(v2.1.1.9050) vctrs fix for `sir`, small documentation fixes

This commit is contained in:
dr. M.S. (Matthijs) Berends 2024-06-15 15:33:49 +02:00
parent 9bf7584d58
commit bdbf5198a2
15 changed files with 248 additions and 165 deletions

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@ -1,6 +1,6 @@
Package: AMR
Version: 2.1.1.9049
Date: 2024-06-14
Version: 2.1.1.9050
Date: 2024-06-15
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by

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@ -84,6 +84,7 @@ S3method(plot,mic)
S3method(plot,resistance_predict)
S3method(plot,sir)
S3method(print,ab)
S3method(print,ab_selector)
S3method(print,av)
S3method(print,bug_drug_combinations)
S3method(print,custom_eucast_rules)

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@ -1,4 +1,4 @@
# AMR 2.1.1.9049
# AMR 2.1.1.9050
*(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(...,
# otherwise, give a 'click to run' popup
parts[cmds & parts %unlike% "[.]"] <- font_url(url = paste0("ide:run:AMR::", parts[cmds & parts %unlike% "[.]"]),
txt = parts[cmds & parts %unlike% "[.]"])
# text starting with `?` must also lead to the help page
parts[parts %like% "^[?]"] <- font_url(url = paste0("ide:help:AMR::", gsub("()", "", gsub("^[?]", "", parts[parts %like% "^[?]"]), fixed = TRUE)),
txt = parts[parts %like% "^[?]"])
msg <- paste0(parts, collapse = "`")
}
msg <- gsub("`(.+?)`", font_grey_bg("\\1"), msg)

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@ -57,59 +57,31 @@
#' 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:
@ -121,7 +93,8 @@
#' 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":
@ -187,6 +160,44 @@
#' }
#'
#'
#' # 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
@ -679,6 +690,16 @@ ab_select_exec <- function(function_name,
)
}
#' @method print ab_selector
#' @export
#' @noRd
print.ab_selector <- function(x, ...) {
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`.",
immediate = TRUE)
cat("Class 'ab_selector'\n")
print(as.character(x), quote = FALSE)
}
#' @method c ab_selector
#' @export
#' @noRd

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@ -462,7 +462,7 @@ eucast_rules <- function(x,
font_red(paste0(
"v", utils::packageDescription("AMR")$Version, ", ",
format(as.Date(utils::packageDescription("AMR")$Date), format = "%Y")
)), "), see ?eucast_rules\n"
)), "), see `?eucast_rules`\n"
))
))
}

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@ -188,7 +188,7 @@ key_antimicrobials <- function(x = NULL,
"No columns available ",
paste0("Only using ", values_new_length, " out of ", values_old_length, " defined columns ")
),
"as key antimicrobials for ", name, "s. See ?key_antimicrobials."
"as key antimicrobials for ", name, "s. See `?key_antimicrobials`."
)
}

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@ -113,7 +113,7 @@ pca <- function(x,
x <- as.data.frame(new_list, stringsAsFactors = FALSE)
if (any(vapply(FUN.VALUE = logical(1), x, function(y) !is.numeric(y)))) {
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)
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)
}
# set column names

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@ -231,7 +231,7 @@ resistance_predict <- function(x,
prediction <- predictmodel$fit
se <- predictmodel$se.fit
} else {
stop("no valid model selected. See ?resistance_predict.")
stop("no valid model selected. See `?resistance_predict`.")
}
# prepare the output dataframe

88
R/sir.R
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@ -158,6 +158,51 @@
#'
#' # 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",
@ -187,36 +232,6 @@
#' 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 ------------------------------------
#'
@ -1121,6 +1136,7 @@ as_sir_method <- function(method_short,
suppressMessages(suppressWarnings(ab_name(ab_current, language = NULL, tolower = TRUE))),
" (", ab_current, ")"
)
notes <- character(0)
# gather all available breakpoints for current MO
breakpoints_current <- breakpoints %pm>%
@ -1165,7 +1181,8 @@ as_sir_method <- function(method_short,
subset(host_match == TRUE)
} else {
# no breakpoint found for this host, so sort on mostly available guidelines
msgs <- c(msgs, paste0("No breakpoints available for ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " - using ", font_bold(breakpoints_current$host[1]), " instead."))
notes <- c(notes, paste0("No breakpoints available for ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " - using ", font_bold(breakpoints_current$host[1]), " instead."))
# msgs <- c(msgs, paste0("No breakpoints available for ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " - using ", font_bold(breakpoints_current$host[1]), " instead."))
}
}
@ -1243,14 +1260,15 @@ as_sir_method <- function(method_short,
mo_user = rep(mo.bak[match(mo_current, df$mo)][1], length(rows)),
ab = rep(ab_current, length(rows)),
mo = rep(breakpoints_current[, "mo", drop = TRUE], length(rows)),
method = rep(method_coerced, length(rows)),
input = as.double(values),
outcome = as.sir(new_sir),
method = rep(method_coerced, length(rows)),
breakpoint_S_R = rep(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
guideline = rep(guideline_coerced, length(rows)),
host = rep(breakpoints_current[, "host", drop = TRUE], length(rows)),
notes = rep(paste0(notes, collapse = " "), length(rows)),
guideline = rep(guideline_coerced, length(rows)),
ref_table = rep(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)),
uti = rep(breakpoints_current[, "uti", drop = TRUE], length(rows)),
breakpoint_S_R = rep(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
stringsAsFactors = FALSE
)
)
@ -1268,6 +1286,8 @@ as_sir_method <- function(method_short,
}
if (isTRUE(rise_warning)) {
message(font_rose_bg(" WARNING "))
} else if (length(notes) > 0) {
message(font_yellow_bg(" NOTES "))
} else if (length(msgs) == 0) {
message(font_green_bg(" OK "))
} else {

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@ -109,10 +109,13 @@ vec_ptype_abbr.disk <- function(x, ...) {
"dsk"
}
vec_ptype2.disk.default <- function (x, y, ..., x_arg = "", y_arg = "") {
x
NA_disk_[0]
}
vec_ptype2.disk.disk <- function(x, y, ...) {
x
NA_disk_[0]
}
vec_cast.disk.disk <- function(x, to, ...) {
as.disk(x)
}
vec_cast.integer.disk <- function(x, to, ...) {
unclass(x)
@ -136,11 +139,11 @@ vec_cast.disk.character <- function(x, to, ...) {
# S3: mic ----
vec_ptype2.mic.default <- function (x, y, ..., x_arg = "", y_arg = "") {
# this will make sure that currently implemented MIC levels are returned
as.mic(x)
NA_mic_[0]
}
vec_ptype2.mic.mic <- function(x, y, ...) {
# this will make sure that currently implemented MIC levels are returned
as.mic(x)
NA_mic_[0]
}
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
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@ -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.

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@ -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

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@ -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 ------------------------------------