Getting Started with fkr

library(fkr)

What does this package do?

fkr makes pretend (fake) data that looks like it came from the Philippines – names, addresses, phone numbers, ages, job titles, and more – without using any real person’s information.

Why would you want “fake” data?

You’re building an app or a form and want to see what it looks like with data in it, before any real user has signed up.
You’re teaching a class or writing a tutorial and need realistic-looking example data, without handing out anyone’s real details.
You’re testing software: trying it with 10,000 pretend customers is much safer (and easier) than testing with real ones.

Every single value this package produces – every name, every address, every number – is made up on the spot. Nothing is looked up from a real database of real people.

A note on ID numbers. Some functions produce values formatted to look like Philippine government ID numbers (an SSS number, a TIN, a PhilSys number, and so on). These are entirely random digits arranged in the right pattern – they are not real ID numbers, they don’t belong to anyone, and they will not work if you try to use them anywhere official. They exist only so that test data looks complete.

Installing fkr

If this is your first time, you’ll need R installed on your computer (see r-project.org). Once R is running, install fkr with:

# install.packages("devtools")
pak::pak("yng-me/fkr")

Then, every time you want to use it, start your R session with:

library(fkr)

Your first fake value

Every function in this package starts with fkr_, so they’re easy to spot and to search for. Let’s make one fake first name:

fkr_first_name(1)
#> [1] "Lorena"

That 1 tells the function “I want one value.” Want five instead? Change the 1 to a 5:

fkr_first_name(5)
#> [1] "Verne"     "Lloyd"     "Fortunata" "Franco"    "Alicia"

That’s the pattern for everything in this package: the first thing you tell a fkr_* function is always how many fake values you want.

fkr_age(5)
#> [1] 0 5 8 1 3
fkr_boolean(5)
#> [1] FALSE  TRUE  TRUE  TRUE FALSE
fkr_civil_status(5)
#> [1] "Married"   "Married"   "Separated" "Single"    "Married"

A quick tour of what you can generate

People

fkr_first_name(3)
#> [1] "Elaine"  "Valiant" "Orion"
fkr_last_name(3)
#> [1] "Velasco"   "Dela Cruz" "Dela Cruz"
fkr_full_name(3)
#> [1] "Valentina Espiritu" "Neil Dela Cruz"     "Leonora Cunanan"
fkr_sex(3)
#> [1] "Male" "Male" "Male"
fkr_civil_status(3)
#> [1] "Single" "Single" "Single"
fkr_age(3)
#> [1] 15 10  3
fkr_birthday(3)
#> [1] "2025-03-28" "2002-12-11" "2021-06-18"

Contact details

fkr_email(3)
#> [1] "[email protected]" "[email protected]"
#> [3] "[email protected]"
fkr_mobile_number(3)
#> [1] "09911740237" "09986938433" "09195927871"
fkr_landline_number(3)
#> [1] "(054) 971 6406" "(043) 971 8009" "(045) 994 1968"

Addresses and places in the Philippines

fkr_region(3)
#> [1] "Region I (Ilocos Region)"   "Region II (Cagayan Valley)"
#> [3] "Negros Island Region (NIR)"
fkr_province(3)
#> [1] "Nueva Ecija"     "Davao del Norte" "Capiz"
fkr_city(3)
#> [1] "Legazpi City" "Dagupan City" "Iligan City"
fkr_barangay(3)
#> [1] "Pag-asa"    "San Miguel" "Poblacion"
fkr_street_address(3)
#> [1] "530 Del Pilar Highway" "14 Rizal Highway"      "961 Recto Road"

Need a full, ready-made address (street, barangay, city, province, region, and ZIP code, all matching each other)? There’s a function for that too, and it gives you back a small table instead of a single value:

fkr_address(3)
#>                    street    barangay           city       province
#> 1       44 Osmena Highway San Vicente     Tabuk City        Kalinga
#> 2 459 Shaw Boulevard Road  Concepcion Koronadal City South Cotabato
#> 3    607 Del Pilar Street  Santo Nino     Tabuk City        Kalinga
#>                                   region  zip
#> 1 Cordillera Administrative Region (CAR) 3800
#> 2              Region XII (SOCCSKSARGEN) 9506
#> 3 Cordillera Administrative Region (CAR) 3800

Government ID-style numbers

As mentioned above, these are random digits in the right pattern – not real IDs.

fkr_sss_number(3)
#> [1] "45-2399799-3" "11-7751447-7" "15-0446348-7"
fkr_tin_number(3)
#> [1] "688-692-847-737" "772-139-002-415" "514-507-562-711"
fkr_philhealth_number(3)
#> [1] "17-404499200-8" "66-273299625-9" "34-973382722-1"
fkr_pagibig_number(3)
#> [1] "0165-5700-8474" "0472-8071-2401" "4851-2818-9493"
fkr_philsys_number(3)
#> [1] "5918-1642-2549-9234" "6031-6810-7962-9059" "3681-9273-4519-5174"

Businesses and jobs

fkr_company(3)
#> [1] "Mendoza & Garcia Construction Co."                  
#> [2] "Fernandez & Velasco Marketing Corp."                
#> [3] "Mendoza & Ignacio Group of Companies Holdings, Inc."
fkr_job_title(3)
#> [1] "Software Developer"      "Store Supervisor"       
#> [3] "Municipal Administrator"
fkr_industry(3)
#> [1] "Real Estate"         "Business Consulting" "Construction"
fkr_product(3)
#> [1] "Bottled Soy Sauce" "Coconut Vinegar"   "Sinigang Mix"
fkr_price(3)
#> [1] "PHP 106.69" "PHP 51.19"  "PHP 15.22"

Small, general-purpose helpers

These aren’t Philippines-specific – they’re handy building blocks for any fake dataset.

fkr_num(5, min = 0, max = 100)          # plain numbers, e.g. a score or a rating
#> [1]  3.96 80.66 67.72 11.87  4.05
fkr_int(5, min = 18, max = 65)          # whole numbers, e.g. an age range
#> [1] 63 19 53 56 34
fkr_boolean(5)                          # TRUE / FALSE, e.g. "has a Facebook account?"
#> [1]  TRUE FALSE  TRUE FALSE FALSE
fkr_uuid(3)                             # a unique ID for each record
#> [1] "1e2ce15d-259c-4b39-9f03-4983c0f44147"
#> [2] "85f18c76-15d6-4d99-8174-cae9600b21d5"
#> [3] "9d9b12f7-13b9-4832-9689-3176ba5bf281"

Two more worth a closer look:

fkr_multiple_response() pretends to be a survey question where someone can pick more than one answer (a “select all that apply” question):

fkr_multiple_response(5, values = c("Email", "Phone", "SMS"), sep = ", ")
#> [1] "Phone, SMS"        "Email, Phone, SMS" "Email, SMS"       
#> [4] "Email, Phone"      "SMS"

fkr_factor() creates a fake category column, where each code has a friendlier label attached – similar to how survey or statistics software lets you store 1/2 but display “Male”/“Female”:

fkr_factor(5, values = c(1, 2), labels = c("Male", "Female"))
#> [1] Male   Female Male   Male   Male  
#> Levels: Male Female

You can also attach a short description of the whole column (not each value) using label:

sex <- fkr_factor(
  5,
  values = c(1, 2), labels = c("Male", "Female"),
  label = "Respondent's biological sex"
)
sex
#> [1] Female Female Male   Male   Female
#> attr(,"label")
#> [1] Respondent's biological sex
#> Levels: Male Female
attr(sex, "label")
#> [1] "Respondent's biological sex"

Building a whole pretend dataset at once

So far we’ve made one column at a time. Most of the time, though, you want a whole table of fake data – like a spreadsheet with many columns and many rows, where each row is one pretend person or one pretend record.

That’s what the fkr() function is for. You give it:

n: how many rows (pretend people) you want, and
specs: a list that says, for each column, which fkr_* function should fill it in.

fkr(5, list(
  first_name = fkr_first_name,
  last_name = fkr_last_name,
  sex = fkr_sex,
  age = fkr_age,
  date_of_birth = fkr_birthday,
  mobile_number = fkr_mobile_number
))
#>   first_name last_name    sex age date_of_birth mobile_number
#> 1     Taylor De Castro   Male   6    2020-03-15   09990148646
#> 2     Sophie     Gomez   Male   5    2021-08-03   09492362615
#> 3     Darwin    Carlos Female  26    2000-01-29   09556728856
#> 4    Miranda   Sanchez   Male  51    1975-01-10   09277267504
#> 5    Ventana  Santiago   Male  61    1964-12-31   09482247608

Notice that we wrote fkr_first_name, not fkr_first_name() – no parentheses. We’re telling fkr() which tool to use for that column; it takes care of calling it the right number of times for you.

If a column’s generator gives back a whole mini-table of its own – like fkr_address() does – fkr() automatically spreads its pieces out into separate columns, named after the column you asked for:

fkr(3, list(
  name = fkr_full_name,
  address = fkr_address
))
#>              name            address_street address_barangay address_city
#> 1 Lynette Mahinay 102 Marcos Highway Street      Kaligayahan     Mamburao
#> 2  Martha Mendoza        734 Katipunan Road            Tibig  Baguio City
#> 3 Toddy Dela Cruz          462 Legarda Road        Poblacion    Parañaque
#>     address_province                         address_region address_zip
#> 1 Occidental Mindoro                        MIMAROPA Region        5106
#> 2            Benguet Cordillera Administrative Region (CAR)        2600
#> 3       Metro Manila          National Capital Region (NCR)        1700

Fixing extra settings for a column

Some generators accept extra settings. For example, fkr_city() can be limited to one province. To use that inside fkr(), wrap it in a small “recipe”:

fkr(5, list(
  city_in_cebu = function(n) fkr_city(n, province = "Cebu")
))
#>   city_in_cebu
#> 1 Mandaue City
#> 2    Cebu City
#> 3    Cebu City
#> 4    Cebu City
#> 5    Cebu City

Read function(n) fkr_city(n, province = "Cebu") as: “to make this column, call fkr_city, asking for n values, always from the province of Cebu.”

Making one column depend on another

Sometimes a column’s value should depend on another column you already made – for example, whether someone is a “Minor” or an “Adult” depends on their age. Add a second ingredient, data, to see the columns built so far:

fkr(6, list(
  age = fkr_age,
  stage = function(n, data) ifelse(data$age < 18, "Minor", "Adult")
))
#>   age stage
#> 1   2 Minor
#> 2   1 Minor
#> 3   1 Minor
#> 4  44 Adult
#> 5  24 Adult
#> 6   4 Minor

Keeping an age and a birthday that agree with each other

If you ask for both an age column and a birthday column in the same fkr() call, fkr is smart about it: it makes sure the birthday it generates actually matches the age in that same row. You don’t have to do anything extra – just ask for both, by name, as shown below:

people <- fkr(5, list(
  age = fkr_age,
  date_of_birth = fkr_birthday
))
people
#>   age date_of_birth
#> 1  38    1988-06-17
#> 2   8    2018-09-22
#> 3  10    2016-08-16
#> 4  14    2012-10-05
#> 5  13    2013-01-12

(If you ever want them to be unrelated – unusual, but possible – wrap one of them in function(n) ..., the same trick shown above for “extra settings.” That tells fkr you’re taking manual control of that column.)

Making your “random” results repeatable

Everything in this package is random by design – that’s the point of fake data! But sometimes you want to get the exact same fake data again, for example to write a tutorial where your readers see the same numbers you did.

Call fkr_seed() with any whole number right before generating your data:

fkr_seed(2024)
fkr_first_name(3)
#> [1] "Sebastian" "Zach"      "Severina"

fkr_seed(2024)
fkr_first_name(3) # identical to the line above
#> [1] "Sebastian" "Zach"      "Severina"

Where to go next

Every function has its own help page. In R, type a question mark followed by the function’s name, e.g. ?fkr_address, to see exactly what settings it accepts.
The package’s README has a shorter, at-a-glance summary of everything covered here.