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 are created using online surname generators and are only in place for practice purposes.
WHONET
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
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.rsi().
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.
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!