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$job()
#> [1] "Therapist, art"
x$name()
#> [1] "Davion Murazik"
x$job()
#> [1] "TEFL teacher"
x$color_name()
#> [1] "LemonChiffon"

locale support

Adding more locales through time, e.g.,

Locale support for job data

ch_job(locale = "en_US", n = 3)
#> [1] "Counsellor"       "Ecologist"        "Financial trader"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Magasinier cariste" "Psychomotricien"    "Animateur nature"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Glavni inspektor zaštite okoliša" "Pregledač vagona"                
#> [3] "Ovlašteni revizor"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Машиніст" "Психолог" "Прокурор"
ch_job(locale = "zh_TW", n = 3)
#> [1] "鐵路車輛駕駛員"   "化學工程研發人員" "硬體測試工程師"

For colors:

ch_color_name(locale = "en_US", n = 3)
#> [1] "DarkOrange"    "BurlyWood"     "PaleGoldenRod"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Грушевий"        "Фіолетовий"      "Темно-брунатний"

More coming soon …

generate a dataset

ch_generate()
#> # A tibble: 10 × 3
#>    name                     job                             phone_number        
#>    <chr>                    <chr>                           <chr>               
#>  1 Kittie Bayer             Accountant, chartered           790.477.8132x020    
#>  2 Geraldo Zulauf           Commissioning editor            +68(7)2415815539    
#>  3 Adelard Hegmann          Psychologist, clinical          001.148.3692x348    
#>  4 Cleola Hettinger         Restaurant manager, fast food   09506901409         
#>  5 Mrs. Nathalia Zulauf     Equality and diversity officer  +75(1)4803864411    
#>  6 Edw Braun                Geophysicist/field seismologist 755.845.0201x0508   
#>  7 Miss Jaunita Effertz PhD Retail manager                  767.075.3022x81938  
#>  8 Nancy Konopelski         Administrator, local government 1-772-466-7294x67130
#>  9 Coleman Rosenbaum-Schoen Diagnostic radiographer         1-010-738-7626      
#> 10 Mrs. Gwenda Powlowski MD Race relations officer          1-879-063-1092x075
ch_generate("job", "phone_number", n = 30)
#> # A tibble: 30 × 2
#>    job                          phone_number      
#>    <chr>                        <chr>             
#>  1 Analytical chemist           (920)534-8748     
#>  2 Civil Service fast streamer  +77(3)6668585195  
#>  3 Records manager              763-920-4265x808  
#>  4 Warehouse manager            848-525-1824x02934
#>  5 Community arts worker        (604)257-5008x903 
#>  6 Education officer, community 575-747-2541x31955
#>  7 Probation officer            813.941.1881x1840 
#>  8 Curator                      157-567-8891x6362 
#>  9 Physiotherapist              813.191.2143x0886 
#> 10 Volunteer coordinator        (078)547-6492     
#> # ℹ 20 more rows

Data types

person name

ch_name()
#> [1] "Kenny Friesen"
ch_name(10)
#>  [1] "Pleasant Adams"           "Mr. Milford Gorczany PhD"
#>  [3] "Deegan Towne"             "Chaim Heathcote"         
#>  [5] "Ephram Bode-Heaney"       "Orlo Bernhard"           
#>  [7] "Gauge Feest"              "Dr. Marcelino Tromp DVM" 
#>  [9] "Sylas Wisoky DVM"         "Gunda Considine"

phone number

ch_phone_number()
#> [1] "1-297-334-6249x463"
ch_phone_number(10)
#>  [1] "185.236.9339x198"   "135-587-2706x72695" "432.261.1842x958"  
#>  [4] "1-510-898-4317"     "637-657-5120x1180"  "970-413-5231x04013"
#>  [7] "+99(3)9365555968"   "858.518.8972"       "449-824-5320"      
#> [10] "809-959-3228x22460"

job

ch_job()
#> [1] "Engineer, chemical"
ch_job(10)
#>  [1] "Multimedia specialist"         "Marketing executive"          
#>  [3] "TEFL teacher"                  "Clinical molecular geneticist"
#>  [5] "Surveyor, mining"              "Engineer, water"              
#>  [7] "Warden/ranger"                 "Theatre manager"              
#>  [9] "Geochemist"                    "Programmer, systems"

credit cards

ch_credit_card_provider()
#> [1] "JCB 16 digit"
ch_credit_card_provider(n = 4)
#> [1] "Voyager"       "Voyager"       "Maestro"       "VISA 13 digit"
ch_credit_card_number()
#> [1] "6011898962312431998"
ch_credit_card_number(n = 10)
#>  [1] "4838754316845"       "4506598649917"       "54321906931129530"  
#>  [4] "3736315271564585"    "3007277329293366"    "6011477512197019225"
#>  [7] "869970814530301513"  "3088373617931330880" "3010620719899192"   
#> [10] "4082160534002"
ch_credit_card_security_code()
#> [1] "254"
ch_credit_card_security_code(10)
#>  [1] "769"  "252"  "839"  "3837" "599"  "717"  "180"  "403"  "241"  "057"

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] "Alma Hickle" NA            NA            NA            NA           
#>  [6] NA            NA            NA            NA            NA

numeric data

testVector$make_missing(x = ch_integer(10))
#>  [1]  NA  NA  NA  NA 658  NA  NA 413  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.