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# 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/r/base/factor.html)
containing the levels `S`, `SDD`, `I`, `R`, `NI`.
Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI
2011-2025, 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, ...)
NA_sir_
is.sir(x)
is_sir_eligible(x, threshold = 0.05)
# Default S3 method
as.sir(x, S = "^(S|U)+$", I = "^(I)+$", R = "^(R)+$",
NI = "^(N|NI|V)+$", SDD = "^(SDD|D|H)+$", 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"),
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,
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"),
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, max_cores = -1, conserve_capped_values = NULL)
sir_interpretation_history(clean = FALSE)
```
## Source
For interpretations of minimum inhibitory concentration (MIC) values and
disk diffusion diameters:
- **CLSI M39: Analysis and Presentation of Cumulative Antimicrobial
Susceptibility Test Data**, 2011-2025, *Clinical and Laboratory
Standards Institute* (CLSI).
<https://clsi.org/standards/products/microbiology/documents/m39/>.
- **CLSI M100: Performance Standard for Antimicrobial Susceptibility
Testing**, 2011-2025, *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**,
2019-2025, *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**, 2011-2025, *European Committee on Antimicrobial
Susceptibility Testing* (EUCAST).
<https://www.eucast.org/clinical_breakpoints>.
- **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)),
1989-2025, *WHO Collaborating Centre for Surveillance of Antimicrobial
Resistance*. <https://whonet.org/>.
## 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)
such as `as.sir(df, penicillins())`.
Otherwise: arguments passed on to methods.
- threshold:
Maximum fraction of invalid antimicrobial interpretations of `x`, see
*Examples*.
- S, I, R, NI, SDD:
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 2025 (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 (2011-2025) and CLSI (2011-2025), 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 treated as-is.
- `<` and `>` are treated as-is.
`"conservative"`
- `<=` and `>=` return `"NI"` (non-interpretable) if the MIC is within
the breakpoint guideline range.
- `<` always returns `"S"`, and `>` always returns `"R"`.
`"standard"` (default)
- `<=` and `>=` return `"NI"` (non-interpretable) if the MIC is within
the breakpoint guideline range.
- `<` and `>` are treated as-is.
`"inverse"`
- `<=` and `>=` are treated as-is.
- `<` always returns `"S"`, and `>` always returns `"R"`.
The default `"standard"` 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).
- 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/expert_rules_and_expected_phenotypes)
(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 contain a data set that is equal in
structure to the
[clinical_breakpoints](https://amr-for-r.org/reference/clinical_breakpoints.md)
data set (same column names and column types). Please note that the
`guideline` argument will be ignored when `reference_data` is manually
set.
- 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`. This requires no
additional packages, as the used `parallel` package is part of base R.
On Windows and on R \< 4.0.0
[`parallel::parLapply()`](https://rdrr.io/r/parallel/clusterApply.html)
will be used, in all other cases the more efficient
[`parallel::mclapply()`](https://rdrr.io/r/parallel/mclapply.html)
will be used.
- max_cores:
Maximum number of cores to use if `parallel = TRUE`. Use a negative
value to subtract that number from the available number of cores, e.g.
a value of `-2` on an 8-core machine means that at most 6 cores will
be used. Defaults to `-1`. There will never be used more cores than
variables to analyse. The available number of cores are detected using
[`parallelly::availableCores()`](https://parallelly.futureverse.org/reference/availableCores.html)
if that package is installed, and base R's
[`parallel::detectCores()`](https://rdrr.io/r/parallel/detectCores.html)
otherwise.
- 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/r/base/factor.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:
as.sir(your_data, ..., parallel = TRUE)
- Operators like "\<=" will be stripped before interpretation. When
using `capped_mic_handling = "conservative"`, an MIC value of e.g.
"\>2" will always return "R", even if the breakpoint according to
the chosen guideline is "\>=4". This is to prevent that capped
values from raw laboratory data would not be treated
conservatively. The default behaviour
(`capped_mic_handling = "standard"`) considers "\>2" to be lower
than "\>=4" and might in this case return "S" or "I".
- **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:
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 2011-2025 and CLSI 2011-2025;
- For **veterinary microbiology**: EUCAST 2021-2025 and CLSI 2019-2025;
- For **ECOFFs** (Epidemiological Cut-off Values): EUCAST 2020-2025 and
CLSI 2022-2025.
The `guideline` argument must be set to e.g., `"EUCAST 2025"` or
`"CLSI 2025"`. 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
[`eucast_rules()`](https://amr-for-r.org/reference/eucast_rules.md)
defined by EUCAST 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 [interpreting 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/newsiandr>).
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).
## 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 Class :character 1st Qu.:63.00 Class :character
#> Median :2009-07-31 Mode :character Median :74.00 Mode :character
#> Mean :2009-11-20 Mean :70.69
#> 3rd Qu.:2014-05-30 3rd Qu.:82.00
#> Max. :2017-12-28 Max. :97.00
#> ward mo PEN
#> Length:2000 Class :mo Class:sir
#> Class :character <NA> :0 %S :25.6% (n=417)
#> Mode :character Unique:90 %SDD : 0.0% (n=0)
#> #1 :B_ESCHR_COLI %I : 0.7% (n=11)
#> #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 2025-11-24 10:38:56 1 MIC amoxicillin Escherich… human 8
#> 2 2025-11-24 10:38:56 1 MIC cipro Escherich… human 0.256
#> 3 2025-11-24 10:38:56 1 DISK tobra Escherich… human 16
#> 4 2025-11-24 10:38:56 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 parallel computing, which is available in base R:
as.sir(df_wide, parallel = TRUE, info = TRUE)
#> ℹ Returning previously coerced values for various antimicrobials. Run
#> `ab_reset_session()` to reset this. This note will be shown once per
#> session.
#>
#> Running in parallel mode using 3 out of 4 cores, on columns 'amoxicillin',
#> 'cipro', 'tobra', 'genta', and 'ERY'...
#> DONE
#>
#>
#> ℹ Run `sir_interpretation_history()` to retrieve a logbook with all details
#> of the breakpoint interpretations.
#> microorganism amoxicillin cipro tobra genta ERY
#> 1 Escherichia coli S I S S R
## 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, ...) ...`.
#> 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, ...) ...`.
#> 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.
#> Interpreting MIC values: 'antibiotic' (ASP, acetylspiramycin), CLSI 2025...
#> Interpreting disk diffusion zones: 'antibiotic' (ASP, acetylspiramycin),
#> CLSI 2025...
#> Interpreting disk diffusion zones: 'antibiotic' (ASP, acetylspiramycin),
#> CLSI 2025...
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `cipro = (function (x, ...) ...`.
#> 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 in index '20' 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
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>, …
# }
```