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$job()
#> [1] "Insurance underwriter"
x$name()
#> [1] "Dr. Ezekiel Legros Sr."
x$job()
#> [1] "Insurance account manager"
x$color_name()
#> [1] "Brown"

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] "Armed forces logistics/support/administrative officer"
#> [2] "Drilling engineer"                                    
#> [3] "Publishing copy"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Opérateur de fabrication de produits alimentaires"
#> [2] "Technicien"                                       
#> [3] "Aquaculteur"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Fotograf"                             
#> [2] "Voditelj skele u nacionalnoj plovidbi"
#> [3] "Autolimar"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Історик"  "Водій"    "Машиніст"
ch_job(locale = "zh_TW", n = 3)
#> [1] "MIS程式設計師" "美療／芳療師"  "資訊專業人員"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "LightSlateGray" "HotPink"        "ForestGreen"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Ніжно-рожевий"   "Зелена мʼята"    "Ультрамариновий"

generate a dataset

ch_generate()
#> # A tibble: 10 × 3
#>    name                   job                         phone_number       
#>    <chr>                  <chr>                       <chr>              
#>  1 Efrem Stanton Jr.      Doctor, general practice    (454)196-5832x66841
#>  2 Griffin Parker         Advertising art director    1-989-227-1920     
#>  3 Dr. Braiden Goyette IV Podiatrist                  684.092.5429x82726 
#>  4 Werner Kunze II        Fine artist                 1-158-569-6973x733 
#>  5 Ms. Ilene Hahn         Community education officer 1-915-862-0841x315 
#>  6 Aden Bogisich V        Surveyor, building          539.749.0954x640   
#>  7 Chloie Jacobs          Geoscientist                025.820.2209       
#>  8 Lora Rau               Journalist, newspaper       1-699-957-4876x4502
#>  9 Hansford Considine     Automotive engineer         772-137-1301x8642  
#> 10 Rollo Funk-Huels       Buyer, industrial           (948)351-0795

ch_generate("job", "phone_number", n = 30)
#> # A tibble: 30 × 2
#>    job                           phone_number       
#>    <chr>                         <chr>              
#>  1 Scientist, research (maths)   1-085-954-9265x8014
#>  2 Bonds trader                  (794)555-0796x10850
#>  3 Podiatrist                    1-438-002-4183x507 
#>  4 Clinical molecular geneticist 571-591-7262x12589 
#>  5 Community pharmacist          586.341.7788x11515 
#>  6 Health physicist              +72(4)0814084813   
#>  7 Leisure centre manager        (150)330-9293x3906 
#>  8 Records manager               00789139342        
#>  9 Fine artist                   1-821-077-1712     
#> 10 Naval architect               +39(1)8754217625   
#> # 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. Finn Bechtelar II"

ch_name(10)
#>  [1] "Aiyana Casper"             "Dr. Brendan Rodriguez DVM"
#>  [3] "Peggy Swift"               "Jerrilyn Powlowski"       
#>  [5] "Dr. Bryant Abernathy IV"   "Little Lynch"             
#>  [7] "Beula Okuneva"             "Francisquita Botsford"    
#>  [9] "Dr. Venie Reynolds"        "Candyce Terry MD"

phone number

ch_phone_number()
#> [1] "(402)943-8800"

ch_phone_number(10)
#>  [1] "328-740-3974x4990"   "(931)136-9980"       "1-586-443-3402x7010"
#>  [4] "03380022256"         "1-410-964-9865"      "(052)158-8081x18272"
#>  [7] "(857)634-6468x918"   "09030895069"         "03608832325"        
#> [10] "+06(3)9657584920"

job

ch_job()
#> [1] "Pharmacist, community"

ch_job(10)
#>  [1] "Ophthalmologist"                "Investment banker, operational"
#>  [3] "Psychotherapist"                "Civil engineer, consulting"    
#>  [5] "Engineer, building services"    "Engineer, technical sales"     
#>  [7] "Biomedical engineer"            "Surveyor, mining"              
#>  [9] "IT technical support officer"   "Engineer, communications"

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] "XDR" "JMD" "BRL"

credit cards

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

ch_credit_card_number()
#> [1] "4363933519243"
ch_credit_card_number(n = 10)
#>  [1] "3756291952838413"   "4025118825390439"   "4593497363459778"  
#>  [4] "55862295532441014"  "4348774484495404"   "869971194735154226"
#>  [7] "3438307179547067"   "869999711314635339" "4007381069954052"  
#> [10] "676288886680409"

ch_credit_card_security_code()
#> [1] "327"
ch_credit_card_security_code(10)
#>  [1] "552"  "881"  "174"  "795"  "436"  "9122" "705"  "247"  "1874" "322"

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] "Hobson Weimann-Ortiz"  NA                      "Mercy Ward"           
#>  [4] "Miss Ethyle Klein DDS" NA                      "Dr. Dawson Huels Jr." 
#>  [7] "Jaycob Morar"          "Trinidad Parker DVM"   NA                     
#> [10] NA

numeric data

testVector$make_missing(x = ch_integer(10))
#>  [1] 596  NA 894  NA  NA 532 736  NA  NA 354

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.