Code Monkey home page Code Monkey logo

reproducible_analysis_demo's Introduction

Demonstration of a reproducible analysis workflow

"The same set of analyses applied to the same set of data should produce the same set of results." Easier said then done! This repository provides a reproducible workflow making using tools such as;

  • Rmarkdown (for reproducible documents)
  • Makefile (for workflow directives)
  • Docker (for isolating the environment for the analysis to run in)
  • DockerHub (image hosting) and GitHub for sharing code + results

Table of contents

Quick start options

Reproduce all analyses

  • install Docker: version used for this demo (version 19.03.0)
  • install Git: version used for this demo (version 2.24.3)
$ git clone https://github.com/singha53/reproducible_analysis_demo.git
$ cd reproducible_analysis_demo
$ make build
$ make run
  • Navigate to http://localhost:8787
  • login using username: rstudio and password: 123
  • open terminal in RStudio and type:
$ make all

Problem

Convert the following set of steps into reproducible workflow:

  1. Get data (.sh)

  2. Run analysis (.py)

  3. Visual results (.r)

  4. Write paper (manually make word document)

Reproducible documents using Rmarkdown (walkthrough)

Rmarkdown

Icon by Muhammad Haq on freeicons.io

Workflow directives using Makefile (walkthrough)

Makefile

Icon by redaxy on freeicons.io

Isolated environment using Docker (walkthrough)

Docker

Icon by fasil on freeicons.io

Sharing analyses using DockerHub and GitHub (walkthrough)

GitHub

Icon by Muhammad Haq on freeicons.io

Bugs and feature requests

Have a bug or a feature request? Please add your request here: https://github.com/singha53/reproducible_analysis_demo/issues.

Contributing

Please feel free to make a pull request if you would like to modify anything.

Copyright and license

Copyright 2021 AMRITPAL SINGH Inc.

Code released under the MIT license.

reproducible_analysis_demo's People

Contributors

singha53-zz avatar

Stargazers

 avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.