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Course Outline
I. Introduction and Preliminaries
1. Course Overview
- Enhancing R's accessibility: R and available Graphical User Interfaces (GUIs)
- RStudio
- Complementary software and documentation resources
- The relationship between R and statistics
- Interactive usage of R
- Initial orientation session
- Accessing help for functions and features
- R syntax, including case sensitivity and other conventions
- Retrieving and correcting previous commands
- Executing commands from files and redirecting output
- Managing data persistence and removing objects
- Best practices in programming: Self-contained scripts, readability (e.g., structured scripts, documentation, markdown)
- Installing packages; understanding CRAN and Bioconductor
2. Data Import
- Text files (using read.delim)
- CSV files
3. Basic Manipulations: Numbers, Vectors, and Arrays
- Understanding vectors and assignment operations
- Vector arithmetic
- Creating regular sequences
- Working with logical vectors
- Handling missing values
- Character vectors
- Index vectors: Selecting and modifying data subsets
- Arrays
- Array indexing and accessing subsections
- Index matrices
- The array() function and 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
- Manipulating data frames
- Attaching arbitrary lists
- Managing the search path
5. Data Manipulation Techniques
- Selecting, subsetting observations and variables
- Filtering and grouping data
- Recoding data and applying transformations
- Aggregation and merging data sets
- Creating partitioned matrices using cbind() and rbind()
- Concatenation functions with arrays
- String manipulation using the stringr package
- Introduction to grep and regexpr
6. Advanced Data Import
- Excel files (XLS, XLSX)
- The readr and readxl packages
- Importing data from SPSS, SAS, Stata, and other formats
- Exporting data to txt, csv, and other formats
7. Grouping, Loops, and Conditional Execution
- Grouped expressions
- Control statements
- Conditional execution: if statements
- Repetitive execution: for loops, repeat, and while loops
- Introduction to apply, lapply, sapply, and tapply functions
8. Creating Functions
- Defining functions
- Optional arguments and default values
- Handling a variable number of arguments
- Understanding scope and its implications
9. 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
- R as a repository of statistical tables
- Examining data set distributions
2. Hypothesis Testing
- Tests concerning population means
- Likelihood ratio tests
- One-sample and two-sample tests
- Chi-square goodness-of-fit test
- Kolmogorov-Smirnov one-sample statistic
- Wilcoxon signed-rank test
- Two-sample tests
- Wilcoxon rank sum test
- Mann-Whitney test
- Kolmogorov-Smirnov test
3. Multiple Hypothesis Testing
- Type I error and False Discovery Rate (FDR)
- Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC)
- Multiple testing procedures (e.g., Benjamini-Hochberg, 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
- Principal Component Analysis (PCA)
- Clustering methods (k-means, hierarchical clustering, k-medoids)
5. Survival Analysis (using the survival package)
- 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: Model comparison techniques
6. Analysis of Variance (ANOVA)
- One-way ANOVA
- Two-way classification in ANOVA
- MANOVA
III. Applied Problems in Bioinformatics
- Introduction to the limma package
- Workflow for microarray data analysis
- Data retrieval from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
- Data processing steps (Quality Control, normalization, differential expression analysis)
- Generating 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.
