Introduction to Computing in the R Software Environment
The R software program has emerged as a popular platform to perform a variety of established techniques and routines in the analysis of biomedical data as well to create new applications. It is also freely available. Getting started with the use of R, however, is typically more difficult for most users than other statistical software programs. Therefore, paced and hands-on instruction in the fundamentals of how to use the software can greatly accelerate productivity. The overarching goal of this course is to provide a broad overview in how to use R for data description, analysis, graphical presentation, and programming. The specific objectives include learning how to:
- Import data files into R;
- Assign objects in R and identify R object types;
- Manipulate data objects in R;
- Conduct basic statistical analysis in R (including generation of graphs and tables);
- Write functions in R and iteratively apply R code and functions;
- Identify strategies for saving R work, and generating reports using R Markdown; and
- Be prepared to take additional advanced courses in data analysis which require computation and programming in R.
Please note that this is not a course in statistics or predictive analytics per se. That is, the course will not teach what statistical techniques to use for various situations or how to interpret the meaning of various statistical tests. Moreover, because there are no prerequisites, it will not be assumed that students have any background in statistics. Instead, the course will provide the general framework for how to perform any statistical technique about which the student is already properly knowledgeable by providing instruction in how to set up data for an analysis, locate canned routines and how to implement them, and present and save findings. For example, this is not a course to learn what logistic regression is and how to implement it in R. Instead, it is a course in how to use R, and if one knows what logistic regression is (e.g., how to interpret regression coefficients and what model assumptions to check), then the course will provide instruction in how to use R to set up one’s data to perform logistic regression, find the R routine for logistic regression, run the routine, present the results, and save the results.
Yea-Hung Chen, PhD, MS
Each week, new material is introduced via an interactive lecture and recommended readings. The lecture will include short, hands-on exercises. The lecture will be followed by a computer lab period, with more in-depth exercises and access to faculty for questions. Homework, in the form of a problem set, is assigned roughly every other week and is due one week after assignment. Feedback and evaluation of student performance will be provided during the lab period and on homework assignments.
Lectures: Wednesdays: 1:00 PM to 2:30 PM, Jan. 16 through Feb. 27.
All course materials and handouts will be posted on the course's online syllabus.
This course does not have a required textbook. We will assign reading from the built-in R help files, academic journals, or other resources. All material will be posted on—or linked to from—the course website.
Grades will be based on total points achieved on the homework assignments. Please note that late assignments are not accepted.
This course is sponsored by the Training in Clinical Research (TICR) Program, and space is limited. Preference is given to UCSF-affiliated personnel. We regret that auditing is not permitted.
To apply for this course, please fill out and submit the application below. Please see our fees page for cost information. The deadline for application is December 21, 2018. Only one application needs to be completed for all courses desired during the quarter.
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