WHONET {AMR}

Data Set with 500 Isolates - WHONET Example

Description

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 antibiotic 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.

Usage

WHONET

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 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.sir().

Details

Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit our website for the download links. The actual files are of course available on our GitHub repository.

Examples

WHONET