At minimum, an IDE typically consists of a source code editor and build automation tools. These interfaces are used to facilitate software development. Generally such interfaces are referred to as integrated development environments (IDE). Several enhanced interfaces for R have been developed.

A new user can (relatively) quickly gain enough skills to obtain, manage, and analyze data in R.

It has an incredible variety of contributed packages.

R is under constant and open development by a diverse and expert core group.

It is one of, if not the, most widely used software environments in data science.

This book focuses on R for several reasons: Each has strengths and weaknesses, and often two or more are used in a single project.

Various statistical and programming software environments are used in data science, including R, Python, SAS, C++, SPSS, and many others. 11.6 A Summary of Useful graphics Functions and Arguments.8.4.2 Michigan Campgrounds Server Logic.8.4 More Advanced Shiny App: Michigan Campgrounds.7.2 Programming: Conditional Statements.6.2 Reading Data with Missing Observations.4.7.2 Logical Subsetting and Data Frames.4.7.1 Modifying or Creating Objects via Subsetting.4.6.1 Accessing Specific Elements of Lists.4.5.1 Accessing Specific Elements of Data Frames.

4.1.2 Accessing Specific Elements of Vectors.

3.2.1 Creating and processing R Markdown documents.2.5 Workspace, Working Directory, and Keeping Organized.2.3.2 Basic descriptive statistics and graphics in R.1.6 How to learn (The most important section in this book!).

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