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Data Set with 500 Isolates - WHONET Example

Source: R/data.R
WHONET.Rd

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.

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.rsi().

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
#> # A tibble: 500 × 53
#>    Identif…¹ Speci…² Organ…³ Country Labor…⁴ Last …⁵ First…⁶ Sex     Age Age c…⁷
#>    <chr>       <int> <chr>   <chr>   <chr>   <chr>   <chr>   <chr> <dbl> <chr>  
#>  1 fe41d7ba…    1748 SPN     Belgium Nation… Abel    B.      F        68 55-74  
#>  2 91f175ec…    1767 eco     The Ne… Nation… Delacr… F.      M        89 75+    
#>  3 cc401505…    1343 eco     The Ne… Nation… Steens… F.      M        85 75+    
#>  4 e864b692…    1894 MAP     Denmark Nation… Beyers… L.      M        62 55-74  
#>  5 3d051fe3…    1739 PVU     Belgium Nation… Hummel  W.      M        86 75+    
#>  6 c80762a0…    1846 103     The Ne… Nation… Eikenb… J.      F        53 25-54  
#>  7 8022d372…    1628 103     Denmark Nation… Leclerc S.      F        77 75+    
#>  8 f3dc5f55…    1493 eco     The Ne… Nation… Delacr… W.      M        53 25-54  
#>  9 15add38f…    1847 eco     France  Nation… Van La… S.      F        63 55-74  
#> 10 fd41248d…    1458 eco     Germany Nation… Moulin  O.      F        75 75+    
#> # … with 490 more rows, 43 more variables: `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>,
#> #   `Inducible clindamycin resistance` <lgl>, Comment <chr>,
#> #   `Date of data entry` <date>, AMP_ND10 <rsi>, AMC_ED20 <rsi>, …