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(v2.1.1.9050) vctrs fix for sir, small documentation fixes
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@ -185,59 +185,31 @@ All data sets in this \code{AMR} package (about microorganisms, antibiotics, SIR
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example_isolates
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# Examples sections below are split into 'base R', 'dplyr', and 'data.table':
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# base R ------------------------------------------------------------------
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# select columns 'IPM' (imipenem) and 'MEM' (meropenem)
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example_isolates[, carbapenems()]
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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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# select only antibiotic columns with DDDs for oral treatment
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example_isolates[, administrable_per_os()]
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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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# 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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# filter with multiple antibiotic selectors using c()
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example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
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# filter + select in one go: get penicillins in carbapenem-resistant strains
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example_isolates[any(carbapenems() == "R"), penicillins()]
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# You can combine selectors with '&' to be more specific. For example,
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# 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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# 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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# Examples sections below are split into 'dplyr', 'base R', and 'data.table':
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\donttest{
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# dplyr -------------------------------------------------------------------
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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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. example_isolates \%>\% select(carbapenems())
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}
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if (require("dplyr")) {
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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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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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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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@ -249,7 +221,8 @@ if (require("dplyr")) {
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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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@ -315,6 +288,44 @@ if (require("dplyr")) {
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}
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# base R ------------------------------------------------------------------
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# select columns 'IPM' (imipenem) and 'MEM' (meropenem)
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example_isolates[, carbapenems()]
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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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# select only antibiotic columns with DDDs for oral treatment
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example_isolates[, administrable_per_os()]
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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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# 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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# filter with multiple antibiotic selectors using c()
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example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
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# filter + select in one go: get penicillins in carbapenem-resistant strains
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example_isolates[any(carbapenems() == "R"), penicillins()]
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# You can combine selectors with '&' to be more specific. For example,
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# 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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# 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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# data.table --------------------------------------------------------------
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# 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
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# For INTERPRETING disk diffusion and MIC values -----------------------
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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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# 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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# 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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# 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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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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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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## Using base R ------------------------------------------------
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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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@ -280,36 +325,6 @@ as.sir(
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guideline = "EUCAST"
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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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df \%>\%
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mutate_at(vars(AMP:TOB), as.sir, mo = "microorganism")
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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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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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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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# For CLEANING existing SIR values ------------------------------------
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