
Introduction to the charlatan package
Scott Chamberlain
2022-06-25
Source:vignettes/charlatan.Rmd
charlatan.Rmd
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 incharlatan
-
fraudster()
- single interface to all fake data methods, - returns vectors/lists of data - this function wraps thech_*()
functions described above
Install
Stable version from CRAN
install.packages("charlatan")
Development version from Github
devtools::install_github("ropensci/charlatan")
high level function
… for all fake data operations
x <- fraudster()
x$job()
#> [1] "Brewing technologist"
x$name()
#> [1] "Lise Kling"
x$job()
#> [1] "Quality manager"
x$color_name()
#> [1] "PaleGoldenRod"
locale support
Adding more locales through time, e.g.,
Locale support for job data
ch_job(locale = "en_US", n = 3)
#> [1] "Product manager"
#> [2] "Aeronautical engineer"
#> [3] "Psychologist, prison and probation services"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Ingénieur en construction automobile"
#> [2] "Ingénieur études et développement en logiciels de simulation"
#> [3] "Guide conférencier des villes et pays d'art et d'histoire"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Odgajatelj u učeničkom domu" "Knjižničar"
#> [3] "Autoelektričar"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Модельєр" "Бібліотекар" "Фрілансер"
ch_job(locale = "zh_TW", n = 3)
#> [1] "塑膠模具技術人員" "演算法開發工程師" "CNC電腦程式編排人員"
For colors:
ch_color_name(locale = "en_US", n = 3)
#> [1] "GhostWhite" "PaleGoldenRod" "Khaki"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Бронзовий" "Темно-рожевий" "Фіолетово-червоний"
More coming soon …
generate a dataset
ch_generate()
#> # A tibble: 10 × 3
#> name job phone_number
#> <chr> <chr> <chr>
#> 1 Alina O'Hara-Stiedemann Exhibitions officer, museum/gallery 601.949.340…
#> 2 Dr. Shawanda Ryan Print production planner 818.140.345…
#> 3 Howell Runte Adult guidance worker 684.413.533…
#> 4 Mr. Deane Bergnaum DVM Sports development officer 895.101.5100
#> 5 Arlie Will Jr. Lighting technician, broadcasting/film/… 01247297529
#> 6 Lovisa Schamberger Magazine journalist (765)763-17…
#> 7 Roland Veum-Kuhlman Chief Strategy Officer 1-130-841-6…
#> 8 Darryle Barton Conservation officer, historic buildings (273)511-26…
#> 9 Mr. Lenwood Wuckert Event organiser 03122954011
#> 10 Jabez Ortiz Administrator, Civil Service 542-887-546…
ch_generate('job', 'phone_number', n = 30)
#> # A tibble: 30 × 2
#> job phone_number
#> <chr> <chr>
#> 1 Set designer (295)588-4512x9702
#> 2 Operations geologist 181-306-7244x8157
#> 3 Barista +38(3)3977141005
#> 4 Broadcast engineer 1-653-931-7044
#> 5 Surveyor, commercial/residential 789.224.3863x979
#> 6 Banker 119.678.7951
#> 7 Legal secretary 132.028.4127x58455
#> 8 Sound technician, broadcasting/film/video 192.065.3644x17688
#> 9 Secretary, company 04127671791
#> 10 Garment/textile technologist (518)808-6327x6449
#> # … with 20 more rows
Data types
person name
ch_name()
#> [1] "Rahsaan Schumm-Thompson"
ch_name(10)
#> [1] "Kathy Farrell PhD" "Randle Schneider"
#> [3] "Bettyjane Torp" "Pranav Hayes"
#> [5] "Miss Corinna Marvin" "Dr. Brianne Gorczany DDS"
#> [7] "Dr. Emmalee Stroman DDS" "Mr. Denny Franecki Jr."
#> [9] "Miss Kimberley Aufderhar PhD" "Capitola Ryan-Hermann"
phone number
ch_phone_number()
#> [1] "1-139-379-3458x153"
ch_phone_number(10)
#> [1] "1-347-320-2042x71344" "246.736.7414x8579" "(211)277-2206"
#> [4] "1-438-013-3391x9453" "(590)621-1232" "957-587-3255x4942"
#> [7] "715.284.3575x08799" "662-337-8668" "881.147.1661x24413"
#> [10] "09552223136"
job
ch_job()
#> [1] "IT technical support officer"
ch_job(10)
#> [1] "Cytogeneticist" "Field seismologist"
#> [3] "Surveyor, minerals" "Seismic interpreter"
#> [5] "Training and development officer" "Medical sales representative"
#> [7] "Surveyor, planning and development" "Podiatrist"
#> [9] "Buyer, retail" "Secretary, company"
credit cards
ch_credit_card_provider()
#> [1] "JCB 16 digit"
ch_credit_card_provider(n = 4)
#> [1] "VISA 16 digit" "Maestro" "Mastercard" "VISA 16 digit"
ch_credit_card_number()
#> [1] "3021576516792197"
ch_credit_card_number(n = 10)
#> [1] "3096523727318357361" "4817785134372217" "53172494484043622"
#> [4] "53085035798204750" "4134852043178071" "3088573971436419567"
#> [7] "4170426501897616" "4245210581993060" "3401725399851055"
#> [10] "4655414847385"
ch_credit_card_security_code()
#> [1] "966"
ch_credit_card_security_code(10)
#> [1] "928" "219" "392" "071" "932" "483" "681" "163" "070" "022"
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()$name)
#> [1] NA NA NA
#> [4] "Mrs. Mae Cole PhD" NA NA
#> [7] NA NA "Malachi Olson"
#> [10] NA
numeric data
testVector$make_missing(x = ch_integer(10))
#> [1] NA NA NA NA NA NA NA NA NA 294
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.