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Course Description
Course Description
This course is designed to offer students an introduction to R programming for the use in epidemiologic research. The course will cover using R for data management, data visualization, and statistical analysis. Data visualization topics includes bar charts, forest plots, geographical mapping, epidemiologic curves and survival curves. Statistical analysis topics include parametric and non-parametric statistical tests, linear regression, logistic regression, and cox proportional hazards. Students should already have mastered basic and intermediate level epidemiological concepts prior to enrolling in this course.
Course Syllabus
Course Syllabus
This class is worth 3 credits . A lecture is given followed by a lab exercise each week. A take-home midterm and a final project are also given.
SYLLABUS_Biggs_TP.docxSample Lectures
Sample Lectures
Lecture3_TeachingPracticum_Biggs.pptxLecture 1: Introducing RStudio
This is the first lecture of the porposed class. The prupose of this lecture is to get students familar with the RStudio interface and to learn how to develop RMarkdown files. Students will also learn how to import data and how to properly manage the data set.
Lecture1_TeachingPracticum_Biggs.pptxLecture 4: Creating Graph in R, Part 1
This is the first part of a three part lecture series on creating graphics in R. This lectures discusses the purpose and qualties of a good graphics and introduces to the R syntax for creating graphics using the ggplot2 package.
Lecture2_TeachingPracticum_Biggs.pptxLecture 11: Applied Survival Analysis in R
This lecture reviews basic principles of survival analysis from previous classworks and introduces the R syntax for conducting survival analysis using the survival and survminer packages.
Lab Assessment Example
Lab Assessment Example
_Assignmet_Biggs.docxLab Exericse 5 - Creating Graphs in R, Part 2
Lab Exericse 5 - Creating Graphs in R, Part 2
This is an example of a lab assessment, which is used to provide students with hands-on practice for making advanced graphs ussing ggplot2 in R. The students are provided time at the end of class to work on the assignment.
The answer key is available by e-mail request.
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