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.

Welcome to the course!

data.table pop quiz

Creating a data.table

Introducing bikes data

Filtering rows in a data.table

Filtering rows using positive integers

Filtering rows using negative integers

Filtering rows using logical vectors

Helpers for filtering

I %like% data.tables

Filtering with %in%

Filtering with %between% and %chin%

Introduction to data.table

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.

Selecting columns from a data.table

Selecting a single column

Selecting columns by name

Deselecting specific columns

Computing on columns the data.table way

Computing in j (I)

Computing in j (II)

Advanced computations in j

Computing in j (III)

Combining i and j

Selecting and Computing on Columns

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.

Computations by groups

Computing stats by groups (I)

Computing stats by groups (II)

Computing multiple stats

Chaining data.table expressions

Ordering rows

What are the top 5 destinations?

What is the most popular destination from each start station?

Combining i, j, and by (I)

Computations in j using .SD

Using .SD (I)

Using .SD (II)

Groupwise Operations

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.

Adding and updating columns by reference

Adding a new column

Updating an existing column (I)

Updating an existing column (II)

Grouped aggregations

Adding columns by group

Updating columns by group

Advanced aggregations

Adding multiple columns (I)

Adding multiple columns (II)

Combining i, j, and by (II)

Reference Semantics

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!

Fast data reading with fread()

Fast reading from disk

Importing a CSV file

Importing selected columns

Importing selected rows

Advanced file reading

Reading large integers

Specifying column classes

Dealing with empty and incomplete lines

Dealing with missing values

Fast data writing with fwrite()

Writing files to disk

Writing date and time columns

Fast writing to disk

Importing and Exporting Data

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.

Intermediate R

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

Data Manipulation with data.table in R

Instructors, DataCamp

Chaining expressions

uniqueN()

uniqueN() together with by

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_5323/subtitles/course_5323_94b39b70fbc4618f5a8baf070f5ade3f.vtt

Groupwise Operations - Chaining data.table expressions

Chaining data.table expressions

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