The ReDaMoR package allows the manipulation of relational data models in R. It provides functions to create, import and save relational data models. These functions are accessible through a graphical user interface made with Shiny.
A public instance of the Shiny app is available here.
The main features of ReDaMoR are the following:
- Create data model from scratch with a graphical user interface
- Save data model in a json format
- Import SQL data model generated with MySQL Workbench
- Document data types as R types (character, numeric, logical, …)
- Add comments to tables and fields to improve model documentation
- Check and auto-correct model constraints (types, keys, uniqueness, mandatory fields)
- Confront data to the model to check their compatibility
The package can also be tested in Rstudio Cloud.
library(ReDaMoR)
m <- model_relational_data()You can also run the code documented in the package vignette.
Introduction
In R, data are often stored in data frames which are tables in which each row represents a record and each column a variable. Because data frames are highly used they have been improved in different objects such as tibble, data.table or AnnotatedDataFrame.
However, in many projects, the data do not fit in only one table but they are organized in different data frames, each of them dealing with a specific concept. These tables are often related to each other by some variables. Depending on the diversity of the concepts, it can be difficult to remember what these tables represent and how they are connected. Fortunately, because they are tables, a set of data frames can be directly documented using a relational data model.
The datamodelr R package provides tools to document relational data. The generate data models are leveraged by the dm R package to interact more easily with relational data.
Here we present the ReDaMoR package which also allows the manipulation of relational data models in R but with an approach quite different from the one implemented in datamodelr. It provides functions to create, import and save relational data models. These functions are accessible through a graphical user interface made with Shiny.
The main features of ReDaMoR are the following:
- Create data model from scratch with a graphical user interface
- Save data model in a json format
- Import SQL data model generated with MySQL Workbench
- Document data types as R types (character, numeric, logical, …)
- Add comments to tables and fields to improve model documentation
- Check and auto-correct model constraints (types, keys, uniqueness, mandatory fields)
- Confront data to the model to check their compatibility
Installation
From CRAN
install.packages("ReDaMoR")Dependencies
The following R packages available on CRAN are required:
- dplyr: A Grammar of Data Manipulation
- magrittr: A Forward-Pipe Operator for R
- visNetwork: Network Visualization using ‘vis.js’ Library
- readr: Read Rectangular Text Data
- shiny: Web Application Framework for R
- shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds
- DT: A Wrapper of the JavaScript Library ‘DataTables’
- rintrojs: Wrapper for the ‘Intro.js’ Library
- colourpicker: A Colour Picker Tool for Shiny and for Selecting Colours in Plots
- jsonlite: A Robust, High Performance JSON Parser and Generator for R
- markdown: Render Markdown with the C Library ‘Sundown’
- rstudioapi: Safely Access the RStudio API
- crayon: Colored Terminal Output
- utils: The R Utils Package
- graphics: The R Graphics Package
- stats: The R Stats Package
From github
devtools::install_github("patzaw/ReDaMoR")Documentation
Documentation is available in this vignette.
Future work
We are working on internalMDB objects which bind the data to their
relational model. These objects will allow the manipulation and the
combination of datasets in a consistent manner. We hope to release the
corresponding package soon.
Acknowledgments
This work was entirely supported by UCB Pharma (Early Solutions department).