R Training (DATA SCIENCE WITH R)
1. Overview of the R language
- Defining the R project
- Obtaining R
- Using the R console
- A sample R session
2. Generating R code
- Basic programming concepts
- Scripts
- Text editors for R
- Graphical User Interfaces (GUIs) for R
- Packages
3. Objects and data structures
- Variable classes (factor, numeric, logical, complex, missing)
- Vectors and matrices
- Data frames and lists
- Data sets included in R packages
- Summarizing and exploring data
4. Dealing with data
- Reading data from external files
- Storing data to external files
- Creating and storing R workspaces
- Basic exploratory graphics
5. Manipulating objects
- Mathematical operations
- Basic matrix computation
- Textual operations
- Searches, strings, and pattern matching
- Regular sequences
- Random sequences
- Sampling from distributions
6. Graphics
- More slicing and extracting data
- Basic plots
- Adding overlaid lines, text, etc.
- Graphical parameters
- Data exploration
- Summary graphics
7. Graphics, (advance)
- Basic graphical troubleshooting
- Brief introduction to regression graphics
- Generating data
8. Programming (basic)
- Functions
- Control structures
- Debugging
9. Hypothesis testing and data handling
- T-tests
- ANOVA
- Sorting/rearranging data structures
10. Linear & logistic regression
- General modeling syntax
- Extracting model results
- Confidence intervals
- Graphics for regression
- Tabular displays
- Extracting model results
- Confidence intervals
- Regression diagnostics
11. Graphics (intermediate)
- 3D graphics
- Graphics presentation
- Interactive graphics
12. Graphics (advanced)
- Animations
- High-density data displays
- Heatmaps & Partitioning graphics
13. Functions & resampling methods for model validation
- Applying functions
- Writing your own functions
- Modifying existing functions
- Permutation testing
- Bootstrapping
- Cross-validation methods