Registration is now open for the remainder of the continuing Fall Data Workshop series, presented by Ryan Womack, Data Librarian.
To go directly to the registration page, click here. A detailed calendar and descriptions of each workshop are below.
All workshops for Fall 2014 will be held in Room 413 on the 4th floor of Alexander Library (169 College Avenue). Workshops are held on Tuesdays from 1:10-2:30 pm according to the schedule below. Room capacity is limited to 25.
Room 413 has R installed on its workstations. You are also welcome to bring your laptop if you want to follow along with the exercises, but this is not required.
If you can’t make the workshops, or would like a preview or refresher, screencast versions of many of the presentations are already available at http://libguides.rutgers.edu/data. Additional screencasts will be added for the newer workshops in the series.
Description of Workshops:
§ Data Visualization in R (Sept 30, Oct 7, Oct 14) discusses principles for effective data visualization, and demonstrates techniques for implementing these using R. Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background. The three parts are:
(Sept 30) Part I – Principles & Use in lattice and ggplot2: discusses classic principles of data visualization (Tufte, Cleveland) and illustrates them with the use of the lattice and ggplot2 packages. Some of the material here overlaps with Intro to R, pt 2, but at a higher level.
(Oct 7) Part II – Miscellany of Methods: illustrates a wide range of specific graphics for different contexts
(Oct 14) Part III – 3-D, Interactive and Big Data: presentation of 3-D data, interactive exploration data, and techniques for large datasets
Additional R resources can be found here: http://libguides.rutgers.edu/data_R
R is freely downloadable from http://r-project.org
§ Time Series in R (Oct 21)
Review of commands and techniques for basic time series analysis in R
§ Survival Analysis in R (Oct 28)
Review of commands and techniques for basic survival analysis in R