Introduction to the charlatan package
Scott Chamberlain
2021-09-24
Source:vignettes/charlatan.Rmd
charlatan.Rmdcharlatan 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
R6objects 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$job()
#> [1] "Psychologist, occupational"
x$name()
#> [1] "Chandler Treutel"
x$job()
#> [1] "Production assistant, radio"
x$color_name()
#> [1] "LightSalmon"locale support
Adding more locales through time, e.g.,
Locale support for job data
ch_job(locale = "en_US", n = 3)
#> [1] "Insurance underwriter" "Oncologist"
#> [3] "Chartered public finance accountant"
ch_job(locale = "fr_FR", n = 3)
#> [1] "Maréchal" "Ingénieur process aval" "Greffier"
ch_job(locale = "hr_HR", n = 3)
#> [1] "Tesar" "Kormilar" "Kuhar"
ch_job(locale = "uk_UA", n = 3)
#> [1] "Медик" "Няня" "Машиніст"
ch_job(locale = "zh_TW", n = 3)
#> [1] "大樓管理員" "生產設備工程師" "珠心算老師"For colors:
ch_color_name(locale = "en_US", n = 3)
#> [1] "LightCyan" "SeaShell" "CadetBlue"
ch_color_name(locale = "uk_UA", n = 3)
#> [1] "Яскраво-рожевий" "Лососевий" "Блідий рожево-ліловий"More coming soon …
generate a dataset
ch_generate()
#> # A tibble: 10 × 3
#> name job phone_number
#> <chr> <chr> <chr>
#> 1 Tyrus Leuschke Chief Operating Officer 04160319532
#> 2 Ms. Willie Heller Water quality scientist +09(8)6325992622
#> 3 Elmina McGlynn Risk manager (216)016-3177
#> 4 Jerome Gerlach Surveyor, building control 00045983492
#> 5 Miss Mara Trantow PhD Community arts worker 415-104-7095
#> 6 Dr. Roger Stark DDS Media planner 327-289-1101x3809
#> 7 Johnathon Kertzmann Equality and diversity officer 862-474-9220
#> 8 Stephenie Hand Clinical psychologist 588.214.5339
#> 9 Mr. Ned Lemke PhD Barista 981-457-4470x776
#> 10 Armani Terry Dancer 1-113-672-4833x909ch_generate('job', 'phone_number', n = 30)
#> # A tibble: 30 × 2
#> job phone_number
#> <chr> <chr>
#> 1 Nurse, adult (269)813-4414x35783
#> 2 Music tutor 450-250-7117
#> 3 Financial planner 01230640264
#> 4 Advertising art director 00918692254
#> 5 Higher education careers adviser 025-380-7029
#> 6 Embryologist, clinical 363-981-9360x85296
#> 7 Historic buildings inspector/conservation officer 725.678.2516
#> 8 Operational investment banker 1-740-398-2950x525
#> 9 Engineer, structural 08576421858
#> 10 Designer, multimedia 504.127.6260x06562
#> # … with 20 more rowsData types
person name
ch_name()
#> [1] "Dr. Sherrill Muller II"ch_name(10)
#> [1] "May Stark-Haley" "Seldon Davis"
#> [3] "Alexus King" "Kyson Lockman"
#> [5] "Mrs. Jasmyn Hauck PhD" "Williard Welch"
#> [7] "Xiomara Franecki-D'Amore" "Gracia Prohaska DVM"
#> [9] "Kerry Skiles" "Shanice Spinka"phone number
ch_phone_number()
#> [1] "(792)092-7879x30916"ch_phone_number(10)
#> [1] "+62(3)7972448032" "(494)799-3942x43907" "503-420-9890"
#> [4] "860-925-7659" "1-303-352-0970" "353-562-1719x460"
#> [7] "+27(3)2144317798" "756-307-4886x288" "(902)683-6690"
#> [10] "790-491-0227x7565"job
ch_job()
#> [1] "Recycling officer"ch_job(10)
#> [1] "Estate manager/land agent" "Textile designer"
#> [3] "Podiatrist" "Engineer, civil (contracting)"
#> [5] "Film/video editor" "Clinical molecular geneticist"
#> [7] "Soil scientist" "Librarian, academic"
#> [9] "Psychologist, educational" "Chief Operating Officer"credit cards
ch_credit_card_provider()
#> [1] "Maestro"
ch_credit_card_provider(n = 4)
#> [1] "VISA 13 digit" "Maestro" "Voyager" "American Express"ch_credit_card_number()
#> [1] "4050064010825"
ch_credit_card_number(n = 10)
#> [1] "6011623173331791501" "4704363407250829" "869995477347918787"
#> [4] "4518437864455430" "869963291405301754" "52726276478525198"
#> [7] "4957354281624621" "675996862667340" "639093438767357"
#> [10] "3528505004802480626"ch_credit_card_security_code()
#> [1] "260"
ch_credit_card_security_code(10)
#> [1] "449" "412" "424" "301" "987" "040" "294" "171" "647" "775"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()$name)
#> [1] "Zackery Rowe-Grimes" "Cara Beier PhD"
#> [3] NA "Hasel Jacobs"
#> [5] "Darla Hartmann" NA
#> [7] "Damarcus Klein" NA
#> [9] "Green Lueilwitz-Stiedemann" "Adriana Wuckert-Greenfelder"numeric data
testVector$make_missing(x = ch_integer(10))
#> [1] 674 761 255 NA 799 517 164 795 223 43Messy 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.