R Tutorial: Joining Data with data.table in R | Intro

Want to learn more? Take the full course at https://learn.datacamp.com/courses/joining-data-with-datatable-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, my name is Scott Ritchie. I'll be your instructor for this course on joining data in R with data table. Welcome, and I look forward to seeing you in the course. A join describes the action of combining information from two different data tables into a single data table. This is a fundamental skill when working with multiple data sources. The majority of R's functions for analyzing and visualizing data are designed to work on a single data frame or data table. But, you'll often find data you want to analyze is spread across multiple datasets, that may come from different sources. For example, you might be working with two data tables in your customer database. One containing their demographic information, shown in blue, and another containing their shipping address, shown in orange. The question is: how do you build a single data table containing all the information about each customer? Joins are an efficient and reliable way of solving this type of problem. In chapter one of the course, you'll learn how to use the merge function to perform four types of joins that you can find in any data-driven language. In chapter two you'll learn how to incorporate joins directly into your data table workflows. In chapter three, you'll learn how to diagnose and avoid common join errors. Finally, in chapter four you'll learn how to concatenate data tables that have the same columns, and how to transform them between wide and long formats. The first skill you need to learn is to identify the join key columns. These are the columns you need in each data table to match the rows between them for a join. No matter what type of join you want to do, you will always need to know which columns to use as join keys. Returning to the customer database examp