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(seeas.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 optionAMR_locale. Uselanguage = NULLorlanguage = ""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.
Value
An integer
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.
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() = 1and 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() >= 1Using only_all_tested has no impact when only using one antibiotic as input.
See also
proportion_* to calculate microbial resistance and susceptibility.
Examples
# example_isolates is a data set available in the AMR package.
# run ?example_isolates for more info.
# base R ------------------------------------------------------------
count_resistant(example_isolates$AMX) # counts "R"
#> [1] 804
count_susceptible(example_isolates$AMX) # counts "S" and "I"
#> [1] 546
count_all(example_isolates$AMX) # counts "S", "I" and "R"
#> [1] 1350
# be more specific
count_S(example_isolates$AMX)
#> [1] 543
count_SI(example_isolates$AMX)
#> [1] 546
count_I(example_isolates$AMX)
#> [1] 3
count_IR(example_isolates$AMX)
#> [1] 807
count_R(example_isolates$AMX)
#> [1] 804
# Count all available isolates
count_all(example_isolates$AMX)
#> [1] 1350
n_sir(example_isolates$AMX)
#> [1] 1350
# 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$AMX)
#> [1] 546
susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX)
#> [1] 546
# dplyr -------------------------------------------------------------
# \donttest{
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)
}
#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
#> # A tibble: 12 × 4
#> ward antibiotic interpretation value
#> <chr> <chr> <ord> <int>
#> 1 Clinical AMX SI 357
#> 2 Clinical AMX R 487
#> 3 Clinical CIP SI 741
#> 4 Clinical CIP R 128
#> 5 ICU AMX SI 158
#> 6 ICU AMX R 270
#> 7 ICU CIP SI 362
#> 8 ICU CIP R 85
#> 9 Outpatient AMX SI 31
#> 10 Outpatient AMX R 47
#> 11 Outpatient CIP SI 78
#> 12 Outpatient CIP R 15
# }