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 data itself was based on our example_isolates data set.

WHONET

Format

A data.frame 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
    Last name of patient

  • First name
    Initial of patient

  • Sex
    Gender of patient

  • Age
    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 antibiotics. You can lookup the abbreviations in the antibiotics data set, or use e.g. ab_name("AMP") to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using as.rsi().

Reference data publicly available

All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.

Read more on our website!

On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!