charlatan

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")

library("charlatan")

high level function

... for all fake data operations

x <- fraudster()
x$job()
#> [1] "Building control surveyor"
x$name()
#> [1] "Dr. Elissa Kassulke"
x$color_name()
#> [1] "MediumBlue"

locale support

Adding more locales through time, e.g.,

Locale support for job data

ch_job(locale = "en_US", n = 3)
#> [1] "Purchasing manager"    "Clinical embryologist" "Product manager"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Conducteur d'engins de travaux publics"
#> [2] "Vitrailliste"                          
#> [3] "Installateur en télécoms"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Arhivski tehničar"                                
#> [2] "Član kabinske posade zrakoplova"                  
#> [3] "Diplomirana medicinska sestra/medicinski tehničar"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Зоолог" "Фермер" "Модель"
ch_job(locale = "zh_TW", n = 3)
#> [1] "發包人員"         "運輸交通專業人員" "導遊"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "NavajoWhite" "HoneyDew"    "Aquamarine"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Абрикосовий"              "Міжнародний помаранчевий"
#> [3] "Умбра"

More coming soon ...

generate a dataset

ch_generate()
#> # A tibble: 10 × 3
#>    name                    job                    phone_number      
#>    <chr>                   <chr>                  <chr>             
#>  1 Mr. Danial Rau          Insurance broker       322.454.0638x452  
#>  2 Ms. Augusta Flatley DDS Air traffic controller 1-412-252-8256x816
#>  3 Ahmed Tromp             Dealer                 738.618.3766      
#>  4 Elle Parker-Pagac       Engineer, petroleum    1-563-823-9417    
#>  5 Nelie Bogisich          Audiological scientist (657)263-8451x928 
#>  6 Dr. Venita Bartoletti   Gaffer                 (701)117-8665x092 
#>  7 Clarke Halvorson        Futures trader         (587)244-0897x4646
#>  8 Keith Marvin            Cytogeneticist         (505)188-7137     
#>  9 Kellan Swift            Primary school teacher 642-015-6852x72341
#> 10 Dr. Shanell Braun       Warden/ranger          586-673-4593x4166

ch_generate('job', 'phone_number', n = 30)
#> # A tibble: 30 × 2
#>    job                                      phone_number       
#>    <chr>                                    <chr>              
#>  1 Geographical information systems officer 00023575617        
#>  2 Industrial/product designer              251.018.7002       
#>  3 Special effects artist                   04931219014        
#>  4 Solicitor                                097.433.7373x183   
#>  5 Research scientist (maths)               1-540-787-9748x7124
#>  6 Retail buyer                             878.896.3368x58978 
#>  7 Engineer, technical sales                09564477842        
#>  8 Volunteer coordinator                    722-926-5502       
#>  9 Museum/gallery exhibitions officer       708.958.6259x3348  
#> 10 Probation officer                        +10(4)9172449874   
#> # … with 20 more rows
#> # ℹ Use `print(n = ...)` to see more rows

person name

ch_name()
#> [1] "Fenton Ryan"

ch_name(10)
#>  [1] "Sylva Klein"             "Kanye Muller PhD"       
#>  [3] "Phoebe Altenwerth"       "Alvie McClure"          
#>  [5] "Nils Mann"               "Santiago Koepp"         
#>  [7] "Jeanmarie Graham-Larkin" "Mr. Humberto Davis PhD" 
#>  [9] "Georgine Zulauf"         "Pascal Schaefer-Feest"

phone number

ch_phone_number()
#> [1] "+57(2)2951130202"

ch_phone_number(10)
#>  [1] "946.325.0782"         "1-121-631-0553"       "669.979.2952x566"    
#>  [4] "(145)481-9199x487"    "594.225.2171x504"     "(910)235-3893x289"   
#>  [7] "1-660-490-0565x59870" "1-340-087-1768x51605" "554-891-7210x6337"   
#> [10] "750.606.3428"

job

ch_job()
#> [1] "Restaurant manager"

ch_job(10)
#>  [1] "Lighting technician, broadcasting/film/video"     
#>  [2] "Chiropodist"                                      
#>  [3] "Wellsite geologist"                               
#>  [4] "Animal nutritionist"                              
#>  [5] "Biomedical scientist"                             
#>  [6] "Risk analyst"                                     
#>  [7] "Historic buildings inspector/conservation officer"
#>  [8] "Intelligence analyst"                             
#>  [9] "Advertising account planner"                      
#> [10] "Engineer, chemical"

credit cards

ch_credit_card_provider()
#> [1] "JCB 16 digit"
ch_credit_card_provider(n = 4)
#> [1] "VISA 13 digit" "Voyager"       "JCB 15 digit"  "VISA 13 digit"

ch_credit_card_number()
#> [1] "4888106530181587"
ch_credit_card_number(n = 10)
#>  [1] "3528539455946294754" "4206980387974"       "561220125494227"    
#>  [4] "4746311035020536"    "4054993945433911"    "869982827809211136" 
#>  [7] "4280701571800733"    "4068247476037565"    "4085741624331754"   
#> [10] "4714129292203"

ch_credit_card_security_code()
#> [1] "599"
ch_credit_card_security_code(10)
#>  [1] "694"  "083"  "7532" "074"  "245"  "354"  "683"  "763"  "998"  "410"

Usage in the wild

• eacton/R-Utility-Belt-ggplot2 (https://github.com/eacton/R-Utility-Belt-ggplot2/blob/836a6bd303fbfde4a334d351e0d1c63f71c4ec68/furry_dataset.R)

Contributors

• Scott Chamberlain (https://github.com/sckott)
• Kyle Voytovich (https://github.com/kylevoyto)
• Martin Pedersen (https://github.com/MartinMSPedersen)

similar art

• wakefield (https://github.com/trinker/wakefield)
• ids (https://github.com/richfitz/ids)
• rcorpora (https://github.com/gaborcsardi/rcorpora)
• synthpop (https://cran.r-project.org/package=synthpop)

Meta

• Please report any issues or bugs.
• License: MIT
• Get citation information for charlatan in R doing citation(package = 'charlatan')
• Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.