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Course Outline
I. Introduction and Preliminaries
1. Course Overview
- Enhancing R's accessibility: Interfaces and available GUIs
- Introduction to RStudio
- Complementary software and documentation resources
- The relationship between R and statistics
- Interactive use of R
- Overview of an introductory session
- Accessing help for functions and features
- R command syntax, case sensitivity, and related conventions
- Reviewing and correcting previous commands
- Running commands from files and redirecting output
- Managing data persistence and object removal
- Best practices in programming: Creating self-contained, readable scripts, using structured formats, documentation, and markdown
- Installing packages: Navigating CRAN and Bioconductor
2. Importing Data
- Text files (using read.delim)
- CSV files
3. Basic Manipulations: Numbers, Vectors, and Arrays
- Defining vectors and assignment operations
- Vector arithmetic
- Creating regular sequences
- Logical vectors
- Handling missing values
- Character vectors
- Index vectors: Selecting and modifying data subsets
- Working with arrays
- Array indexing and accessing subsections
- Using index matrices
- The array() function and basic array operations (e.g., multiplication, transposition)
- Other object types
4. Lists and Data Frames
- Understanding lists
- Building and modifying lists
- Concatenating lists
- Working with data frames
- Creating data frames
- Interacting with data frames
- Attaching arbitrary lists
- Managing the search path
5. Data Manipulation Techniques
- Selecting and subsetting observations and variables
- Filtering and grouping data
- Recoding and data transformations
- Aggregation and combining datasets
- Creating partitioned matrices using cbind() and rbind()
- Using the concatenation function with arrays
- String manipulation using the stringr package
- Introduction to grep and regexpr
6. Advanced Data Import Methods
- Working with XLS and XLSX files
- Utilizing 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
- Iterative execution: for loops, repeat, and while loops
- Introduction to apply, lapply, sapply, and tapply functions
8. Creating Functions in R
- Defining custom functions
- Optional arguments and default values
- Handling variable numbers of arguments
- Understanding scope and its implications
9. Basic Graphics in R
- Generating 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 set of statistical tables
- Examining the distribution of data sets
2. Hypothesis Testing
- Tests concerning a Population Mean
- 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
- Understanding Type I Error and False Discovery Rate (FDR)
- ROC curves and AUC metrics
- Multiple testing procedures (Benjamini-Hochberg, Bonferroni, etc.)
4. Linear Regression Models
- Generic functions for extracting model information
- Updating fitted models
- Generalized linear models
- Model 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 (using the survival package)
- Understanding survival objects in R
- Kaplan-Meier estimates, log-rank tests, and parametric regression
- Calculating confidence bands
- Analysis of censored (interval censored) data
- Cox Proportional Hazards models with constant covariates
- Cox Proportional Hazards models with time-dependent covariates
- Simulation for model comparison
6. Analysis of Variance (ANOVA)
- One-Way ANOVA
- Two-Way Classification ANOVA
- Multivariate ANOVA (MANOVA)
III. Applied Problems in Bioinformatics
- Short introduction to the limma package
- Workflow for microarray data analysis
- Downloading data from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
- Data processing steps: Quality control, normalization, and differential expression analysis
- Creating and interpreting volcano plots
- Clustering examples and generating 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.
