### Tutorial: Introduction to R and Data Visualization

Instructor: Abel Rodriguez, Professor, Applied Mathematics and Statistics, University of California, Santa Cruz, abel@ams.ucsc.edu

This short course covers two distinct but interrelated topics: it provides both an introduction to R (one of the premier languages for statistical analysis)
and to data visualization techniques (and how to implement them using R). The course is divided into 6 sessions of 105 minutes each (for a notional total of
10.5 hours of instruction). *Participants are expected to bring their own computers to work alongside the instructor on examples.*

There are numerous books and online courses on R. There is also a multiplicity of books on Data Visualization. Below are my favorites, and I have based important portions of the course on material extracted from them.

- Venables, William N., Smith, D.M. and the R Core Team.
*An introduction to R*. Available at https://cran.r-project.org/doc/manuals/R-intro.pdf - Maindonald, John, and John Braun.
*Data analysis and graphics using R: an example-based approach*. Vol. 10. Cambridge University Press, 2006. - Venables, William N., and Brian D. Ripley.
*Modern applied statistics with S-PLUS*. Springer Science & Business Media, 2013. - Cairo, Alberto.
*The Functional Art: An introduction to information graphics and visualization*. New Riders, 2012. - Yau, Nathan.
*Visualize this!*. John Wiley & Sons, 2012.

#### Content

##### Session 1. Introduction to R

- What is R? Basic syntax and operations.
- Objects: vectors, arrays, data frames, lists.
- Flow control.
- Functions and vectorization.
- Loading datasets.
- Descriptive statistics.
- Packages.

##### Session 2. Useful tools in R

- Linear algebra.
- Scripting.
- Optimization.

##### Session 3. Introduction to Data Visualization

- General principles
- Pre-attentive processing
- Choosing visual cues
- Choosing coordinate systems
- Choose scales
- Choosing context information

##### Session 4. Data Visualization in R

- Case study: Combining Time Series and Part-to-Whole Relationships
- Case study: Hierarchically classified data.
- Case study: Periodic Data
- Case study: Two time series with different scales
- Case study: Uncertainty bands

##### Session 5. Basic data analysis in R

- Fitting linear models.
- Fitting generalized linear models
- Multivariate statistics

##### Session 6. Advanced Topics

- Greek letters and math notation in R
- Mapping
- Network data
- Trees and dendrograms
- Reproducible research