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Course Outline
Introduction and preliminaries
- Enhancing R usability and exploring available GUIs
- Overview of the R environment
- Compatible software and documentation resources
- The relationship between R and statistics
- Interactive use of R
- Initial introductory session
- Accessing help for functions and features
- R command syntax, including case sensitivity
- Retrieving and correcting previous commands
- Executing commands from files and redirecting output
- Managing data persistence and removing objects
Simple manipulations; numbers and vectors
- Understanding vectors and assignment operations
- Performing vector arithmetic
- Generating regular sequences
- Working with logical vectors
- Handling missing values
- Utilizing character vectors
- Using index vectors to select and modify data subsets
- Introduction to other object types
Objects, their modes and attributes
- Intrinsic attributes: mode and length
- Modifying the length of an object
- Retrieving and setting attributes
- Understanding the class of an object
Ordered and unordered factors
- Specific examples of factors
- Using the tapply() function and handling ragged arrays
- Working with ordered factors
Arrays and matrices
- Introduction to arrays
- Array indexing and accessing subsections
- Utilizing index matrices
- The array() function
- Mixed vector and array arithmetic: The recycling rule
- Calculating the outer product of two arrays
- Performing generalized transposes of arrays
- Matrix operations
- Matrix multiplication
- Solving linear equations and matrix inversion
- Computing eigenvalues and eigenvectors
- Singular value decomposition and determinants
- Least squares fitting and QR decomposition
- Constructing partitioned matrices using cbind() and rbind()
- Concatenation with arrays
- Generating frequency tables from factors
Lists and data frames
- Understanding lists
- Constructing and modifying lists
- Concatenating lists
- Working with data frames
- Creating data frames
- Using attach() and detach()
- Manipulating data frames
- Attaching arbitrary lists
- Managing the search path
Reading data from files
- Using the read.table() function
- Using the scan() function
- Accessing built-in datasets
- Loading data from additional R packages
- Editing data
Probability distributions
- Utilizing R as a repository of statistical tables
- Examining the distribution of data sets
- Conducting 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
- More advanced examples
- Efficiency factors in block designs
- Dropping all names in a printed array
- Recursive numerical integration
- Understanding scope
- Customizing the environment
- Classes, generic functions, and object orientation
Statistical models in R
- Defining statistical models and formulae
- Working with contrasts
- Linear models
- Generic functions for extracting model information
- Analysis of variance and model comparison
- ANOVA tables
- Updating fitted models
- Generalized linear models
- Understanding families
- Using the glm() function
- Nonlinear least squares and maximum likelihood models
- Least squares methods
- Maximum likelihood estimation
- Introduction to some non-standard models
Graphical procedures
- High-level plotting commands
- Using the plot() function
- Visualizing multivariate data
- Displaying graphics
- Arguments for high-level plotting functions
- Low-level plotting commands
- Mathematical annotation
- Hershey vector fonts
- Interacting with graphics
- Configuring graphics parameters
- Making permanent changes via the par() function
- Applying temporary changes through function arguments
- Graphics parameters list
- Graphical elements
- Axes and tick marks
- Figure margins
- Multiple figure environments
- Device drivers
- Generating PostScript diagrams for typeset documents
- Managing multiple graphics devices
- Dynamic graphics
Packages
- Standard packages
- Contributed packages and CRAN
- Understanding namespaces
Requirements
A solid understanding of statistical principles.
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.
