Monthly Archives: December, 2016

Survival Analysis in R video available

As promised earlier, the “special topic” material on Survival Analysis is now available on YouTube in lieu of in-person sessions.  Take a look at the Survival Analysis in R Playlist.

Survival analysis deals with data that may have truncated observations, called censored data.  A typical example is studying the time until failure of a part in engineering, or failure of a part of the human body in medicine (colloquially known as “disease”).  We usually have some accurate data on when the problem occurs until the point that the end of the study is reached.  Then we will have some subjects that survived without failure until the end of the study, but we are uncertain just how long they would have lasted until failure.  The methods of survival analysis account for this partial uncertainty in the data.  R can deal with almost all necessary aspects of survival analysis, but requires some mixing and matching of packages to get the best results, as shown in the videos.

As always, my YouTube videos are fueled by music behind the scenes.  Giving a throwback shoutout to Public Image Limited, some holiday Twice, plus the usual Mongolian suspects.

Statistical Software and Data Workshops, Spring 2017

Rutgers University Libraries Data Services Workshop Series (New Brunswick)

Spring 2017

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!

Logistics

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.

Calendar of workshops

Monday (LSM)

12 noon – 3 pm

  Tuesday (Alexander)

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.

  • SPSS is widely used statistical software with strengths in survey analysis and other social science disciplines.  Copies of the workshop materials, a screencast, and additional SPSS resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208425. SPSS is made available by OIRT at a discounted academic rate, currently $100/academic year.  Find it at software.rutgers.edu.  SPSS is also available in campus computer labs and via the Apps server (see below).
  • Stata is flexible and allows relatively easy access to programming features.  It is popular in economics among other areas.  Copies of the workshop materials, a screencast, and additional Stata resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208427. Stata is made available by OIRT via campus license with no additional charge to install for Rutgers users.  Find it at software.rutgers.edu.
  • SAS is a powerful and long-standing system that handles large data sets well, and is popular in the pharmaceutical industry, among other applications. Copies of the workshop materials, a screencast, and additional SAS resources can be found here: http://libguides.rutgers.edu/content.php?pid=115296&sid=1208423. SAS is made available by OIRT at a discounted academic rate, currently $100/academic year.  Find it at software.rutgers.edu.  SAS is also available in campus computer labs, online via the SAS University Edition cloud service, and via the Apps server (see below).

Note: Accessing software via apps.rutgers.edu

SPSS, SAS, Stata, and R are available for remote access on apps.rutgers.eduapps.rutgers.edu does not require any software installation, but you must activate the service first at netid.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:

  • Statistical Techniques: getting around in R, descriptive statistics, regression, significance tests, working with packages
  • Graphics:  comparison of graphing techniques in base R, lattice, and ggplot2 packages
  • Data Manipulation:  data import and transformation, additional methods for working with large data sets, also plyr and other packages useful for manipulation.

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:

  • 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.
  • Miscellany of Methods: illustrates a wide range of specific graphics for different contexts
  • 3-D, Interactive, and Big Data: presentation of 3-D data, interactive exploration data, and techniques for large datasets. Relevant packages such as shiny and tessera are explored.

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

  • Reproducible research describes the growing movement to make the products of research accessible and usable by others in order to verify, replicate, and extend research findings.  Reviews how to plan research, to create publications, code, and data in open, reusable formats, and maximize the impact of shared research findings.  Examples in LaTeX and Rmarkdown are discussed, along with platforms for reusability such as the Open Science Foundation.

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.