This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antimicrobial results are from our example_isolates data set. All patient names were created using online surname generators and are only in place for practice purposes.
Format
A tibble with 500 observations and 53 variables:
- Identification number
 ID of the sample
- Specimen number
 ID of the specimen
- Organism
 Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using- as.mo().
- Country
 Country of origin
- Laboratory
 Name of laboratory
- Last name
 Fictitious last name of patient
- First name
 Fictitious initial of patient
- Sex
 Fictitious gender of patient
- Age
 Fictitious age of patient
- Age category
 Age group, can also be looked up using- age_groups()
- Date of admission
 Date of hospital admission
- Specimen date
 Date when specimen was received at laboratory
- Specimen type
 Specimen type or group
- Specimen type (Numeric)
 Translation of- "Specimen type"
- Reason
 Reason of request with Differential Diagnosis
- Isolate number
 ID of isolate
- Organism type
 Type of microorganism, can also be looked up using- mo_type()
- Serotype
 Serotype of microorganism
- Beta-lactamase
 Microorganism produces beta-lactamase?
- ESBL
 Microorganism produces extended spectrum beta-lactamase?
- Carbapenemase
 Microorganism produces carbapenemase?
- MRSA screening test
 Microorganism is possible MRSA?
- Inducible clindamycin resistance
 Clindamycin can be induced?
- Comment
 Other comments
- Date of data entry
 Date this data was entered in WHONET
- AMP_ND10:CIP_EE
 28 different antimicrobials. You can lookup the abbreviations in the antimicrobials data set, or use e.g.- ab_name("AMP")to get the official name immediately. Before analysis, you should transform this to a valid antimicrobial class, using- as.sir().
Download Our Reference Data
All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.
For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.
Visit our website for direct download links, or explore the actual files in our GitHub repository.
Examples
WHONET
#> # A tibble: 500 × 53
#>    `Identification number` `Specimen number` Organism Country         Laboratory
#>    <chr>                               <int> <chr>    <chr>           <chr>     
#>  1 fe41d7bafa                           1748 SPN      Belgium         National …
#>  2 91f175ec37                           1767 eco      The Netherlands National …
#>  3 cc4015056e                           1343 eco      The Netherlands National …
#>  4 e864b692f5                           1894 MAP      Denmark         National …
#>  5 3d051fe345                           1739 PVU      Belgium         National …
#>  6 c80762a08d                           1846 103      The Netherlands National …
#>  7 8022d3727c                           1628 103      Denmark         National …
#>  8 f3dc5f553d                           1493 eco      The Netherlands National …
#>  9 15add38f6c                           1847 eco      France          National …
#> 10 fd41248def                           1458 eco      Germany         National …
#> # ℹ 490 more rows
#> # ℹ 48 more variables: `Last name` <chr>, `First name` <chr>, Sex <chr>,
#> #   Age <dbl>, `Age category` <chr>, `Date of admission` <date>,
#> #   `Specimen date` <date>, `Specimen type` <chr>,
#> #   `Specimen type (Numeric)` <dbl>, Reason <chr>, `Isolate number` <int>,
#> #   `Organism type` <chr>, Serotype <chr>, `Beta-lactamase` <lgl>, ESBL <lgl>,
#> #   Carbapenemase <lgl>, `MRSA screening test` <lgl>, …