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$job()
#> [1] "Pilot, airline"
x$name()
#> [1] "Kit Olson"
x$job()
#> [1] "Civil Service fast streamer"
x$color_name()
#> [1] "PaleGoldenRod"

locale support

Adding more locales through time, e.g.,

Locale support for job data

ch_job(locale = "en_US", n = 3)
#> [1] "Community arts worker" "Exercise physiologist" "Bookseller"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Auxiliaire de vie sociale"   "Chef monteur"               
#> [3] "Biologiste en environnement"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Pregledač vagona"                                       
#> [2] "Vozač teretnog motornog vozila i autobusa"              
#> [3] "Djelatnik koji obavlja poslove izvođenja glasnog pucnja"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Випробувач" "Мірошник"   "Швачка"
ch_job(locale = "zh_TW", n = 3)
#> [1] "播音／配音人員" "麵包師"         "產品維修人員"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "RoyalBlue" "Pink"      "Khaki"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Колір засмаги"   "Темно-кораловий" "Блаватний"

More coming soon …

generate a dataset

ch_generate()
#> # A tibble: 10 x 3
#>    name                       job                           phone_number  
#>    <chr>                      <chr>                         <chr>         
#>  1 Dr. Rube Jenkins           Garment/textile technologist  03032473162   
#>  2 Nicolas Rohan II           Community arts worker         832.223.2113x…
#>  3 Lisette Kunde              Chemical engineer             894-412-4188x…
#>  4 Tanika Bayer               Teacher, music                317-851-3598x…
#>  5 Wesley Paucek              Housing manager/officer       049.669.5051  
#>  6 Casey Walter               Immunologist                  00838982753   
#>  7 Nakisha DuBuque-Runolfsson Solicitor, Scotland           546-479-6195x…
#>  8 Ray Nolan                  Broadcast engineer            046-837-3872x…
#>  9 Cindy Rosenbaum-Zboncak    Development worker, community (342)135-9921 
#> 10 Dr. Reino Romaguera        Best boy                      720-227-6908
ch_generate('job', 'phone_number', n = 30)
#> # A tibble: 30 x 2
#>    job                                      phone_number       
#>    <chr>                                    <chr>              
#>  1 Secretary, company                       (176)766-6539x98163
#>  2 Accommodation manager                    756-275-2881x5444  
#>  3 Technical author                         1-238-563-1014x9137
#>  4 Diagnostic radiographer                  +25(9)2759353471   
#>  5 Trade mark attorney                      632-364-4032x3837  
#>  6 Fisheries officer                        03784472866        
#>  7 Public affairs consultant                642-477-1823x9911  
#>  8 Geographical information systems officer 590-683-8806x87386 
#>  9 Medical laboratory scientific officer    747.240.4935       
#> 10 Physicist, medical                       389.193.5038       
#> # ... with 20 more rows

Data types

person name

ch_name()
#> [1] "Miss Fiona Hettinger DVM"
ch_name(10)
#>  [1] "Ronal McDermott"       "Eric Hand"            
#>  [3] "Mr. Kasey Roberts Jr." "Mrs. Olivine Osinski" 
#>  [5] "Delma Dickinson DVM"   "Milo Glover"          
#>  [7] "Abdiel Yost"           "Latanya King"         
#>  [9] "Sabastian Zboncak"     "Dr. Dora Hammes DVM"

phone number

ch_phone_number()
#> [1] "624.842.8023x0490"
ch_phone_number(10)
#>  [1] "+48(4)9316992700"  "+09(0)7272220687"  "1-770-726-3350"   
#>  [4] "719.617.7928"      "+25(1)4441957520"  "1-601-264-1417"   
#>  [7] "192-090-5794x8002" "00622724252"       "398-551-3270x449" 
#> [10] "(210)784-5702x647"

job

ch_job()
#> [1] "Museum/gallery curator"
ch_job(10)
#>  [1] "Nature conservation officer" "Engineer, mining"           
#>  [3] "Risk analyst"                "Designer, furniture"        
#>  [5] "Education officer, museum"   "Higher education lecturer"  
#>  [7] "Music tutor"                 "Research scientist (maths)" 
#>  [9] "Lexicographer"               "Occupational hygienist"

credit cards

ch_credit_card_provider()
#> [1] "Maestro"
ch_credit_card_provider(n = 4)
#> [1] "Mastercard"       "Voyager"          "VISA 16 digit"   
#> [4] "American Express"
ch_credit_card_number()
#> [1] "3748464464469461"
ch_credit_card_number(n = 10)
#>  [1] "4800004198534897"    "54958287613799373"   "676231076965784"    
#>  [4] "3337013655555534144" "4407106818436689"    "3528609408271169955"
#>  [7] "4108643086250"       "3158593336043226738" "6011140503841684099"
#> [10] "4424599784454216"
ch_credit_card_security_code()
#> [1] "188"
ch_credit_card_security_code(10)
#>  [1] "368"  "7546" "8950" "422"  "320"  "387"  "250"  "804"  "454"  "712"

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] "Krystal Wilderman-Crist" "Mr Nevin Stehr"         
#>  [3] "Harris Kris"             "Ilona Bergstrom"        
#>  [5] "Mr Tanner Sipes IV"      "Arther Hegmann"         
#>  [7] "Sherilyn Lubowitz"       "Dr Bee Harvey md"       
#>  [9] "Agusta Runte-Dickinson"  "Alphonso Koepp"         
#> [11] "Burr Gibson"             "Soloman Murray"         
#> [13] "Jerold Hamill"           "Pattie Gorczany m.d."   
#> [15] "Dr Vessie Herman"        "Irvine Bradtke-Hackett" 
#> [17] "Mr Tristen Schimmel"     "Jack Carter"            
#> [19] "Hurley Bauch"            "Efrain Kirlin-Gleichner"
#> [21] "Ethan Boehm I"           "Willaim Stokes"         
#> [23] "Bev Kihn-Murazik"        "Mauricio Rippin"        
#> [25] "Dr Tilden Littel Jr"     "Abie Erdman"            
#> [27] "Austin Kuhlman"          "Byron Hills"            
#> [29] "Bernita Reichert Ph.D."  "Kristi Hickle"          
#> [31] "Leslee Bartell DVM"      "Nia Connelly"           
#> [33] "Mrs. Velda Dickens md"   "Susanna VonRueden"      
#> [35] "Garrick Langosh"         "Davon Gerlach"          
#> [37] "Dr Barbra Reynolds DVM"  "Dr. Lupe Mitchell md"   
#> [39] "Wilson Carter II"        "Omari Kuvalis"          
#> [41] "Bernice Bergnaum"        "Raegan Braun-Lindgren"  
#> [43] "Ardis Walter"            "Miss Luciana Lynch DVM" 
#> [45] "Emmitt Yundt"            "Johnson Funk"           
#> [47] "Ebert Spencer IV"        "Earnest Cummerata"      
#> [49] "Moody Koch Jr."          "Lex Lehner Sr"

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