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Course Outline
Introduction and Preliminaries
- Making R user-friendly, R and available GUIs
- RStudio
- Related software and documentation
- R and statistics
- Using R interactively
- An introductory session
- Getting help with functions and features
- R commands, case sensitivity, etc.
- Recall and correction of previous commands
- Executing commands from or diverting output to a file
- Data permanency and removing objects
Simple Manipulations; Numbers and Vectors
- Vectors and assignment
- Vector arithmetic
- Generating regular sequences
- Logical vectors
- Missing values
- Character vectors
- Index vectors; selecting and modifying subsets of a dataset
- Other types of objects
Objects, Their Modes, and Attributes
- Intrinsic attributes: mode and length
- Changing the length of an object
- Getting and setting attributes
- The class of an object
Arrays and Matrices
- Arrays
- Array indexing. Subsections of an array
- Index matrices
- The array() function
- The 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 the QR decomposition
- Forming partitioned matrices, 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
- Making data frames
- attach() and detach()
- Working with data frames
- Attaching arbitrary lists
- Managing the search path
Data Manipulation
- Selecting, subsetting observations and variables
- Filtering, grouping
- Recoding, transformations
- Aggregation, combining datasets
- Character manipulation, stringr package
Reading Data
- Txt files
- CSV files
- XLS, XLSX files
- SPSS, SAS, Stata,… and other formats data
- Exporting data to txt, csv, and other formats
- Accessing data from databases using SQL language
Probability Distributions
- 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 Your Own Functions
- Simple examples
- Defining new binary operators
- Named arguments and defaults
- The '...' argument
- Assignments within functions
- More 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
Graphical Procedures
- High-level plotting commands
- The plot() function
- Displaying multivariate data
- Display graphics
- Arguments to high-level plotting functions
- Basic visualization graphs
- Multivariate relations with lattice and ggplot packages
- Using graphics parameters
- Graphics parameters list
Automated and Interactive Reporting
- Combining output from R 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.
