Data Set with 500 Isolates - WHONET Example

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.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
#> Warning: The 'rsi' class has been replaced with 'sir'. Transform your 'rsi' columns
#> to 'sir' with as.sir(), e.g.:
#>   your_data %>% mutate_if(is.rsi, as.sir)
#> # 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>, …