Session 1: Tuesday May 24th | Session 2: Thursday May 26th
Session 3: Tuesday May 31st | Session 4: Thursday June 2nd
Overview
This webinar series “An Introduction to RStudio for SAS Users” provides an overview of RStudio for users who have had prior training in the SAS programming language, but are new to the R language. R is a free, open-source language and environment for statistical computing and graphics, with an extensive collection of features for data management, manipulation and analyses. This series introduces users to the basic elements and functionalities of R programming while framing the fundamental features of the R language in terms of how they differ from SAS (a programming language that users are already familiar with). In so doing, the series aims to highlight the fundamental aspects of R as an object-oriented language, and the flexibility this affords. The webinar series will be divided into four 2-hour sessions.
Prior required knowledge
Familiarity with SAS and basic methods for descriptive statistics, statistical inference, and linear regression will be assumed.
Webinar objectives
By the end of this webinar series, participants will be able to:
- Navigate within the main windows of the RStudio user interface; import and export data; write R code to access, manipulate, and analyse data from an object-oriented perspective; and extend R functionality through the use of R packages.
- Use R to generate descriptive statistics and implement methods commonly used for statistical analyses, for example two sample inference and linear regression.
- Use R to generate graphs and plots commonly used for data visualization and presentation, for example scatter plots, histograms and bar plots.
Webinar Format
The interactive webinar software will provide remote access for students to view the instructor's screen, listen to the lecture in real time, and ask questions. The instructor will provide lecture slides (PowerPoint) for pre-reading prior to the start of the webinar. For practice between webinar sessions and for follow up study, students will also receive training data and programming code for use with the statistical package: R.
Students can download the R software package for use on their computers through this site: https://www.rstudio.com/products/rstudio/download/
Pre-Webinar Reading
Students can review the best practices document: STAN 104 | RStudio for SAS Users
This document can be found within the Population Data BC Education and Training Unit’s free online courses and resources. To access this document and related educational resources please visit the following web page to create a PopData account and enroll in the training resources of your choice, https://training.popdata.bc.ca/
Workshop fees
Regular Rate: $265
Student Rate: $165
Course content
Session 1: Getting started with RStudio and object-oriented programming
- Getting started
- The RStudio interface
- R syntax
- R objects
- Object oriented programming vs SAS Steps
Session 2: R Functions and object-oriented programming
- R Functions
- Getting data into RStudio
- Basic data manipulation in R
- Getting data out of RStudio
- R functions vs SAS functions and proc statements
- Basic R functions vs packages
- Debugging code in R
Session 3: Descriptive statistics and regression
- Functions for descriptive statistics
- Functions for statistical inference
- Functions for linear regression
- Saving output
Session 4: Working with data sub-sets and plotting
- Applying functions to sub-sets of data and notable differences from SAS programming
- Plotting data and results
- ‘Do’ loops and notable differences from SAS programming
- Additional resources
Instructor Biography
Ethan Gough, MPH, PhD, is a Post-doctoral Fellow at the University of British Columbia, School of Population and Public Health. He completed a PhD in Epidemiology at McGill University, prior to which he worked as the lead Epidemiologist at the Ministry of Health in Belize. The focus of his research is childhood undernutrition in low- and middle-income countries, with a particular emphasis on the potential role of the intestinal microbiota in childhood malnourishment, and the statistical methods for analyzing such high dimensional data.
