Home/Summer Camp/Statistical Programming and Data Analysis with R Course
Statistical Programming and Data Analysis with R Course 2017-07-13T13:38:15+00:00

Statistical Programming and Data Analysis with R Course

Session 1: June 4 – 8, 2018
Albuquerque, NM – Embassy Suites


FAQVenue Info
$1,795 Faculty/Professional or $1,095 Student/Post-Doc

Payment Options
Per Course Qty
Professionalshow details + $1,795 (USD)  
Studentshow details + $1,095 (USD)  

Course Syllabus

Monday June 4, 2018
9:00-10:45 Welcome & Introduction to Open Source/Licensing
10:45-11:00 Snack and Refreshment Break
11:00-12:30 Installing R and Survey of Editors
12:30-1:30 Lunch
1:30-3:15 Basic Commands
3:15-3:30 Snack and Refreshment Break
3:30-5:00 Basic Commands
5:00-8:30 Social hour and dinner for all Stats Camp attendees!
Tuesday June 5, 2018
9:00-10:45 Data Objects
10:45-11:00 Snack and Refreshment Break
11:00-12:30 Data Objects
12:30-1:30 Lunch
1:30-3:15 Simple Programming
3:15-3:30 Snack and Refreshment Break
3:30-5:00 Simple Programming
Wednesday June 6, 2018
9:00-10:45 Simple Analysis
10:45-11:00 Snack and Refreshment Break
11:00-12:30 Simple Analysis
12:30-1:30 Lunch
1:30-3:15 Graphing
3:15-3:30 Snack and Refreshment Break
3:30-5:00 Graphing
Thursday June 7, 2018
9:00-10:45 CFA/SEM with lavaan
10:45-11:00 Snack and Refreshment Break
11:00-12:30 CFA/SEM with lavaan
12:30-1:30 Lunch
1:30-3:15 Missing Data Analysis
3:15-3:30 Snack and Refreshment Break
3:30-5:00 Missing Data Analysis
Friday June 8, 2018
9:00-10:45 Individual Consultations
10:45-11:00 Snack and Refreshment Break
11:00-12:30 Individual Consultations
12:30-1:30 Lunch
1:30-~3:30 Individual Consultations

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Course Resources
  • Breakfast (Embassy guests), Lunches, & Snacks Daily

Statistical Methods Course Description

Overview: This 5-day summer camp will introduce participants to the R software platform for data analysis. R is a freely available, open source software platform that is growing in both popularity and capacity.

Participants in this camp will learn the principles of R-based data analysis and how to use a number of useful packages that are available in R. Integrated practice problems will fix ideas for attendees as they follow along with the lectures on their laptops.

Topics:

  1. Introduction to the R ecosystem and the “open-source” philosophy
  2. Running R and installing third-party packages from CRAN
  3. Introduction to R’s user interface and syntactic structure
  4. Data structures and data management in R
  5. Simple programming in R
  6. Basic statistical analyses in R: Descriptives, regression, ANOVA, t-test
  7. Graphing
  8. Missing data analysis in R
  9. Using Quark for treating missing data
  10. Using lavaan for structural equation modeling

Instructor: Kyle M. Lang

Instructor_KyleLangKyle is a senior research associate at the Texas Tech Institute for Measurement, Methodology, Analysis, and Policy. He earned his Ph.D. in quantitative psychology from the University of Kansas in 2015. Kyle’s research focuses on missing data analysis and Bayesian statistics with a particular emphasis on developing and evaluating multiple imputation techniques for use with difficult missing data problems (e.g., imputation in large datasets, high-dimensional imputation models). He also has extensive experience applying cutting edge statistical methods such as those for testing mediation and moderation to substantive research questions in fields such as psychology, education, social work, and political science as both a statistical consultant and a collaborating researcher. One common theme across all of Kyle’s research is high-performance statistical computing. He is the development team lead for the quark project—an R package that creates multiple imputations via principal components regression, and he regularly teaches classes and workshops on statistical programming with R. Kyle has been involved in Stats Camp every year since 2009. He has provided general statistical consulting for all of the courses offered, taught classes on Mediation and Moderation, Missing Data Analysis, and Statistical Programing with R, and given numerous guest lectures on topics such as mixture modeling, regularized regression modeling, and Bayesian structural equation modeling.

Software and Computer Support

A laptop with the latest version of R installed is highly recommended. R can be downloaded, for free, at the R-project website: https://www.r-project.org/. R’s built-in text editor is very primitive, so a stand-alone text editor with which to write R code and a plug-in allowing execution of R code directly from the text editor is highly recommended. The following are three text editor/plug-in combinations that work very well with R:

  1. RStudio (https://www.rstudio.com/)
  2. EMACS with ESS (http://vgoulet.act.ulaval.ca/en/emacs/)
  3. Notepad++ (https://notepad-plus-plus.org/) with NppToR (http://sourceforge.net/projects/npptor/)

Course Audience

The camp is ideal for life-science investigators, biostatisticians, program evaluators, and R & D researchers—anyone who is interested in data analysis with R.

Recommended Reading

Matloff, N. (2011). The art of R programming: A tour of statistical software design. San Francisco: No Starch Press.

Teetor, P. (2011). R cookbook. Sebastopol, CA: O’Reilly Media, Inc.

Venables, W. N., & Ripley, B. D. (2013). Modern applied statistics with S-PLUS. New York: Springer Science & Business Media.

Verzani, J. (2014). Using R for introductory statistics. Boca Raton, FL: CRC Press.

Statistical Programming and Data Analysis with R Seminar Kyle Lang TTU