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Datacamp

Data Manipulation with data.table in R

via Datacamp

Overview

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

The data.table package provides a high-performance version of base R's data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. This course shows you how to create, subset, and manipulate data.tables. You'll also learn about the database-inspired features of data.tables, including built-in groupwise operations. The course concludes with fast methods of importing and exporting tabular text data such as CSV files. Upon completion of the course, you will be able to use data.table in R for a more efficient manipulation and analysis process. Throughout the course you'll explore the San Francisco Bay Area bike share trip dataset from 2014.

Syllabus

Introduction to data.table
-This chapter introduces data.tables as a drop-in replacement for data.frames and shows how to use data.table's i argument to filter rows.

Selecting and Computing on Columns
-Just as the i argument lets you filter rows, the j argument of data.table lets you select columns and also perform computations. The syntax is far more convenient and flexible when compared to data.frames.

Groupwise Operations
-This chapter introduces data.table's by argument that lets you perform computations by groups. By the end of this chapter, you will master the concise DT[i, j, by] syntax of data.table.

Reference Semantics
-You will learn about a unique feature of data.table in this chapter: modifying existing data.tables in place. Modifying data.tables in place makes your operations incredibly fast and is easy to learn.

Importing and Exporting Data
-Not only does the data.table package help you perform incredibly fast computations, it can also help you read and write data to disk with amazing speeds. This chapter focuses on data.table's fread() and fwrite() functions which let you import and export flat files quickly and easily!

Taught by

Matt Dowle and Arun Srinivasan

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