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, please see Examples.
count_resistant() should be used to count resistant isolates, count_susceptible() should be used to count susceptible isolates.
count_resistant(..., only_all_tested = FALSE) count_susceptible(..., only_all_tested = FALSE) count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_S(..., only_all_tested = FALSE) count_all(..., only_all_tested = FALSE) n_rsi(..., only_all_tested = FALSE) count_df( data, translate_ab = "name", language = get_locale(), combine_SI = TRUE, combine_IR = FALSE )
Arguments
| ... | one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with |
|---|---|
| only_all_tested | (for combination therapies, i.e. using more than one variable for |
| data | a |
| translate_ab | a column name of the antibiotics data set to translate the antibiotic abbreviations to, using |
| language | language of the returned text, defaults to system language (see |
| combine_SI | a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter |
| combine_IR | a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter |
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_rsi() is an alias of count_all(). They can be used to count all available isolates, i.e. where all input antibiotics 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 rsi class (created with as.rsi()) and counts the number of S's, I's and R's. It also supports grouped variables. The function rsi_df() works exactly like count_df(), but adds the percentage of S, I and R.
Stable lifecycle
The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
Interpretation of R and S/I
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/).
R = Resistant
A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.S = Susceptible
A microorganism is categorised as Susceptible, standard dosing regimen, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.I = Increased exposure, but still susceptible
A microorganism is categorised as Susceptible, Increased exposure when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
This AMR package honours this new 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 antibiotics/variables that you test them for. See this example for two antibiotics, 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 include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
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.
Read more on our website!
On our website https://msberends.gitlab.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
See also
proportion_* to calculate microbial resistance and susceptibility.
Examples
# example_isolates is a data set available in the AMR package. ?example_isolates 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$AMX) n_rsi(example_isolates$AMX) # n_rsi() 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_rsi(example_isolates$AMX) if (require("dplyr")) { example_isolates %>% group_by(hospital_id) %>% 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_rsi(CIP), # same - analogous to n_distinct total = n()) # NOT the number of tested isolates! # 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(hospital_id, AMX, CIP) %>% group_by(hospital_id) %>% count_df(translate = FALSE) }