Screencast versions of the workshops listed above. Maximize the viewer size and resolution for the best results...
The Introduction to R workshops are split into three parts:
Session 1 - Statistical Techniques: Descriptive Statistics, Regression, Significance, Finding Additional Packages
Session 2 - Graphics: comparison of graphing techniques of basic R, lattice, and ggplot2 packages
Session 3 - Data Manipulation: Data Import and Transformation
plus an extra session on Time Series.
Click on the links below to download the materials.
R scripts for Special Topics workshops
Rutgers University Libraries Data Services Workshop Series (New Brunswick)
In Spring 2017, Ryan Womack, Data Librarian, will repeat the series of workshops on statistical software, data visualization, and reproducible research as part of the Rutgers University Libraries Data Services. A detailed calendar and descriptions of each workshop are below. This semester each workshop topic will be repeated twice, once at the Library of Science and Medicine on Busch Campus, and once at Alexander Library on College Ave. These sessions will be identical except for location. Sessions will run approximately 3 hours. Workshops in parts will divide the time in thirds. For example, the first SPSS, Stata, and SAS workshop (running from 12-3 pm) would start with SPSS at 12 pm, Stata at 1 pm, and SAS at 2 pm. You are free to come only to those segments that interest you. There is no need to register, just come!
Location: The Library of Science and Medicine (LSM on Busch) workshops will be held in the Conference Room on the 1st floor of LSM on Mondays from 12 to 3 pm. The Alexander Library (College Ave) workshops will be held in room 413 of the Scholarly Communication Center (4th floor of Alexander Library) from on Tuesdays from 1:10 to 4:10 pm.
For both locations, you are encouraged to bring your own laptop to work in your native environment. Alternatively, at Alexander Library, you can use a library desktop computer instead of your own laptop. At LSM, we will have laptops available to borrow for the session if you don’t bring your own. Room capacity is 25 in both locations, first come, first served.
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 and https://youtube.com/librarianwomack. Additional screencasts are continually being added to this series. Note that the “special topics” [Time Series, Survival Analysis, and Big Data] are no longer offered in person, but are available via screencast [Survival Analysis coming soon].
Calendar of workshops
12 noon – 3 pm
1:10 pm -4:10 pm
|January 23||Introduction to SPSS, Stata, and SAS||January 24|
|January 30||Introduction to R||January 31|
|February 6||Data Visualization in R||February 7|
|February 13||Reproducible Research||February 14|
Description of Workshops:
§ Introduction to SPSS, Stata, and SAS (January 23 or January 24) provides overviews of these three popular commercial statistical software programs, covering the basics of navigation, loading data, graphics, and elementary descriptive statistics and regression using a sample dataset. If you are already using these packages with some degree of success, you may find these sessions too basic for you.
Note: Accessing software via apps.rutgers.edu
§ Introduction to R (January 30 or January 31) – This session provides a three-part orientation to the R programming environment. R is freely available, open source statistical software that has been widely adopted in the research community. Due to its open nature, thousands of additional packages have been created by contributors to implement the latest statistical techniques, making R a very powerful tool. No prior knowledge is assumed. The three parts cover:
Additional R resources, including handouts, scripts, and screencast versions of the workshops, can be found here: http://libguides.rutgers.edu/data_R
R is freely downloadable from http://r-project.org
§ Data Visualization in R (February 6 or February 7) 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:
Additional R resources can be found here: http://libguides.rutgers.edu/data_R
R is freely downloadable from http://r-project.org
§ Reproducible Research (February 13 or February 14) covers
Additional resources on reproducible research and data management, including presentation slides, can be found here: http://libguides.rutgers.edu/datamanagement
§ Special Topics
Note that the following special topics are no longer covered by in-person workshops, but are available via screencast.
R is open source software for statistical analysis. Being open source (Gnu GPL licensed) doesn't just mean that the software is free. It means that you can use it for a variety of applications, and install it virtually anywhere you'd like, without any restrictions. Open source also means that the code for all statistical procedures and analysis can be independently checked and verified. The activity community of R users is constantly developing new add-on packages that use the latest techniques, which you are free to do as well. And, being free, you can always have access to the latest version of the software, no matter where you are.
R is also a programming language, which makes it easy to document, reuse and reproduce all the steps of your statistical analysis.
Help Commands within R
• help.start() - launches interactive help system
• help(function) or ?function launch the manual pages
describing a function
• example(function) provides detailed examples
• for help on a whole package, try library(help=packagename)
• apropos and help.search (deep vs. fuzzy search, respectively)
These are some miscellaneous useful and interesting links that may help you accomplish some specific tasks in R.