# Introduction to the charlatan package

#### Scott Chamberlain

#### 2020-01-23

`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$job()
    #> [1] "Engineer, automotive"
    x$name()
    #> [1] "Ms. Aleta O'Kon DDS"
    x$job()
    #> [1] "Occupational therapist"
    x$color_name()
    #> [1] "GreenYellow"

## locale support

Adding more locales through time, e.g.,

Locale support for job data

    ch_job(locale = "en_US", n = 3)
    #> [1] "Broadcast engineer"           "Product designer"
    #> [3] "Research scientist (medical)"
    ch_job(locale = "fr_FR", n = 3)
    #> [1] "Hydraulicien"
    #> [2] "Technicien d'exploitation de l'eau"
    #> [3] "Conseiller en économie sociale et familiale"
    ch_job(locale = "hr_HR", n = 3)
    #> [1] "Fizioterapeutski tehničar"
    #> [2] "Odgajatelj u učeničkom domu"
    #> [3] "Odgovorna osoba za ocjenjivanje sukladnosti građevinskih proizvoda"
    ch_job(locale = "uk_UA", n = 3)
    #> [1] "Бібліограф" "Секретар"   "Астроном"
    ch_job(locale = "zh_TW", n = 3)
    #> [1] "生管助理"       "客戶服務人員"   "心理學研究人員"

For colors:

    ch_color_name(locale = "en_US", n = 3)
    #> [1] "Cyan"       "PapayaWhip" "SteelBlue"
    ch_color_name(locale = "uk_UA", n = 3)
    #> [1] "Циннвальдит"     "Лазуровий"       "Ультрамариновий"

More coming soon …

## generate a dataset

    ch_generate()
    #> # A tibble: 10 x 3
    #>    name                    job                               phone_number
    #>    <chr>                   <chr>                             <chr>
    #>  1 Raymon Donnelly         Sports administrator              1-411-205-0557x5577
    #>  2 Miss Lavinia Baumbach … Theatre director                  233.994.1552x8646
    #>  3 Lora Collier DVM        Health and safety adviser         1-823-830-2291x4940
    #>  4 Mr. Dean Beahan DDS     Senior tax professional/tax insp… 554-742-9675
    #>  5 Lyda Ryan               Database administrator            (917)024-9152x4035
    #>  6 Frida Armstrong         Hydrologist                       1-041-553-5156x541…
    #>  7 Rufus Howell-Koelpin    Engineer, structural              485.333.1282x2256
    #>  8 Louvenia VonRueden      Therapist, art                    686.933.8333
    #>  9 Mellisa Keeling         Exhibitions officer, museum/gall… (229)582-6317x324
    #> 10 Callie McDermott        Chartered management accountant   869.393.1925x77477

    ch_generate('job', 'phone_number', n = 30)
    #> # A tibble: 30 x 2
    #>    job                                   phone_number
    #>    <chr>                                 <chr>
    #>  1 English as a second language teacher  (446)462-8829x54784
    #>  2 Company secretary                     179.566.0567x187
    #>  3 Clothing/textile technologist         1-175-044-1387
    #>  4 Technical sales engineer              830-117-2214
    #>  5 Economist                             +19(8)9889303313
    #>  6 Nurse, mental health                  (789)114-2432x2078
    #>  7 Merchant navy officer                 +83(9)6026295373
    #>  8 English as a foreign language teacher 1-573-165-9844x12523
    #>  9 Medical technical officer             1-403-479-2141
    #> 10 Engineer, civil (contracting)         1-690-568-3657
    #> # … with 20 more rows

## Data types

### person name

    ch_name()
    #> [1] "Grace Deckow"

    ch_name(10)
    #>  [1] "Elbridge Dooley Sr."   "Zaida Stracke"         "Enrique VonRueden"
    #>  [4] "Mr. Verle Koss"        "Hildred Conn"          "Kenyon Howell III"
    #>  [7] "Delma Mertz"           "Shona Stark"           "Dr. Vonetta Armstrong"
    #> [10] "Dr. Vashti Bailey PhD"

### phone number

    ch_phone_number()
    #> [1] "1-835-651-8717"

    ch_phone_number(10)
    #>  [1] "(604)405-5027"        "126-480-4735x48552"   "1-086-496-1111x36766"
    #>  [4] "042-754-0729x558"     "332.006.1737x456"     "006.891.7617"
    #>  [7] "275.525.7273x319"     "1-873-705-3585"       "1-122-719-7284x2682"
    #> [10] "649.394.9233x2258"

### job

    ch_job()
    #> [1] "Geologist, wellsite"

    ch_job(10)
    #>  [1] "Trading standards officer"  "Physicist, medical"
    #>  [3] "Energy engineer"            "Hydrographic surveyor"
    #>  [5] "Medical illustrator"        "Designer, fashion/clothing"
    #>  [7] "Statistician"               "Health service manager"
    #>  [9] "Transport planner"          "Air traffic controller"

### credit cards

    ch_credit_card_provider()
    #> [1] "Diners Club / Carte Blanche"
    ch_credit_card_provider(n = 4)
    #> [1] "Voyager"       "Voyager"       "VISA 13 digit" "JCB 15 digit"

    ch_credit_card_number()
    #> [1] "3492673232590227"
    ch_credit_card_number(n = 10)
    #>  [1] "3034632417047426"    "060495293791547"     "6011320717243771773"
    #>  [4] "4220373428719897"    "3088879464576096560" "4437842902292"
    #>  [7] "4266417098802460"    "869988023143281969"  "869956493318574944"
    #> [10] "52577607417221095"

    ch_credit_card_security_code()
    #> [1] "4848"
    ch_credit_card_security_code(10)
    #>  [1] "478"  "453"  "0035" "928"  "610"  "303"  "263"  "064"  "191"  "235"

## 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                         "Vivien Pacocha-McDermott"
    #>  [3] "Glendora VonRueden"       NA
    #>  [5] "Dr. Nicolas Lehner"       NA
    #>  [7] "Megan Bartell"            NA
    #>  [9] NA                         "Lelia Emard"

### numeric data

    testVector$make_missing(x = ch_integer(10))
    #>  [1]  NA  NA 635  NA  29  NA 582  NA  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.