ECOL592: Introduction to R
About the Course
R is an incredibly powerful tool for statistical analysis, data management, and visualization that has been increasingly used for ecological applications. It is open source, free, and available for all operating systems. Although it is widely available, R is considered a difficult language to learn— especially for those coming from a limited background in command line programming.
This course aims to introduce users to R in an interactive, hands-on format. We will focus on topics that will be common to the needs of all ecologists such as structuring, summarizing, manipulating, and plotting data. Later portions of the course will devote time to more individual-specific needs of real data sets. Class sessions will be exercise-driven and the course will include guest lectures from graduate students who have had success using R in their research.
Expected Learning Outcomes
Class Format
Each week we will go over material by working through new code together. Interspersed throughout the code are questions that ask you to apply what you've just learned in the same context as the lecture. There will be 5 assignments that ask you to apply what you've learned in a whole new context. Each assignment will also contain challenge questions that require you to extend what you know about the course material and combine it with new techniques that you'll have to seek out on your own. Additional questions are also available should you want more practice.
Justification for Class Format
The course structure is built around two premises:
1) A solid foundation of some basic computer science skills will make for more successful self-teaching when specific applications of R are needed.
2) The process of struggling to generate new code from a blank script file is a far more efficient way to learn R than copy-pasting someone else's code.
R is an incredibly powerful tool for statistical analysis, data management, and visualization that has been increasingly used for ecological applications. It is open source, free, and available for all operating systems. Although it is widely available, R is considered a difficult language to learn— especially for those coming from a limited background in command line programming.
This course aims to introduce users to R in an interactive, hands-on format. We will focus on topics that will be common to the needs of all ecologists such as structuring, summarizing, manipulating, and plotting data. Later portions of the course will devote time to more individual-specific needs of real data sets. Class sessions will be exercise-driven and the course will include guest lectures from graduate students who have had success using R in their research.
Expected Learning Outcomes
- A basic command of the R language
- A better sense of how to collect and enter data for future analysis
- A head start on something directly applicable to your research
- Tools for learning more about what R can offer your research
Class Format
Each week we will go over material by working through new code together. Interspersed throughout the code are questions that ask you to apply what you've just learned in the same context as the lecture. There will be 5 assignments that ask you to apply what you've learned in a whole new context. Each assignment will also contain challenge questions that require you to extend what you know about the course material and combine it with new techniques that you'll have to seek out on your own. Additional questions are also available should you want more practice.
Justification for Class Format
The course structure is built around two premises:
1) A solid foundation of some basic computer science skills will make for more successful self-teaching when specific applications of R are needed.
2) The process of struggling to generate new code from a blank script file is a far more efficient way to learn R than copy-pasting someone else's code.
Access content from the buttons below or the drop down menu at the top of the page.
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Lectures
Downloadable script files and brief description of covered topics |
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Assignments & Practice
Additional practice questions with a wide range of difficulty |
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Datasets
All of the datasets we used for lectures and assignments |
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Administration
Course handouts and evaluation forms |
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Blog
Updates and general musings |
