count.RdThese functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.
count_R and count_IR can be used to count resistant isolates, count_S and count_SI can be used to count susceptible isolates.
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)
| ... | 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 |
| 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 This is outdated, see parameter |
Wickham H. Tidy Data. The Journal of Statistical Software, vol. 59, 2014. http://vita.had.co.nz/papers/tidy-data.html
Integer
These functions are meant to count isolates. Use the portion_* functions to calculate microbial resistance.
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_S(...) + count_IR(...).
The function count_df takes any variable from data that has an "rsi" class (created with as.rsi) and counts the amounts of S, I and R. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
The function rsi_df works exactly like count_df, but adds the percentage of S, I and R.
In 2019, EUCAST has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
S - Susceptible, standard dosing regimen: 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 - Susceptible, increased exposure: 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.
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.
This AMR package honours this new insight. Use portion_SI to determine antimicrobial susceptibility and count_SI to count susceptible isolates.
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, Antibiotic A and Antibiotic B, about how portion_SI 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() portion_S() + portion_I() + portion_R() == 1
and that, in combination therapies, for only_all_tested = FALSE applies that:
count_S() + count_I() + count_R() >= count_all() portion_S() + portion_I() + portion_R() >= 1
Using only_all_tested has no impact when only using one antibiotic as input.
On our website https://msberends.gitlab.io/AMR you can find a 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.
portion_* to calculate microbial resistance and susceptibility.
# example_isolates is a data set available in the AMR package. ?example_isolates # Count resistant isolates count_R(example_isolates$AMX) count_IR(example_isolates$AMX) # Or susceptible isolates count_S(example_isolates$AMX) count_SI(example_isolates$AMX) # Count all available isolates count_all(example_isolates$AMX) n_rsi(example_isolates$AMX) # Since n_rsi counts available isolates, you can # calculate back to count e.g. non-susceptible isolates. # This results in the same: count_SI(example_isolates$AMX) portion_SI(example_isolates$AMX) * n_rsi(example_isolates$AMX) library(dplyr) example_isolates %>% group_by(hospital_id) %>% summarise(R = count_R(CIP), I = count_I(CIP), S = count_S(CIP), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_rsi(CIP), # same - analogous to n_distinct total = n()) # NOT the number of tested isolates! # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. # Please mind that `portion_SI` calculates percentages right away instead. count_SI(example_isolates$AMC) # 1433 count_all(example_isolates$AMC) # 1879 count_SI(example_isolates$GEN) # 1399 count_all(example_isolates$GEN) # 1855 with(example_isolates, count_SI(AMC, GEN)) # 1764 with(example_isolates, n_rsi(AMC, GEN)) # 1936 # Get portions S/I/R immediately of all rsi 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)