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

Contributing

See the Contributing to charlatan vignette

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$job()
#> [1] "Exercise physiologist"
x$name()
#> [1] "Mahalie Volkman"
x$job()
#> [1] "Pharmacist, hospital"
x$color_name()
#> [1] "Salmon"

locale support

Adding more locales through time, e.g.,

Locale support for job data

ch_job(locale = "en_US", n = 3)
#> [1] "Comptroller"                 "Applications developer"     
#> [3] "Conservator, museum/gallery"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Conseiller en fusion-acquisition" "Technicien automobile"           
#> [3] "Économe de flux"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Revident"                        "Dentalni asistent"              
#> [3] "Inženjer medicinske radiologije"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Астроном"   "Антрополог" "Ріелтор"
ch_job(locale = "zh_TW", n = 3)
#> [1] "排版人員"           "英文翻譯／口譯人員" "染整技術人員"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "DarkSeaGreen"  "Violet"        "DarkSlateGray"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Дерева"         "Темно-лазурний" "Зелений"

More coming soon …

generate a dataset

ch_generate()
#> # A tibble: 10 × 3
#>    name                     job                                    phone_number 
#>    <chr>                    <chr>                                  <chr>        
#>  1 Shona Howell             Tax adviser                            (297)747-155…
#>  2 Missouri Hoppe-Gerlach   Dispensing optician                    657-442-1745…
#>  3 Miss Exa Lindgren DVM    Surveyor, insurance                    1-393-703-89…
#>  4 Marilou Hilll            Civil Service administrator            03436190752  
#>  5 Mr. Hoke Hansen          Teacher, adult education               816.530.1379 
#>  6 Ms. Violeta Ebert        Psychiatric nurse                      577-572-9397…
#>  7 Kieth Kiehn IV           Teacher, English as a foreign language 030-742-7026…
#>  8 Mikaila Wintheiser-Bruen Museum/gallery conservator             (305)030-717…
#>  9 Baylie Armstrong         Recruitment consultant                 178.968.3856…
#> 10 Gerard Quigley Jr.       Airline pilot                          765.313.3182
ch_generate('job', 'phone_number', n = 30)
#> # A tibble: 30 × 2
#>    job                                          phone_number       
#>    <chr>                                        <chr>              
#>  1 Paramedic                                    916.806.7738       
#>  2 Engineer, electronics                        1-558-738-5481x4670
#>  3 Education officer, environmental             1-777-390-2418x927 
#>  4 Production designer, theatre/television/film 177.776.3900       
#>  5 Marine scientist                             06918434812        
#>  6 Control and instrumentation engineer         089-980-1061x5075  
#>  7 Licensed conveyancer                         082.027.2276x7008  
#>  8 Operational researcher                       134.883.0475x1393  
#>  9 Management consultant                        844-455-4954x0276  
#> 10 Optometrist                                  264.670.0122x820   
#> # … with 20 more rows
#> # ℹ Use `print(n = ...)` to see more rows

Data types

person name

ch_name()
#> [1] "Akeem Schaden"
ch_name(10)
#>  [1] "Allen Littel"          "Dr. Yadira Mosciski"   "Ms. Teela Rath"       
#>  [4] "Griselda Macejkovic"   "Loula Moen"            "Geri Hermiston"       
#>  [7] "Latrice Mueller-Purdy" "Bessie Greenfelder"    "Brittnay Beahan"      
#> [10] "Marely Harvey-Haag"

phone number

ch_phone_number()
#> [1] "694.142.2479x8330"
ch_phone_number(10)
#>  [1] "729.696.4235x432"    "(410)948-5947x078"   "558.791.9758"       
#>  [4] "(019)599-4783x5009"  "204-331-9183"        "(478)845-1133"      
#>  [7] "631.713.7642"        "1-647-843-4062x1199" "705-007-7353x9342"  
#> [10] "(178)325-2557x56382"

job

ch_job()
#> [1] "Nurse, children's"
ch_job(10)
#>  [1] "Commercial art gallery manager"         
#>  [2] "IT technical support officer"           
#>  [3] "Adult guidance worker"                  
#>  [4] "Librarian, public"                      
#>  [5] "Diplomatic Services operational officer"
#>  [6] "Control and instrumentation engineer"   
#>  [7] "Engineer, manufacturing"                
#>  [8] "Chartered certified accountant"         
#>  [9] "Administrator, Civil Service"           
#> [10] "Designer, industrial/product"

credit cards

ch_credit_card_provider()
#> [1] "Diners Club / Carte Blanche"
ch_credit_card_provider(n = 4)
#> [1] "JCB 16 digit"     "Voyager"          "American Express" "JCB 16 digit"
ch_credit_card_number()
#> [1] "53179035548247676"
ch_credit_card_number(n = 10)
#>  [1] "502059506159116"     "3088615926186023845" "3002877866616823"   
#>  [4] "4383703635318773"    "180088446044038768"  "869901859495367367" 
#>  [7] "675928121803937"     "3096439026343250946" "4850304535923"      
#> [10] "52642312402226446"
ch_credit_card_security_code()
#> [1] "417"
ch_credit_card_security_code(10)
#>  [1] "071"  "824"  "5435" "775"  "2961" "976"  "2281" "363"  "188"  "732"

Missing data

charlatan makes it very easy to generate fake data with missing entries. First, you need to run MissingDataProvider() and then make an appropriate make_missing() call specifying the data type to be generated. This method picks a random number (N) of slots in the input make_missing vector and then picks N random positions that will be replaced with NA matching the input class.

testVector <- MissingDataProvider$new()

character strings

testVector$make_missing(x = ch_generate()$name) 
#>  [1] NA                   "Marge Bogan"        "Ester Hilll"       
#>  [4] "Rome Barton"        "Mervyn Huels"       NA                  
#>  [7] NA                   "Sonya Pollich"      "Mr. Bernard Raynor"
#> [10] "Dr. Aniya Waters"

numeric data

testVector$make_missing(x = ch_integer(10)) 
#>  [1] NA NA NA NA NA NA NA NA NA NA

logicals

set.seed(123)
testVector$make_missing(x = sample(c(TRUE, FALSE), 10, replace = TRUE)) 
#>  [1]  TRUE    NA    NA FALSE  TRUE    NA FALSE FALSE    NA  TRUE

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] "Destiney Dicki"            "Mrs Freddie Pouros d.d.s."
#>  [3] "Jefferey Lesch"            "Inga Dach"                
#>  [5] "Keyshawn Schaefer"         "Ferdinand Bergstrom"      
#>  [7] "Justen Simonis"            "Ms. Doloris Stroman md"   
#>  [9] "Mrs Ermine Heidenreich"    "Marion Corwin"            
#> [11] "Jalen Grimes"              "Mr. Sullivan Hammes IV"   
#> [13] "Adrien Vandervort-Dickens" "Dr Sharif Kunde"          
#> [15] "Marlena Reichert d.d.s."   "Mr. Brandan Oberbrunner"  
#> [17] "Lloyd Adams Sr"            "Keesha Schowalter"        
#> [19] "Randy Ziemann"             "Gina Sanford"             
#> [21] "Cornell Funk"              "Yadiel Collier"           
#> [23] "Kamryn Johnson"            "Tyesha Schmeler"          
#> [25] "Ernie Hegmann-Graham"      "Zackery Runolfsdottir"    
#> [27] "Cleveland Predovic"        "Melvyn Hickle"            
#> [29] "Larry Nienow I"            "Nicola Langosh Ph.D."     
#> [31] "Ebenezer Fadel V"          "Andrae Hand-Eichmann"     
#> [33] "Shamar Harvey"             "Miss Lynn Altenwerth"     
#> [35] "Willene McLaughlin-Mohr"   "Kyree Kutch"              
#> [37] "Ms Delpha Grant"           "Ms. Icie Crooks"          
#> [39] "Loney Jenkins-Lindgren"    "Shania Donnelly DVM"      
#> [41] "Dr Patric Veum"            "Amirah Rippin DVM"        
#> [43] "Randle Hilpert"            "Soren Dare"               
#> [45] "Roderic Walter"            "Farah Daugherty DDS"      
#> [47] "Ryland Ledner"             "Girtha Harvey DVM"        
#> [49] "Tyrique Spencer"           "Mr Olan Bernhard"

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