RStudio is becoming Posit in October. Learn more at posit.co
RStudio is becoming Posit in October.
Learn more at posit.co
The premier IDE for R
RStudio anywhere using a web browser
Put Shiny applications online
Shiny, R Markdown, Tidyverse and more
Next level training for you and your team
Do, share, teach and learn data science
An easy way to access R packages
Let us host your Shiny applications
A single home for R & Python Data Science Teams
Scale, develop, and collaborate across R & Python
Easily share your insights
Control and distribute packages
RStudio
RStudio Server
Shiny Server
R Packages
RStudio Academy
RStudio Cloud
RStudio Public Package Manager
shinyapps.io
RStudio Team
RStudio Workbench
RStudio Connect
RStudio Package Manager
rstudio::conf 2018 Interoperabilityerability
Building Spark ML pipelines with sparklyr
March 4, 2018
We provide an overview of the recently implemented Pipelines API in sparklyr, an R package for interfacing with Apache Spark. This new feature allows users to build and tune data transformation and machine learning pipelines that are interoperable with Scala and Python, simplifying handoffs between data science and data engineering. We go over the components of pipelines and walk through practical examples.
Kevin is a software engineer working on open source packages for big data analytics and machine learning. He has held data science positions in a variety of industries and was a credentialed actuary. He likes mixing cocktails and studying about wine.