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Course Outline
I. Introduction and Preliminaries
1. Overview
- Making R more user-friendly: R and available GUIs
- RStudio
- Related software and documentation
- R and statistics
- Interactive use of R
- Initial orientation session
- Obtaining help for functions and features
- R commands, case sensitivity, and other conventions
- Recalling and correcting previous commands
- Executing commands from files or redirecting output
- Managing data persistence and removing objects
- Best practices in programming: creating self-contained scripts, enhancing readability through structured code, documentation, and markdown
- Installing packages: CRAN and Bioconductor
2. Reading Data
- Text files (using read.delim)
- CSV files
3. Basic Manipulations: Numbers, Vectors, and Arrays
- Vectors and assignment operations
- Vector arithmetic
- Generating regular sequences
- Logical vectors
- Handling missing values
- Character vectors
- Index vectors for selecting and modifying data subsets
- Arrays
- Array indexing and accessing subsections
- Index matrices
- Using the array() function and performing basic array operations (e.g., multiplication, transposition)
- Other object types
4. Lists and Data Frames
- Lists
- Constructing and modifying lists
- Concatenating lists
- Data frames
- Creating data frames
- Working with data frames
- Attaching arbitrary lists
- Managing the search path
5. Data Manipulation
- Selecting and subsetting observations and variables
- Filtering and grouping
- Recoding and transforming data
- Aggregation and combining datasets
- Creating partitioned matrices using cbind() and rbind()
- Using concatenation functions with arrays
- Character manipulation using the stringr package
- Introduction to grep and regexpr
6. Advanced Data Reading
- XLS and XLSX files
- readr and readxl packages
- Importing data from SPSS, SAS, Stata, and other formats
- Exporting data to text, CSV, and other formats
6. Grouping, Loops, and Conditional Execution
- Grouped expressions
- Control statements
- Conditional execution: if statements
- Repetitive execution: for loops, repeat, and while
- Introduction to apply, lapply, sapply, and tapply
7. Functions
- Creating custom functions
- Optional arguments and default values
- Handling variable numbers of arguments
- Scope and its implications
8. Basic Graphics in R
- Creating graphs
- Density plots
- Dot plots
- Bar plots
- Line charts
- Pie charts
- Boxplots
- Scatter plots
- Combining multiple plots
II. Statistical Analysis in R
1. Probability Distributions
- Utilizing R as a statistical table reference
- Examining data distribution sets
2. Hypothesis Testing
- Tests concerning population means
- Likelihood Ratio Test
- One-sample and two-sample tests
- Chi-Square Goodness-of-Fit Test
- Kolmogorov-Smirnov One-Sample Statistic
- Wilcoxon Signed-Rank Test
- Two-Sample Test
- Wilcoxon Rank Sum Test
- Mann-Whitney Test
- Kolmogorov-Smirnov Test
3. Multiple Hypothesis Testing
- Type I Error and False Discovery Rate (FDR)
- ROC curves and Area Under the Curve (AUC)
- Multiple Testing Procedures (e.g., BH, Bonferroni)
4. Linear Regression Models
- Generic functions for extracting model information
- Updating fitted models
- Generalized linear models (GLMs)
- Families
- The glm() function
- Classification techniques
- Logistic Regression
- Linear Discriminant Analysis
- Unsupervised learning methods
- Principal Components Analysis
- Clustering Methods (k-means, hierarchical clustering, k-medoids)
5. Survival Analysis (survival package)
- Creating survival objects in R
- Kaplan-Meier estimates, log-rank tests, and parametric regression
- Confidence bands
- Analysis of censored (interval censored) data
- Cox Proportional Hazards (PH) models with constant covariates
- Cox PH models with time-dependent covariates
- Simulation: Comparing models (regression model comparison)
6. Analysis of Variance (ANOVA)
- One-Way ANOVA
- Two-Way Classification of ANOVA
- MANOVA
III. Bioinformatics Case Studies
- Short introduction to the limma package
- Microarray data analysis workflow
- Data download from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
- Data processing steps: QC, normalization, differential expression
- Volcano plots
- Clustering examples and heatmaps
28 Hours
Testimonials (2)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The real life applications using Statcan and CER as examples.
