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Joining Data with data.table in R

This course will show you how to combine and merge datasets with data.table.

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4 Hours13 Videos47 Exercises13,663 Learners3950 XPData Manipulation with R Track

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Course Description

In the real world, data sets typically come split across many tables while most data analysis functions in R are designed to work with single tables of data. In this course, you'll learn how to effectively combine data sets into single tables using data.table. You'll learn how to add columns from one table to another table, how to filter a table based on observations in another table, and how to identify records across multiple tables matching complex criteria. Along the way, you'll learn how to troubleshoot failed join operations and best practices for working with complex data sets. After completing this course you'll be well on your way to be a data.table master!
  1. 1

    Joining Multiple data.tables

    Free

    This chapter will show you how to perform simple joins that will enable you to combine information spread across multiple tables.

    Play Chapter Now
    Welcome to the course
    50 xp
    Exploring data.tables
    100 xp
    Identifying join keys
    50 xp
    Multiple data.tables, multiple keys
    50 xp
    The merge function
    50 xp
    Inner join
    100 xp
    Full join
    100 xp
    Left and right joins
    50 xp
    Left join
    100 xp
    Right join
    100 xp
    Mastering simple joins
    100 xp
  2. 2

    Joins Using data.table Syntax

    In this chapter you will perform joins using the data.table syntax, set and view data.table keys, and perform anti-joins.

    Play Chapter Now
    Joins using data.table syntax
    50 xp
    Right join with the data.table syntax
    100 xp
    Inner join with the data.table syntax
    100 xp
    Anti-joins
    100 xp
    Setting and viewing data.table keys
    50 xp
    Setting keys
    100 xp
    Getting keys
    100 xp
    Incorporating joins into your data.table workflow
    50 xp
    Exploring the Australian population
    100 xp
    Finding multiple matches
    100 xp
    Exploring world life expectancy
    100 xp
  3. 3

    Diagnosing and Fixing Common Join Problems

    This chapter will discuss common problems and errors encountered when performing data.table joins and show you how to troubleshoot and avoid them.

    Play Chapter Now
    Complex keys
    50 xp
    Keys with different names
    100 xp
    Multi-column keys
    100 xp
    Multi-key, single-key
    100 xp
    Tricky columns
    50 xp
    Column name suffixes
    100 xp
    Joining a data.frame
    100 xp
    Duplicate matches
    50 xp
    Joining with missing values
    100 xp
    Filtering duplicates
    100 xp
    Joining and filtering duplicates
    100 xp
  4. 4

    Concatenating and Reshaping data.tables

    In the last chapter of this course you'll learn how to concatenate observations from multiple tables together, how to identify observations present in one table but not another, and how to reshape tables between long and wide formats.

    Play Chapter Now
    Concatenating two or more data.tables
    50 xp
    Concatenating data.table variables
    100 xp
    Concatenating a list of data.tables
    100 xp
    Set operations
    50 xp
    Identifying observations shared by multiple tables
    100 xp
    Removing duplicates while combining tables
    100 xp
    Identifying observations unique to a table
    100 xp
    Melting data.tables
    50 xp
    Melting a wide table
    100 xp
    More melts
    100 xp
    Casting data.tables
    50 xp
    Casting a long table
    100 xp
    Casting multiple columns
    100 xp
    Splitting by multiple groups
    100 xp

In the following tracks

Data Manipulation with R

Datasets

IMDBNetflixAustralia PopulationLife ExpectancySchool DatabaseHeart DiseaseEbola CasesGDP

Collaborators

Sumedh Panchadhar
Eunkyung Park
Richie Cotton

Prerequisites

Data Manipulation with data.table in R
Scott Ritchie Headshot

Scott Ritchie

Postdoctoral Researcher in Systems Genomics

Scott Ritchie is a Post-doctoral Researcher in the field of systems genomics. He applies and develops tools to analyse genetic and molecular data in population studies of common diseases. He is a daily user of R and the data.table package. He has contributed to development of the data.table package and to course material used by Software Carpentry. He holds an MSc in Bioinformatics and a PhD in systems biology.
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