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# Interpret MIC and Disk Diffusion as SIR, or Clean Existing SIR Data

Source: `R/sir.R`
`as.sir.Rd`

Clean up existing SIR values, or interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI. `as.sir()` transforms the input to a new class `sir`, which is an ordered factor containing the levels `S`, `SDD`, `I`, `R`, `NI`.

Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026, see Details. All breakpoints used for interpretation are available in our clinical_breakpoints data set.

## Usage

    as.sir(x, ..., enforce_method = "auto")

    NA_sir_

    is.sir(x)

    is_sir_eligible(x, threshold = 0.05)

    # Default S3 method
    as.sir(
      x,
      S = "^(S|U|1)+$",
      I = "^(I|2)+$",
      R = "^(R|3)+$",
      NI = "^(N|NI|V|4)+$",
      SDD = "^(SDD|D|H|5)+$",
      WT = "^(WT|6)+$",
      NWT = "^(NWT|7)+$",
      NS = "^(NS|8)+$",
      info = interactive(),
      ...
    )

    # S3 method for class 'mic'
    as.sir(
      x,
      mo = NULL,
      ab = deparse(substitute(x)),
      guideline = getOption("AMR_guideline", "EUCAST"),
      uti = NULL,
      capped_mic_handling = getOption("AMR_capped_mic_handling", "standard"),
      as_wt_nwt = identical(breakpoint_type, "ECOFF"),
      add_intrinsic_resistance = FALSE,
      reference_data = AMR::clinical_breakpoints,
      substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint",
        FALSE),
      include_screening = getOption("AMR_include_screening", FALSE),
      include_PKPD = getOption("AMR_include_PKPD", TRUE),
      breakpoint_type = getOption("AMR_breakpoint_type", "human"),
      host = NULL,
      language = get_AMR_locale(),
      verbose = FALSE,
      info = interactive(),
      conserve_capped_values = NULL,
      ...
    )

    # S3 method for class 'disk'
    as.sir(
      x,
      mo = NULL,
      ab = deparse(substitute(x)),
      guideline = getOption("AMR_guideline", "EUCAST"),
      uti = NULL,
      as_wt_nwt = identical(breakpoint_type, "ECOFF"),
      add_intrinsic_resistance = FALSE,
      reference_data = AMR::clinical_breakpoints,
      substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint",
        FALSE),
      include_screening = getOption("AMR_include_screening", FALSE),
      include_PKPD = getOption("AMR_include_PKPD", TRUE),
      breakpoint_type = getOption("AMR_breakpoint_type", "human"),
      host = NULL,
      language = get_AMR_locale(),
      verbose = FALSE,
      info = interactive(),
      ...
    )

    # S3 method for class 'data.frame'
    as.sir(
      x,
      ...,
      col_mo = NULL,
      guideline = getOption("AMR_guideline", "EUCAST"),
      uti = NULL,
      capped_mic_handling = getOption("AMR_capped_mic_handling", "standard"),
      as_wt_nwt = identical(breakpoint_type, "ECOFF"),
      add_intrinsic_resistance = FALSE,
      reference_data = AMR::clinical_breakpoints,
      substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint",
        FALSE),
      include_screening = getOption("AMR_include_screening", FALSE),
      include_PKPD = getOption("AMR_include_PKPD", TRUE),
      breakpoint_type = getOption("AMR_breakpoint_type", "human"),
      host = NULL,
      language = get_AMR_locale(),
      verbose = FALSE,
      info = interactive(),
      parallel = FALSE,
      conserve_capped_values = NULL
    )

    sir_interpretation_history(clean = FALSE)

## Arguments

x

  Vector of values (for class `mic`: MIC values in mg/L, for class `disk`: a disk diffusion radius in millimetres).

...

  For using on a data.frame: selection of columns to apply `as.sir()` to. Supports tidyselect language such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and can thus also be antimicrobial selectors, e.g. `as.sir(df, penicillins())`.

enforce_method

  A character string to force interpretation as a specific method, useful when the S3 class of `x` is lost (e.g., when called from Python via rpy2). Must be one of `"auto"` (default), `"mic"`, or `"disk"`.

  Otherwise: arguments passed on to methods.

threshold

  Maximum fraction of invalid antimicrobial interpretations of `x`, see Examples.

S, I, R, NI, SDD, WT, NWT, NS

  A case-independent regular expression to translate input to this result. This regular expression will be run after all non-letters and whitespaces are removed from the input.

info

  A logical to print information about the process, defaults to `TRUE` only in interactive sessions.

mo

  A vector (or column name) with characters that can be coerced to valid microorganism codes with `as.mo()`, can be left empty to determine it automatically.

ab

  A vector (or column name) with characters that can be coerced to a valid antimicrobial drug code with `as.ab()`.

guideline

  A guideline name (or column name) to use for SIR interpretation. Defaults to EUCAST 2026 (the latest implemented EUCAST guideline in the clinical_breakpoints data set), but can be set with the package option `AMR_guideline`. Currently supports EUCAST (2011-2026) and CLSI (2011-2026), see Details. Using a column name allows for straightforward interpretation of historical data, which must be analysed in the context of, for example, different years.

uti

  (Urinary Tract Infection) a vector (or column name) with logicals (`TRUE` or `FALSE`) to specify whether a UTI specific interpretation from the guideline should be chosen. For using `as.sir()` on a data.frame, this can also be a column containing logicals or when left blank, the data set will be searched for a column 'specimen', and rows within this column containing 'urin' (such as 'urine', 'urina') will be regarded isolates from a UTI. See Examples.

capped_mic_handling

  A character string that controls how MIC values with a cap (i.e., starting with `<`, `<=`, `>`, or `>=`) are interpreted. Supports the following options:

  `"none"`

  - `<=`, `<`, `>` and `>=` are ignored.

  `"conservative"` (default)

  - `<=`, `<`, `>` and `>=` return `"NI"` (non-interpretable) if the true MIC could be at either side of the breakpoint.

  - This is the only mode that preserves uncertainty for ECOFFs.

  `"standard"`

  - `<=` and `>=` return `"NI"` (non-interpretable) if the true MIC could be at either side of the breakpoint.

  - `<` always returns `"S"`, regardless of the breakpoint.

  - `>` always returns `"R"`, regardless of the breakpoint.

  `"lenient"`

  - `<=` and `<` always return `"S"`, regardless of the breakpoint.

  - `>=` and `>` always return `"R"`, regardless of the breakpoint.

  The default `"conservative"` setting ensures cautious handling of uncertain values while preserving interpretability. This option can also be set with the package option `AMR_capped_mic_handling`.

as_wt_nwt

  A logical to return `"WT"`/`"NWT"` instead of `"S"`/`"R"`. Defaults to `TRUE` when using ECOFFs, i.e., when `breakpoint_type` is set to `"ECOFF"`.

add_intrinsic_resistance

  (only useful when using a EUCAST guideline) a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in Klebsiella species. Determination is based on the intrinsic_resistant data set, that itself is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021).

reference_data

  A data.frame to be used for interpretation, which defaults to the clinical_breakpoints data set. Changing this argument allows for using own interpretation guidelines. This argument must have the same column names as the clinical_breakpoints data set. Column types are coerced automatically where possible: the `mo` column is passed through `as.mo()`, the `ab` column through `as.ab()`, and plain character, numeric, or logical columns are cast to the expected type. When `reference_data` is manually set, the `guideline` argument is optional: if omitted (or if its value does not match any row in the custom data), all rows in `reference_data` are considered. If `guideline` is set to a value that exists in the `guideline` column of the custom data, only matching rows are used — useful when a single custom table contains multiple guidelines. For the R classification, the EUCAST convention is used by default: MIC values `> breakpoint_R` and disk diffusion values `< breakpoint_R` are classified as R, with values between `breakpoint_S` and `breakpoint_R` classified as I (or SDD). Only when using the standard clinical_breakpoints with a CLSI guideline are the closed-interval rules (`>= breakpoint_R` for MIC, `<= breakpoint_R` for disk) applied; custom `reference_data` always uses the open-interval (EUCAST) convention regardless of the guideline name.

substitute_missing_r_breakpoint

  A logical to indicate that a missing clinical breakpoints for R (resistant) must be substituted with R - the default is `FALSE`. Some (especially CLSI) breakpoints only have a breakpoint for S, meaning that the outcome can only be `"S"` or `NA`. Setting this to `TRUE` will convert the `NA`s in these cases to `"R"`. Can also be set with the package option `AMR_substitute_missing_r_breakpoint`.

include_screening

  A logical to indicate that clinical breakpoints for screening are allowed - the default is `FALSE`. Can also be set with the package option `AMR_include_screening`.

include_PKPD

  A logical to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is `TRUE`. Can also be set with the package option `AMR_include_PKPD`.

breakpoint_type

  The type of breakpoints to use, either `"ECOFF"`, `"animal"`, or `"human"`. ECOFF stands for Epidemiological Cut-Off values. The default is `"human"`, which can also be set with the package option `AMR_breakpoint_type`. If `host` is set to values of veterinary species, this will automatically be set to `"animal"`.

host

  A vector (or column name) with characters to indicate the host. Only useful for veterinary breakpoints, as it requires `breakpoint_type = "animal"`. The values can be any text resembling the animal species, even in any of the 28 supported languages of this package. For foreign languages, be sure to set the language with `set_AMR_locale()` (though it will be automatically guessed based on the system language).

language

  Language to convert values set in `host` when using animal breakpoints. Use one of these supported language names or ISO 639-1 codes: English (en), Arabic (ar), Bengali (bn), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Hindi (hi), Indonesian (id), Italian (it), Japanese (ja), Korean (ko), Norwegian (no), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swahili (sw), Swedish (sv), Turkish (tr), Ukrainian (uk), Urdu (ur), or Vietnamese (vi).

verbose

  A logical to indicate that all notes should be printed during interpretation of MIC values or disk diffusion values.

conserve_capped_values

  Deprecated, use `capped_mic_handling` instead.

col_mo

  Column name of the names or codes of the microorganisms (see `as.mo()`) - the default is the first column of class `mo`. Values will be coerced using `as.mo()`.

parallel

  A logical to indicate if parallel computing must be used, defaults to `FALSE`. Requires the `future.apply` package. A non-sequential `future::plan()` must already be active before setting `parallel = TRUE` — for example, `future::plan(future::multisession)`. An error is thrown if `parallel = TRUE` is used without a plan set by the user. Parallelism distributes columns (and optionally row batches) across workers; it is most beneficial when there are many antibiotic columns and a large number of rows.

clean

  A logical to indicate whether previously stored results should be forgotten after returning the 'logbook' with results.

## Value

Ordered factor with new class `sir`

## Details

Note: The clinical breakpoints in this package were validated through, and imported from, WHONET. The public use of this `AMR` package has been endorsed by both CLSI and EUCAST. See clinical_breakpoints for more information.

### How it Works

The `as.sir()` function can work in four ways:

1. For cleaning raw / untransformed data. The data will be cleaned to only contain valid values, namely: S for susceptible, I for intermediate or 'susceptible, increased exposure', R for resistant, NI for non-interpretable, and SDD for susceptible dose-dependent. Each of these can be set using a regular expression. Furthermore, `as.sir()` will try its best to clean with some intelligence. For example, mixed values with SIR interpretations and MIC values such as `"<0.25; S"` will be coerced to `"S"`. Combined interpretations for multiple test methods (as seen in laboratory records) such as `"S; S"` will be coerced to `"S"`, but a value like `"S; I"` will return `NA` with a warning that the input is invalid.

2. For interpreting minimum inhibitory concentration (MIC) values according to EUCAST or CLSI. You must clean your MIC values first using `as.mic()`, that also gives your columns the new data class `mic`. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` argument.

   - Example to apply using `dplyr`:

         your_data %>% mutate_if(is.mic, as.sir)
         your_data %>% mutate(across(where(is.mic), as.sir))
         your_data %>% mutate_if(is.mic, as.sir, ab = "column_with_antibiotics", mo = "column_with_microorganisms")
         your_data %>% mutate_if(is.mic, as.sir, ab = c("cipro", "ampicillin", ...), mo = c("E. coli", "K. pneumoniae", ...))

         # for veterinary breakpoints, also set `host`:
         your_data %>% mutate_if(is.mic, as.sir, host = "column_with_animal_species", guideline = "CLSI")

         # fast processing with parallel computing (requires future.apply):
         as.sir(your_data, ..., parallel = TRUE)

   - Operators like "<=" will be considered according to the `capped_mic_handling` setting. At default, an MIC value of e.g. ">2" will return "NI" (non-interpretable) if the breakpoint is 4-8; the true MIC could be at either side of the breakpoint. This is to prevent that capped values from raw laboratory data would not be treated conservatively.

   - Note: When using CLSI as the guideline, MIC values must be log2-based doubling dilutions. Values not in this format, will be automatically rounded up to the nearest log2 level as CLSI instructs, and a warning will be thrown.

3. For interpreting disk diffusion diameters according to EUCAST or CLSI. You must clean your disk zones first using `as.disk()`, that also gives your columns the new data class `disk`. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` argument.

   - Example to apply using `dplyr`:

         your_data %>% mutate_if(is.disk, as.sir)
         your_data %>% mutate(across(where(is.disk), as.sir))
         your_data %>% mutate_if(is.disk, as.sir, ab = "column_with_antibiotics", mo = "column_with_microorganisms")
         your_data %>% mutate_if(is.disk, as.sir, ab = c("cipro", "ampicillin", ...), mo = c("E. coli", "K. pneumoniae", ...))

         # for veterinary breakpoints, also set `host`:
         your_data %>% mutate_if(is.disk, as.sir, host = "column_with_animal_species", guideline = "CLSI")

         # fast processing with parallel computing (requires future.apply):
         as.sir(your_data, ..., parallel = TRUE)

4. For interpreting a complete data set, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running `as.sir(your_data)`.

For points 2, 3 and 4: Use `sir_interpretation_history()` to retrieve a data.frame with all results of all previous `as.sir()` calls. It also contains notes about interpretation, and the exact input and output values.

### Supported Guidelines

For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are:

- For clinical microbiology: EUCAST 2011-2026 and CLSI 2011-2026;

- For veterinary microbiology: EUCAST 2021-2026 and CLSI 2019-2026;

- For ECOFFs (Epidemiological Cut-off Values): EUCAST 2020-2026 and CLSI 2022-2026.

The `guideline` argument must be set to e.g., `"EUCAST 2026"` or `"CLSI 2026"`. By simply using `"EUCAST"` (the default) or `"CLSI"` as input, the latest included version of that guideline will automatically be selected. Importantly, using a column name of your data instead, allows for straightforward interpretation of historical data that must be analysed in the context of, for example, different years.

You can set your own data set using the `reference_data` argument. The `guideline` argument will then be ignored.

It is also possible to set the default guideline with the package option `AMR_guideline` (e.g. in your `.Rprofile` file), such as:

      options(AMR_guideline = "CLSI")
      options(AMR_guideline = "CLSI 2018")
      options(AMR_guideline = "EUCAST 2020")
      # or to reset:
      options(AMR_guideline = NULL)

### Working with Veterinary Breakpoints

When using veterinary breakpoints (i.e., setting `breakpoint_type = "animal"`), a column with animal species must be available or set manually using the `host` argument. The column must contain names like "dogs", "cats", "cattle", "swine", "horses", "poultry", or "aquatic". Other animal names like "goats", "rabbits", or "monkeys" are also recognised but may not be available in all guidelines. Matching is case-insensitive and accepts Latin-based synonyms (e.g., "bovine" for cattle and "canine" for dogs).

Regarding choice of veterinary guidelines, these might be the best options to set before analysis:

      options(AMR_guideline = "CLSI")
      options(AMR_breakpoint_type = "animal")

### After Interpretation

After using `as.sir()`, you can use the `interpretive_rules()` to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.

To determine which isolates are multi-drug resistant, be sure to run `mdro()` (which applies the MDR/PDR/XDR guideline from 2012 at default) on a data set that contains S/I/R values. Read more about detecting multidrug-resistant organisms here.

### Other

The function `is.sir()` detects if the input contains class `sir`. If the input is a data.frame or list, it iterates over all columns/items and returns a logical vector.

The base R function `as.double()` can be used to retrieve quantitative values from a `sir` object: `"S"` = 1, `"I"`/`"SDD"` = 2, `"R"` = 3. All other values are rendered `NA`. Note: Do not use `as.integer()`, since that (because of how R works internally) will return the factor level indices, and not these aforementioned quantitative values.

The function `is_sir_eligible()` returns `TRUE` when a column contains at most 5% potentially invalid antimicrobial interpretations, and `FALSE` otherwise. The threshold of 5% can be set with the `threshold` argument. If the input is a data.frame, it iterates over all columns and returns a logical vector.

`NA_sir_` is a missing value of the new `sir` class, analogous to e.g. base R's `NA_character_`.

## 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/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/).

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.

## Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visit our website for direct download links, or explore the actual files in our GitHub repository.

## References

For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:

- CLSI M39: Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 2011-2026, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.

- CLSI M100: Performance Standard for Antimicrobial Susceptibility Testing, 2011-2026, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/.

- CLSI VET01: Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals, 2019-2026, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet01/.

- EUCAST Breakpoint tables for interpretation of MICs and zone diameters, 2011-2026, European Committee on Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/clinical-breakpoint-tables/.

- WHONET as a source for machine-reading the clinical breakpoints (read more here), 1989-2026, WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. https://whonet.org/.

## See also

`as.mic()`, `as.disk()`, `as.mo()`

## Examples

    example_isolates
    #> # A tibble: 2,000 × 46
    #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX
    #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
    #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA
    #> # ℹ 1,990 more rows
    #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
    #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
    #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
    #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
    #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
    #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …

    summary(example_isolates[, 1:10]) # see all SIR results at a glance
    #>       date                 patient          age              gender
    #>  Min.   :2002-01-02   Length   :2000   Min.   : 0.00   Length   :2000
    #>  1st Qu.:2005-07-31   N.unique : 981   1st Qu.:63.00   N.unique :   2
    #>  Median :2009-07-31   N.blank  :   0   Median :74.00   N.blank  :   0
    #>  Mean   :2009-11-20   Min.nchar:   6   Mean   :70.69   Min.nchar:   1
    #>  3rd Qu.:2014-05-30   Max.nchar:   6   3rd Qu.:82.00   Max.nchar:   1
    #>  Max.   :2017-12-28                    Max.   :97.00
    #>         ward           mo                  PEN
    #>  Length   :2000   Class :mo             Class:sir
    #>  N.unique :   3   <NA>  :0              %S   :25.6% (n=417)
    #>  N.blank  :   0   Unique:90             %SDD : 0.0% (n=0)
    #>  Min.nchar:   3   #1    :B_ESCHR_COLI   %I   : 0.7% (n=11)
    #>  Max.nchar:  10   #2    :B_STPHY_CONS   %R   :73.7% (n=1201)
    #>                   #3    :B_STPHY_AURS   %NI  : 0.0% (n=0)
    #>     OXA                   FLC                   AMX
    #>  Class:sir             Class:sir             Class:sir
    #>  %S   :68.8% (n=251)   %S   :70.5% (n=665)   %S   :40.2% (n=543)
    #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)
    #>  %I   : 0.0% (n=0)     %I   : 0.0% (n=0)     %I   : 0.2% (n=3)
    #>  %R   :31.2% (n=114)   %R   :29.5% (n=278)   %R   :59.6% (n=804)
    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)

    # create some example data sets, with combined MIC values and disk zones
    df_wide <- data.frame(
      microorganism = "Escherichia coli",
      amoxicillin = as.mic(8),
      cipro = as.mic(0.256),
      tobra = as.disk(16),
      genta = as.disk(18),
      ERY = "R"
    )
    df_long <- data.frame(
      bacteria = rep("Escherichia coli", 4),
      antibiotic = c("amoxicillin", "cipro", "tobra", "genta"),
      mics = as.mic(c(0.01, 1, 4, 8)),
      disks = as.disk(c(6, 10, 14, 18)),
      guideline = c("EUCAST 2021", "EUCAST 2022", "EUCAST 2023", "EUCAST 2024")
    )
    # and clean previous SIR interpretation logs
    x <- sir_interpretation_history(clean = TRUE)

    # For INTERPRETING disk diffusion and MIC values -----------------------

    # most basic application:
    as.sir(df_wide)
    #>      microorganism amoxicillin cipro tobra genta ERY
    #> 1 Escherichia coli           S     I     S     S   R

    # return a 'logbook' about the results:
    sir_interpretation_history()
    #> # A tibble: 4 × 18
    #>   datetime            index method ab_given    mo_given   host_given input_given
    #>   <dttm>              <int> <chr>  <chr>       <chr>      <chr>      <chr>
    #> 1 2026-08-20 12:34:53     1 MIC    amoxicillin Escherich… human      8
    #> 2 2026-08-20 12:34:53     1 MIC    cipro       Escherich… human      0.256
    #> 3 2026-08-20 12:34:53     1 DISK   tobra       Escherich… human      16
    #> 4 2026-08-20 12:34:53     1 DISK   genta       Escherich… human      18
    #> # ℹ 11 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>,
    #> #   outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
    #> #   breakpoint_S_R <chr>, site <chr>

    # \donttest{

    ## Using dplyr -------------------------------------------------
    if (require("dplyr")) {
      # approaches that all work without additional arguments:
      df_wide %>% mutate_if(is.mic, as.sir)
      df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
      df_wide %>% mutate(across(where(is.mic), as.sir))

      df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)
      df_wide %>% mutate(across(amoxicillin:tobra, as.sir))

      df_wide %>% mutate(across(aminopenicillins(), as.sir))

      # approaches that all work with additional arguments:
      df_long %>%
        # given a certain data type, e.g. MIC values
        mutate_if(is.mic, as.sir,
          mo = "bacteria",
          ab = "antibiotic",
          guideline = "guideline"
        )
      df_long %>%
        mutate(across(
          where(is.mic),
          function(x) {
            as.sir(x,
              mo = "bacteria",
              ab = "antibiotic",
              guideline = "CLSI"
            )
          }
        ))
      df_wide %>%
        # given certain columns, e.g. from 'cipro' to 'genta'
        mutate_at(vars(cipro:genta), as.sir,
          mo = "bacteria",
          guideline = "CLSI"
        )
      df_wide %>%
        mutate(across(
          cipro:genta,
          function(x) {
            as.sir(x,
              mo = "bacteria",
              guideline = "CLSI"
            )
          }
        ))

      # for veterinary breakpoints, add 'host':
      df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
      df_long %>%
        # given a certain data type, e.g. MIC values
        mutate_if(is.mic, as.sir,
          mo = "bacteria",
          ab = "antibiotic",
          host = "animal_species",
          guideline = "CLSI"
        )
      df_long %>%
        mutate(across(
          where(is.mic),
          function(x) {
            as.sir(x,
              mo = "bacteria",
              ab = "antibiotic",
              host = "animal_species",
              guideline = "CLSI"
            )
          }
        ))
      df_wide %>%
        mutate_at(vars(cipro:genta), as.sir,
          mo = "bacteria",
          ab = "antibiotic",
          host = "animal_species",
          guideline = "CLSI"
        )
      df_wide %>%
        mutate(across(
          cipro:genta,
          function(x) {
            as.sir(x,
              mo = "bacteria",
              host = "animal_species",
              guideline = "CLSI"
            )
          }
        ))

      # to include information about urinary tract infections (UTI)
      data.frame(
        mo = "E. coli",
        nitrofuratoin = c("<= 2", 32),
        from_the_bladder = c(TRUE, FALSE)
      ) %>%
        as.sir(uti = "from_the_bladder")

      data.frame(
        mo = "E. coli",
        nitrofuratoin = c("<= 2", 32),
        specimen = c("urine", "blood")
      ) %>%
        as.sir() # automatically determines urine isolates

      df_wide %>%
        mutate_at(vars(cipro:genta), as.sir, mo = "E. coli", uti = TRUE)
    }
    #> ℹ For `aminopenicillins()` using column amoxicillin
    #> Warning: There was 1 warning in `mutate()`.
    #> ℹ In argument: `across(...)`.
    #> Caused by warning:
    #> ! Some MICs were converted to the nearest higher log2 level, following the CLSI
    #> interpretation guideline.
    #> Warning: There was 1 warning in `mutate()`.
    #> ℹ In argument: `cipro = (function (x, ..., enforce_method = "auto") ...`.
    #> Caused by warning:
    #> ! Some MICs were converted to the nearest higher log2 level, following the CLSI
    #> interpretation guideline.
    #> Warning: There was 1 warning in `mutate()`.
    #> ℹ In argument: `across(...)`.
    #> Caused by warning:
    #> ! Some MICs were converted to the nearest higher log2 level, following the CLSI
    #> interpretation guideline.
    #> Warning: There was 1 warning in `mutate()`.
    #> ℹ In argument: `mics = (function (x, ..., enforce_method = "auto") ...`.
    #> Caused by warning:
    #> ! Some MICs were converted to the nearest higher log2 level, following the CLSI
    #> interpretation guideline.
    #> Warning: There was 1 warning in `mutate()`.
    #> ℹ In argument: `across(...)`.
    #> Caused by warning:
    #> ! Some MICs were converted to the nearest higher log2 level, following the CLSI
    #> interpretation guideline.
    #> Warning: There was 1 warning in `mutate()`.
    #> ℹ In argument: `cipro = (function (x, ..., enforce_method = "auto") ...`.
    #> Caused by warning:
    #> ! Some MICs were converted to the nearest higher log2 level, following the CLSI
    #> interpretation guideline.
    #> Warning: There was 1 warning in `mutate()`.
    #> ℹ In argument: `across(...)`.
    #> Caused by warning:
    #> ! Some MICs were converted to the nearest higher log2 level, following the CLSI
    #> interpretation guideline.
    #>      microorganism amoxicillin cipro tobra genta ERY
    #> 1 Escherichia coli           8  <NA>     S     S   R

    ## Using base R ------------------------------------------------

    # for single values
    as.sir(
      x = as.mic(2),
      mo = as.mo("S. pneumoniae"),
      ab = "AMP",
      guideline = "EUCAST"
    )
    #> Class <sir>
    #> [1] R

    as.sir(
      x = as.disk(18),
      mo = "Strep pneu", # `mo` will be coerced with as.mo()
      ab = "ampicillin", # and `ab` with as.ab()
      guideline = "EUCAST"
    )
    #> Class <sir>
    #> [1] R

    # For CLEANING existing SIR values -------------------------------------

    as.sir(c("S", "SDD", "I", "R", "NI", "A", "B", "C"))
    #> Warning: in `as.sir()`: 3 results truncated (38%) that were invalid antimicrobial
    #> interpretations: "A", "B", and "C"
    #> Class <sir>
    #> [1] S    SDD  I    R    NI   <NA> <NA> <NA>
    as.sir("<= 0.002; S") # will return "S"
    #> Class <sir>
    #> [1] S

    as.sir(c(1, 2, 3))
    #> ℹ `as.sir()`: Interpreting input value 1 as "S", 2 as "I", and 3 as "R"
    #> Class <sir>
    #> [1] S I R
    as.sir(c(1, 2, 3), S = 3, I = 2, R = 1)
    #> ℹ `as.sir()`: Interpreting input value 1 as "R", 2 as "I", and 3 as "S"
    #> Class <sir>
    #> [1] R I S

    sir_data <- as.sir(c(rep("S", 474), rep("I", 36), rep("R", 370)))
    is.sir(sir_data)
    #> [1] TRUE
    plot(sir_data) # for percentages

    barplot(sir_data) # for frequencies

    # as common in R, you can use as.integer() to return factor indices:
    as.integer(as.sir(c("S", "SDD", "I", "R", "NI", NA)))
    #> [1]  1  2  3  4  5 NA

    # but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R:
    as.double(as.sir(c("S", "SDD", "I", "R", "NI", NA)))
    #> [1]  1  2  2  3 NA NA

    # the dplyr way
    if (require("dplyr")) {
      example_isolates %>%
        mutate_at(vars(PEN:RIF), as.sir)
      # same:
      example_isolates %>%
        as.sir(PEN:RIF)

      # fastest way to transform all columns with already valid AMR results to class `sir`:
      example_isolates %>%
        mutate_if(is_sir_eligible, as.sir)

      # since dplyr 1.0.0, this can also be the more impractical:
      # example_isolates %>%
      #   mutate(across(where(is_sir_eligible), as.sir))
    }
    #> # A tibble: 2,000 × 46
    #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX
    #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
    #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA
    #> # ℹ 1,990 more rows
    #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
    #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
    #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
    #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
    #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
    #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
    # }