Learn how to perform meaningful data analysis using the R language and software environment, even if you have minimal or no prior programming experience. Through comprehensive tutorials and guided exercises in this class, you will learn how to use the essential R tools and techniques you need to effectively analyze data, including working with various data types and fundamental programming concepts. The course demonstrates practical data analysis applications in action by covering everything from importing raw data to publishing final results and insights.
Audience:
This class is designed for non-programmers who want to gain a strong foundational understanding of R and data analysis.
Prerequisites:
No prior programming experience is required. Some background in statistics is helpful but not essential for success.
Course Objectives:
- Write a simple R program and discover the full scope of what the language can do
- Use various data types such as vectors, arrays, lists, data frames, and string variables
- Execute code conditionally or repeatedly using branches and loops
- Learn to use R add-on packages for extended functionality
Course Outline
The R Language
1. Introduction
- What is R, and its capabilities
- Installing R on your system
- Choosing an appropriate IDE (Integrated Development Environment)
- Emacs with ESS plugin
- Eclipse with Architect
- RStudio IDE
- Revolution-R Enterprise
- Live-R Web Interface
- Other IDEs and text editors
- Writing your first R program
- How to get help in R
- Installing extra related software and packages
2. A Scientific Calculator
- Mathematical operations and vector arithmetic
- Assigning and using variables
- Special numbers and their uses
- Logical vectors and boolean operations
- Summary and key takeaways
- Test your knowledge: quiz questions
- Test your knowledge: practical exercises
3. Inspecting Variables and Your Workspace
- Classes and data structures
- Different types of numbers
- Other common classes in R
- Checking and changing classes
- Examining variables in detail
- Understanding the workspace
4. Vectors, Matrices, and Arrays
- Vectors and vector operations
- Creating sequences
- Vector lengths and dimensions
- Naming vector elements
- Indexing vectors and subsetting
- Vector recycling and repetition rules
- Matrices and multidimensional arrays
- Creating arrays and matrices
- Rows, columns, and dimensions
- Row, column, and dimension names
- Indexing and subsetting arrays
- Combining matrices
- Array arithmetic and operations
5. Lists and Data Frames
- Lists and list operations
- Creating lists
- Atomic and recursive variable types
- List dimensions and arithmetic
- Indexing and subsetting lists
- Converting between vectors and lists
- Combining lists
- NULL and special values
- Pairlists
- Data frames and their structure
- Creating data frames
- Indexing and subsetting data frames
- Basic data frame manipulation
6. Environments and Functions
- Environments and scoping
- Functions and function design
- Creating and calling functions
- Passing functions to and from other functions
- Variable scope and lifetime
7. Strings and Factors
- Strings and string operations
- Constructing and printing strings
- Formatting numbers and output
- Special characters and escape sequences
- Changing case and string manipulation
- Extracting substrings
- Splitting strings
- File paths and path operations
- Factors and categorical data
- Creating factors
- Changing factor levels
- Dropping unused factor levels
- Ordered factors and ordinal data
- Converting continuous variables to categorical
- Converting categorical variables to continuous
- Generating factor levels
- Combining factors
8. Flow Control and Loops
- Flow control and decision making
- If and else statements
- Vectorized conditional operations
- Multiple selection and switch statements
- Loops and iteration
- Repeat loops
- While loops
- For loops
9. Advanced Looping
- Replication and repeated evaluation
- Looping over lists
- Looping over arrays and matrices
- Multiple-input apply functions
- Instant vectorization techniques
- Split-apply-combine pattern
- The plyr package for data manipulation
10. Packages
- Loading and using packages
- The search path
- Libraries and installed packages
- Installing packages
- Maintaining and updating packages
11. Dates and Times
- Date and time classes and formats
- POSIX dates and times
- The date class
- Other date classes and formats
- Conversion to and from strings
- Parsing dates from various formats
- Formatting dates for output
- Time zones and their handling
- Arithmetic with dates and times
- Lubridate package for date operations
