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Data Manipulation with data.table in R

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

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4 Hours15 Videos59 Exercises21,658 Learners5050 XP

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Course Description

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.
  1. 1

    Introduction to data.table

    Free

    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.

    Play Chapter Now
    Welcome to the course!
    50 xp
    data.table pop quiz
    50 xp
    Creating a data.table
    100 xp
    Introducing bikes data
    100 xp
    Filtering rows in a data.table
    50 xp
    Filtering rows using positive integers
    100 xp
    Filtering rows using negative integers
    100 xp
    Filtering rows using logical vectors
    100 xp
    Helpers for filtering
    50 xp
    I %like% data.tables
    100 xp
    Filtering with %in%
    100 xp
    Filtering with %between% and %chin%
    100 xp
  2. 2

    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.

    Play Chapter Now
    Selecting columns from a data.table
    50 xp
    Selecting a single column
    50 xp
    Selecting columns by name
    100 xp
    Deselecting specific columns
    100 xp
    Computing on columns the data.table way
    50 xp
    Computing in j (I)
    100 xp
    Computing in j (II)
    100 xp
    Advanced computations in j
    50 xp
    Computing in j (III)
    100 xp
    Combining i and j
    100 xp
  3. 3

    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.

    Play Chapter Now
    Computations by groups
    50 xp
    Computing stats by groups (I)
    100 xp
    Computing stats by groups (II)
    100 xp
    Computing multiple stats
    100 xp
    Chaining data.table expressions
    50 xp
    Ordering rows
    100 xp
    What are the top 5 destinations?
    100 xp
    What is the most popular destination from each start station?
    100 xp
    Combining i, j, and by (I)
    100 xp
    Computations in j using .SD
    50 xp
    Using .SD (I)
    100 xp
    Using .SD (II)
    100 xp
  4. 4

    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.

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    Adding and updating columns by reference
    50 xp
    Adding a new column
    100 xp
    Updating an existing column (I)
    100 xp
    Updating an existing column (II)
    100 xp
    Grouped aggregations
    50 xp
    Adding columns by group
    100 xp
    Updating columns by group
    100 xp
    Advanced aggregations
    50 xp
    Adding multiple columns (I)
    100 xp
    Adding multiple columns (II)
    100 xp
    Combining i, j, and by (II)
    100 xp
  5. 5

    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!

    Play Chapter Now
    Fast data reading with fread()
    50 xp
    Fast reading from disk
    100 xp
    Importing a CSV file
    100 xp
    Importing selected columns
    100 xp
    Importing selected rows
    100 xp
    Advanced file reading
    50 xp
    Reading large integers
    100 xp
    Specifying column classes
    100 xp
    Dealing with empty and incomplete lines
    100 xp
    Dealing with missing values
    100 xp
    Fast data writing with fwrite()
    50 xp
    Writing files to disk
    100 xp
    Writing date and time columns
    100 xp
    Fast writing to disk
    100 xp

Collaborators

Benjamin Feder
Eunkyung Park
Sumedh Panchadhar
Richie Cotton

Prerequisites

Intermediate R
Matt Dowle Headshot

Matt Dowle

Author of data.table

Matt Dowle is the main author of the data.table package. Matt has worked for some of the world’s largest financial organizations and has been programming in R for over a decade.
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Arun Srinivasan Headshot

Arun Srinivasan

R's data.table co-developer

Arun Srinivasan is originally from Tamilnadu, India. He holds a Bachelors degree in Electronics engineering and a Masters degree in Bioinformatics. He started using R in 2010 and has contributed to R's data.table package since late 2013. He currently lives in London, where he works as a developer and analyst in Finance. He has a passion for developing tools and algorithms facilitating analyses on large data.
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