# Conduct AMR data analysis

**Note:** values on this page will change with every website update
since they are based on randomly created values and the page was written
in [R Markdown](https://rmarkdown.rstudio.com/). However, the
methodology remains unchanged. This page was generated on 20 August
2026.

## Introduction

Conducting AMR data analysis unfortunately requires in-depth knowledge
from different scientific fields, which makes it hard to do right. At
least, it requires:

- Good questions (always start with those!) and reliable data
- A thorough understanding of (clinical) epidemiology, to understand the
  clinical and epidemiological relevance and possible bias of results
- A thorough understanding of (clinical) microbiology/infectious
  diseases, to understand which microorganisms are causal to which
  infections and the implications of pharmaceutical treatment, as well
  as understanding intrinsic and acquired microbial resistance
- Experience with data analysis with microbiological tests and their
  results, to understand the determination and limitations of MIC values
  and their interpretations to SIR values
- Availability of the biological taxonomy of microorganisms and probably
  normalisation factors for pharmaceuticals, such as defined daily doses
  (DDD)
- Available (inter-)national guidelines, and profound methods to apply
  them

Of course, we cannot instantly provide you with knowledge and
experience. But with this `AMR` package, we aimed at providing (1) tools
to simplify antimicrobial resistance data cleaning, transformation and
analysis, (2) methods to easily incorporate international guidelines and
(3) scientifically reliable reference data, including the requirements
mentioned above.

The `AMR` package enables standardised and reproducible AMR data
analysis, with the application of evidence-based rules, determination of
first isolates, translation of various codes for microorganisms and
antimicrobial drugs, determination of (multi-drug) resistant
microorganisms, and calculation of antimicrobial resistance, prevalence
and future trends.

## Preparation

For this tutorial, we will create fake demonstration data to work with.

You can skip to [Cleaning the data](#cleaning-the-data) if you already
have your own data ready. If you start your analysis, try to make the
structure of your data generally look like this:

|    date    | patient_id |        mo        | AMX | CIP |
|:----------:|:----------:|:----------------:|:---:|:---:|
| 2026-08-20 |    abcd    | Escherichia coli |  S  |  S  |
| 2026-08-20 |    abcd    | Escherichia coli |  S  |  R  |
| 2026-08-20 |    efgh    | Escherichia coli |  R  |  S  |

### Needed R packages

As with many uses in R, we need some additional packages for AMR data
analysis. Our package works closely together with the [tidyverse
packages](https://www.tidyverse.org)
[`dplyr`](https://dplyr.tidyverse.org/) and
[`ggplot2`](https://ggplot2.tidyverse.org) by RStudio. The tidyverse
tremendously improves the way we conduct data science - it allows for a
very natural way of writing syntaxes and creating beautiful plots in R.

We will also use the `cleaner` package, that can be used for cleaning
data and creating frequency tables.

``` r

library(dplyr)
library(ggplot2)
library(AMR)

# (if not yet installed, install with:)
# install.packages(c("dplyr", "ggplot2", "AMR"))
```

The `AMR` package contains a data set `example_isolates_unclean`, which
might look data that users have extracted from their laboratory systems:

``` r

example_isolates_unclean
#> # A tibble: 3,000 × 8
#>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN  
#>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr>
#>  1 J3         A        2012-11-21 E. coli       R     I     S     S    
#>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S    
#>  3 P3         A        2014-09-19 E. coli       R     S     S     S    
#>  4 P10        A        2015-12-10 E. coli       S     I     S     S    
#>  5 B7         A        2015-03-02 E. coli       S     S     S     S    
#>  6 W3         A        2018-03-31 S. aureus     R     S     R     S    
#>  7 J8         A        2016-06-14 E. coli       R     S     S     S    
#>  8 M3         A        2015-10-25 E. coli       R     S     S     S    
#>  9 J3         A        2019-06-19 E. coli       S     S     S     S    
#> 10 G6         A        2015-04-27 S. aureus     S     S     S     S    
#> # ℹ 2,990 more rows

# we will use 'our_data' as the data set name for this tutorial
our_data <- example_isolates_unclean
```

For AMR data analysis, we would like the microorganism column to contain
valid, up-to-date taxonomy, and the antibiotic columns to be cleaned as
SIR values as well.

### Taxonomy of microorganisms

With [`as.mo()`](https://amr-for-r.org/reference/as.mo.md), users can
transform arbitrary microorganism names or codes to current taxonomy.
The `AMR` package contains up-to-date taxonomic data. To be specific,
currently included data were retrieved on 07 May 2026.

The codes of the AMR packages that come from
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) are short, but
still human readable. More importantly,
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) supports all kinds
of input:

``` r

as.mo("Klebsiella pneumoniae")
#> Class <mo>
#> [1] B_KLBSL_PNMN
as.mo("K. pneumoniae")
#> Class <mo>
#> [1] B_KLBSL_PNMN
as.mo("KLEPNE")
#> Class <mo>
#> [1] B_KLBSL_PNMN
as.mo("KLPN")
#> Class <mo>
#> [1] B_KLBSL_PNMN
```

The first character in above codes denote their taxonomic kingdom, such
as Bacteria (B), Fungi (F), and Protozoa (P).

The `AMR` package also contain functions to directly retrieve taxonomic
properties, such as the name, genus, species, family, order, and even
Gram-stain. They all start with `mo_` and they use
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) internally, so
that still any arbitrary user input can be used:

``` r

mo_family("K. pneumoniae")
#> [1] "Enterobacteriaceae"
mo_genus("K. pneumoniae")
#> [1] "Klebsiella"
mo_species("K. pneumoniae")
#> [1] "pneumoniae"

mo_gramstain("Klebsiella pneumoniae")
#> [1] "Gram-negative"

mo_ref("K. pneumoniae")
#> [1] "Trevisan, 1887"

mo_snomed("K. pneumoniae")
#> [[1]]
#> [1] "1098101000112102" "446870005"        "1098201000112108" "409801009"       
#> [5] "56415008"         "714315002"        "713926009"
```

Now we can thus clean our data:

``` r

our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE)
#> ℹ Retrieved values from the `microorganisms.codes` data set for "ESCCOL",
#>   "KLEPNE", "STAAUR", and "STRPNE".
#> ℹ Microorganism translation was uncertain for four microorganisms. Run
#>   `mo_uncertainties()` to review these uncertainties, or use
#>   `add_custom_microorganisms()` to add custom entries.
```

Apparently, there was some uncertainty about the translation to
taxonomic codes. Let’s check this:

``` r

mo_uncertainties()
#> Matching scores are based on the resemblance between the input and the full
#> taxonomic name, and the pathogenicity in humans. See `mo_matching_score()`.
#> Colour keys:  0.000-0.549  0.550-0.649  0.650-0.749  0.750-1.000 
#> -------------------------------------------------------------------------------
#> "E. coli" -> Escherichia coli (B_ESCHR_COLI, 0.688)
#> Also matched: Enterococcus crotali (0.650), Escherichia coli coli (0.643),
#> Escherichia coli expressing (0.611), Enterobacter cowanii (0.600), Enterococcus
#> columbae (0.595), Enterococcus camelliae (0.591), Enterococcus casseliflavus
#> (0.577), Enterobacter cloacae cloacae (0.571), Enterobacter cloacae complex
#> (0.571), and Enterobacter cloacae dissolvens (0.565)
#> -------------------------------------------------------------------------------
#> "K. pneumoniae" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786)
#> -------------------------------------------------------------------------------
#> "S. aureus" -> Staphylococcus aureus (B_STPHY_AURS, 0.690)
#> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus argenteus
#> (0.625), Staphylococcus aureus anaerobius (0.625), Streptomyces aureus (0.618),
#> Staphylococcus auricularis (0.615), Streptomyces azureus (0.609), Salmonella
#> Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness (0.587),
#> and Staphylococcus argensis (0.587)
#> -------------------------------------------------------------------------------
#> "S. pneumoniae" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750)
#> Also matched: Streptococcus parapneumoniae (0.714), Streptococcus
#> pseudopneumoniae (0.700), Serratia proteamaculans quinivorans (0.557),
#> Streptococcus phocae salmonis (0.552), Serratia proteamaculans quinovora
#> (0.545), Sphingomonas piscinae (0.538), Streptococcus pseudoporcinus (0.536),
#> Staphylococcus piscifermentans (0.533), Staphylococcus pseudintermedius
#> (0.532), and Serratia proteamaculans proteamaculans (0.526)
#> ℹ Only the first 10 other matches of each record are shown. Run ``
#>   `print(mo_uncertainties(), n = ...)` `` to view more entries, or save
#>   `mo_uncertainties()` to an object.
```

That’s all good.

### Antibiotic results

The column with antibiotic test results must also be cleaned. The `AMR`
package comes with three new data types to work with such test results:
`mic` for minimal inhibitory concentrations (MIC), `disk` for disk
diffusion diameters, and `sir` for SIR data that have been interpreted
already. This package can also determine SIR values based on MIC or disk
diffusion values, read more about that on the
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) page.

For now, we will just clean the SIR columns in our data using dplyr:

``` r

# method 1, be explicit about the columns:
our_data <- our_data %>%
  mutate_at(vars(AMX:GEN), as.sir)

# method 2, let the AMR package determine the eligible columns
our_data <- our_data %>%
  mutate_if(is_sir_eligible, as.sir)

# result:
our_data
#> # A tibble: 3,000 × 8
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN  
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir>
#>  1 J3         A        2012-11-21 B_ESCHR_COLI   R     I     S     S  
#>  2 R7         A        2018-04-03 B_KLBSL_PNMN   R     I     S     S  
#>  3 P3         A        2014-09-19 B_ESCHR_COLI   R     S     S     S  
#>  4 P10        A        2015-12-10 B_ESCHR_COLI   S     I     S     S  
#>  5 B7         A        2015-03-02 B_ESCHR_COLI   S     S     S     S  
#>  6 W3         A        2018-03-31 B_STPHY_AURS   R     S     R     S  
#>  7 J8         A        2016-06-14 B_ESCHR_COLI   R     S     S     S  
#>  8 M3         A        2015-10-25 B_ESCHR_COLI   R     S     S     S  
#>  9 J3         A        2019-06-19 B_ESCHR_COLI   S     S     S     S  
#> 10 G6         A        2015-04-27 B_STPHY_AURS   S     S     S     S  
#> # ℹ 2,990 more rows
```

This is basically it for the cleaning, time to start the data inclusion.

### First isolates

We need to know which isolates we can *actually* use for analysis
without repetition bias.

To conduct an analysis of antimicrobial resistance, you must [only
include the first isolate of every patient per
episode](https:/pubmed.ncbi.nlm.nih.gov/17304462/) (Hindler *et al.*,
Clin Infect Dis. 2007). If you would not do this, you could easily get
an overestimate or underestimate of the resistance of an antibiotic.
Imagine that a patient was admitted with an MRSA and that it was found
in 5 different blood cultures the following weeks (yes, some countries
like the Netherlands have these blood drawing policies). The resistance
percentage of oxacillin of all isolates would be overestimated, because
you included this MRSA more than once. It would clearly be [selection
bias](https://en.wikipedia.org/wiki/Selection_bias).

The Clinical and Laboratory Standards Institute (CLSI) appoints this as
follows:

> *(…) When preparing a cumulative antibiogram to guide clinical
> decisions about empirical antimicrobial therapy of initial infections,
> **only the first isolate of a given species per patient, per analysis
> period (eg, one year) should be included, irrespective of body site,
> antimicrobial susceptibility profile, or other phenotypical
> characteristics (eg, biotype)**. The first isolate is easily
> identified, and cumulative antimicrobial susceptibility test data
> prepared using the first isolate are generally comparable to
> cumulative antimicrobial susceptibility test data calculated by other
> methods, providing duplicate isolates are excluded.*  
> [M39-A4 Analysis and Presentation of Cumulative Antimicrobial
> Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter
> 6.4](https://clsi.org/standards/products/microbiology/documents/m39/)

This `AMR` package includes this methodology with the
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
function and is able to apply the four different methods as defined by
[Hindler *et al.* in
2007](https://academic.oup.com/cid/article/44/6/867/364325):
phenotype-based, episode-based, patient-based, isolate-based. The right
method depends on your goals and analysis, but the default
phenotype-based method is in any case the method to properly correct for
most duplicate isolates. Read more about the methods on the
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
page.

The outcome of the function can easily be added to our data:

``` r

our_data <- our_data %>%
  mutate(first = first_isolate(info = TRUE))
#> ℹ Determining first isolates using an episode length of 365 days
#> ℹ Using column bacteria as input for `col_mo`.
#> ℹ Column first is SIR eligible (despite only having empty values), since it
#>   seems to be cefozopran (ZOP)
#> ℹ Using column date as input for `col_date`.
#> ℹ Using column patient_id as input for `col_patient_id`.
#> ℹ Basing inclusion on all antimicrobial results, using a points threshold of 2
#> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a
#> microbial ID was available)
```

So only 91% is suitable for resistance analysis! We can now filter on it
with the [`filter()`](https://dplyr.tidyverse.org/reference/filter.html)
function, also from the `dplyr` package:

``` r

our_data_1st <- our_data %>%
  filter(first == TRUE)
```

For future use, the above two syntaxes can be shortened:

``` r

our_data_1st <- our_data %>%
  filter_first_isolate()
```

So we end up with 2 724 isolates for analysis. Now our data looks like:

``` r

our_data_1st
#> # A tibble: 2,724 × 9
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl>
#>  1 J3         A        2012-11-21 B_ESCHR_COLI   R     I     S     S   TRUE 
#>  2 R7         A        2018-04-03 B_KLBSL_PNMN   R     I     S     S   TRUE 
#>  3 P3         A        2014-09-19 B_ESCHR_COLI   R     S     S     S   TRUE 
#>  4 P10        A        2015-12-10 B_ESCHR_COLI   S     I     S     S   TRUE 
#>  5 B7         A        2015-03-02 B_ESCHR_COLI   S     S     S     S   TRUE 
#>  6 W3         A        2018-03-31 B_STPHY_AURS   R     S     R     S   TRUE 
#>  7 M3         A        2015-10-25 B_ESCHR_COLI   R     S     S     S   TRUE 
#>  8 J3         A        2019-06-19 B_ESCHR_COLI   S     S     S     S   TRUE 
#>  9 G6         A        2015-04-27 B_STPHY_AURS   S     S     S     S   TRUE 
#> 10 P4         A        2011-06-21 B_ESCHR_COLI   S     S     S     S   TRUE 
#> # ℹ 2,714 more rows
```

Time for the analysis.

## Analysing the data

The base R [`summary()`](https://rdrr.io/r/base/summary.html) function
gives a good first impression, as it comes with support for the new `mo`
and `sir` classes that we now have in our data set:

``` r

summary(our_data_1st)
#>      patient_id        hospital         date              bacteria           
#>  Length   :2724   Length   :2724   Min.   :2011-01-01   Class :mo            
#>  N.unique : 260   N.unique :   3   1st Qu.:2013-04-07   <NA>  :0             
#>  N.blank  :   0   N.blank  :   0   Median :2015-06-03   Unique:4             
#>  Min.nchar:   2   Min.nchar:   1   Mean   :2015-06-09   #1    :B_ESCHR_COLI  
#>  Max.nchar:   3   Max.nchar:   1   3rd Qu.:2017-08-11   #2    :B_STPHY_AURS  
#>                                    Max.   :2019-12-27   #3    :B_STRPT_PNMN  
#>     AMX                    AMC                    CIP                
#>  Class:sir              Class:sir              Class:sir             
#>  %S   :41.6% (n=1133)   %S   :52.6% (n=1432)   %S   :52.5% (n=1431)  
#>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     
#>  %I   :16.4% (n=446)    %I   :12.2% (n=333)    %I   : 6.5% (n=176)   
#>  %R   :42.0% (n=1145)   %R   :35.2% (n=959)    %R   :41.0% (n=1117)  
#>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     
#>     GEN                  first        
#>  Class:sir              Mode:logical  
#>  %S   :61.0% (n=1661)   TRUE:2724     
#>  %SDD : 0.0% (n=0)                    
#>  %I   : 3.0% (n=82)                   
#>  %R   :36.0% (n=981)                  
#>  %NI  : 0.0% (n=0)

glimpse(our_data_1st)
#> Rows: 2,724
#> Columns: 9
#> $ patient_id <chr> "J3", "R7", "P3", "P10", "B7", "W3", "M3", "J3", "G6", "P4"…
#> $ hospital   <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…
#> $ date       <date> 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02…
#> $ bacteria   <mo> "B_ESCHR_COLI", "B_KLBSL_PNMN", "B_ESCHR_COLI", "B_ESCHR_COL…
#> $ AMX        <sir> R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,…
#> $ AMC        <sir> I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,…
#> $ CIP        <sir> S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…
#> $ GEN        <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,…
#> $ first      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…

# number of unique values per column:
sapply(our_data_1st, n_distinct)
#> patient_id   hospital       date   bacteria        AMX        AMC        CIP 
#>        260          3       1854          4          3          3          3 
#>        GEN      first 
#>          3          1
```

### Availability of species

To just get an idea how the species are distributed, create a frequency
table with [`count()`](https://amr-for-r.org/reference/count.md) based
on the name of the microorganisms:

``` r

our_data %>%
  count(mo_name(bacteria), sort = TRUE)
#> # A tibble: 4 × 2
#>   `mo_name(bacteria)`          n
#>   <chr>                    <int>
#> 1 Escherichia coli          1518
#> 2 Staphylococcus aureus      730
#> 3 Streptococcus pneumoniae   426
#> 4 Klebsiella pneumoniae      326

our_data_1st %>%
  count(mo_name(bacteria), sort = TRUE)
#> # A tibble: 4 × 2
#>   `mo_name(bacteria)`          n
#>   <chr>                    <int>
#> 1 Escherichia coli          1321
#> 2 Staphylococcus aureus      682
#> 3 Streptococcus pneumoniae   402
#> 4 Klebsiella pneumoniae      319
```

### Select and filter with antimicrobial selectors

Using so-called antimicrobial class selectors, you can select or filter
columns based on the antimicrobial class that your antimicrobial results
are in:

``` r

our_data_1st %>%
  select(date, aminoglycosides())
#> ℹ For `aminoglycosides()` using column GEN
#> (gentamicin)
#> # A tibble: 2,724 × 2
#>    date       GEN  
#>    <date>     <sir>
#>  1 2012-11-21   S  
#>  2 2018-04-03   S  
#>  3 2014-09-19   S  
#>  4 2015-12-10   S  
#>  5 2015-03-02   S  
#>  6 2018-03-31   S  
#>  7 2015-10-25   S  
#>  8 2019-06-19   S  
#>  9 2015-04-27   S  
#> 10 2011-06-21   S  
#> # ℹ 2,714 more rows

our_data_1st %>%
  select(bacteria, betalactams())
#> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC
#>   (amoxicillin/clavulanic acid)
#> # A tibble: 2,724 × 3
#>    bacteria     AMX   AMC  
#>    <mo>         <sir> <sir>
#>  1 B_ESCHR_COLI   R     I  
#>  2 B_KLBSL_PNMN   R     I  
#>  3 B_ESCHR_COLI   R     S  
#>  4 B_ESCHR_COLI   S     I  
#>  5 B_ESCHR_COLI   S     S  
#>  6 B_STPHY_AURS   R     S  
#>  7 B_ESCHR_COLI   R     S  
#>  8 B_ESCHR_COLI   S     S  
#>  9 B_STPHY_AURS   S     S  
#> 10 B_ESCHR_COLI   S     S  
#> # ℹ 2,714 more rows

our_data_1st %>%
  select(bacteria, where(is.sir))
#> # A tibble: 2,724 × 5
#>    bacteria     AMX   AMC   CIP   GEN  
#>    <mo>         <sir> <sir> <sir> <sir>
#>  1 B_ESCHR_COLI   R     I     S     S  
#>  2 B_KLBSL_PNMN   R     I     S     S  
#>  3 B_ESCHR_COLI   R     S     S     S  
#>  4 B_ESCHR_COLI   S     I     S     S  
#>  5 B_ESCHR_COLI   S     S     S     S  
#>  6 B_STPHY_AURS   R     S     R     S  
#>  7 B_ESCHR_COLI   R     S     S     S  
#>  8 B_ESCHR_COLI   S     S     S     S  
#>  9 B_STPHY_AURS   S     S     S     S  
#> 10 B_ESCHR_COLI   S     S     S     S  
#> # ℹ 2,714 more rows

# filtering using antimicrobial selectors is also possible:
our_data_1st %>%
  filter(any(aminoglycosides() == "R"))
#> ℹ For `aminoglycosides()` using column GEN
#> (gentamicin)
#> # A tibble: 981 × 9
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl>
#>  1 J5         A        2017-12-25 B_STRPT_PNMN   R     S     S     R   TRUE 
#>  2 X1         A        2017-07-04 B_STPHY_AURS   R     S     S     R   TRUE 
#>  3 B3         A        2016-07-24 B_ESCHR_COLI   S     S     S     R   TRUE 
#>  4 V7         A        2012-04-03 B_ESCHR_COLI   S     S     S     R   TRUE 
#>  5 C9         A        2017-03-23 B_ESCHR_COLI   S     S     S     R   TRUE 
#>  6 R1         A        2018-06-10 B_STPHY_AURS   S     S     S     R   TRUE 
#>  7 S2         A        2013-07-19 B_STRPT_PNMN   S     S     S     R   TRUE 
#>  8 P5         A        2019-03-09 B_STPHY_AURS   S     S     S     R   TRUE 
#>  9 Q8         A        2019-08-10 B_STPHY_AURS   S     S     S     R   TRUE 
#> 10 K5         A        2013-03-15 B_STRPT_PNMN   S     S     S     R   TRUE 
#> # ℹ 971 more rows

our_data_1st %>%
  filter(all(betalactams() == "R"))
#> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC
#>   (amoxicillin/clavulanic acid)
#> # A tibble: 462 × 9
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl>
#>  1 M7         A        2013-07-22 B_STRPT_PNMN   R     R     S     S   TRUE 
#>  2 R10        A        2013-12-20 B_STPHY_AURS   R     R     S     S   TRUE 
#>  3 R7         A        2015-10-25 B_STPHY_AURS   R     R     S     S   TRUE 
#>  4 R8         A        2019-10-25 B_STPHY_AURS   R     R     S     S   TRUE 
#>  5 B6         A        2016-11-20 B_ESCHR_COLI   R     R     R     R   TRUE 
#>  6 I7         A        2015-08-19 B_ESCHR_COLI   R     R     S     S   TRUE 
#>  7 N3         A        2014-12-29 B_STRPT_PNMN   R     R     R     S   TRUE 
#>  8 Q2         A        2019-09-22 B_ESCHR_COLI   R     R     S     S   TRUE 
#>  9 X7         A        2011-03-20 B_ESCHR_COLI   R     R     S     R   TRUE 
#> 10 V1         A        2018-08-07 B_STPHY_AURS   R     R     S     S   TRUE 
#> # ℹ 452 more rows

# even works in base R (since R 3.0):
our_data_1st[all(betalactams() == "R"), ]
#> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC
#>   (amoxicillin/clavulanic acid)
#> # A tibble: 462 × 9
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl>
#>  1 M7         A        2013-07-22 B_STRPT_PNMN   R     R     S     S   TRUE 
#>  2 R10        A        2013-12-20 B_STPHY_AURS   R     R     S     S   TRUE 
#>  3 R7         A        2015-10-25 B_STPHY_AURS   R     R     S     S   TRUE 
#>  4 R8         A        2019-10-25 B_STPHY_AURS   R     R     S     S   TRUE 
#>  5 B6         A        2016-11-20 B_ESCHR_COLI   R     R     R     R   TRUE 
#>  6 I7         A        2015-08-19 B_ESCHR_COLI   R     R     S     S   TRUE 
#>  7 N3         A        2014-12-29 B_STRPT_PNMN   R     R     R     S   TRUE 
#>  8 Q2         A        2019-09-22 B_ESCHR_COLI   R     R     S     S   TRUE 
#>  9 X7         A        2011-03-20 B_ESCHR_COLI   R     R     S     R   TRUE 
#> 10 V1         A        2018-08-07 B_STPHY_AURS   R     R     S     S   TRUE 
#> # ℹ 452 more rows
```

### Generate antibiograms

The `AMR` package supports 28 different languages for antibiograms and
provides four types, as proposed by Klinker *et al.* (2021, [DOI
10.1177/20499361211011373](https://doi.org/10.1177/20499361211011373)):

1.  **Traditional Antibiogram (TA)** – susceptibility of a species to
    individual antibiotics
2.  **Combination Antibiogram (CA)** – susceptibility of a species to
    combination regimens
3.  **Syndromic Antibiogram (SA)** – susceptibility of a species,
    stratified by clinical syndrome or setting
4.  **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)** –
    estimated empirical coverage of a *regimen* for a *syndrome*,
    weighted by pathogen incidence and with quantified uncertainty

**If your goal is to guide empirical therapy, WISCA should be your
default.** The reason is simple: when you start empirical treatment, you
do not know which pathogen is causing the infection. Your next patient
will not present with a species label attached to them. What matters is
the probability that the *regimen* you choose will cover *whatever
pathogen turns out to be the cause*, given the local epidemiology of the
syndrome. Traditional antibiograms do not answer that question. They
fragment information by species, ignore how frequently each species
causes the syndrome, do not evaluate combination regimens, and provide
no measure of uncertainty. WISCA addresses all of these limitations
using a Bayesian framework (Hebert *et al.*, 2012; Bielicki *et al.*,
2016). See the [WISCA
vignette](https://amr-for-r.org/articles/WISCA.html) for the full
explanation.

Traditional, combination, and syndromic antibiograms remain useful for
**surveillance** purposes, i.e., tracking resistance trends per species
over time. But if you care about clinical impact, about choosing the
right empirical regimen for your patient, use WISCA.

For starters, this is what the included `example_isolates` data set
looks like:

``` 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>, …
```

#### WISCA (recommended for empirical therapy guidance)

Use the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
function, or equivalently `antibiogram(..., wisca = TRUE)`. WISCA
produces a single coverage estimate per regimen for the entire syndrome,
weighted by pathogen incidence, with a 95% credible interval from
Bayesian Monte Carlo simulation:

``` r

wisca_result <- example_isolates %>%
  wisca(
    antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
    minimum = 10
  ) # Recommended threshold: ≥30
wisca_result
```

| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|
| 70% (64.4-75.3%) | 93.6% (92.1-95%) | 89.8% (87-92.3%) |

The output tells you: *“given the species distribution in your data,
there is an estimated X% probability that this regimen covers the
infection, with 95% credible interval \[lower, upper\]”*. That is the
clinically relevant question.

For **syndrome-specific** or **patient-specific WISCA**, use the
`syndromic_group` argument or group your data first. You can stratify by
anything: ward, age group, risk profile, acquisition type. The
`syndromic_group` argument accepts any column or expression:

``` r

wisca_out <- example_isolates %>%
  top_n_microorganisms(n = 10) %>%
  group_by(
    age_group = age_groups(age, c(25, 50, 75)),
    gender
  ) %>%
  wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))

wisca_out
```

| age_group | gender | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|:---|:---|
| 0-24 | F | 57% (29.7-81.3%) | 70.8% (45.2-88.9%) | 66.1% (43-86.8%) |
| 0-24 | M | 59.7% (31.2-85.9%) | 76.1% (57.1-91.7%) | 59.5% (31.7-85.1%) |
| 25-49 | F | 67.9% (44.6-89.7%) | 93.8% (87.4-98.1%) | 87% (70-97%) |
| 25-49 | M | 56.9% (26.6-86.2%) | 91.1% (82-97.2%) | 76.5% (51.4-93.5%) |
| 50-74 | F | 68% (54.1-81.8%) | 96.9% (94.6-98.5%) | 90.3% (82-96.2%) |
| 50-74 | M | 67% (56-78.5%) | 96.7% (94-98.5%) | 86.7% (77.3-94.5%) |
| 75+ | F | 73.2% (61.9-84.2%) | 97.7% (95.9-99%) | 92.7% (85.6-97.2%) |
| 75+ | M | 74% (63.6-82.6%) | 97.9% (96-99%) | 94.7% (89.3-97.9%) |

Keep in mind that more granular stratification produces more relevant
estimates for each subgroup, but with wider credible intervals due to
smaller sample sizes. There is always a trade-off between granularity
and precision. If local numbers are small, consider pooling data from
multiple sites (Bielicki *et al.*, 2016).

For reliable WISCA results, ensure your data includes **only first
isolates** (use
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md))
and consider filtering for **the top *n* species** (use
[`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md)),
since rare contaminants can distort coverage estimates.

After creating the WISCA model, assessments can be done on the
distributions of the Monte Carlo simulations that WISCA carried out:

``` r

wisca_plot(wisca_out)
```

![](AMR_files/figure-html/wisca_plots-1.png)

``` r

wisca_plot(wisca_out, wisca_plot_type = "posterior_coverage")
```

![](AMR_files/figure-html/wisca_plots-2.png)

``` r


# a ggplot2 extension for WISCAs and other antibiograms:
ggplot2::autoplot(wisca_out)
```

![](AMR_files/figure-html/wisca_plots-3.png)

#### Traditional Antibiogram

If you need per-species susceptibility rates, e.g., for AMR surveillance
reports, the traditional antibiogram remains the right tool. It reports
the proportion of susceptible isolates per species per antibiotic:

``` r

antibiogram(example_isolates,
  antibiotics = c(aminoglycosides(), carbapenems())
)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#>   (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
```

| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:---|:---|:---|:---|:---|:---|:---|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |

Notice that the
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
function automatically prints in the right format when using Quarto or R
Markdown (such as this page), and even applies italics for taxonomic
names (by using
[`italicise_taxonomy()`](https://amr-for-r.org/reference/italicise_taxonomy.md)
internally).

It also uses the language of your OS if this is either English, Arabic,
Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek,
Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish,
Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish,
Ukrainian, Urdu, or Vietnamese. In this next example, we force the
language to be Spanish using the `language` argument:

``` r

antibiogram(example_isolates,
  mo_transform = "gramstain",
  antibiotics = aminoglycosides(),
  ab_transform = "name",
  language = "es"
)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#>   (amikacin), and KAN (kanamycin)
```

| Patógeno | Amikacina | Gentamicina | Kanamicina | Tobramicina |
|:---|:---|:---|:---|:---|
| Gram negativo | 98% (96-99%,N=256) | 96% (95-98%,N=684) | 0% (0-10%,N=35) | 96% (94-97%,N=686) |
| Gram positivo | 0% (0-1%,N=436) | 63% (60-66%,N=1170) | 0% (0-1%,N=436) | 34% (31-38%,N=665) |

#### Combination Antibiogram

A combination antibiogram shows how much additional susceptibility a
second agent adds for a given species. This is useful for surveillance
of combination regimens, but note that it is still species-stratified
and does not account for pathogen incidence in the syndrome:

``` r

combined_ab <- antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
  ab_transform = NULL
)
combined_ab
```

| Pathogen | TZP | TZP + GEN | TZP + TOB |
|:---|:---|:---|:---|
| CoNS | 30% (16-49%,N=33) | 97% (95-99%,N=274) | NA |
| *E. coli* | 94% (92-96%,N=416) | 100% (98-100%,N=459) | 99% (97-100%,N=461) |
| *K. pneumoniae* | 89% (77-96%,N=53) | 93% (83-98%,N=58) | 93% (83-98%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 100% (90-100%,N=34) | 100% (90-100%,N=34) |
| *S. aureus* | NA | 100% (98-100%,N=231) | 100% (96-100%,N=91) |
| *S. epidermidis* | NA | 100% (97-100%,N=128) | 100% (92-100%,N=46) |
| *S. hominis* | NA | 100% (95-100%,N=74) | 100% (93-100%,N=53) |
| *S. pneumoniae* | 100% (97-100%,N=112) | 100% (97-100%,N=112) | 100% (97-100%,N=112) |

#### Syndromic Antibiogram

A syndromic antibiogram stratifies per-species susceptibility by
clinical context (ward, specimen type, etc.). It adds clinical context
to the traditional antibiogram but is still species-level, without
incidence weighting or uncertainty quantification. For surveillance by
setting this is fine; for empirical therapy guidance, WISCA is
preferred:

``` r

antibiogram(example_isolates,
  antibiotics = c(aminoglycosides(), carbapenems()),
  syndromic_group = "ward"
)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#>   (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
```

| Syndromic Group | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:---|:---|:---|:---|:---|:---|:---|:---|
| Clinical | CoNS | NA | 89% (84-93%,N=205) | 57% (39-74%,N=35) | NA | 57% (39-74%,N=35) | 26% (12-45%,N=31) |
| ICU | CoNS | NA | 79% (68-88%,N=73) | NA | NA | NA | NA |
| Outpatient | CoNS | NA | 84% (66-95%,N=31) | NA | NA | NA | NA |
| Clinical | *E. coli* | 100% (97-100%,N=104) | 98% (96-99%,N=297) | 100% (99-100%,N=266) | NA | 100% (99-100%,N=276) | 98% (96-99%,N=299) |
| ICU | *E. coli* | 100% (93-100%,N=52) | 99% (95-100%,N=137) | 100% (97-100%,N=133) | NA | 100% (97-100%,N=118) | 96% (92-99%,N=137) |
| Clinical | *K. pneumoniae* | NA | 92% (81-98%,N=51) | 100% (92-100%,N=44) | NA | 100% (92-100%,N=46) | 92% (81-98%,N=51) |
| Clinical | *P. mirabilis* | NA | 100% (88-100%,N=30) | NA | NA | NA | 100% (88-100%,N=30) |
| Clinical | *S. aureus* | NA | 99% (95-100%,N=150) | NA | NA | NA | 97% (89-100%,N=63) |
| ICU | *S. aureus* | NA | 100% (95-100%,N=66) | NA | NA | NA | NA |
| Clinical | *S. epidermidis* | NA | 82% (72-90%,N=79) | NA | NA | NA | 55% (39-70%,N=44) |
| ICU | *S. epidermidis* | NA | 72% (60-82%,N=75) | NA | NA | NA | 41% (26-58%,N=41) |
| Clinical | *S. hominis* | NA | 96% (85-99%,N=45) | NA | NA | NA | 94% (79-99%,N=31) |
| Clinical | *S. pneumoniae* | 0% (0-5%,N=78) | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) |
| ICU | *S. pneumoniae* | 0% (0-12%,N=30) | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) |

#### Plotting antibiograms

All antibiogram types, including WISCA, can be plotted using
[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
from the `ggplot2` package, since this `AMR` package provides an
extension to that function:

``` r

autoplot(wisca_result)
```

![](AMR_files/figure-html/unnamed-chunk-10-1.png)

To calculate antimicrobial resistance in a more sensible way, also by
correcting for too few results, we use the
[`resistance()`](https://amr-for-r.org/reference/proportion.md) and
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
functions.

### Resistance percentages

The functions
[`resistance()`](https://amr-for-r.org/reference/proportion.md) and
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md) can
be used to calculate antimicrobial resistance or susceptibility. For
more specific analyses, the functions
[`proportion_S()`](https://amr-for-r.org/reference/proportion.md),
[`proportion_SI()`](https://amr-for-r.org/reference/proportion.md),
[`proportion_I()`](https://amr-for-r.org/reference/proportion.md),
[`proportion_IR()`](https://amr-for-r.org/reference/proportion.md) and
[`proportion_R()`](https://amr-for-r.org/reference/proportion.md) can be
used to determine the proportion of a specific antimicrobial outcome.

All these functions contain a `minimum` argument, denoting the minimum
required number of test results for returning a value. These functions
will otherwise return `NA`. The default is `minimum = 30`, following the
[CLSI M39-A4
guideline](https://clsi.org/standards/products/microbiology/documents/m39/)
for applying microbial epidemiology.

As per the EUCAST guideline of 2019, we calculate resistance as the
proportion of R
([`proportion_R()`](https://amr-for-r.org/reference/proportion.md),
equal to
[`resistance()`](https://amr-for-r.org/reference/proportion.md)) and
susceptibility as the proportion of S and I
([`proportion_SI()`](https://amr-for-r.org/reference/proportion.md),
equal to
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)).
These functions can be used on their own:

``` r

our_data_1st %>% resistance(AMX)
#> [1] 0.4203377
```

Or can be used in conjunction with
[`group_by()`](https://dplyr.tidyverse.org/reference/group_by.html) and
[`summarise()`](https://dplyr.tidyverse.org/reference/summarise.html),
both from the `dplyr` package:

``` r

our_data_1st %>%
  group_by(hospital) %>%
  summarise(amoxicillin = resistance(AMX))
#> # A tibble: 3 × 2
#>   hospital amoxicillin
#>   <chr>          <dbl>
#> 1 A              0.340
#> 2 B              0.551
#> 3 C              0.370
```

### Interpreting MIC and Disk Diffusion Values

Minimal inhibitory concentration (MIC) values and disk diffusion
diameters can be interpreted into clinical breakpoints (SIR) using
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md). Here’s an
example with randomly generated MIC values for *Klebsiella pneumoniae*
and ciprofloxacin:

``` r

set.seed(123)
mic_values <- random_mic(100)
sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")

my_data <- tibble(MIC = mic_values, SIR = sir_values)
my_data
#> # A tibble: 100 × 2
#>         MIC SIR  
#>       <mic> <sir>
#>  1 <=0.0001   S  
#>  2   0.0160   S  
#>  3 >=8.0000   R  
#>  4   0.0320   S  
#>  5   0.0080   S  
#>  6  64.0000   R  
#>  7   0.0080   S  
#>  8   0.1250   S  
#>  9   0.0320   S  
#> 10   0.0002   S  
#> # ℹ 90 more rows
```

This allows direct interpretation according to EUCAST or CLSI
breakpoints, facilitating automated AMR data processing.

### Plotting MIC and SIR Interpretations

We can visualise MIC distributions and their SIR interpretations using
`ggplot2`, using the new
[`scale_y_mic()`](https://amr-for-r.org/reference/plot.md) for the
y-axis and
[`scale_colour_sir()`](https://amr-for-r.org/reference/plot.md) to
colour-code SIR categories.

``` r

# add a group
my_data$group <- rep(c("A", "B", "C", "D"), each = 25)

ggplot(
  my_data,
  aes(x = group, y = MIC, colour = SIR)
) +
  geom_jitter(width = 0.2, size = 2) +
  geom_boxplot(fill = NA, colour = "grey40") +
  scale_y_mic() +
  scale_colour_sir() +
  labs(
    title = "MIC Distribution and SIR Interpretation",
    x = "Sample Groups",
    y = "MIC (mg/L)"
  )
```

![](AMR_files/figure-html/mic_plot-1.png)

This plot provides an intuitive way to assess susceptibility patterns
across different groups while incorporating clinical breakpoints.

For a more straightforward and less manual approach, `ggplot2`’s
function
[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
has been extended by this package to directly plot MIC and disk
diffusion values:

``` r

autoplot(mic_values)
```

![](AMR_files/figure-html/autoplot-1.png)

``` r


# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
```

![](AMR_files/figure-html/autoplot-2.png)

------------------------------------------------------------------------

*Author: Dr. Matthijs Berends, 23rd June 2026*
