Teaching

R for Biologists – BIO4351K (Spring 2022)

This course will broadly introduce biologists* to the statistics programming language R. The course will focus on the programming aspects of R using Base-R and tidyverse. This includes fundamentals like accessing the RStudio environment, loading, analyzing, and visualizing data, declaring variables, as well as navigating through and installing new modules. 

*Please note that this course is for Aquatic Biology, Biology, Microbiology, and     Wildlife Majors only and has limited seating.

Goal:

The goal of this course is to present an introductory treatment of data manipulation and analyses with Base-R and tidyverse (packages ggplot2, dplyr, tidyr, readr, and tibble) in the RStudio integrated developing environment. Special emphasis will be placed on utilizing biologically relevant data. The course will focus on unifying concepts to make reading and writing R syntax more accessible and intuitive. In addition to data analyses, this course will also teach students how to use R to create effective data visualizations. 

Students will master the most common data manipulation challenges, become familiar with functions that help organize biological data, and work more effectively with data frames.  

Objectives: 

1. Practice correctly installing R, Rstudio, and additional packages. 
2. Classify distinct types of R objects and data. 
3. Use R operators and manage data import/export. 
4. Demonstrate working knowledge of data transformation. 
5. Implement exploratory data analysis.
6.  Understand how to effectively illustrate biological data.
7. Employ iterations: R Loops and vectorization.

Description of Instructional Methodologies: 

This course will be delivered as a traditional lecture-discussion format course with practice on data analysis and coding. Students will individually be assigned exercises/quizzes based on the material discussed in the class. Students will also be assigned in-class and take-home group projects for more challenging material.

Suggested Textbook(s) and Other Learning Resources:

REQUIRED:

Ismay C., & Kim A., Y. (2019). Statistical Inference Via Data Science: A ModernDive Into R and the Tidyverse. CRC Press. Free PDF of latest edition available at https://moderndive.com/index.html

Grolemund, G., & Wickham, H. (2017). R for Data Science. O’Reilly Media.                    Free PDF of earlier edition available at https://r4ds.had.co.nz/introduction.html 

RECOMMENDED: 

Sanders, S. (2020). Introduction to R for Biologists. https://ncgas.org/training/r_textbook_full.pdf 

Wickham, H., Navarro, D., & Pedersen, T. L. (2020). Ggplot2: Elegant Graphics for Data Analysis. Springer.                                                                                                                                        Free PDF of earlier edition available at https://ggplot2-book.org/index.html 

Paradis, E., (2005). R for Beginners.                                                                                https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf 

OTHER SOURCES: 

Venables, W. N., Smith, D. M., Gentleman, R., Ihaka, R., Maechler, M., & R Core Team. (2021). An Introduction to R.                                                                                             https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Preface 

RStudio PBC (2020). RStudio Education.                                 https://education.rstudio.com/learn/ 

RStudio PBC (2020). RStudio Cheatsheets.  https://www.rstudio.com/resources/cheatsheets/ 

Callie,R., C. (2020). For Undergraduates: Coding in R. https://www.calliechappell.com/blog/coding-in-r  

Koçak, H. R for Biologists.                                                            (https://www.rforbiologists.org/#)

PLEASE FILL OUT THIS FORM IF INTERESTED IN ENLISTING FOR THE COURSE

Course Flyer

Loading...

Taking too long?

Reload document

| Open in new tab