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Course Outline
I. Introduction and preliminaries
1. Overview
- Enhancing the R experience: R and available Graphical User Interfaces (GUIs)
- Overview of RStudio
- Related software and documentation resources
- The relationship between R and statistics
- Interactive use of R
- Conducting an introductory session
- Accessing help for functions and features
- R commands, case sensitivity, and syntax rules
- Recalling and correcting previous commands
- Executing commands from files and directing output
- Managing data persistence and removing objects
- Best practices in programming: Creating self-contained scripts, ensuring readability through structured scripts, documentation, and markdown
- Installing packages via CRAN and Bioconductor
2. Reading data
- TXT files (using read.delim)
- CSV files
3. Basic manipulations; numbers, vectors, and arrays
- Understanding vectors and assignment
- Vector arithmetic
- Generating 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
- Using the array() function and performing simple 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, subsetting observations, and variables
- Filtering and grouping data
- Recoding and data transformations
- Aggregation and combining data sets
- Creating partitioned matrices using cbind() and rbind()
- Using the concatenation function with arrays
- Character manipulation using the stringr package
- Introduction to grep and regexpr
6. Advanced data reading techniques
- XLS and XLSX files
- Using 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. Functions
- Creating custom functions
- Optional arguments and default values
- Handling variable numbers of arguments
- Understanding scope and its implications
9. Basic graphics in R
- Creating a graph
- Density plots
- Dot plots
- Bar plots
- Line charts
- Pie charts
- Boxplots
- Scatter plots
- Combining multiple plots
II. Statistical analysis in R
1. Probability distributions
- Using R as a collection of statistical tables
- Examining data distribution
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 (Bonferroni, Benjamini-Hochberg, etc.)
4. Linear regression models
- Generic functions for extracting model information
- Updating fitted models
- Generalized linear models
- 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)
- Working with survival objects in R
- Kaplan-Meier estimates, log-rank test, and parametric regression
- Calculating 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 of ANOVA
- MANOVA
III. Worked problems in bioinformatics
- Short introduction to the limma package
- Microarray data analysis workflow
- Downloading data from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
- Data processing steps: 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.
