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Course Outline
Day 1
Introduction and Preliminaries
- Enhancing R usability: R and available graphical user interfaces (GUIs)
- Introduction to RStudio
- Related software and documentation resources
- The connection between R and statistics
- Interactive usage of R
- Overview of an introductory session
- Obtaining help for functions and features
- R commands, case sensitivity, and related conventions
- Recalling and correcting previous commands
- Executing commands from files or redirecting output to files
- Data persistence and removing objects
Simple Manipulations: Numbers and Vectors
- Vectors and assignment operations
- Arithmetic operations on vectors
- Generating regular sequences
- Logical vectors
- Handling missing values
- Character vectors
- Index vectors: selecting and modifying subsets of datasets
- 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
- A practical example
- The tapply() function and ragged arrays
- Ordered factors
Arrays and Matrices
- Arrays
- Array indexing and subsections
- Index matrices
- The array() function
- Mixed vector and array arithmetic: the recycling rule
- 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
- Forming partitioned matrices using cbind() and rbind()
- The concatenation function with arrays
- Generating frequency tables from factors
Day 2
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 and subsetting observations and variables
- Filtering and grouping data
- 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 formats
- Exporting data to txt, csv, and other formats
- Accessing database data using SQL
Probability Distributions
- R as a comprehensive set of statistical tables
- Examining the distribution of a dataset
- One-sample and two-sample tests
Grouping, Loops, and Conditional Execution
- Grouped expressions
- Control statements
- Conditional execution: if statements
- Repetitive execution: 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
- Efficiency factors in block designs
- Removing all names from a printed array
- Recursive numerical integration
- Scope
- Customizing the 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
- Families
- The glm() function
- Classification
- Logistic Regression
- Linear Discriminant Analysis
- Unsupervised learning
- Principal Components Analysis
- Clustering methods (k-means, hierarchical clustering, k-medoids)
- Survival analysis
- Survival objects in R
- Kaplan-Meier estimation
- Confidence bands
- Cox proportional hazards models with constant covariates
- Cox proportional hazards models with time-dependent covariates
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 using the lattice and ggplot packages
- Using graphical parameters
- List of graphical parameters
Automated and Interactive Reporting
- Combining R output with text
- Creating HTML and PDF documents
Requirements
A solid understanding of statistical concepts is required.
21 Hours
Testimonials (3)
That Haytham started with the basics and gave us enough time to do the examples and ensure that we were at the same page before we moved on to the next topic.
Jaco Dreyer - Africa Health Research Institute
Course - R Fundamentals
I enjoyed that it was very hands-on, so we were constantly having the chance to try things on, rather than just sitting listening to a lecture (for example). I felt like I am now able to go away and start using R, which I haven't been able to do before
Kathy Baisley - Africa Health Research Institute
Course - R Fundamentals
Day 1 and Day 2 were really straight forward for me and really enjoyed that experience.
