Introduction to the charlatan package

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

Why would you want to make fake data that looks real? 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 (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

See the Creating realistic data vignette for a few realistic examples.

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

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

high level function

… for all fake data operations

x <- fraudster()
x$job()
#> [1] "Scientist, research (maths)"
x$name()
#> [1] "Ralph Hand"
x$job()
#> [1] "Futures trader"
x$color_name()
#> [1] "LightSlateGray"

locale support

Here we create 3 jobs, for different locales:

  • English USA
  • French, France
  • Croatian, Croatia
  • Ukrainian, Ukraine
  • Chinese, Taiwan
ch_job(locale = "en_US", n = 3)
#> [1] "Barrister's clerk" "Stage manager"     "Recycling officer"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Moniteur de ski"   "Militaire du rang" "Styliste"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Kuhar"                                              
#> [2] "Medicinski biokemičar specijalist"                  
#> [3] "Vodoinstalater, instalater grijanja i klimatizacije"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Драматург"   "Фольклорист" "Економіст"
ch_job(locale = "zh_TW", n = 3)
#> [1] "電子商務技術主管" "家庭代工"         "生產管理主管"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "MistyRose"     "Chartreuse"    "DarkSlateGray"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Візантійський"      "Зелене море"        "Фіолетово-червоний"

generate a dataset

ch_generate()
#> # A tibble: 10 × 3
#>    name                        job                            phone_number      
#>    <chr>                       <chr>                          <chr>             
#>  1 Shelvia Grady               Sub                            (928)246-5867     
#>  2 Clarabelle O'Connell-Harber Health physicist               +78(5)8487961127  
#>  3 Arnav Windler III           Youth worker                   919.660.3368x197  
#>  4 Geary Stracke               Education administrator        963-347-2985      
#>  5 Mr. Casper Kub V            Manufacturing engineer         356.570.7457      
#>  6 Ali Stanton                 Ecologist                      (423)336-6737x841…
#>  7 Moses Jaskolski             Production assistant, radio    478.546.3060x1337 
#>  8 Delfina Quitzon             Equality and diversity officer (291)445-2397x1244
#>  9 Ellyn Zboncak               Designer, multimedia           (363)404-8430x091…
#> 10 Elder Weissnat-Mante        Private music teacher          04900551937
ch_generate("job", "phone_number", n = 30)
#> # A tibble: 30 × 2
#>    job                         phone_number      
#>    <chr>                       <chr>             
#>  1 Holiday representative      104.593.2425      
#>  2 Tax adviser                 422.282.4579x32804
#>  3 Archivist                   973-844-5714      
#>  4 Civil engineer, consulting  227-647-9165      
#>  5 Health promotion specialist 524-124-9888      
#>  6 Cabin crew                  594.195.2935x55837
#>  7 Trade mark attorney         734-348-7242      
#>  8 Sales executive             395.238.9402x1678 
#>  9 Electronics engineer        +15(6)7624223383  
#> 10 Designer, interior/spatial  416.376.1102      
#> # ℹ 20 more rows

Data types, localized

We can create locale specific versions of:

  • Colors
  • Companies
  • Elements (of the periodic table)
  • Files
  • Internet
  • Jobs
  • Lorem
  • Persons
  • Phone numbers
  • Social Security Numbers
  • Taxonomies
  • UserAgent

Examples:

person name

ch_name()
#> [1] "Dr. Cora McLaughlin"
ch_name(10)
#>  [1] "Elmer Wisoky"              "Carleen Cronin"           
#>  [3] "Litha Schmitt"             "Marlene Schoen"           
#>  [5] "Rolland Barton"            "Daniel Schamberger-Wunsch"
#>  [7] "Philomena Wolff MD"        "Regis Botsford"           
#>  [9] "Dr. Kip Fadel DVM"         "Salome Hayes"

phone number

ch_phone_number()
#> [1] "568.346.9449"
ch_phone_number(10)
#>  [1] "(192)982-9874"       "1-769-068-4586"      "(809)630-0190x23964"
#>  [4] "1-220-502-9958"      "+01(6)4924224323"    "918.587.3218x640"   
#>  [7] "643-059-1326"        "620-738-4359"        "+52(2)3364262615"   
#> [10] "(077)170-2302x614"

job

ch_job()
#> [1] "Photographer"
ch_job(10)
#>  [1] "Education officer, museum" "Investment analyst"       
#>  [3] "Hospital doctor"           "Materials engineer"       
#>  [5] "Media planner"             "Engineer, technical sales"
#>  [7] "Proofreader"               "Professor Emeritus"       
#>  [9] "Trade mark attorney"       "Surveyor, rural practice"

Data types, universal

Some data types are not localized (arguably the files and user_agents, are mostly universal too).

  • Currency
  • credit card
  • Sequence (DNA)
  • Numerics (doubles, integers, numbers from a distribution; uniform, normal, log-normal, and beta)
  • Miscellaneous (booleans, language codes)
  • DOIs (Digital Object Identifiers, used in scientific journals)
  • Coordinates (GPS coordinates)

currency

ch_currency(3)
#> [1] "KPW" "DOP" "MGA"

credit cards

ch_credit_card_provider()
#> [1] "Discover"
ch_credit_card_provider(n = 4)
#> [1] "VISA 16 digit"    "American Express" "Mastercard"       "VISA 16 digit"
ch_credit_card_number()
#> [1] "180040765650319825"
ch_credit_card_number(n = 10)
#>  [1] "4300224010091703"    "210018633218769261"  "3750915422050268"   
#>  [4] "6011322469940040114" "4653932341742"       "6011587896742328299"
#>  [7] "6011865796162696998" "4648409223472"       "3041944532807011"   
#> [10] "4149119433873"
ch_credit_card_security_code()
#> [1] "115"
ch_credit_card_security_code(10)
#>  [1] "102" "777" "563" "798" "827" "416" "815" "781" "950" "374"

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 NA NA NA NA NA NA NA NA NA

numeric data

testVector$make_missing(x = ch_integer(10))
#>  [1]  NA 641  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 DDS"  
#>  [3] "Ms. Jada Lesch"            "Inga Dach"                
#>  [5] "Keyshawn Schaefer"         "Ferdinand Bergstrom"      
#>  [7] "Justen Simonis"            "Ms. Doloris Stroman DVM"  
#>  [9] "Mrs. Ermine Heidenreich"   "Marion Corwin"            
#> [11] "Jalen Grimes"              "Mr. Sullivan Hammes IV"   
#> [13] "Adrien Vandervort-Dickens" "Dr. Sharif Kunde"         
#> [15] "Marlena Reichert PhD"      "Mr. Brandan Oberbrunner"  
#> [17] "Lloyd Adams III"           "Randy Ziemann"            
#> [19] "Gina Sanford"              "Cornell Funk"             
#> [21] "Yadiel Collier"            "Kamryn Johnson"           
#> [23] "Tyesha Schmeler"           "Ernie Hegmann-Graham"     
#> [25] "Zackery Runolfsdottir"     "Cleveland Predovic"       
#> [27] "Melvyn Hickle"             "Larry Nienow IV"          
#> [29] "Vilma Rutherford"          "Wiliam Ziemann-Fadel"     
#> [31] "Mrs. Kathy Halvorson"      "Mirtie Harvey-Shanahan"   
#> [33] "Eliezer Pfeffer"           "Dr. Shep Buckridge"       
#> [35] "Kyree Kutch"               "Ms. Delpha Grant"         
#> [37] "Ms. Icie Crooks"           "Loney Jenkins-Lindgren"   
#> [39] "Shania Donnelly DVM"       "Dr. Patric Veum"          
#> [41] "Amirah Rippin DVM"         "Randle Hilpert"           
#> [43] "Soren Dare"                "Roderic Walter"           
#> [45] "Farah Daugherty MD"        "Marva Crooks"             
#> [47] "Ryland Ledner"             "Girtha Harvey DDS"        
#> [49] "Staci 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.