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Course Outline
I. Introduction and preliminaries
1. Overview
- Enhancing R usability: R and available graphical user interfaces (GUIs)
- RStudio
- Related software and documentation
- R and statistics
- Interactive use of R
- An introductory session
- Obtaining help with functions and features
- R commands, case sensitivity, and related concepts
- Recalling and correcting previous commands
- Executing commands from or redirecting output to a file
- Data permanency and object removal
- Good programming practices: self-contained scripts, readability through structured code, documentation, and markdown
- Installing packages: CRAN and Bioconductor
2. Reading data
- Text 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 and simple array operations, such as multiplication and 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 and subsetting observations and variables
- Filtering and grouping
- Recoding and transformations
- Aggregation and combining data sets
- Forming partitioned matrices using cbind() and rbind()
- The concatenation function with arrays
- Character manipulation using the stringr package
- Introduction to grep and regexpr
6. More on Reading data
- XLS and XLSX files
- readr and readxl packages
- SPSS, SAS, Stata, and other data formats
- Exporting data to text, 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 numbers of arguments
- Scope and its consequences
8. Simple graphics in R
- Creating graphs
- 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 collection of statistical tables
- Examining the distribution of a data set
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 false discovery rate (FDR)
- ROC curves and AUC
- Multiple testing procedures (Benjamini-Hochberg, Bonferroni, etc.)
4. Linear regression models
- Generic functions for extracting model information
- Updating fitted models
- Generalised 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 proportional hazards models with constant covariates
- Cox proportional hazards models with 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
- Microarray data analysis workflow
- Data download from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
- Data processing (quality control, normalisation, 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.
