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Course Outline
Introduction and preliminaries
- Making R user-friendly: Overview of R and available GUIs
- RStudio
- Related software and documentation
- The relationship between R and statistics
- Interactive use of R
- Introductory session
- Obtaining help for functions and features
- R commands, case sensitivity, etc.
- Recalling and correcting previous commands
- Executing commands from or redirecting output to a file
- 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 data subsets
- Other object types
Objects, their modes, and attributes
- Intrinsic attributes: mode and length
- Changing object length
- Getting and setting attributes
- Object class
Arrays and matrices
- Arrays
- Array indexing and subsections
- Index matrices
- The array() function
- Outer product of two arrays
- Generalized transpose of an array
- Matrix facilities
- 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
- 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
Data manipulation
- Selecting, subsetting observations, and variables
- Filtering and grouping
- Recoding and transformations
- Aggregation and combining datasets
- Character manipulation using the stringr package
Reading data
- Text files
- CSV files
- XLS and XLSX files
- SPSS, SAS, Stata, and other data formats
- Exporting data to txt, csv, and other formats
- Accessing data from databases using SQL
Probability distributions
- Utilizing R as a set of statistical tables
- Examining the distribution of a dataset
- 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 custom functions
- Simple examples
- Defining new binary operators
- Named arguments and defaults
- The '...' argument
- Assignments within functions
- Advanced examples
- Efficiency factors in block designs
- Removing all names in a printed array
- Recursive numerical integration
- Scope
- Customizing the environment
- Classes, generic functions, and object orientation
Graphical procedures
- High-level plotting commands
- The plot() function
- Displaying multivariate data
- Display graphics
- Arguments for high-level plotting functions
- Basic visualization graphs
- Multivariate relations with lattice and ggplot packages
- Using graphics parameters
- Graphics parameter list
Automated and interactive reporting
- Integrating R output with text
14 Hours
Testimonials (1)
It was very informative and professionally held. Wojteks knowledge level was so advanced that he could basically answer any question and he was willing to put effort into fitting the training to my personal needs.
