Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
I. Introduction and Preliminaries
1. Overview
- Enhancing R's usability: R and available Graphical User Interfaces (GUIs)
- RStudio
- Complementary software and documentation
- The relationship between R and statistics
- Interactive use of R
- Initial orientation session
- Obtaining assistance with functions and features
- R command syntax, including case sensitivity
- Retrieving and correcting previous commands
- Executing commands from files or redirecting output
- Data persistence and object removal
- Best practices in programming: Self-contained scripts, readability, structured code, 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
- 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 subsections
- Index matrices
- The array() function and basic array operations (e.g., multiplication, transposition)
- Other object types
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 datasets
- Creating partitioned matrices using cbind() and rbind()
- The concatenation function in relation to arrays
- Character manipulation using the stringr package
- Introduction to grep and regexpr
6. Further Data Reading Techniques
- XLS and XLSX files
- The 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 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 plots
II. Statistical Analysis in R
1. Probability Distributions
- R as a repository for statistical tables
- Examining data distribution
2. Hypothesis Testing
- Tests concerning population means
- 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
- 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
- Analysis of censored (interval censored) data
- Cox PH models with constant covariates
- Cox PH 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 (QC, normalization, differential expression)
- 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.
