The premier IDE for R
RStudio anywhere using a web browser
Put Shiny applications online
Shiny, R Markdown, Tidyverse and more
Do, share, teach and learn data science
Let us host your Shiny applications
The premier software bundle for data science teams
RStudio for the Enterprise
Connect data scientists with decision makers
Control and distribute packages
Webinars Working with Spark RStudio Pro Administration
Part 3 - Advanced features of sparklyr
August 23, 2017
RStudio recently announced a new open-source package called sparklyr that facilitates a connection between R and Spark using a full-fledged dplyr backend with support for the entirety of Spark’s MLlib library. Due to Spark’s ability to interact with distributed data with little latency, it is becoming an attractive tool for interfacing with large datasets in an interactive environment. In addition to handling the storage of data, Spark also incorporates a variety of other tools including stream processing, computing on graphs, and a distributed machine learning framework. Some of these tools are available to R programmers via the sparklyr package.
In this four-part series, we’ll discuss how to leverage Spark’s capabilities in a modern R environment. The sparklyr Series:
Javier is experienced in technologies ranging from desktop, web, mobile and backend; to augmented reality and deep learning applications. He previously worked in Microsoft Research and SAP and holds a double degree in Mathematics and Software Engineering. Javier is the creator of packages like sparklyr, r2d3, cloudml and author of "Mastering Spark with R".