'Shiny' Based Record Linkage Tool

A bridge is created between existing robust open-source record linkage algorithms and an urgently needed user-friendly platform that removes financial and technical barriers, setting a new standard for data interoperability in public health and bioinformatics. The 'fastLink' algorithms are used for matching. Ted Enamorado et al. (2019) .

ShinyLink - RShiny Based Record Linkage Tool

About

ShinyLink is the bridge between existing vigorous open-source record linkage algorithms and an urgently needed accessible platform that eliminates cost and programming barriers and delivers a public health and bioinformatics precedent toward increased data interchangeability.

Installation

To install a stable version, you can install ShinyLink from CRAN.

install.packages("ShinyLink")

To get a bug fix, or use a feature from the development version, you can install ShinyLink from GitHub.

Windows platform

For windows users, please make sure that you have the latest R (>= 4.2.2) and RTools (>= 4.2) installed

• Download link for R: https://cran.r-project.org/
• Download link for RTools: https://cran.r-project.org/bin/windows/Rtools/

MacOS X platform

Download and install the latest version of R (>= 4.2.2) for your Mac From CRAN: https://cran.r-project.org/bin/macosx/

Install devtools and ShinyLink

Launch R and run these commands to install

if(!require(httpuv)) install.packages("httpuv")
if(!require(devtools)) install.packages("devtools")

devtools::install_github("cdc-addm/ShinyLink",dependencies=TRUE)

Start using ShinyLink

By typing this command in the R Console to start using ShinyLink

ShinyLink::run_app()

Code style

Since this is a collaborative project, please adhere to the following code formatting conventions: * We use the tidyverse style guide (https://style.tidyverse.org/). * Please write roxygen2 comments as full sentences, starting with a capital letter and ending with a period. Brevity is preferred (e.g., “Calculates standard deviation” is preferred over “This method calculates and returns a standard deviation of given set of numbers”).

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/v1/examples.