Development

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.

It parses a fitted R model object, and returns a formula in Tidy Eval code that calculates the predictions. It works with several databases back-ends because it leverages dplyr and dbplyr for the final SQL translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.

A shiny gadget to create ggplot2 charts interactively with drag-and-drop to map your variables. You can quickly visualize your data accordingly to their type, export to PNG or PowerPoint, and retrieve the code to reproduce the chart.

Two methods for keeping your Shiny app organized while avoiding hardcoding. Also, a tip for adding multiple languages (internationalization) for your app.

renv