**An Introduction to R and Data Visualization**

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.***Instructor**

Abel Rodriguez

Professor, Applied Mathematics and Statistics, University of California, Santa Cruz

Email: abel@ams.ucsc.edu

**Bibliography**

**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.