Hello R users! We have a big announcement this week as well, DataCamp just released our first Spark in R course: Introduction to Spark in R using sparklyr! This course is taught by Richie Cotton.
R is mostly optimized to help you write data analysis code quickly and readably. Apache Spark is designed to analyze huge datasets quickly. The
sparklyr package lets you write
dplyr R code that runs on a Spark cluster, giving you the best of both worlds. This course teaches you how to manipulate Spark DataFrames using both the
dplyr interface and the native interface to Spark, as well as trying machine learning techniques. Throughout the course, you’ll explore the Million Song Dataset.
Introduction to Spark in R using sparklyr features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will teach you how to work with Spark in R!
What you’ll learn:
Chapter 1 – Light My Fire: Starting To Use Spark With dplyr Syntax
Starting off you will learn how Spark and R complement each other, how to get data to and from Spark, and how to manipulate Spark data frames using dplyr syntax.
Chapter 2 – Tools of the Trade: Advanced dplyr Usage
In the second chapter, you will learn more about using the
dplyr interface to Spark, including advanced field selection, calculating groupwise statistics, and joining data frames.
Chapter 3 – Going Native: Use The Native Interface to Manipulate Spark DataFrames
In chapter 3, you’ll learn about Spark’s machine learning data transformation features and functionality for manipulating native DataFrames.
Chapter 4 – Case Study: Learning to be a Machine: Running Machine Learning Models on Spark
The final chapter is a case study in which you learn to use
sparklyr‘s machine learning routines, by predicting the year in which a song was released.