# Calculate resistance of isolates — portion • AMR (for R)
These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples. portion_R and portion_IR can be used to calculate resistance, portion_S and portion_SI can be used to calculate susceptibility.

# Calculate resistance of isolates

`portion.Rd`

These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in `dplyr`s `summarise` and support grouped variables, see Examples.

`portion_R` and `portion_IR` can be used to calculate resistance, `portion_S` and `portion_SI` can be used to calculate susceptibility.

    portion_R(..., minimum = 30, as_percent = FALSE,
      also_single_tested = FALSE)

    portion_IR(..., minimum = 30, as_percent = FALSE,
      also_single_tested = FALSE)

    portion_I(..., minimum = 30, as_percent = FALSE,
      also_single_tested = FALSE)

    portion_SI(..., minimum = 30, as_percent = FALSE,
      also_single_tested = FALSE)

    portion_S(..., minimum = 30, as_percent = FALSE,
      also_single_tested = FALSE)

    portion_df(data, translate_ab = getOption("get_antibiotic_names",
      "official"), minimum = 30, as_percent = FALSE, combine_IR = FALSE)

## Arguments

... | one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with `as.rsi` if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
minimum | the minimal amount of available isolates. Any number lower than `minimum` will return `NA` with a warning. The default number of `30` isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
as_percent | a logical to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of `0.123456` will then be returned as `"12.3%"`.
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 `portion_S` and R in case of `portion_R`). This would lead to selection bias in almost all cases.
data | a `data.frame` containing columns with class `rsi` (see `as.rsi`)
translate_ab | a column name of the `antibiotics` data set to translate the antibiotic abbreviations to, using `abname`. This can be set with `getOption("get_antibiotic_names")`.
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)

## Source

M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.

Wickham H. Tidy Data. The Journal of Statistical Software, vol. 59, 2014. http://vita.had.co.nz/papers/tidy-data.html

## Value

Double or, when `as_percent = TRUE`, a character.

## Details

Remember that you should filter your table to let it contain only first isolates! Use `first_isolate` to determine them in your data set.

These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the `count` functions to count isolates. Low counts can infuence the outcome - these `portion` functions may camouflage this, since they only return the portion albeit being dependent on the `minimum` parameter.

`portion_df` takes any variable from `data` that has an `"rsi"` class (created with `as.rsi`) and calculates the portions 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"`.

The old `rsi` function is still available for backwards compatibility but is deprecated.
To calculate the probability (p) of susceptibility of one antibiotic, we use this formula:

To calculate the probability (p) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator).
For two antibiotics:
For three antibiotics:
And so on.

## See also

`count_*` to count resistant and susceptible isolates.

## Examples

    # NOT RUN {
    # septic_patients is a data set available in the AMR package. It is true, genuine data.
    ?septic_patients

    # Calculate resistance
    portion_R(septic_patients$amox)
    portion_IR(septic_patients$amox)

    # Or susceptibility
    portion_S(septic_patients$amox)
    portion_SI(septic_patients$amox)

    # Do the above with pipes:
    library(dplyr)
    septic_patients %>% portion_R(amox)
    septic_patients %>% portion_IR(amox)
    septic_patients %>% portion_S(amox)
    septic_patients %>% portion_SI(amox)

    septic_patients %>%
      group_by(hospital_id) %>%
      summarise(p = portion_S(cipr),
                n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr

    septic_patients %>%
      group_by(hospital_id) %>%
      summarise(R = portion_R(cipr, as_percent = TRUE),
                I = portion_I(cipr, as_percent = TRUE),
                S = portion_S(cipr, as_percent = TRUE),
                n = n_rsi(cipr), # works like n_distinct in dplyr
                total = n())     # NOT the amount of tested isolates!

    # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
    # so we can see that combination therapy does a lot more than mono therapy:
    septic_patients %>% portion_S(amcl)       # S = 67.1%
    septic_patients %>% count_all(amcl)       # n = 1576

    septic_patients %>% portion_S(gent)       # S = 74.0%
    septic_patients %>% count_all(gent)       # n = 1855

    septic_patients %>% portion_S(amcl, gent) # S = 92.0%
    septic_patients %>% count_all(amcl, gent) # n = 1517

    septic_patients %>%
      group_by(hospital_id) %>%
      summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
                cipro_n = count_all(cipr),
                genta_p = portion_S(gent, as_percent = TRUE),
                genta_n = count_all(gent),
                combination_p = portion_S(cipr, gent, as_percent = TRUE),
                combination_n = count_all(cipr, gent))

    # Get portions S/I/R immediately of all rsi columns
    septic_patients %>%
      select(amox, cipr) %>%
      portion_df(translate = FALSE)

    # It also supports grouping variables
    septic_patients %>%
      select(hospital_id, amox, cipr) %>%
      group_by(hospital_id) %>%
      portion_df(translate = FALSE)

    # }# NOT RUN {
    # calculate current empiric combination therapy of Helicobacter gastritis:
    my_table %>%
      filter(first_isolate == TRUE,
             genus == "Helicobacter") %>%
      summarise(p = portion_S(amox, metr),  # amoxicillin with metronidazole
                n = count_all(amox, metr))
    # }