datafaker

DataFakeR is an R package designed to help you generate sample of fake data preserving specified assumptions about the original one.

https://github.com/openpharma/datafaker

Keywords from Contributors

Repository

DataFakeR is an R package designed to help you generate sample of fake data preserving specified assumptions about the original one.

README.Rmd

---
output: github_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = TRUE,        
  echo = TRUE,         # echo code?
  message = TRUE,     # Show messages
  warning = TRUE,     # Show warnings
  fig.width = 8,       # Default plot width
  fig.height = 6,      # .... height
  dpi = 200,           # Plot resolution
  fig.align = "center",
  fig.path = "man/figures/README-"
)
knitr::opts_chunk$set()  # Figure alignment   
library(DataFakeR)
set.seed(123)
options(tibble.width = Inf)
```

# DataFakeR 

[![version](https://img.shields.io/static/v1.svg?label=github.com&message=v.0.1.3&color=ff69b4)](https://openpharma.github.io/DataFakeR/)
[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-success.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

## Overview

DataFakeR is an R package designed to help you generate sample of fake data preserving specified assumptions about the original one. 


##  DataFakeR 0.1.3 is now available!


## Installation

- from CRAN

```
install.packages("DataFakeR")
```

- latest version from Github

```
remotes::install_github(
  "openpharma/DataFakeR"
)
```

## Learning DataFakeR

If you are new to DataFakeR, look at the **[Welcome Page](https://openpharma.github.io/DataFakeR/articles/main.html)**.

You may find there a list of useful articles that will guide you through the package functionality.

## Usage

### Configure schema YAML structure

```
# 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: books
```

### Define custom simulation methods if needed

```{r}
books <- 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)
)

```

### Source schema (and check table and column dependencies)

```{r}
options("dfkr_verbose" = TRUE) # set `dfkr_verbose` option to see the workflow progress
sch <- schema_source("schema_books.yml")
```

```{r tbls_dep}
schema_plot_deps(sch)
```

```{r books_dep}
schema_plot_deps(sch, "books")
```

### Run data simulation

```{r}
sch <- schema_simulate(sch)
```

### Check the results

```{r}
schema_get_table(sch, "books")
```

```{r}
schema_get_table(sch, "borrowed")
```

## Acknowledgment

**The package was created thanks to [Roche](https://www.roche.com/) support and contributions from RWD Insights Engineering Team.**

Special thanks to:

- [Adam Foryś](mailto:adam.forys@gmail.com) for technical support, numerous suggestions for the current and future implementation of the package.
- [Adam Leśniewski](mailto:alesniewski123@gmail.com) for challenging limitations of the package by providing multiple real-world test scenarios (and wonderful hex sticker!).
- [Paweł Kawski](mailto:pawel.kawski@gmail.com) for indication of initial assumptions about the package based on real-world medical data.
- [Kamil Wais](mailto:kamil.wais@gmail.com) for highlighting the need for the package and its relevance to real-world applications.

## Lifecycle

DataFakeR 0.1.3 is at experimental stage. If you find bugs or post an issue on GitHub page at 

## Getting help

There are two main ways to get help with `DataFakeR`

1. Reach the package author via email: krystian8207@gmail.com.
2. Post an issue on our GitHub page at [https://github.com/openpharma/DataFakeR/issues](https://github.com/openpharma/DataFakeR/issues).

Owner

GitHub Events

Committers

Last synced: over 2 years ago

Issues and Pull Requests

Last synced: 11 months ago

Packages