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Course Outline
Introduction and preliminaries
- Making R user-friendly: Overview of R and available GUIs
- Introduction to RStudio
- Related software and documentation resources
- The relationship between R and statistics
- Interactive use of R
- Initial orientation session
- Accessing help for functions and features
- Understanding R commands, case sensitivity, and syntax
- Recalling and editing previous commands
- Executing commands from files and redirecting output
- Managing object permanence and removing objects
Simple manipulations; numbers and vectors
- Creating and assigning vectors
- Performing vector arithmetic
- Generating regular sequences
- Working with logical vectors
- Handling missing values
- Working with character vectors
- Using index vectors to select and modify data subsets
- Other object types
Objects, their modes and attributes
- Intrinsic attributes: mode and length
- Altering object length
- Retrieving and setting attributes
- Object classes
Arrays and matrices
- Understanding arrays
- Array indexing and accessing subsections
- Index matrices
- The array() function
- Calculating the outer product of two arrays
- Generalized array transposition
- Matrix capabilities
- Matrix multiplication
- Solving linear equations and matrix inversion
- Calculating eigenvalues and eigenvectors
- Singular value decomposition and determinants
- Least squares fitting and QR decomposition
- Creating partitioned matrices using cbind() and rbind()
- The concatenation function applied to arrays
- Generating frequency tables from factors
Lists and data frames
- Understanding 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 data
- Recoding and transformations
- Aggregation and combining datasets
- Character manipulation using the stringr package
Reading data
- Importing TXT files
- Importing CSV files
- Importing XLS and XLSX files
- Importing data from SPSS, SAS, Stata, and other formats
- Exporting data to TXT, CSV, and other formats
- Accessing database data using SQL
Probability distributions
- R as a repository of statistical tables
- Analyzing data distribution
- Conducting one- and two-sample tests
Grouping, loops and conditional execution
- Grouped expressions
- Control statements
- Conditional execution with 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
- Removing names from printed arrays
- Recursive numerical integration
- Scoping rules
- Customizing the environment
- Classes, generic functions, and object orientation
Graphical procedures
- High-level plotting commands
- The plot() function
- Visualizing multivariate data
- Display graphics
- Arguments for high-level plotting functions
- Basic visualization graphs
- Analyzing multivariate relations with lattice and ggplot packages
- Utilizing graphics parameters
- Graphics parameters list
Time series Forecasting
- Seasonal adjustment
- Moving average techniques
- Exponential smoothing
- Extrapolation
- Linear prediction
- Trend estimation
- Ensuring stationarity and ARIMA modeling
Econometric methods (causal methods)
- Regression analysis
- Multiple linear regression
- Multiple non-linear regression
- Validating regression models
- Forecasting based on regression results
21 Hours
Testimonials (2)
Good detail on what R is used for and how to start using it right away
Hoss Shenassa - Trimac Management Services LP
Course - Introduction to R with Time Series Analysis
the matter was well presented and in an orderly manner.
