Open Source Resources

A collection of open-source resources.

Quarto

An open-source scientific and technical publishing system.

  • Author using Jupyter notebooks or with plain text markdown in your favorite editor.
  • Create dynamic content with Python, R, Julia, and Observable.
  • Publish reproducible, production quality articles, presentations, dashboards, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more.

View Quarto’s Get Started documentation to install Quarto and use the tutorials to learn the basics.

To install Quarto on a Linux server, see the Install Quarto guide.

Shiny

Shiny is package that makes it easy to build interactive web apps straight from R & Python.

You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions.

Visit the Shiny website for more information.

R Markdown

R Markdown documents allow you to embed code chunks (of R or other languages) in Markdown documents and are fully reproducible. Use multiple languages including R, Python, and SQL.

R Markdown supports 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.

Visit the R Markdown website for more information.

Plumber

Plumber allows you to create a web API by merely decorating your existing R source code with special comments.

Posit Connect automatically manages the dependent packages and files your API has and recreates an environment that closely mimicks your local development environment on the server.

Visit the Plumber website for more information.

Tidyverse

The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

The core tidyverse includes the packages that you’re likely to use in everyday data analyses.

Visit the Tidyverse website for more information.

Using RStudio with databases

RStudio makes it easy to access and analyze your data with R. Posit Professional Drivers are ODBC data connectors that help you connect to the most popular databases.

Spark

Sparklyr is an R interface for Apache Spark that allows you to:

  • Install and connect to Spark using YARN, Mesos, Livy or Kubernetes
  • Use dplyr to filter and aggregate Spark datasets and streams then bring them into R for analysis and visualization
  • Use MLlib, H2O, XGBoost and GraphFrames to train models at scale in Spark
  • Create interoperable machine learning pipelines and productionize them with MLeap
  • Create extensions that call the full Spark API or run distributed R code to support new functionality

Visit spark.posit.co for more information.

Tensorflow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

A set of Python modules composes the TensorFlow API that enable constructing and executing TensorFlow graphs. The tensorflow package for R provides access to the complete TensorFlow API from within R.

Visit tensorflow.rstudio.com for more information.

Keras

Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Keras has the following key features:

  • Allows the same code to run on CPU or on GPU, seamlessly.
  • User-friendly API which makes it easy to prototype deep learning models.
  • Built-in support for convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both.
  • Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine.
keras package for R

The keras package for R provides access to the Keras API from within R.

Visit keras.posit.co for more information.

Back to top