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Course Outline
I. Introduction and Preliminaries
1. Overview
- Making R more accessible: R and available GUIs
- RStudio
- Related software and documentation
- R and statistics
- Using R interactively
- An introductory session
- Getting help with functions and features
- R commands, case sensitivity, etc.
- Recalling and correcting previous commands
- Executing commands from or redirecting output to a file
- Data persistence and removing objects
- Good programming practices: Self-contained scripts, readability (e.g., structured scripts, documentation, markdown)
- Installing packages: CRAN and Bioconductor
2. Reading Data
- Text files (read.delim)
- CSV files
3. Simple Manipulations: Numbers, Vectors, and Arrays
- Vectors and assignment
- Vector arithmetic
- Generating regular sequences
- Logical vectors
- Missing values
- Character vectors
- Index vectors: selecting and modifying subsets of a data set
- Arrays
- Array indexing: Subsections of an array
- Index matrices
- The array() function and simple operations on arrays (e.g., multiplication, transposition)
- Other types of objects
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, subsetting observations and variables
- Filtering and grouping
- Recoding and transformations
- Aggregation and combining data sets
- Forming partitioned matrices: cbind() and rbind()
- The concatenation function, c(), 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
- 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 functions
- Optional arguments and default values
- Variable number of arguments
- Scope and its consequences
8. Basic Graphics in R
- Creating a graph
- Density plots
- Dot plots
- Bar plots
- Line charts
- Pie charts
- Boxplots
- Scatter plots
- Combining plots
II. Statistical Analysis in R
1. Probability Distributions
- R as a set of statistical tables
- Examining the distribution of a data set
2. Hypothesis Testing
- Tests concerning a Population Mean
- Likelihood Ratio Test
- One- 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 (BH, Bonferroni, etc.)
4. Linear Regression Models
- Generic functions for extracting model information
- Updating fitted models
- Generalized Linear Models (GLM)
- Families
- The glm() function
- Classification
- Logistic Regression
- Linear Discriminant Analysis
- Unsupervised Learning
- Principal Components Analysis
- Clustering Methods (k-means, hierarchical clustering, k-medoids)
5. Survival Analysis (survival package)
- Survival objects in R
- Kaplan-Meier estimate, log-rank test, parametric regression
- Confidence bands
- Censored (interval-censored) data analysis
- Cox 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 of ANOVA
- 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, normalization, differential expression)
- Volcano plot
- 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.
