Course Outline
Day 1
Introduction and Preliminaries
- Making R more user-friendly: Overview of R and available Graphical User Interfaces (GUIs)
- RStudio environment
- Related software tools and documentation resources
- The relationship between R and statistics
- Interactive usage of R
- An introductory session overview
- Obtaining help with functions and features
- R command syntax, including case sensitivity
- Reviewing and correcting previous commands
- Executing commands from files and redirecting output
- Managing data persistence and removing objects
Simple Manipulations: Numbers and Vectors
- Vectors and assignment operations
- Vector arithmetic
- Generating regular sequences
- Logical vectors
- Handling missing values
- Character vectors
- Index vectors: selecting and modifying subsets of a dataset
- Other object types
Objects: Modes and Attributes
- Intrinsic attributes: mode and length
- Modifying the length of an object
- Retrieving and setting attributes
- Object classes
Ordered and Unordered Factors
- Specific examples
- Using the tapply() function and handling ragged arrays
- Understanding ordered factors
Arrays and Matrices
- Working with arrays
- Array indexing and subsections
- Index matrices
- The array() function
- Mixed vector and array arithmetic: The recycling rule
- Calculating the outer product of two arrays
- Generalized transpose of an array
- Matrix facilities
- Matrix multiplication
- Solving linear equations and matrix inversion
- Eigenvalues and eigenvectors
- Singular value decomposition and determinants
- Least squares fitting and QR decomposition
- Creating partitioned matrices using cbind() and rbind()
- Concatenation functions applied to arrays
- Generating frequency tables from factors
Day 2
Lists and Data Frames
- Understanding lists
- Constructing and modifying lists
- Concatenating lists
- Data frames
- Creating data frames
- Using attach() and detach()
- Working effectively with data frames
- Attaching arbitrary lists
- Managing the search path
Data Manipulation
- Selecting and subsetting observations and variables
- Filtering and grouping data
- Recoding and transforming data
- Aggregation and combining datasets
- String manipulation using the stringr package
Reading Data
- Importing TXT files
- Importing CSV files
- Importing XLS and XLSX files
- Handling SPSS, SAS, Stata, and other data formats
- Exporting data to TXT, CSV, and other formats
- Accessing database data using SQL language
Probability Distributions
- Using R as a collection of statistical tables
- Examining the distribution of a dataset
- One- and two-sample statistical tests
Grouping, Loops, and Conditional Execution
- Grouped expressions
- Control statements
- Conditional execution using if statements
- Repetitive execution using for loops, repeat, and while
Day 3
Writing Your Own Functions
- Simple examples
- Defining new binary operators
- Named arguments and default values
- The '...' argument
- Assignments within functions
- Advanced examples
- Calculating efficiency factors in block designs
- Dropping all names in a printed array
- Implementing recursive numerical integration
- Understanding scope
- Customizing the R environment
- Classes, generic functions, and object-oriented programming
Statistical Analysis in R
- Linear regression models
- Generic functions for extracting model information
- Updating fitted models
- Generalized linear models
- Model families
- Using the glm() function
- Classification techniques
- Logistic Regression
- Linear Discriminant Analysis
- Unsupervised learning
- Principal Components Analysis (PCA)
- Clustering Methods (k-means, hierarchical clustering, k-medoids)
- Survival analysis
- Survival objects in R
- Kaplan-Meier estimates
- Confidence bands
- Cox PH models with constant covariates
- Cox PH models with time-dependent covariates
Graphical Procedures
- High-level plotting commands
- The plot() function
- Displaying multivariate data
- Visualizing graphics
- Arguments for high-level plotting functions
- Basic visualization graphs
- Analyzing multivariate relations using lattice and ggplot packages
- Using graphics parameters
- Understanding the graphics parameters list
Automated and Interactive Reporting
- Integrating R output with text
Creating HTML and PDF Documents
Testimonials (6)
At the end of the class, we had a great overview of the language, we were provided tools to continue learning and were provided suggestions on how to continue learning. We covered AI/ML information.
Victor Prado - Global Knowledge Network Training Ltd
Course - R
The R-programming overview training is quite intensive but Tomasz is always helpful, energetic and up to date. On top of it, he is passionate about R. I would highly recommend his R sessions to anyone interested in R.
Luiza Panoschi - Global Knowledge Network Training Ltd
Course - R
Practice exercises were relevant and very helpful to reinforce the knowledge.
Andy Kwan - Environment and Climate Change Canada
Course - R
Follow-along exercises after slide presentation kept engagement.
Robin White - Environment and Climate Change Canada
Course - R
Michael was very knowledgeable and clear in his instruction of the training. Course was well structured to teach the desired subject as well as the right amount of room was left to adjust to fit our needs better. Over all, I am very happy with the course.
Brock Batey - Environment and Climate Change Canada
Course - R
I really enjoyed the knowledge of the trainer.
