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(..., also_single_tested = FALSE) count_IR(..., also_single_tested = FALSE) count_I(..., also_single_tested = FALSE) count_SI(..., also_single_tested = FALSE) count_S(..., also_single_tested = FALSE) count_all(...) n_rsi(...) count_df(data, translate_ab = getOption("get_antibiotic_names", "official"), combine_IR = FALSE)
| ... | one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with |
|---|---|
| also_single_tested | a logical to indicate whether (in combination therapies) also observations should be included where not all antibiotics were tested, but at least one of the tested antibiotics contains a target interpretation (e.g. S in case of |
| data | a |
| translate_ab | a column name of the |
| 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. IR (susceptible vs. non-susceptible) |
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.
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(...).
count_df takes any variable from data that has an "rsi" class (created with as.rsi) and counts the amounts of R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
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.
portion_* to calculate microbial resistance and susceptibility.
# NOT RUN { # septic_patients is a data set available in the AMR package. It is true, genuine data. ?septic_patients # Count resistant isolates count_R(septic_patients$amox) count_IR(septic_patients$amox) # Or susceptible isolates count_S(septic_patients$amox) count_SI(septic_patients$amox) # Count all available isolates count_all(septic_patients$amox) n_rsi(septic_patients$amox) # Since n_rsi counts available isolates, you can # calculate back to count e.g. non-susceptible isolates. # This results in the same: count_IR(septic_patients$amox) portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox) library(dplyr) septic_patients %>% group_by(hospital_id) %>% summarise(R = count_R(cipr), I = count_I(cipr), S = count_S(cipr), n1 = count_all(cipr), # the actual total; sum of all three n2 = n_rsi(cipr), # same - analogous to n_distinct total = n()) # NOT the number of tested isolates! # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. # Please mind that `portion_S` calculates percentages right away instead. count_S(septic_patients$amcl) # S = 1342 (71.4%) count_all(septic_patients$amcl) # n = 1879 count_S(septic_patients$gent) # S = 1372 (74.0%) count_all(septic_patients$gent) # n = 1855 with(septic_patients, count_S(amcl, gent)) # S = 1660 (92.3%) with(septic_patients, # n = 1798 n_rsi(amcl, gent)) # Get portions S/I/R immediately of all rsi columns septic_patients %>% select(amox, cipr) %>% count_df(translate = FALSE) # It also supports grouping variables septic_patients %>% select(hospital_id, amox, cipr) %>% group_by(hospital_id) %>% count_df(translate = FALSE) # }