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

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](https://rdrr.io/pkg/data.table/man/fctr.html) 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](https://amr-for-r.org/reference/clinical_breakpoints.md)
data set.

## Usage

``` r
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`](https://amr-for-r.org/reference/as.mic.md): MIC values in
  mg/L, for class [`disk`](https://amr-for-r.org/reference/as.disk.md):
  a disk diffusion radius in millimetres).

- ...:

  For using on a [data.frame](https://rdrr.io/r/base/data.frame.html):
  selection of columns to apply `as.sir()` to. Supports [tidyselect
  language](https://tidyselect.r-lib.org/reference/starts_with.html)
  such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and
  can thus also be [antimicrobial
  selectors](https://amr-for-r.org/reference/antimicrobial_selectors.md),
  e.g. `as.sir(df, penicillins())`.

- enforce_method:

  A [character](https://rdrr.io/r/base/character.html) 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](https://rdrr.io/r/base/regex.html) 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](https://rdrr.io/r/base/logical.html) to print information
  about the process, defaults to `TRUE` only in [interactive
  sessions](https://rdrr.io/r/base/interactive.html).

- mo:

  A vector (or column name) with
  [character](https://rdrr.io/r/base/character.html)s that can be
  coerced to valid microorganism codes with
  [`as.mo()`](https://amr-for-r.org/reference/as.mo.md), can be left
  empty to determine it automatically.

- ab:

  A vector (or column name) with
  [character](https://rdrr.io/r/base/character.html)s that can be
  coerced to a valid antimicrobial drug code with
  [`as.ab()`](https://amr-for-r.org/reference/as.ab.md).

- 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](https://amr-for-r.org/reference/clinical_breakpoints.md)
  data set), but can be set with the package option
  [`AMR_guideline`](https://amr-for-r.org/reference/AMR-options.md).
  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
  [logical](https://rdrr.io/r/base/logical.html)s (`TRUE` or `FALSE`) to
  specify whether a UTI specific interpretation from the guideline
  should be chosen. For using `as.sir()` on a
  [data.frame](https://rdrr.io/r/base/data.frame.html), this can also be
  a column containing [logical](https://rdrr.io/r/base/logical.html)s 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](https://rdrr.io/r/base/character.html) 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`](https://amr-for-r.org/reference/AMR-options.md).

- as_wt_nwt:

  A [logical](https://rdrr.io/r/base/logical.html) 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](https://rdrr.io/r/base/logical.html) 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](https://amr-for-r.org/reference/intrinsic_resistant.md)
  data set, that itself is based on ['EUCAST Expert Rules' and 'EUCAST
  Intrinsic Resistance and Unusual Phenotypes'
  v3.3](https://www.eucast.org/bacteria/important-additional-information/expert-rules/)
  (2021).

- reference_data:

  A [data.frame](https://rdrr.io/r/base/data.frame.html) to be used for
  interpretation, which defaults to the
  [clinical_breakpoints](https://amr-for-r.org/reference/clinical_breakpoints.md)
  data set. Changing this argument allows for using own interpretation
  guidelines. This argument must have the same column names as the
  [clinical_breakpoints](https://amr-for-r.org/reference/clinical_breakpoints.md)
  data set. Column types are coerced automatically where possible: the
  `mo` column is passed through
  [`as.mo()`](https://amr-for-r.org/reference/as.mo.md), the `ab` column
  through [`as.ab()`](https://amr-for-r.org/reference/as.ab.md), 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](https://amr-for-r.org/reference/clinical_breakpoints.md)
  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](https://rdrr.io/r/base/logical.html) 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`](https://amr-for-r.org/reference/AMR-options.md).

- include_screening:

  A [logical](https://rdrr.io/r/base/logical.html) to indicate that
  clinical breakpoints for screening are allowed - the default is
  `FALSE`. Can also be set with the package option
  [`AMR_include_screening`](https://amr-for-r.org/reference/AMR-options.md).

- include_PKPD:

  A [logical](https://rdrr.io/r/base/logical.html) 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`](https://amr-for-r.org/reference/AMR-options.md).

- 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`](https://amr-for-r.org/reference/AMR-options.md).
  If `host` is set to values of veterinary species, this will
  automatically be set to `"animal"`.

- host:

  A vector (or column name) with
  [character](https://rdrr.io/r/base/character.html)s 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()`](https://amr-for-r.org/reference/translate.md)
  (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](https://en.wikipedia.org/wiki/ISO_639-1): 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](https://rdrr.io/r/base/logical.html) 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()`](https://amr-for-r.org/reference/as.mo.md)) - the default
  is the first column of class
  [`mo`](https://amr-for-r.org/reference/as.mo.md). Values will be
  coerced using [`as.mo()`](https://amr-for-r.org/reference/as.mo.md).

- parallel:

  A [logical](https://rdrr.io/r/base/logical.html) to indicate if
  parallel computing must be used, defaults to `FALSE`. Requires the
  [`future.apply`](https://future.apply.futureverse.org/reference/future_lapply.html)
  package. **A non-sequential
  [`future::plan()`](https://future.futureverse.org/reference/plan.html)
  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](https://rdrr.io/r/base/logical.html) to indicate whether
  previously stored results should be forgotten after returning the
  'logbook' with results.

## Value

Ordered [factor](https://rdrr.io/pkg/data.table/man/fctr.html) with new
class `sir`

## Details

*Note: The clinical breakpoints in this package were validated through,
and imported from, [WHONET](https://whonet.org). The public use of this
`AMR` package has been endorsed by both CLSI and EUCAST. See
[clinical_breakpoints](https://amr-for-r.org/reference/clinical_breakpoints.md)
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](https://rdrr.io/r/base/regex.html). 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()`](https://amr-for-r.org/reference/as.mic.md), that
    also gives your columns the new data class
    [`mic`](https://amr-for-r.org/reference/as.mic.md). 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()`](https://amr-for-r.org/reference/as.disk.md), that also
    gives your columns the new data class
    [`disk`](https://amr-for-r.org/reference/as.disk.md). 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](https://rdrr.io/r/base/data.frame.html) 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`](https://amr-for-r.org/reference/AMR-options.md) (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()`](https://amr-for-r.org/reference/interpretive_rules.md)
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()`](https://amr-for-r.org/reference/mdro.md) (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](https://amr-for-r.org/reference/mdro.md).

### Other

The function `is.sir()` detects if the input contains class `sir`. If
the input is a [data.frame](https://rdrr.io/r/base/data.frame.html) or
[list](https://rdrr.io/r/base/list.html), it iterates over all
columns/items and returns a
[logical](https://rdrr.io/r/base/logical.html) vector.

The base R function [`as.double()`](https://rdrr.io/r/base/double.html)
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()`](https://rdrr.io/r/base/integer.html), 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](https://rdrr.io/r/base/data.frame.html), it iterates over
all columns and returns a [logical](https://rdrr.io/r/base/logical.html)
vector.

`NA_sir_` is a missing value of the new `sir` class, analogous to e.g.
base R's [`NA_character_`](https://rdrr.io/r/base/NA.html).

## 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()`](https://amr-for-r.org/reference/proportion.md)
(equal to
[`proportion_SI()`](https://amr-for-r.org/reference/proportion.md)) to
determine antimicrobial susceptibility and
[`count_susceptible()`](https://amr-for-r.org/reference/count.md) (equal
to [`count_SI()`](https://amr-for-r.org/reference/count.md)) 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](https://amr-for-r.org/articles/datasets.html), or explore the
actual files in [our GitHub
repository](https://github.com/msberends/AMR/tree/main/data-raw/datasets).

## 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](https://amr-for-r.org/reference/clinical_breakpoints.html#imported-from-whonet)),
  1989-2026, *WHO Collaborating Centre for Surveillance of Antimicrobial
  Resistance*. <https://whonet.org/>.

## See also

[`as.mic()`](https://amr-for-r.org/reference/as.mic.md),
[`as.disk()`](https://amr-for-r.org/reference/as.disk.md),
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md)

## Examples

``` r
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>, …
# }
```
