R Programming for Developers

Overview

Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this course you will learn how to load data, assemble and disassemble data objects, navigate R s environment system, write your own functions, and use all of R s programming tools.

Prerequisites

Previous experience of a programming language or attendance of our R Programming for the Beginner is required for this course.

Getting Started

• The R User Interface
• Objects
• Functions
• Sample with Replacement
• Writing Your Own Functions
• The Function Constructor
• Arguments
• Scripts

Packages and Help Pages

• Packages
• install.packages
• library
• Getting Help with Help Pages
• Parts of a Help Page
• Getting More Help

Atomic Vectors

• Doubles
• Integers
• Characters
• Logicals
• Complex and Raw

Attributes

• Names
• Dim

Storing Data

• Matrices
• Arrays
• Class
• Dates and Times
• Factors
• Coercion
• Lists
• Data Frames
• Loading Data
• Saving Data

R Notation

• Selecting Values
• Positive Integers
• Negative Integers
• Zero
• Blank Spaces
• Logical Values
• Names
• Dollar Signs and Double Brackets

Modifying Values

• Changing Values in Place
• Logical Subsetting
• Logical Tests
• Boolean Operators
• Missing Information
• na.rm
• is.na

Environments

• Environments
• Working with Environments
• The Active Environment
• Scoping Rules
• Assignment
• Evaluation
• Closures

Strategy

• Sequential Steps
• Parallel Cases
• if Statements
• else Statements
• Lookup Tables
• Code Comments

S3

• The S3 System
• Attributes
• Generic Functions
• Methods
• Method Dispatch
• Classes
• S3 and Debugging
• S4 and R5

Loops

• Expected Values
• expand.grid
• for Loops
• while Loops
• repeat Loops

Speed

• Vectorized Code
• How to Write Vectorized Code
• How to Write Fast for Loops in R
• Vectorized Code in Practice
• Loops Versus Vectorized Code