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

Welcome to the course

Exploring data.tables

Identifying join keys

Multiple data.tables, multiple keys

The merge function

Inner join

Full join

Left and right joins

Left join

Right join

Mastering simple joins

Joining Multiple data.tables 

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

Joins using data.table syntax

Right join with the data.table syntax

Inner join with the data.table syntax

Anti-joins

Setting and viewing data.table keys

Setting keys

Getting keys

Incorporating joins into your data.table workflow

Exploring the Australian population

Finding multiple matches

Exploring world life expectancy

Joins Using data.table Syntax

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

Complex keys

Keys with different names

Multi-column keys

Multi-key, single-key

Tricky columns

Column name suffixes

Joining a data.frame

Duplicate matches

Joining with missing values

Filtering duplicates

Joining and filtering duplicates

Diagnosing and Fixing Common Join Problems

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.

Concatenating two or more data.tables

Concatenating data.table variables

Concatenating a list of data.tables

Set operations

Identifying observations shared by multiple tables

Removing duplicates while combining tables

Identifying observations unique to a table

Melting data.tables

Melting a wide table

More melts

Casting data.tables

Casting a long table

Casting multiple columns

Splitting by multiple groups

Concatenating and Reshaping data.tables

IMDB

Netflix

Australia Population

Life Expectancy

School Database

Heart Disease

Ebola Cases

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!


Data Manipulation with data.table in R

Learn how to combine and merge datasets, add columns from one table to another, filter based on observations in another table, and more with data.table

Joining data with data.table in R

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

Joining Data with data.table in R

Postdoctoral Researcher in Systems Genomics

Concatenating data.tables

Same columns, different data.tables

Concatenation functions

The rbind() function

Adding an identifier column

Handling missing columns

The rbindlist() function

Handling different column orders

`data.tables` with different column names

Pitfalls of `use.names = FALSE`

Differing defaults

Let's practice!

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Concatenating and Reshaping data.tables - Concatenating two or more data.tables

Concatenating two or more data.tables

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