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Course Outline
I. Introduction and Preliminaries
1. Overview
- Enhancing R's usability: R and available GUIs
- Introduction to RStudio
- Related software and documentation resources
- The connection between R and statistics
- Interactive usage of R
- Conducting an introductory session
- Obtaining help for functions and features
- R commands: case sensitivity and syntax
- Recalling 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. 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: 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
- Understanding 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 data
- Recoding and data transformations
- Aggregation and combining datasets
- Creating partitioned matrices using cbind() and rbind()
- The concatenation function combined with arrays
- Character manipulation using the stringr package
- Introduction to grep and regexpr
6. Advanced Data Reading
- XLS and XLSX files
- Utilizing readr and readxl packages
- Handling SPSS, SAS, Stata, and other data formats
- Exporting data to TXT, 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 a variable number 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
- R as a repository of statistical tables
- Examining data distribution patterns
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
- Type I Error and False Discovery Rate (FDR)
- ROC curves and AUC
- Multiple Testing Procedures (e.g., BH, Bonferroni)
4. Linear Regression Models
- Generic functions for extracting model information
- Updating fitted models
- Generalized Linear Models (GLM)
- Model Families
- The glm() function
- Classification Techniques
- Logistic Regression
- Linear Discriminant Analysis
- Unsupervised Learning
- Principal Components Analysis
- Clustering Methods (k-means, hierarchical clustering, k-medoids)
5. Survival Analysis (using the survival package)
- Survival objects in R
- Kaplan-Meier estimates, log-rank test, 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 (comparing regression models)
6. Analysis of Variance (ANOVA)
- One-Way ANOVA
- Two-Way Classification in ANOVA
- Multivariate Analysis of Variance (MANOVA)
III. Worked Problems in Bioinformatics
- 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: Quality Control (QC), normalization, and differential expression analysis
- Creating 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.
