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Introduction to the R Statistical Computing Environment
March 10, 2020 @ 11:00 am - 5:00 pm$250
A 6-hour workshop taught by Mariola Moeyaert, Ph.D.Register
The R statistical programming language and computing environment is a free, open-source package for statistical analysis. R has become the de-facto standard in statistics (see Muenchen, 2018; Suda, 2017) and is widely used in the social, health, physical, and computational sciences. Researchers transition to R because it is powerful, flexible, makes routine data analysis easy and has a large and rapidly growing community of users.
This workshop is designed to introduce the R statistical environment to those who are looking to use R for applied statistical tasks. Topics include data coding and management, as well as how to perform basic descriptive, bivariate and multivariate analyses.
This workshop is hands-on and held through WebEx, a video conferencing application. The instructor will share R code with the participants. The participants are encouraged to copy the code in R or write code along with the instructor, and participate in the carefully designed exercises that will be interspersed throughout the day.
Participants are strongly encouraged to have the most recent version of R installed. Participants are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. RStudio will be used during the workshop. This software is free and available for Windows, Mac, and Linux platforms.
By the end of this workshop, participants should be able to import data, manipulate data, and run statistical models. The workshop will facilitate the use of R, to a level that enables participants to employ the software in their own work. Participants will understand the fundamentals of how R “thinks” so that they can begin to use R independently.
Pricing and schedule
Time: Wednesday, March 10, 11AM to 5PM (EST)
We offer $50 discounts for graduate students and $25 discounts for multiple workshop enrollments. Find out about our discounts here.
The main workshop material is scheduled for six hours. Time permitting, Dr. Moeyaert will answer questions about participants’ specific research projects. Participants can ask questions via chat, microphone, or telephone. In order to allow sufficient time for questions, the number of workshop participants is limited to 30.
Who should attend?
This workshop is designed for anyone to learn R. No prior knowledge of R is assumed. Participants should have a basic understanding of data management and fundamental bivariate and multivariate statistics (e.g., simple t-test, analysis of variance, correlation analysis, and multiple linear regression).
- Introduction to the R Statistical environment
Importing and exporting data
Basic data structures in R
Working with data (merging, sorting)
Understanding R functions and help files
- Exploring Data in R
Descriptive statistics and exploratory data plots
Bivariate techniques (e.g., t-tests, chi-square tests) and bivariate plots
- Analyzing Data in R
Linear Models: Estimation and diagnostics
About the instructor
Dr. Mariola Moeyaert is an Associate Professor Statistics within the division of Educational Psychology and Methodology at UAlbany. She received her PhD in Educational Statistics from the Katholieke Universiteit Leuven (KU Leuven) in Belgium and worked as a Postdoctoral fellow at the Center of Advanced Study in Education at the City University of New York. Her major research interests and publications are in the field of multilevel analysis, meta-analysis and interrupted time series analysis. She is also interested in Bayesian statistics, bootstrapping, structural equation modeling, longitudinal data analysis, and international comparative research. She (co)authored about 47 international publications, in such journals as Psychological Methods, Multivariate Behavior Research, Journal of School Psychology, Behavior Research Methods, School Psychology Quarterly, etc., reporting about developments in research methodology (including several computing intensive Monte Carlo simulation studies) and about applications of statistical models on educational/medical data. Currently, she is involved as PI and Co-PI in research projects funded by the Institute of Education Sciences about methodological advancements in the field of multilevel meta-analysis. You can find more information about Dr. Mariola Moeyaert by following these two links (1) https://www.