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Course Outline
Introduction and Preliminaries
- Making R more user-friendly: R and available GUIs
- The R environment
- Related software and documentation
- R and statistics
- Interactive use of R
- Introduction to the course
- Obtaining help for functions and features
- R commands, case sensitivity, and other conventions
- Recalling and correcting previous commands
- Executing commands from files or directing output to files
- Managing data persistence and removing objects
Basic Manipulations: Numbers and Vectors
- Vectors and assignment
- Vector arithmetic
- Generating regular sequences
- Logical vectors
- Handling missing values
- Character vectors
- Index vectors: Selecting and modifying subsets of data
- Other object types
Objects, Modes, and Attributes
- Intrinsic attributes: mode and length
- Changing object length
- Retrieving and setting attributes
- Object classes
Ordered and Unordered Factors
- A practical example
- The tapply() function and ragged arrays
- Ordered factors
Arrays and Matrices
- Arrays
- Array indexing and subsections
- Index matrices
- The array() function
- Mixed vector and array arithmetic: The recycling rule
- The outer product of two arrays
- Generalized transpose of an array
- Matrix operations
- Matrix multiplication
- Linear equations and inversion
- Eigenvalues and eigenvectors
- Singular value decomposition and determinants
- Least squares fitting and QR decomposition
- Creating partitioned matrices using cbind() and rbind()
- The concatenation function with arrays
- Generating frequency tables from factors
Lists and Data Frames
- Lists
- Constructing and modifying lists
- Concatenating lists
- Data frames
- Creating data frames
- Using attach() and detach()
- Working with data frames
- Attaching arbitrary lists
- Managing the search path
Reading Data from Files
- The read.table() function
- The scan() function
- Accessing built-in datasets
- Loading data from other R packages
- Editing data
Probability Distributions
- R as a repository of statistical tables
- Examining data distribution
- One- and two-sample tests
Grouping, Loops, and Conditional Execution
- Grouped expressions
- Control statements
- Conditional execution: if statements
- Repetitive execution: for loops, repeat, and while
Writing Your Own Functions
- Simple examples
- Defining new binary operators
- Named arguments and default values
- The '...' argument
- Assignments within functions
- Advanced examples
- Efficiency factors in block designs
- Dropping all names in a printed array
- Recursive numerical integration
- Scope
- Customizing the environment
- Classes, generic functions, and object orientation
Statistical Models in R
- Defining statistical models and formulae
- Contrasts
- Linear models
- Generic functions for extracting model information
- Analysis of variance and model comparison
- ANOVA tables
- Updating fitted models
- Generalized linear models
- Families
- The glm() function
- Nonlinear least squares and maximum likelihood models
- Least squares
- Maximum likelihood
- Some non-standard models
Graphical Procedures
- High-level plotting commands
- The plot() function
- Displaying multivariate data
- Display graphics
- Arguments to high-level plotting functions
- Low-level plotting commands
- Mathematical annotation
- Hershey vector fonts
- Interacting with graphics
- Using graphical parameters
- Permanent changes: The par() function
- Temporary changes: Arguments to graphics functions
- Graphics parameters list
- Graphical elements
- Axes and tick marks
- Figure margins
- Multiple figure environment
- Device drivers
- PostScript diagrams for typeset documents
- Multiple graphics devices
- Dynamic graphics
Packages
- Standard packages
- Contributed packages and CRAN
- Namespaces
Requirements
A solid understanding of statistical concepts is required.
21 Hours
Testimonials (3)
We had many varying levels of skill in the class which created the need for more thorough explanations at times to ensure understanding. Pace and structure was generally pleasant.
Gary Munn - Vodacom
Course - Introduction to R
Hands on examples were the most helpful.
Sean Kaukas
Course - Introduction to R
I genuinely enjoyed working 1:1 with Gunner.
