charlatan makes fake data, inspired from and borrowing some code from Python’s faker

Why would you want to make fake data? Here’s some possible use cases to give you a sense for what you can do with this package:

  • Students in a classroom setting learning any task that needs a dataset.
  • People doing simulations/modeling that need some fake data
  • Generate fake dataset of users for a database before actual users exist
  • Complete missing spots in a dataset
  • Generate fake data to replace sensitive real data with before public release
  • Create a random set of colors for visualization
  • Generate random coordinates for a map
  • Get a set of randomly generated DOIs (Digial Object Identifiers) to assign to fake scholarly artifacts
  • Generate fake taxonomic names for a biological dataset
  • Get a set of fake sequences to use to test code/software that uses sequence data

Package API

  • Low level interfaces: All of these are R6 objects that a user can initialize and then call methods on. These contain all the logic that the below interfaces use.
  • High level interfaces: There are high level functions prefixed with ch_*() that wrap low level interfaces, and are meant to be easier to use and provide an easy way to make many instances of a thing.
  • ch_generate() - generate a data.frame with fake data, choosing which columns to include from the data types provided in charlatan
  • fraudster() - single interface to all fake data methods, - returns vectors/lists of data - this function wraps the ch_*() functions described above

Install

Stable version from CRAN

install.packages("charlatan")

Development version from Github

devtools::install_github("ropensci/charlatan")
library("charlatan")

high level function

… for all fake data operations

x <- fraudster()
x$job()
#> [1] "Chief Technology Officer"
x$name()
#> [1] "Gottlieb Greenfelder"
x$job()
#> [1] "Scientist, product/process development"
x$color_name()
#> [1] "DarkViolet"

locale support

Adding more locales through time, e.g.,

Locale support for job data

ch_job(locale = "en_US", n = 3)
#> [1] "Housing manager/officer" "Theme park manager"     
#> [3] "Office manager"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Auxiliaire spécialisé vétérinaire" "Économe de flux"                  
#> [3] "Bottier"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Klobučar"          "Graditelj brodova" "Pismoslikar"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Програміст" "Професор"   "Дерун"
ch_job(locale = "zh_TW", n = 3)
#> [1] "農藝/畜產研究人員"  "軟體專案管理師"      "系統整合/ERP專案師"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "SandyBrown"    "DarkSlateGray" "PowderBlue"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Лимонно-кремовий"       "Блідо-каштановий"      
#> [3] "Помаранчево-персиковий"

More coming soon …

generate a dataset

ch_generate()
#> # A tibble: 10 x 3
#>    name                job                               phone_number      
#>    <chr>               <chr>                             <chr>             
#>  1 Kassie Leffler      Ecologist                         490.780.0864      
#>  2 Tierra Watsica      Insurance account manager         (452)316-8793x9081
#>  3 Debbi Harber        Engineer, chemical                1-301-135-5169x984
#>  4 Jeramie Walter      Editorial assistant               (665)154-2744x2274
#>  5 Rickie Morissette   Restaurant manager                1-329-085-4956    
#>  6 Julio Farrell       Corporate treasurer               720-855-1751x2252 
#>  7 Sherie Gislason     Plant breeder/geneticist          767-335-0245      
#>  8 Lucinda Armstrong   Radiation protection practitioner 02079832938       
#>  9 Carrol Howe MD      Contracting civil engineer        +68(4)9165838423  
#> 10 Dr. Socorro Schmitt Designer, furniture               852-928-8670
ch_generate('job', 'phone_number', n = 30)
#> # A tibble: 30 x 2
#>    job                                   phone_number      
#>    <chr>                                 <chr>             
#>  1 Legal secretary                       (606)596-6104     
#>  2 Emergency planning/management officer +99(0)0779574693  
#>  3 Food technologist                     07047666301       
#>  4 Quality manager                       936.390.4298      
#>  5 Economist                             +25(9)7508933600  
#>  6 Actor                                 (977)091-9322     
#>  7 Civil Service fast streamer           770.644.3003x529  
#>  8 Acupuncturist                         703-830-2719x354  
#>  9 Lobbyist                              (494)107-0538x3345
#> 10 Health promotion specialist           1-231-435-4566x864
#> # ... with 20 more rows

Data types

person name

ch_name()
#> [1] "Eulalia Walker-Hand"
ch_name(10)
#>  [1] "Lular Schulist"        "Miss Willie Mante DDS"
#>  [3] "Kole Price"            "Kinsey Hahn"          
#>  [5] "Lindy Hettinger"       "Myron Miller"         
#>  [7] "Clara O'Kon-Bosco"     "Lakisha Murazik"      
#>  [9] "Ms. Aiyanna Ernser"    "Regenia Crooks"

phone number

ch_phone_number()
#> [1] "225-861-1056"
ch_phone_number(10)
#>  [1] "(397)709-4341x8258"   "588-372-5015x37545"   "613-137-7911x883"    
#>  [4] "1-600-128-4597x99950" "670-069-8527x6732"    "658.651.4977x94270"  
#>  [7] "+46(6)3770984906"     "1-546-751-0949"       "(459)629-1280x4323"  
#> [10] "+20(2)7575786577"

job

ch_job()
#> [1] "Contracting civil engineer"
ch_job(10)
#>  [1] "Diplomatic Services operational officer"
#>  [2] "Exhibitions officer, museum/gallery"    
#>  [3] "Clinical psychologist"                  
#>  [4] "Interpreter"                            
#>  [5] "Electrical engineer"                    
#>  [6] "Sports coach"                           
#>  [7] "Radio broadcast assistant"              
#>  [8] "Museum/gallery exhibitions officer"     
#>  [9] "Broadcast engineer"                     
#> [10] "Personal assistant"

credit cards

ch_credit_card_provider()
#> [1] "VISA 16 digit"
ch_credit_card_provider(n = 4)
#> [1] "VISA 16 digit" "Mastercard"    "Mastercard"    "VISA 16 digit"
ch_credit_card_number()
#> [1] "55102041677140379"
ch_credit_card_number(n = 10)
#>  [1] "6011605711290457945" "3041537239690522"    "3112768344523864506"
#>  [4] "4486249701104"       "4253802266283"       "4992229219738163"   
#>  [7] "4672722860615828"    "4987676760612"       "4752221113018"      
#> [10] "3764355598669753"
ch_credit_card_security_code()
#> [1] "711"
ch_credit_card_security_code(10)
#>  [1] "395" "324" "582" "562" "227" "595" "574" "110" "050" "275"

Messy data

Real data is messy, right? charlatan makes it easy to create messy data. This is still in the early stages so is not available across most data types and languages, but we’re working on it.

For example, create messy names:

ch_name(50, messy = TRUE)
#>  [1] "Cason Hagenes"               "Angela Cassin"              
#>  [3] "Drew Ritchie"                "Math Bergnaum"              
#>  [5] "Wash Brown"                  "Ransom Green-Keebler"       
#>  [7] "Elick Boyer DDS"             "Joretta Hirthe"             
#>  [9] "Dr Solon Schneider"          "Jaden D'Amore"              
#> [11] "Delmas Schmidt"              "Ramiro Howell-Goldner"      
#> [13] "Margarett Pouros DVM"        "Una McClure"                
#> [15] "Miracle Yost m.d."           "Dr Beaulah Vandervort Ph.D."
#> [17] "Yadira Grimes"               "Holland Koch"               
#> [19] "Migdalia Flatley"            "Miss Ferne Haag"            
#> [21] "Dr Chelsi Jaskolski"         "Maryanne Heaney"            
#> [23] "Gerold Klein"                "Dr. Stephon Kreiger"        
#> [25] "Lydell Abernathy Sr."        "Ms Della Gulgowski"         
#> [27] "Boyd Quitzon"                "Jerald Kerluke"             
#> [29] "Dr Domenick Mills"           "Dr. Thurlow Connelly"       
#> [31] "Raheem Robel"                "Britney O'Keefe-Greenholt"  
#> [33] "Kacey Hane"                  "Germaine Feeney"            
#> [35] "Myrta Rosenbaum"             "Dequan Sawayn"              
#> [37] "Harvie Crona"                "Adelyn Feest-Wehner"        
#> [39] "Corda Jerde"                 "Savon Armstrong"            
#> [41] "Summer Cartwright"           "Chyna Stehr-Schiller"       
#> [43] "Levie Torphy"                "Anne Tillman"               
#> [45] "Nana Abbott"                 "Dr. Neta Funk"              
#> [47] "Dr. Vance Stracke"           "Love Bogan"                 
#> [49] "Laquita Bartell"             "Nikolas Block-Beatty"

Right now only suffixes and prefixes for names in en_US locale are supported. Notice above some variation in prefixes and suffixes.