Course description
R Programming for Developers
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.
Upcoming start dates
Choose between 5 start dates
Suitability - Who should attend?
Prerequisites
Previous experience of a programming language or attendance of our R Programming Introduction is required for this course.
Training Course Content
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
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