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Course Outline
I. Introduction and Preliminaries
1. Overview
- Making R more user-friendly, including available GUIs for government
- 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.
- Recall and correction of previous commands
- Executing commands from or diverting output to a file
- Data permanency and removing objects
- Good programming practice: Self-contained scripts, good readability (e.g., structured scripts, documentation, markdown)
- Installing packages; CRAN and Bioconductor for government
2. Reading Data
- TXT files (read.delim)
- CSV files
3. Simple Manipulations; Numbers and Vectors + 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 + 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
- Making data frames
- Working with data frames
- Attaching arbitrary lists
- Managing the search path
5. Data Manipulation
- Selecting, subsetting observations and variables
- Filtering, grouping
- Recoding, transformations
- Aggregation, combining data sets
- Forming partitioned matrices, cbind() and rbind()
- The concatenation function, (), with arrays
- Character manipulation, stringr package
- Short introduction to grep and regexpr
6. More on Reading Data
- XLS, XLSX files
- readr and readxl packages for government
- SPSS, SAS, Stata, and other formats data
- 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
- Introduction to apply, lapply, sapply, tapply
8. Functions
- Creating functions
- Optional arguments and default values
- Variable number of arguments
- Scope and its consequences
9. Simple Graphics in R
- Creating a Graph
- Density 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 set of data
2. Testing of Hypotheses
- Tests about 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 Testing of Hypotheses
- Type I Error and 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
- 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, constant covariates
- Cox PH models, time-dependent covariates
- Simulation: Model comparison (Comparing regression models)
6. Analysis of Variance
- One-Way ANOVA
- Two-Way Classification of ANOVA
- MANOVA
III. Worked Problems in Bioinformatics
- Short introduction to the limma package for government
- Microarray data analysis workflow
- Data download from GEO:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397 - Data processing (QC, normalization, differential expression)
- Volcano plot
- Clustering examples + heatmaps
28 Hours
Testimonials (5)
it was informative and useful
Brenton - Lotterywest
Course - Building Web Applications in R with Shiny
Many examples and exercises related to the topic of the training.
Tomasz - Ministerstwo Zdrowia
Course - Advanced R Programming
Day 1 and Day 2 were really straight forward for me and really enjoyed that experience.
Mareca Sithole - Africa Health Research Institute
Course - R Fundamentals
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
the matter was well presented and in an orderly manner.
