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$job()
#> [1] "Retail banker"
x$name()
#> [1] "Geneva Hahn"
x$job()
#> [1] "Local government officer"
x$color_name()
#> [1] "Navy"

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] "Public librarian"          "Editor, magazine features"
#> [3] "Herpetologist"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Hydrologue"      "Géologue minier" "Pédiatre"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Konzervator tehničar"                           
#> [2] "Pediker"                                        
#> [3] "Stručni suradnik ovlaštenog inženjera geodezije"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Реставратор" "Підприємець" "Каскадер"
ch_job(locale = "zh_TW", n = 3)
#> [1] "特殊工程師"       "安親班老師"       "類比IC設計工程師"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "Azure"     "Cornsilk"  "LawnGreen"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Ніжно-блакитний" "Зелене море"     "Ціан (колір)"

generate a dataset

ch_generate()
#> # A tibble: 10 × 3
#>    name                   job                              phone_number        
#>    <chr>                  <chr>                            <chr>               
#>  1 Mr. Sammie O'Reilly    Television production assistant  326-318-3851x116    
#>  2 Ms. Charlie Schoen     Therapist, speech and language   1-950-659-3121x93103
#>  3 Ms. Bonita Kessler MD  Adult nurse                      1-480-353-4414x99590
#>  4 Miss Marget Sawayn DDS Furniture conservator/restorer   (525)751-5753x10373 
#>  5 North Schumm           Animal nutritionist              1-099-828-5738      
#>  6 Dr. Octavia Harber MD  Magazine features editor         908.295.0724x3725   
#>  7 Cressie Vandervort     Theatre director                 1-469-687-8198x12080
#>  8 Natalee Crist          Cabin crew                       +83(1)9044595393    
#>  9 Vonnie Bergstrom MD    Higher education careers adviser (439)969-0309x172   
#> 10 Cadence Feeney         Therapist, occupational          1-578-166-5130

ch_generate("job", "phone_number", n = 30)
#> # A tibble: 30 × 2
#>    job                                   phone_number       
#>    <chr>                                 <chr>              
#>  1 English as a foreign language teacher 868-774-7172x6811  
#>  2 Engineer, agricultural                1-234-699-5943x855 
#>  3 Psychotherapist, child                (274)133-4184      
#>  4 Medical physicist                     770-832-0187       
#>  5 Teacher, music                        (603)619-8601x405  
#>  6 Scientific laboratory technician      (024)436-5539x40762
#>  7 Pilot, airline                        1-626-903-9861x110 
#>  8 Commercial horticulturist             05030117281        
#>  9 Financial adviser                     (078)498-3591      
#> 10 Designer, textile                     793.889.4295x19267 
#> # 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] "Ford Kozey"

ch_name(10)
#>  [1] "Miss Djuana Torp"      "Leonard Gibson III"    "Lotta Schroeder"      
#>  [4] "Nelia Predovic"        "Janelle Boehm"         "Neil Torp III"        
#>  [7] "Dr. Nikhil Prosacco"   "Gerardo Oberbrunner"   "Dr. Coleton Zulauf IV"
#> [10] "Stephany Hettinger"

phone number

ch_phone_number()
#> [1] "(529)147-8015x295"

ch_phone_number(10)
#>  [1] "1-296-083-7092"      "+06(4)5354985327"    "1-083-499-2356x917" 
#>  [4] "1-957-736-0706x1350" "1-977-443-4656"      "896.959.5109"       
#>  [7] "(250)970-3677x763"   "1-102-087-5014x2969" "070.285.1226"       
#> [10] "(658)509-0112"

job

ch_job()
#> [1] "Equities trader"

ch_job(10)
#>  [1] "Leisure centre manager"      "Newspaper journalist"       
#>  [3] "IT sales professional"       "Teacher, adult education"   
#>  [5] "Lawyer"                      "Soil scientist"             
#>  [7] "Scientist, research (maths)" "Local government officer"   
#>  [9] "Therapist, occupational"     "Agricultural consultant"

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] "GBP" "IDR" "BSD"

credit cards

ch_credit_card_provider()
#> [1] "American Express"
ch_credit_card_provider(n = 4)
#> [1] "JCB 15 digit"  "VISA 16 digit" "JCB 15 digit"  "Mastercard"

ch_credit_card_number()
#> [1] "210090811545037005"
ch_credit_card_number(n = 10)
#>  [1] "210021413356967638" "3438031621338988"   "55817578986016614" 
#>  [4] "4245188512154244"   "3028153078999751"   "3053222722836035"  
#>  [7] "210007411722342005" "53404431760496801"  "3732305459543133"  
#> [10] "3713741823905249"

ch_credit_card_security_code()
#> [1] "712"
ch_credit_card_security_code(10)
#>  [1] "729" "343" "016" "251" "933" "405" "340" "792" "441" "181"

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] "Darrel Gislason"             "Erling Spinka"              
#>  [3] "Mikhail Schuppe"             "Janessa Ziemann"            
#>  [5] "Heaven Hauck"                "Sue Ferry"                  
#>  [7] "Rasheed Ankunding-Steuber"   NA                           
#>  [9] "Vickey Hettinger-Swaniawski" "Artelia Ondricka"

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 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.