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These functions can be used to determine first weighted isolates by considering the phenotype for isolate selection (see first_isolate()). Using a phenotype-based method to determine first isolates is more reliable than methods that disregard phenotypes.


  x = NULL,
  col_mo = NULL,
  universal = c("ampicillin", "amoxicillin/clavulanic acid", "cefuroxime",
    "piperacillin/tazobactam", "ciprofloxacin", "trimethoprim/sulfamethoxazole"),
  gram_negative = c("gentamicin", "tobramycin", "colistin", "cefotaxime", "ceftazidime",
  gram_positive = c("vancomycin", "teicoplanin", "tetracycline", "erythromycin",
    "oxacillin", "rifampin"),
  antifungal = c("anidulafungin", "caspofungin", "fluconazole", "miconazole", "nystatin",
  only_sir_columns = FALSE,

all_antimicrobials(x = NULL, only_sir_columns = FALSE, ...)

  type = c("points", "keyantimicrobials"),
  ignore_I = TRUE,
  points_threshold = 2,



a data.frame with antibiotics columns, like AMX or amox. Can be left blank to determine automatically


column name of the names or codes of the microorganisms (see - the default is the first column of class mo. Values will be coerced using


names of broad-spectrum antimicrobial drugs, case-insensitive. Set to NULL to ignore. See Details for the default antimicrobial drugs


names of antibiotic drugs for Gram-positives, case-insensitive. Set to NULL to ignore. See Details for the default antibiotic drugs


names of antibiotic drugs for Gram-negatives, case-insensitive. Set to NULL to ignore. See Details for the default antibiotic drugs


names of antifungal drugs for fungi, case-insensitive. Set to NULL to ignore. See Details for the default antifungal drugs


a logical to indicate whether only columns must be included that were transformed to class sir (see as.sir()) on beforehand (default is FALSE)


ignored, only in place to allow future extensions

y, z

character vectors to compare


type to determine weighed isolates; can be "keyantimicrobials" or "points", see Details


logical to indicate whether antibiotic interpretations with "I" will be ignored when type = "keyantimicrobials", see Details


minimum number of points to require before differences in the antibiogram will lead to inclusion of an isolate when type = "points", see Details


The key_antimicrobials() and all_antimicrobials() functions are context-aware. This means that the x argument can be left blank if used inside a data.frame call, see Examples.

The function key_antimicrobials() returns a character vector with 12 antimicrobial results for every isolate. The function all_antimicrobials() returns a character vector with all antimicrobial drug results for every isolate. These vectors can then be compared using antimicrobials_equal(), to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (".") by key_antimicrobials() and ignored by antimicrobials_equal().

Please see the first_isolate() function how these important functions enable the 'phenotype-based' method for determination of first isolates.

The default antimicrobial drugs used for all rows (set in universal) are:

  • Ampicillin

  • Amoxicillin/clavulanic acid

  • Cefuroxime

  • Ciprofloxacin

  • Piperacillin/tazobactam

  • Trimethoprim/sulfamethoxazole

The default antimicrobial drugs used for Gram-negative bacteria (set in gram_negative) are:

  • Cefotaxime

  • Ceftazidime

  • Colistin

  • Gentamicin

  • Meropenem

  • Tobramycin

The default antimicrobial drugs used for Gram-positive bacteria (set in gram_positive) are:

  • Erythromycin

  • Oxacillin

  • Rifampin

  • Teicoplanin

  • Tetracycline

  • Vancomycin

The default antimicrobial drugs used for fungi (set in antifungal) are:

  • Anidulafungin

  • Caspofungin

  • Fluconazole

  • Miconazole

  • Nystatin

  • Voriconazole

See also


# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates.

# output of the `key_antimicrobials()` function could be like this:
strainA <- "SSSRR.S.R..S"

# those strings can be compared with:
antimicrobials_equal(strainA, strainB, type = "keyantimicrobials")
#> [1] TRUE
# TRUE, because I is ignored (as well as missing values)

antimicrobials_equal(strainA, strainB, type = "keyantimicrobials", ignore_I = FALSE)
#> [1] FALSE
# FALSE, because I is not ignored and so the 4th [character] differs

# \donttest{
if (require("dplyr")) {
  # set key antibiotics to a new variable
  my_patients <- example_isolates %>%
    mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`
      # now calculate first isolates
      first_regular = first_isolate(col_keyantimicrobials = FALSE),
      # and first WEIGHTED isolates
      first_weighted = first_isolate(col_keyantimicrobials = "keyab")

  # Check the difference in this data set, 'weighted' results in more isolates:
  sum(my_patients$first_regular, na.rm = TRUE)
  sum(my_patients$first_weighted, na.rm = TRUE)
#> [1] 1376
# }