Introduction to the charlatan package

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 (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

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$job()
#> [1] "Cabin crew"
x$name()
#> [1] "Dr. Jerrad Braun I"
x$job()
#> [1] "Nature conservation officer"
x$color_name()
#> [1] "OldLace"

locale support

Adding more locales through time, e.g.,

Locale support for job data

ch_job(locale = "en_US", n = 3)
#> [1] "Research scientist (physical sciences)"
#> [2] "Fisheries officer"                     
#> [3] "Barrister's clerk"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Secrétaire"                        "Chargé d'études économiques"      
#> [3] "Responsable de réseau eau potable"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Pekar"     "Dimnjačar" "Stolar"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Диригент"  "Шериф"     "Бізнесмен"
ch_job(locale = "zh_TW", n = 3)
#> [1] "景觀設計師"       "水電工程繪圖人員" "生管助理"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "DarkSeaGreen" "FloralWhite"  "Crimson"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Циннвальдит" "Мисливський" "Болотний"

More coming soon …

generate a dataset

ch_generate()
#> # A tibble: 10 × 3
#>    name                     job                                     phone_number
#>    <chr>                    <chr>                                   <chr>       
#>  1 Ms. Kaiya Price          Nurse, mental health                    785.017.914…
#>  2 Dr. Jimmy Schroeder III  Conservation officer, historic buildin… 725.143.2597
#>  3 Sincere Bergstrom        Therapist, sports                       1-916-970-6…
#>  4 Dr. Arnulfo Pfeffer Sr.  Clinical biochemist                     1-365-171-5…
#>  5 Tory Grimes              Homeopath                               708-660-5063
#>  6 Mrs. Rosann Goyette DDS  Comptroller                             798.114.6720
#>  7 Mathew Rosenbaum         Best boy                                1-354-322-0…
#>  8 Mr. Ned Satterfield      Education officer, community            1-046-445-1…
#>  9 Mrs. Tamala Pfannerstill Systems analyst                         872.098.7255
#> 10 Drury Lebsack            Furniture designer                      +82(6)45367…

ch_generate("job", "phone_number", n = 30)
#> # A tibble: 30 × 2
#>    job                         phone_number       
#>    <chr>                       <chr>              
#>  1 Copywriter, advertising     1-607-535-6368x2083
#>  2 Artist                      (313)991-9209x307  
#>  3 Astronomer                  219-519-5350x631   
#>  4 Podiatrist                  605-348-7548x2551  
#>  5 Ophthalmologist             030.652.3669x590   
#>  6 Tour manager                1-698-204-8063x1869
#>  7 Civil Service administrator (977)289-7863x321  
#>  8 Secretary/administrator     02700464227        
#>  9 Regulatory affairs officer  890-328-7343       
#> 10 Merchandiser, retail        (862)723-8754x4726 
#> # ℹ 20 more rows

Data types

person name

ch_name()
#> [1] "Carmela Ortiz"

ch_name(10)
#>  [1] "Pairlee Littel"       "Christi Von"          "Mrs. Ilah Wolff"     
#>  [4] "Scarlet Yost"         "Dylan Kris-Hilpert"   "Benjamin Goodwin PhD"
#>  [7] "Wayde Gerlach Jr."    "Zackary Lubowitz"     "Izola Breitenberg"   
#> [10] "Kordell Koss"

phone number

ch_phone_number()
#> [1] "+47(5)8942794052"

ch_phone_number(10)
#>  [1] "605.504.8723x2537"  "959-610-0196x41378" "764.273.9364"      
#>  [4] "034-339-3355"       "+21(7)8493099694"   "+44(9)4592304063"  
#>  [7] "233-868-1649"       "(595)631-6235"      "(261)603-0359"     
#> [10] "730-510-8884"

job

ch_job()
#> [1] "Astronomer"

ch_job(10)
#>  [1] "Investment banker, operational"     "Building surveyor"                 
#>  [3] "Tourist information centre manager" "IT sales professional"             
#>  [5] "Community education officer"        "Environmental consultant"          
#>  [7] "Midwife"                            "Lecturer, higher education"        
#>  [9] "Television floor manager"           "Editor, film/video"

credit cards

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

ch_credit_card_number()
#> [1] "3528550731134503600"
ch_credit_card_number(n = 10)
#>  [1] "4585899757655707"    "3158280292025722921" "3158508720231237934"
#>  [4] "3042184030913503"    "6011041407397559236" "3793640823277919"   
#>  [7] "6011577972662048019" "6011677876244004748" "4501657767479919"   
#> [10] "4449143559441677"

ch_credit_card_security_code()
#> [1] "000"
ch_credit_card_security_code(10)
#>  [1] "536" "829" "197" "290" "927" "864" "991" "716" "255" "812"

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  NA  NA  NA  NA  NA  NA  NA  NA 269

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.

Scott Chamberlain

Roel M. Hogervorst

2023-12-04