Introduction to Spark with sparklyr in R

Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.
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4 Hours4 Videos50 Exercises16,522 Learners
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Course Description

R is mostly optimized to help you write data analysis code quickly and readably. Apache Spark is designed to analyze huge datasets quickly. The <code>sparklyr</code> package lets you write <code>dplyr</code> 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 <code>dplyr</code> interface and the native interface to Spark, as well as trying machine learning techniques. Throughout the course, you'll explore the Million Song Dataset.

  1. 1

    Light My Fire: Starting To Use Spark With dplyr Syntax

    In which you 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.
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  2. 2

    Tools of the Trade: Advanced dplyr Usage

    In which you learn more about using the dplyr interface to Spark, including advanced field selection, calculating groupwise statistics, and joining data frames.
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  3. 3

    Going Native: Use The Native Interface to Manipulate Spark DataFrames

    In which you learn about Spark's machine learning data transformation features, and functionality for manipulating native DataFrames.
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  4. 4

    Case Study: Learning to be a Machine: Running Machine Learning Models on Spark

    A case study in which you learn to use sparklyr's machine learning routines, by predicting the year in which a song was released.
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In the following tracks
Big DataMachine Learning Scientist
Tom Jeon
Intermediate R
Richie Cotton Headshot

Richie Cotton

Curriculum Architect at DataCamp
Richie is a Learning Solutions Architect at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
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