count: Count Available Isolates
In AMR: Antimicrobial Resistance Data Analysis

count

R Documentation

Count Available Isolates

Description

These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in summarise() from the dplyr package and also support grouped variables, see Examples.

count_resistant() should be used to count resistant isolates, count_susceptible() should be used to count susceptible isolates.

Usage

count_resistant(..., only_all_tested = FALSE)

count_susceptible(..., only_all_tested = FALSE)

count_S(..., only_all_tested = FALSE)

count_SI(..., only_all_tested = FALSE)

count_I(..., only_all_tested = FALSE)

count_IR(..., only_all_tested = FALSE)

count_R(..., only_all_tested = FALSE)

count_all(..., only_all_tested = FALSE)

n_sir(..., only_all_tested = FALSE)

count_df(data, translate_ab = "name", language = get_AMR_locale(),
  combine_SI = TRUE)

Arguments

`...`	One or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with `as.sir()` if needed.
`only_all_tested`	(for combination therapies, i.e. using more than one variable for `...`): a logical to indicate that isolates must be tested for all antimicrobials, see section Combination Therapy below.
`data`	A data.frame containing columns with class `sir` (see `as.sir()`).
`translate_ab`	A column name of the antimicrobials data set to translate the antibiotic abbreviations to, using `ab_property()`.
`language`	Language of the returned text - the default is the current system language (see `get_AMR_locale()`) and can also be set with the package option `AMR_locale`. Use `language = NULL` or `language = ""` to prevent translation.
`combine_SI`	A logical to indicate whether all values of S, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is `TRUE`.

Details

These functions are meant to count isolates. Use the resistance()/susceptibility() functions to calculate microbial resistance/susceptibility.

The function count_resistant() is equal to the function count_R(). The function count_susceptible() is equal to the function count_SI().

The function n_sir() is an alias of count_all(). They can be used to count all available isolates, i.e. where all input antimicrobials have an available result (S, I or R). Their use is equal to n_distinct(). Their function is equal to count_susceptible(...) + count_resistant(...).

The function count_df() takes any variable from data that has an sir class (created with as.sir()) and counts the number of S's, I's and R's. It also supports grouped variables. The function sir_df() works exactly like count_df(), but adds the percentage of S, I and R.

Value

An integer

Interpretation of SIR

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr).

This AMR package follows insight; use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.

Combination Therapy

When using more than one variable for ... (= combination therapy), use only_all_tested to only count isolates that are tested for all antimicrobials/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how susceptibility() works to calculate the %SI:

--------------------------------------------------------------------
                    only_all_tested = FALSE  only_all_tested = TRUE
                    -----------------------  -----------------------
 Drug A    Drug B   considered   considered  considered   considered
                    susceptible    tested    susceptible    tested
--------  --------  -----------  ----------  -----------  ----------
 S or I    S or I        X            X           X            X
   R       S or I        X            X           X            X
  <NA>     S or I        X            X           -            -
 S or I      R           X            X           X            X
   R         R           -            X           -            X
  <NA>       R           -            -           -            -
 S or I     <NA>         X            X           -            -
   R        <NA>         -            -           -            -
  <NA>      <NA>         -            -           -            -
--------------------------------------------------------------------

Please note that, in combination therapies, for only_all_tested = TRUE applies that:

    count_S()    +   count_I()    +   count_R()    = count_all()
  proportion_S() + proportion_I() + proportion_R() = 1

and that, in combination therapies, for only_all_tested = FALSE applies that:

    count_S()    +   count_I()    +   count_R()    >= count_all()
  proportion_S() + proportion_I() + proportion_R() >= 1

Using only_all_tested has no impact when only using one antibiotic as input.

Examples

# example_isolates is a data set available in the AMR package.
# run ?example_isolates for more info.

# base R ------------------------------------------------------------
count_resistant(example_isolates$AMX) # counts "R"
count_susceptible(example_isolates$AMX) # counts "S" and "I"
count_all(example_isolates$AMX) # counts "S", "I" and "R"

# be more specific
count_S(example_isolates$AMX)
count_SI(example_isolates$AMX)
count_I(example_isolates$AMX)
count_IR(example_isolates$AMX)
count_R(example_isolates$AMX)

# Count all available isolates
count_all(example_isolates$AMX)
n_sir(example_isolates$AMX)

# n_sir() is an alias of count_all().
# Since it counts all available isolates, you can
# calculate back to count e.g. susceptible isolates.
# These results are the same:
count_susceptible(example_isolates$AMX)
susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX)

# dplyr -------------------------------------------------------------

if (require("dplyr")) {
  example_isolates %>%
    group_by(ward) %>%
    summarise(
      R = count_R(CIP),
      I = count_I(CIP),
      S = count_S(CIP),
      n1 = count_all(CIP), # the actual total; sum of all three
      n2 = n_sir(CIP), # same - analogous to n_distinct
      total = n()
    ) # NOT the number of tested isolates!

  # Number of available isolates for a whole antibiotic class
  # (i.e., in this data set columns GEN, TOB, AMK, KAN)
  example_isolates %>%
    group_by(ward) %>%
    summarise(across(aminoglycosides(), n_sir))

  # Count co-resistance between amoxicillin/clav acid and gentamicin,
  # so we can see that combination therapy does a lot more than mono therapy.
  # Please mind that `susceptibility()` calculates percentages right away instead.
  example_isolates %>% count_susceptible(AMC) # 1433
  example_isolates %>% count_all(AMC) # 1879

  example_isolates %>% count_susceptible(GEN) # 1399
  example_isolates %>% count_all(GEN) # 1855

  example_isolates %>% count_susceptible(AMC, GEN) # 1764
  example_isolates %>% count_all(AMC, GEN) # 1936

  # Get number of S+I vs. R immediately of selected columns
  example_isolates %>%
    select(AMX, CIP) %>%
    count_df(translate = FALSE)

  # It also supports grouping variables
  example_isolates %>%
    select(ward, AMX, CIP) %>%
    group_by(ward) %>%
    count_df(translate = FALSE)
}

AMR documentation built on June 8, 2025, 10:17 a.m.