#'
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#' if (require("dplyr")) {
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#' # select only antibiotic columns with DDDs for oral treatment
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#'. example_isolates %>% select(administrable_per_os())
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#' }
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#'
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#' if (require("dplyr")) {
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#' # get AMR for all aminoglycosides e.g., per ward:
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#' example_isolates %>%
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#' group_by(ward) %>%
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#' summarise(across(aminoglycosides(), resistance))
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#' summarise(across(aminoglycosides(),
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#' resistance))
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#' }
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#' if (require("dplyr")) {
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#' # You can combine selectors with '&' to be more specific:
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@ -121,7 +93,8 @@
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#' example_isolates %>%
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#' filter(mo_genus() %in% c("Escherichia", "Klebsiella")) %>%
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#' group_by(ward) %>%
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#' summarise(across(not_intrinsic_resistant(), resistance))
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#' summarise_at(not_intrinsic_resistant(),
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#' resistance)
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#' }
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#' if (require("dplyr")) {
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#' # get susceptibility for antibiotics whose name contains "trim":
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@ -187,6 +160,44 @@
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#' }
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#'
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#'
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#' # base R ------------------------------------------------------------------
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#'
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#' # select columns 'IPM' (imipenem) and 'MEM' (meropenem)
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#' example_isolates[, carbapenems()]
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#'
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#' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
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#' example_isolates[, c("mo", aminoglycosides())]
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#'
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#' # select only antibiotic columns with DDDs for oral treatment
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#' example_isolates[, administrable_per_os()]
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#'
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#' # filter using any() or all()
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#' example_isolates[any(carbapenems() == "R"), ]
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#' subset(example_isolates, any(carbapenems() == "R"))
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#'
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#' # filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
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#' example_isolates[any(carbapenems()), ]
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#' example_isolates[all(carbapenems()), ]
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#'
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#' # filter with multiple antibiotic selectors using c()
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#' example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
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#'
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#' # filter + select in one go: get penicillins in carbapenem-resistant strains
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#' example_isolates[any(carbapenems() == "R"), penicillins()]
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#'
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#' # You can combine selectors with '&' to be more specific. For example,
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#' # penicillins() would select benzylpenicillin ('peni G') and
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#' # administrable_per_os() would select erythromycin. Yet, when combined these
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#' # drugs are both omitted since benzylpenicillin is not administrable per os
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#' # and erythromycin is not a penicillin:
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#' example_isolates[, penicillins() & administrable_per_os()]
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#'
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#' # ab_selector() applies a filter in the `antibiotics` data set and is thus
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#' # very flexible. For instance, to select antibiotic columns with an oral DDD
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#' # of at least 1 gram:
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#' example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
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#'
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#'
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#' # data.table --------------------------------------------------------------
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#'
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#' # data.table is supported as well, just use it in the same way as with
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@ -679,6 +690,16 @@ ab_select_exec <- function(function_name,
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)
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}
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#' @method print ab_selector
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#' @export
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#' @noRd
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print.ab_selector <- function(x, ...) {
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warning_("It should never be needed to print an antibiotic selector class. Are you using data.table? Then add the argument `with = FALSE`, see our examples at `?ab_selector`.",
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immediate = TRUE)
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cat("Class 'ab_selector'\n")
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print(as.character(x), quote = FALSE)
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}
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#' @method c ab_selector
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#' @export
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#' @noRd
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@ -462,7 +462,7 @@ eucast_rules <- function(x,
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font_red(paste0(
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"v", utils::packageDescription("AMR")$Version, ", ",
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format(as.Date(utils::packageDescription("AMR")$Date), format = "%Y")
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)), "), see ?eucast_rules\n"
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)), "), see `?eucast_rules`\n"
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))
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))
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}
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@ -188,7 +188,7 @@ key_antimicrobials <- function(x = NULL,
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"No columns available ",
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paste0("Only using ", values_new_length, " out of ", values_old_length, " defined columns ")
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),
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"as key antimicrobials for ", name, "s. See ?key_antimicrobials."
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"as key antimicrobials for ", name, "s. See `?key_antimicrobials`."
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)
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}
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2
R/pca.R
2
R/pca.R
@ -113,7 +113,7 @@ pca <- function(x,
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x <- as.data.frame(new_list, stringsAsFactors = FALSE)
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if (any(vapply(FUN.VALUE = logical(1), x, function(y) !is.numeric(y)))) {
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warning_("in `pca()`: be sure to first calculate the resistance (or susceptibility) of variables with antimicrobial test results, since PCA works with numeric variables only. See Examples in ?pca.", call = FALSE)
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warning_("in `pca()`: be sure to first calculate the resistance (or susceptibility) of variables with antimicrobial test results, since PCA works with numeric variables only. See Examples in `?pca`.", call = FALSE)
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}
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# set column names
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@ -231,7 +231,7 @@ resistance_predict <- function(x,
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prediction <- predictmodel$fit
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se <- predictmodel$se.fit
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} else {
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stop("no valid model selected. See ?resistance_predict.")
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stop("no valid model selected. See `?resistance_predict`.")
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}
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# prepare the output dataframe
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88
R/sir.R
88
R/sir.R
@ -158,6 +158,51 @@
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#'
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#' # For INTERPRETING disk diffusion and MIC values -----------------------
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#'
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#' \donttest{
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#' ## Using dplyr -------------------------------------------------
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#' if (require("dplyr")) {
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#' # approaches that all work without additional arguments:
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#' df %>% mutate_if(is.mic, as.sir)
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#' df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
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#' df %>% mutate(across(where(is.mic), as.sir))
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#' df %>% mutate_at(vars(AMP:TOB), as.sir)
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#' df %>% mutate(across(AMP:TOB, as.sir))
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#'
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#' # approaches that all work with additional arguments:
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#' df %>% mutate_if(is.mic, as.sir, mo = "column1", guideline = "CLSI")
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#' df %>% mutate(across(where(is.mic),
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#' function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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#' df %>% mutate_at(vars(AMP:TOB), as.sir, mo = "column1", guideline = "CLSI")
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#' df %>% mutate(across(AMP:TOB,
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#' function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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#'
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#' # for veterinary breakpoints, add 'host':
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#' df %>% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "species_column")
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#' df %>% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "horse")
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#' df %>% mutate(across(where(is.mic),
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#' function(x) as.sir(x, guideline = "CLSI", host = "species_column")))
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#' df %>% mutate_at(vars(AMP:TOB), as.sir, guideline = "CLSI", host = "species_column")
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#' df %>% mutate(across(AMP:TOB,
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#' function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
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#'
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#' # to include information about urinary tract infections (UTI)
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#' data.frame(mo = "E. coli",
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#' nitrofuratoin = c("<= 2", 32),
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#' from_the_bladder = c(TRUE, FALSE)) %>%
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#' as.sir(uti = "from_the_bladder")
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#'
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#' data.frame(mo = "E. coli",
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#' nitrofuratoin = c("<= 2", 32),
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#' specimen = c("urine", "blood")) %>%
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#' as.sir() # automatically determines urine isolates
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#'
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#' df %>%
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#' mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
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#' }
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#'
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#'
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#' ## Using base R ------------------------------------------------
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#'
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#' # a whole data set, even with combined MIC values and disk zones
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#' df <- data.frame(
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#' microorganism = "Escherichia coli",
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@ -187,36 +232,6 @@
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#' guideline = "EUCAST"
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#' )
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#'
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#' \donttest{
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#' # the dplyr way
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#' if (require("dplyr")) {
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#' df %>% mutate_if(is.mic, as.sir)
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#' df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
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#' df %>% mutate(across(where(is.mic), as.sir))
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#' df %>% mutate_at(vars(AMP:TOB), as.sir)
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#' df %>% mutate(across(AMP:TOB, as.sir))
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#'
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#' df %>%
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#' mutate_at(vars(AMP:TOB), as.sir, mo = "microorganism")
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#'
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#' # to include information about urinary tract infections (UTI)
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#' data.frame(
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#' mo = "E. coli",
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#' NIT = c("<= 2", 32),
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#' from_the_bladder = c(TRUE, FALSE)
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#' ) %>%
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#' as.sir(uti = "from_the_bladder")
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#'
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#' data.frame(
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#' mo = "E. coli",
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#' NIT = c("<= 2", 32),
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#' specimen = c("urine", "blood")
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#' ) %>%
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#' as.sir() # automatically determines urine isolates
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#'
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#' df %>%
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#' mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
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#' }
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#'
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#' # For CLEANING existing SIR values ------------------------------------
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#'
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@ -1121,6 +1136,7 @@ as_sir_method <- function(method_short,
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suppressMessages(suppressWarnings(ab_name(ab_current, language = NULL, tolower = TRUE))),
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" (", ab_current, ")"
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)
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notes <- character(0)
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# gather all available breakpoints for current MO
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breakpoints_current <- breakpoints %pm>%
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@ -1165,7 +1181,8 @@ as_sir_method <- function(method_short,
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subset(host_match == TRUE)
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} else {
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# no breakpoint found for this host, so sort on mostly available guidelines
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msgs <- c(msgs, paste0("No breakpoints available for ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " - using ", font_bold(breakpoints_current$host[1]), " instead."))
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notes <- c(notes, paste0("No breakpoints available for ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " - using ", font_bold(breakpoints_current$host[1]), " instead."))
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# msgs <- c(msgs, paste0("No breakpoints available for ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " - using ", font_bold(breakpoints_current$host[1]), " instead."))
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}
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}
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@ -1243,14 +1260,15 @@ as_sir_method <- function(method_short,
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mo_user = rep(mo.bak[match(mo_current, df$mo)][1], length(rows)),
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ab = rep(ab_current, length(rows)),
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mo = rep(breakpoints_current[, "mo", drop = TRUE], length(rows)),
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method = rep(method_coerced, length(rows)),
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input = as.double(values),
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outcome = as.sir(new_sir),
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method = rep(method_coerced, length(rows)),
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breakpoint_S_R = rep(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
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guideline = rep(guideline_coerced, length(rows)),
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host = rep(breakpoints_current[, "host", drop = TRUE], length(rows)),
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notes = rep(paste0(notes, collapse = " "), length(rows)),
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guideline = rep(guideline_coerced, length(rows)),
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ref_table = rep(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)),
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uti = rep(breakpoints_current[, "uti", drop = TRUE], length(rows)),
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breakpoint_S_R = rep(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
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stringsAsFactors = FALSE
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)
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)
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@ -1268,6 +1286,8 @@ as_sir_method <- function(method_short,
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}
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if (isTRUE(rise_warning)) {
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message(font_rose_bg(" WARNING "))
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} else if (length(notes) > 0) {
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message(font_yellow_bg(" NOTES "))
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} else if (length(msgs) == 0) {
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message(font_green_bg(" OK "))
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} else {
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15
R/vctrs.R
15
R/vctrs.R
@ -109,10 +109,13 @@ vec_ptype_abbr.disk <- function(x, ...) {
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"dsk"
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}
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vec_ptype2.disk.default <- function (x, y, ..., x_arg = "", y_arg = "") {
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x
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NA_disk_[0]
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}
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vec_ptype2.disk.disk <- function(x, y, ...) {
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x
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NA_disk_[0]
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}
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vec_cast.disk.disk <- function(x, to, ...) {
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as.disk(x)
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}
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vec_cast.integer.disk <- function(x, to, ...) {
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unclass(x)
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@ -136,11 +139,11 @@ vec_cast.disk.character <- function(x, to, ...) {
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# S3: mic ----
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vec_ptype2.mic.default <- function (x, y, ..., x_arg = "", y_arg = "") {
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# this will make sure that currently implemented MIC levels are returned
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as.mic(x)
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NA_mic_[0]
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}
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vec_ptype2.mic.mic <- function(x, y, ...) {
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# this will make sure that currently implemented MIC levels are returned
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as.mic(x)
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NA_mic_[0]
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}
|
||||
vec_cast.mic.mic <- function(x, to, ...) {
|
||||
# this will make sure that currently implemented MIC levels are returned
|
||||
@ -187,6 +190,10 @@ vec_ptype2.sir.sir <- function(x, y, ...) {
|
||||
vec_ptype2.character.sir <- function(x, y, ...) {
|
||||
NA_sir_[0]
|
||||
}
|
||||
vec_cast.sir.sir <- function(x, to, ...) {
|
||||
# this makes sure that old SIR objects (with S/I/R) are converted to the current structure (S/SDD/I/R/NI)
|
||||
as.sir(x)
|
||||
}
|
||||
vec_cast.character.sir <- function(x, to, ...) {
|
||||
as.character(x)
|
||||
}
|
||||
|
||||
11
R/zzz.R
11
R/zzz.R
@ -62,13 +62,15 @@ AMR_env$sir_interpretation_history <- data.frame(
|
||||
mo_user = character(0),
|
||||
ab = set_clean_class(character(0), c("ab", "character")),
|
||||
mo = set_clean_class(character(0), c("mo", "character")),
|
||||
method = character(0),
|
||||
input = double(0),
|
||||
outcome = NA_sir_[0],
|
||||
method = character(0),
|
||||
breakpoint_S_R = character(0),
|
||||
guideline = character(0),
|
||||
host = character(0),
|
||||
notes = character(0),
|
||||
guideline = character(0),
|
||||
ref_table = character(0),
|
||||
uti = logical(0),
|
||||
breakpoint_S_R = character(0),
|
||||
stringsAsFactors = FALSE
|
||||
)
|
||||
|
||||
@ -95,6 +97,7 @@ AMR_env$sup_1_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$sup_1 %o
|
||||
s3_register("pillar::pillar_shaft", "sir")
|
||||
s3_register("pillar::pillar_shaft", "mic")
|
||||
s3_register("pillar::pillar_shaft", "disk")
|
||||
# no type_sum of disk, that's now in vctrs::vec_ptype_full
|
||||
s3_register("pillar::type_sum", "ab")
|
||||
s3_register("pillar::type_sum", "av")
|
||||
s3_register("pillar::type_sum", "mo")
|
||||
@ -153,6 +156,7 @@ AMR_env$sup_1_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$sup_1 %o
|
||||
s3_register("vctrs::vec_ptype_abbr", "disk")
|
||||
s3_register("vctrs::vec_ptype2", "disk.default")
|
||||
s3_register("vctrs::vec_ptype2", "disk.disk")
|
||||
s3_register("vctrs::vec_cast", "disk.disk")
|
||||
s3_register("vctrs::vec_cast", "integer.disk")
|
||||
s3_register("vctrs::vec_cast", "disk.integer")
|
||||
s3_register("vctrs::vec_cast", "double.disk")
|
||||
@ -179,6 +183,7 @@ AMR_env$sup_1_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$sup_1 %o
|
||||
s3_register("vctrs::vec_ptype2", "character.sir")
|
||||
s3_register("vctrs::vec_cast", "character.sir")
|
||||
s3_register("vctrs::vec_cast", "sir.character")
|
||||
s3_register("vctrs::vec_cast", "sir.sir")
|
||||
|
||||
# if mo source exists, fire it up (see mo_source())
|
||||
if (tryCatch(file.exists(getOption("AMR_mo_source", "~/mo_source.rds")), error = function(e) FALSE)) {
|
||||
|
||||
Binary file not shown.
@ -185,59 +185,31 @@ All data sets in this \code{AMR} package (about microorganisms, antibiotics, SIR
|
||||
example_isolates
|
||||
|
||||
|
||||
# Examples sections below are split into 'base R', 'dplyr', and 'data.table':
|
||||
|
||||
|
||||
# base R ------------------------------------------------------------------
|
||||
|
||||
# select columns 'IPM' (imipenem) and 'MEM' (meropenem)
|
||||
example_isolates[, carbapenems()]
|
||||
|
||||
# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
|
||||
example_isolates[, c("mo", aminoglycosides())]
|
||||
|
||||
# select only antibiotic columns with DDDs for oral treatment
|
||||
example_isolates[, administrable_per_os()]
|
||||
|
||||
# filter using any() or all()
|
||||
example_isolates[any(carbapenems() == "R"), ]
|
||||
subset(example_isolates, any(carbapenems() == "R"))
|
||||
|
||||
# filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
|
||||
example_isolates[any(carbapenems()), ]
|
||||
example_isolates[all(carbapenems()), ]
|
||||
|
||||
# filter with multiple antibiotic selectors using c()
|
||||
example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
|
||||
|
||||
# filter + select in one go: get penicillins in carbapenem-resistant strains
|
||||
example_isolates[any(carbapenems() == "R"), penicillins()]
|
||||
|
||||
# You can combine selectors with '&' to be more specific. For example,
|
||||
# penicillins() would select benzylpenicillin ('peni G') and
|
||||
# administrable_per_os() would select erythromycin. Yet, when combined these
|
||||
# drugs are both omitted since benzylpenicillin is not administrable per os
|
||||
# and erythromycin is not a penicillin:
|
||||
example_isolates[, penicillins() & administrable_per_os()]
|
||||
|
||||
# ab_selector() applies a filter in the `antibiotics` data set and is thus
|
||||
# very flexible. For instance, to select antibiotic columns with an oral DDD
|
||||
# of at least 1 gram:
|
||||
example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
|
||||
# Examples sections below are split into 'dplyr', 'base R', and 'data.table':
|
||||
|
||||
\donttest{
|
||||
# dplyr -------------------------------------------------------------------
|
||||
|
||||
if (require("dplyr")) {
|
||||
tibble(kefzol = random_sir(5)) \%>\%
|
||||
select(cephalosporins())
|
||||
. example_isolates \%>\% select(carbapenems())
|
||||
}
|
||||
|
||||
if (require("dplyr")) {
|
||||
# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
|
||||
example_isolates \%>\% select(mo, aminoglycosides())
|
||||
}
|
||||
|
||||
if (require("dplyr")) {
|
||||
# select only antibiotic columns with DDDs for oral treatment
|
||||
. example_isolates \%>\% select(administrable_per_os())
|
||||
}
|
||||
|
||||
if (require("dplyr")) {
|
||||
# get AMR for all aminoglycosides e.g., per ward:
|
||||
example_isolates \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(across(aminoglycosides(), resistance))
|
||||
summarise(across(aminoglycosides(),
|
||||
resistance))
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
# You can combine selectors with '&' to be more specific:
|
||||
@ -249,7 +221,8 @@ if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
filter(mo_genus() \%in\% c("Escherichia", "Klebsiella")) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(across(not_intrinsic_resistant(), resistance))
|
||||
summarise_at(not_intrinsic_resistant(),
|
||||
resistance)
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
# get susceptibility for antibiotics whose name contains "trim":
|
||||
@ -315,6 +288,44 @@ if (require("dplyr")) {
|
||||
}
|
||||
|
||||
|
||||
# base R ------------------------------------------------------------------
|
||||
|
||||
# select columns 'IPM' (imipenem) and 'MEM' (meropenem)
|
||||
example_isolates[, carbapenems()]
|
||||
|
||||
# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
|
||||
example_isolates[, c("mo", aminoglycosides())]
|
||||
|
||||
# select only antibiotic columns with DDDs for oral treatment
|
||||
example_isolates[, administrable_per_os()]
|
||||
|
||||
# filter using any() or all()
|
||||
example_isolates[any(carbapenems() == "R"), ]
|
||||
subset(example_isolates, any(carbapenems() == "R"))
|
||||
|
||||
# filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
|
||||
example_isolates[any(carbapenems()), ]
|
||||
example_isolates[all(carbapenems()), ]
|
||||
|
||||
# filter with multiple antibiotic selectors using c()
|
||||
example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
|
||||
|
||||
# filter + select in one go: get penicillins in carbapenem-resistant strains
|
||||
example_isolates[any(carbapenems() == "R"), penicillins()]
|
||||
|
||||
# You can combine selectors with '&' to be more specific. For example,
|
||||
# penicillins() would select benzylpenicillin ('peni G') and
|
||||
# administrable_per_os() would select erythromycin. Yet, when combined these
|
||||
# drugs are both omitted since benzylpenicillin is not administrable per os
|
||||
# and erythromycin is not a penicillin:
|
||||
example_isolates[, penicillins() & administrable_per_os()]
|
||||
|
||||
# ab_selector() applies a filter in the `antibiotics` data set and is thus
|
||||
# very flexible. For instance, to select antibiotic columns with an oral DDD
|
||||
# of at least 1 gram:
|
||||
example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
|
||||
|
||||
|
||||
# data.table --------------------------------------------------------------
|
||||
|
||||
# data.table is supported as well, just use it in the same way as with
|
||||
|
||||
@ -251,6 +251,51 @@ summary(example_isolates) # see all SIR results at a glance
|
||||
|
||||
# For INTERPRETING disk diffusion and MIC values -----------------------
|
||||
|
||||
\donttest{
|
||||
## Using dplyr -------------------------------------------------
|
||||
if (require("dplyr")) {
|
||||
# approaches that all work without additional arguments:
|
||||
df \%>\% mutate_if(is.mic, as.sir)
|
||||
df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
|
||||
df \%>\% mutate(across(where(is.mic), as.sir))
|
||||
df \%>\% mutate_at(vars(AMP:TOB), as.sir)
|
||||
df \%>\% mutate(across(AMP:TOB, as.sir))
|
||||
|
||||
# approaches that all work with additional arguments:
|
||||
df \%>\% mutate_if(is.mic, as.sir, mo = "column1", guideline = "CLSI")
|
||||
df \%>\% mutate(across(where(is.mic),
|
||||
function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
|
||||
df \%>\% mutate_at(vars(AMP:TOB), as.sir, mo = "column1", guideline = "CLSI")
|
||||
df \%>\% mutate(across(AMP:TOB,
|
||||
function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
|
||||
|
||||
# for veterinary breakpoints, add 'host':
|
||||
df \%>\% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "species_column")
|
||||
df \%>\% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "horse")
|
||||
df \%>\% mutate(across(where(is.mic),
|
||||
function(x) as.sir(x, guideline = "CLSI", host = "species_column")))
|
||||
df \%>\% mutate_at(vars(AMP:TOB), as.sir, guideline = "CLSI", host = "species_column")
|
||||
df \%>\% mutate(across(AMP:TOB,
|
||||
function(x) as.sir(x, mo = "column1", 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 \%>\%
|
||||
mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
|
||||
}
|
||||
|
||||
|
||||
## Using base R ------------------------------------------------
|
||||
|
||||
# a whole data set, even with combined MIC values and disk zones
|
||||
df <- data.frame(
|
||||
microorganism = "Escherichia coli",
|
||||
@ -280,36 +325,6 @@ as.sir(
|
||||
guideline = "EUCAST"
|
||||
)
|
||||
|
||||
\donttest{
|
||||
# the dplyr way
|
||||
if (require("dplyr")) {
|
||||
df \%>\% mutate_if(is.mic, as.sir)
|
||||
df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
|
||||
df \%>\% mutate(across(where(is.mic), as.sir))
|
||||
df \%>\% mutate_at(vars(AMP:TOB), as.sir)
|
||||
df \%>\% mutate(across(AMP:TOB, as.sir))
|
||||
|
||||
df \%>\%
|
||||
mutate_at(vars(AMP:TOB), as.sir, mo = "microorganism")
|
||||
|
||||
# to include information about urinary tract infections (UTI)
|
||||
data.frame(
|
||||
mo = "E. coli",
|
||||
NIT = c("<= 2", 32),
|
||||
from_the_bladder = c(TRUE, FALSE)
|
||||
) \%>\%
|
||||
as.sir(uti = "from_the_bladder")
|
||||
|
||||
data.frame(
|
||||
mo = "E. coli",
|
||||
NIT = c("<= 2", 32),
|
||||
specimen = c("urine", "blood")
|
||||
) \%>\%
|
||||
as.sir() # automatically determines urine isolates
|
||||
|
||||
df \%>\%
|
||||
mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
|
||||
}
|
||||
|
||||
# For CLEANING existing SIR values ------------------------------------
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user