Transform Input to Disk Diffusion Diameters

This transforms a vector to a new class disk, which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 6 and 50.

Usage

as.disk(x, na.rm = FALSE)

NA_disk_

is.disk(x)

Format

An object of class disk (inherits from integer) of length 1.

Arguments

x

vector

na.rm

a logical indicating whether missing values should be removed

Value

An integer with additional class disk

Details

Interpret disk values as SIR values with as.sir(). It supports guidelines from EUCAST and CLSI.

Disk diffusion growth zone sizes must be between 6 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 6-50 range will return NA.

NA_disk_ is a missing value of the new disk class.

See also

as.sir()

Examples

# transform existing disk zones to the `disk` class (using base R)
df <- data.frame(
  microorganism = "Escherichia coli",
  AMP = 20,
  CIP = 14,
  GEN = 18,
  TOB = 16
)
df[, 2:5] <- lapply(df[, 2:5], as.disk)
str(df)
#> 'data.frame':	1 obs. of  5 variables:
#>  $ microorganism: chr "Escherichia coli"
#>  $ AMP          : 'disk' int 20
#>  $ CIP          : 'disk' int 14
#>  $ GEN          : 'disk' int 18
#>  $ TOB          : 'disk' int 16

# \donttest{
# transforming is easier with dplyr:
if (require("dplyr")) {
  df %>% mutate(across(AMP:TOB, as.disk))
}
#>      microorganism AMP CIP GEN TOB
#> 1 Escherichia coli  20  14  18  16
# }

# interpret disk values, see ?as.sir
as.sir(
  x = as.disk(18),
  mo = "Strep pneu", # `mo` will be coerced with as.mo()
  ab = "ampicillin", # and `ab` with as.ab()
  guideline = "EUCAST"
)
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#>   all the details of the breakpoint interpretations. Note that some
#>   microorganisms might not have breakpoints for each antimicrobial drug in
#>   EUCAST 2023.
#> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2023...
#>  OK 
#> Class 'sir'
#> [1] R

# interpret whole data set, pretend to be all from urinary tract infections:
as.sir(df, uti = TRUE)
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#>   all the details of the breakpoint interpretations. Note that some
#>   microorganisms might not have breakpoints for each antimicrobial drug in
#>   EUCAST 2023.
#> Interpreting disk diffusion zones: column 'AMP' (ampicillin), EUCAST
#> 2023...
#>  OK 
#> Interpreting disk diffusion zones: column 'CIP' (ciprofloxacin), EUCAST
#> 2023...
#>  OK 
#> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST
#> 2023...
#>  OK 
#> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST
#> 2023...
#>  OK 
#>      microorganism AMP  CIP GEN TOB
#> 1 Escherichia coli   S <NA>   S   S