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Introduction to Spark with sparklyr in R

Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.

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4 Hours4 Videos50 Exercises18,158 Learners

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Course Description

Explore the Advantages of R, Spark, and sparklyr

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 4-hour 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.

Load Data into Spark and Manipulate Spark DataFrames

You’ll start this Spark course by investigating how Spark and R work well together and practicing loading data, ready for cleaning, transformation, and analysis. You’ll use Spark frames and dplyr syntax to manipulate your data by filtering and arranging rows, and mutating and summarizing columns.

Delve into Big Data Analysis with Spark MLib

This course focuses on building your skills and confidence in analyzing huge datasets. The final chapters take you through Spark’s machine learning data transformation features and offer you the chance to practice sparklyr’s machine learning routines by using it to make predictions using gradient boosted trees and random forests. "
  1. 1

    Light My Fire: Starting To Use Spark With dplyr Syntax

    Free

    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.

    Play Chapter Now
    Getting started
    50 xp
    Made for each other
    50 xp
    Here be dragons
    50 xp
    The connect-work-disconnect pattern
    100 xp
    Copying data into Spark
    100 xp
    Big data, tiny tibble
    100 xp
    Exploring the structure of tibbles
    100 xp
    Selecting columns
    100 xp
    Filtering rows
    100 xp
    Arranging rows
    100 xp
    Mutating columns
    100 xp
    Summarizing columns
    100 xp
  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.

    Play Chapter Now
    Leveling up
    50 xp
    Mother's little helper (1)
    100 xp
    Mother's little helper (2)
    100 xp
    Selecting unique rows
    100 xp
    Common people
    100 xp
    Collecting data back from Spark
    100 xp
    Storing intermediate results
    100 xp
    Groups: great for music, great for data
    100 xp
    Groups of mutants
    100 xp
    Advanced Selection II: The SQL
    100 xp
    Left joins
    100 xp
    Anti joins
    100 xp
    Semi joins
    100 xp
  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.

    Play Chapter Now
    Two new interfaces
    50 xp
    Popcorn double feature
    50 xp
    Transforming continuous variables to logical
    100 xp
    Transforming continuous variables into categorical (1)
    100 xp
    Transforming continuous variables into categorical (2)
    100 xp
    More than words: tokenization (1)
    100 xp
    More than words: tokenization (2)
    100 xp
    More than words: tokenization (3)
    100 xp
    Sorting vs. arranging
    100 xp
    Exploring Spark data types
    100 xp
    Shrinking the data by sampling
    100 xp
    Training/testing partitions
    100 xp
  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.

    Play Chapter Now
    Machine learning on Spark
    50 xp
    Machine learning functions
    50 xp
    (Hey you) What's that sound?
    100 xp
    Working with parquet files
    100 xp
    Come together
    100 xp
    Partitioning data with a group effect
    100 xp
    Gradient boosted trees: modeling
    100 xp
    Gradient boosted trees: prediction
    100 xp
    Gradient boosted trees: visualization
    100 xp
    Random Forest: modeling
    100 xp
    Random Forest: prediction
    100 xp
    Random Forest: visualization
    100 xp
    Comparing model performance
    100 xp

In the following tracks

Big Data with RMachine Learning Scientist with R

Datasets

Anti-joinBoth-model-responsesGbt-model-responsesInner-joinLeft-joinPredicted vs actualResidual densitySemi-joinTimbreTimbre parquetTitle text parquetTrack data parquetTrack data to model parquetTrack data to predict parquetTrack metadata

Collaborators

Tom Jeon

Prerequisites

Intermediate R
Richie Cotton HeadshotRichie Cotton

Data Evangelist at DataCamp

Richie is a Data Evangelist 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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