Development

The R task Force - R experts for all your needs

All the best Open Source & Software as a Tool (SaaS) tools in one place, ranked by developers and companies using them. Compare and browse tech stacks from thousands of companies and software developers from around the world.

Astral is the best way to manage your starred repositories on GitHub using tags, notes and a powerful search feature.

The book covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.

An intuitive and practical approach to data analysis, data preparation and machine learning, suitable for all ages!

Unpacking Data Science One Step At A Time.

A curated list of awesome READMEs. Contribute to matiassingers/awesome-readme development by creating an account on GitHub.

crosswalkr

I am working on a Shiny application which allows the user to upload data, do some analysis and processing on each variable in the data, and finally use the processed variables to build a statistical model. As there may be hundreds of variables in the data, the user may want ...

A book for Geospatial Health Analysis with R.

Well organized and easy to understand Web building tutorials with lots of examples of how to use HTML, CSS, JavaScript, SQL, PHP, Python, Bootstrap, Java and XML.

Business Analytics with R

This is a DT Editor Shiny app. Contribute to jienagu/DT-Editor development by creating an account on GitHub.

Interactive charts for your web pages.

Using Git and GitHub with R, Rstudio, and R Markdown

Turn your analyses into high quality documents, reports, presentations and dashboards with R Markdown. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. Use multiple languages including R, Python, and SQL. R Markdown supports a reproducible workflow for dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.

The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.

Provenance tracking for R. Contribute to NCEAS/recordr development by creating an account on GitHub.