Introduction to the charlatan package

In charlatan: Make Fake Data

knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  warning = FALSE,
  message = FALSE
)

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$job()
x$name()
x$job()
x$color_name()

locale support

Adding more locales through time, e.g.,

Locale support for job data

ch_job(locale = "en_US", n = 3)
ch_job(locale = "fr_FR", n = 3)
ch_job(locale = "hr_HR", n = 3)
ch_job(locale = "uk_UA", n = 3)
ch_job(locale = "zh_TW", n = 3)

For colors:

ch_color_name(locale = "en_US", n = 3)
ch_color_name(locale = "uk_UA", n = 3)

More coming soon ...

generate a dataset

ch_generate()

ch_generate("job", "phone_number", n = 30)

Data types

person name

ch_name()

ch_name(10)

phone number

ch_phone_number()

ch_phone_number(10)

job

ch_job()

ch_job(10)

credit cards

ch_credit_card_provider()
ch_credit_card_provider(n = 4)

ch_credit_card_number()
ch_credit_card_number(n = 10)

ch_credit_card_security_code()
ch_credit_card_security_code(10)

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)

numeric data

testVector$make_missing(x = ch_integer(10))

logicals

set.seed(123)
testVector$make_missing(x = sample(c(TRUE, FALSE), 10, replace = 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)

Right now only suffixes and prefixes for names in en_US locale are supported. Notice above some variation in prefixes and suffixes.

charlatan documentation built on Sept. 13, 2023, 5:08 p.m.