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DataFakeR is an R package designed to help you generate sample of fake data preserving specified assumptions about the original one.
install.packages("DataFakeR")remotes::install_github(
"openpharma/DataFakeR"
)If you are new to DataFakeR, look at the Welcome Page.
You may find there a list of useful articles that will guide you through the package functionality.
# schema_books.yml
public:
tables:
books:
nrows: 10
columns:
book_id:
type: char(8)
formula: !expr paste0(substr(author, 1, 4), substr(title, 1, 4), substr(bought, 1, 4))
author:
type: varchar
spec: name
title:
type: varchar
spec: book
spec_params:
add_second: true
genre:
type: varchar
values: [Fantasy, Adventure, Horror, Romance]
bought:
type: date
range: ['2020-01-02', '2021-06-01']
amount:
type: smallint
range: [1, 99]
na_ratio: 0.2
purchase_id:
type: varchar
check_constraints:
purchase_id_check:
column: purchase_id
expression: !expr purchase_id == paste0('purchase_', bought)
borrowed:
nrows: 30
columns:
book_id:
type: char(8)
not_null: true
user_id:
type: char(10)
foreign_keys:
book_id_fkey:
columns: book_id
references:
columns: book_id
table: booksbooks <- function(n, add_second = FALSE) {
first <- c("Learning", "Amusing", "Hiding", "Symbols", "Hunting", "Smile")
second <- c("Of", "On", "With", "From", "In", "Before")
third <- c("My", "Your", "The", "Common", "Mysterious", "A")
fourth <- c("Future", "South", "Technology", "Forest", "Storm", "Dreams")
second_res <- NULL
if (add_second) {
second_res <- sample(second, n, replace = TRUE)
}
paste(
sample(first, n, replace = TRUE), second_res,
sample(third, n, replace = TRUE), sample(fourth, n, replace = TRUE)
)
}
simul_spec_character_book <- function(n, unique, spec_params, ...) {
spec_params$n <- n
DataFakeR::unique_sample(
do.call(books, spec_params),
spec_params = spec_params, unique = unique
)
}
set_faker_opts(
opt_simul_spec_character = opt_simul_spec_character(book = simul_spec_character_book)
)options("dfkr_verbose" = TRUE) # set `dfkr_verbose` option to see the workflow progress
sch <- schema_source("schema_books.yml")schema_plot_deps(sch)
schema_plot_deps(sch, "books")
sch <- schema_simulate(sch)
#> =====> Simulating table 'books' started..
#> ===> Simulating column 'author' started..
#> ===> Simulating column 'title' started..
#> ===> Simulating column 'genre' started..
#> ===> Simulating column 'bought' started..
#> ===> Simulating column 'amount' started..
#> ===> Simulating column 'book_id' started..
#> ===> Simulating column 'purchase_id' started..
#> =====> Simulating table 'borrowed' started..
#> ===> Simulating column 'book_id' started..
#> ===> Simulating column 'user_id' started..schema_get_table(sch, "books")
#> # A tibble: 10 × 7
#> book_id author title
#> <chr> <chr> <chr>
#> 1 DormAmus2021 Dorman Abshire Amusing In Common Forest
#> 2 Dr. Symb2020 Dr. Montie Kihn Symbols In My Future
#> 3 SharAmus2021 Sharde Howell MD Amusing With Your Forest
#> 4 Dr. Lear2020 Dr. Maggie Lind Learning From A Storm
#> 5 NathSmil2020 Nathanael Upton-Prosacco Smile Of Common Future
#> 6 AnasSmil2021 Anastacia Dickens Smile In Common Forest
#> 7 RyleSymb2020 Ryleigh Brekke Symbols From Mysterious Storm
#> 8 HortAmus2020 Hortense Rosenbaum Amusing Before Common Technology
#> 9 MariHidi2021 Mariana Auer-Sauer Hiding On The Forest
#> 10 TrisSmil2021 Tristen Larkin Smile With The South
#> genre bought amount purchase_id
#> <chr> <date> <int> <chr>
#> 1 Adventure 2021-04-13 17 purchase_2021-04-13
#> 2 Horror 2020-03-16 81 purchase_2020-03-16
#> 3 Adventure 2021-01-06 55 purchase_2021-01-06
#> 4 Adventure 2020-02-02 NA purchase_2020-02-02
#> 5 Adventure 2020-04-13 93 purchase_2020-04-13
#> 6 Romance 2021-03-02 2 purchase_2021-03-02
#> 7 Horror 2020-08-09 42 purchase_2020-08-09
#> 8 Adventure 2020-10-12 NA purchase_2020-10-12
#> 9 Horror 2021-05-27 47 purchase_2021-05-27
#> 10 Horror 2021-05-30 72 purchase_2021-05-30schema_get_table(sch, "borrowed")
#> # A tibble: 30 × 2
#> book_id user_id
#> <chr> <chr>
#> 1 DormAmus2021 PKPFJGYlKQ
#> 2 SharAmus2021 YiitBNRqgN
#> 3 RyleSymb2020 ZmFaiKZrsn
#> 4 RyleSymb2020 hKKanzSLlW
#> 5 AnasSmil2021 vvTGnzCNAP
#> 6 DormAmus2021 BZcsAzAjzm
#> 7 RyleSymb2020 gEfcYAuUVw
#> 8 SharAmus2021 oVcYOaJXBc
#> 9 HortAmus2020 YDCQQTGlce
#> 10 AnasSmil2021 uLrpKuAFVd
#> # … with 20 more rowsThe package was created thanks to Roche support and contributions from RWD Insights Engineering Team.
Special thanks to:
DataFakeR 0.1.3 is at experimental stage. If you find bugs or post an issue on GitHub page at https://github.com/openpharma/DataFakeR/issues
There are two main ways to get help with DataFakeR
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.