rOpenSci: The charlatan package

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Build Status Build status cran checks codecov cran version rstudio mirror downloads

charlatan makes fake data, inspired from and borrowing some code from Python’s faker (https://github.com/joke2k/faker)

Make fake data for:

  • person names
  • jobs
  • phone numbers
  • colors: names, hex, rgb
  • credit cards
  • DOIs
  • numbers in range and from distributions
  • gene sequences
  • geographic coordinates
  • emails
  • URIs, URLs, and their parts
  • IP addresses
  • more coming …

Possible use cases for charlatan:

  • 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 (Digital 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

Reasons to use charlatan:

  • Lite weight, few dependencies
  • Relatively comprehensive types of data, and more being added
  • Comprehensive set of languages supported, more being added
  • Useful R features such as creating entire fake data.frame’s

Installation

cran version

install.packages("charlatan")

dev version

remotes::install_github("ropensci/charlatan")

high level function

… for all fake data operations

x <- fraudster()
x$job()
#> [1] "Agricultural consultant"
x$name()
#> [1] "Britney Pollich"
x$color_name()
#> [1] "HoneyDew"

locale support

Adding more locales through time, e.g.,

Locale support for job data

ch_job(locale = "en_US", n = 3)
#> [1] "Development worker, international aid"
#> [2] "Geoscientist"                         
#> [3] "Engineer, technical sales"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Chef de mission humanitaire" "Entraîneur de chevaux"      
#> [3] "Officier de marine"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Ovlašteni inženjer strojarstva" "Knjižničarski savjetnik"       
#> [3] "Zlatar"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Економіст"   "Модель"      "Палеонтолог"
ch_job(locale = "zh_TW", n = 3)
#> [1] "飛行機師"         "塑膠射出技術人員" "手工包裝工"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "SaddleBrown"  "LemonChiffon" "ForestGreen"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Зелений чай"   "Ніжно-рожевий" "Смарагдовий"

More coming soon …

generate a dataset

ch_generate()
#> # A tibble: 10 x 3
#>    name                 job                                     phone_number    
#>    <chr>                <chr>                                   <chr>           
#>  1 Miss Kayleigh Dietr… Lighting technician, broadcasting/film… 500.521.7362x47…
#>  2 Lim O'Kon            Designer, fashion/clothing              179-726-6648    
#>  3 Tamisha Mayert PhD   Sub                                     535.439.1715x11…
#>  4 Jordy Stamm          Soil scientist                          +57(1)2448847839
#>  5 Leighton Langosh     Clinical biochemist                     673.628.3141x63…
#>  6 Bambi Fritsch        Optician, dispensing                    +05(2)9665064998
#>  7 Erlinda Welch        Database administrator                  844-661-0630    
#>  8 Mr. Myer Padberg     Education administrator                 1-961-436-8520  
#>  9 Amin Beahan          Hotel manager                           994.778.2680x64…
#> 10 Derl Kulas IV        Office manager                          237.117.2042x70…
ch_generate('job', 'phone_number', n = 30)
#> # A tibble: 30 x 2
#>    job                                phone_number       
#>    <chr>                              <chr>              
#>  1 Government social research officer 1-233-523-0412x4350
#>  2 Geophysical data processor         1-068-624-4456x3615
#>  3 Civil Service administrator        756.909.1036x6157  
#>  4 Animator                           977-515-5664       
#>  5 Production manager                 +81(8)3608969316   
#>  6 Public librarian                   (433)179-5585      
#>  7 Health visitor                     (979)259-6778x85903
#>  8 Higher education lecturer          045.270.9911x946   
#>  9 Web designer                       1-819-943-8977x8767
#> 10 Audiological scientist             411.429.8116x1221  
#> # … with 20 more rows

person name

ch_name()
#> [1] "Marcellus Wintheiser"
ch_name(10)
#>  [1] "Leia Greenholt PhD"        "Tatyana Renner-Bartoletti"
#>  [3] "Leonia Wolff DVM"          "Berneice Marvin-Zulauf"   
#>  [5] "Dr. Reggie Reichert Sr."   "Valentina Fisher"         
#>  [7] "Alena King"                "Mabelle Brakus"           
#>  [9] "Tyreke D'Amore-Klein"      "Clarine Mraz PhD"

phone number

ch_phone_number()
#> [1] "391.480.9016x29138"
ch_phone_number(10)
#>  [1] "1-444-462-8908"       "1-607-225-7080"       "1-042-282-7832x489"  
#>  [4] "1-187-719-1456"       "+62(9)2775060147"     "09880850028"         
#>  [7] "477.760.3248x0189"    "100-214-2675"         "(636)600-3735x7625"  
#> [10] "1-972-824-8726x55089"

job

ch_job()
#> [1] "Recycling officer"
ch_job(10)
#>  [1] "Metallurgist"                         
#>  [2] "Trade mark attorney"                  
#>  [3] "Printmaker"                           
#>  [4] "Editor, commissioning"                
#>  [5] "Product/process development scientist"
#>  [6] "Personnel officer"                    
#>  [7] "Art gallery manager"                  
#>  [8] "Nurse, mental health"                 
#>  [9] "Leisure centre manager"               
#> [10] "Biochemist, clinical"

credit cards

ch_credit_card_provider()
#> [1] "Maestro"
ch_credit_card_provider(n = 4)
#> [1] "JCB 15 digit"                "Voyager"                    
#> [3] "JCB 15 digit"                "Diners Club / Carte Blanche"
ch_credit_card_number()
#> [1] "55822361446116206"
ch_credit_card_number(n = 10)
#>  [1] "3498363999948883"   "180067076908985096" "4062017321928"     
#>  [4] "4170398299942"      "4527661145377"      "51638673599454307" 
#>  [7] "4854103984796010"   "4618290138974861"   "4502776360832164"  
#> [10] "54706214706241050"
ch_credit_card_security_code()
#> [1] "751"
ch_credit_card_security_code(10)
#>  [1] "119"  "680"  "432"  "254"  "888"  "285"  "743"  "284"  "5914" "286"

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