Special Topics in Earth and Planetary Science (390-0-08)
R Data Science
Elsa C Anderson
Technological Institute F285: Mon 11:00AM - 12:50PM
Overview of class
R Data Science: As we are in the era of ‘big data', the quantity and quality of data available for environmental, ecological and earth science research has exploded over the past few decades. The free and open-source R programming language has become a powerful tool in data analysis in scientific research. This course offers an introduction to the fundamentals of data science using the programming language, R. The course contents span from basic R programming skills to advanced skills including data management, visualization and analysis of spatial data such as weather and satellite imagery data. By conducting hands-on exercises and an extensive project, students will develop dynamic and reproducible outputs based on their own fields of interests. This course does not require prior coding experience.
There are no prerequisites for this course.
Students will recognize the fundamentals of R programming language.
Students will read, clean, merge and transform data attributes appropriately.
Students will effectively display and communicate spatial, temporal and textual data.
Students will process, analyze and interpret spatial data using R program.
Students will apply R data science skills into analyzing and presenting cases based on real-world problems.
The course will focus on programming in the R language. Typical class sessions will consist of a short lecture followed by interactive exercises and activities as well as one two-hour lab section. All teaching and exercises are done from RStudio. Students will obtain extensive hands-on coding experience in class.
Class Materials (Required)
Students' performances are evaluated by four components. They are class participation, assignments, package presentation and project. Assignments are given to test technical aspects using R language. Student should introduce an R package to the class about the functions and utilizations. Each student should develop a project using data analysis to tell a story about a topic of interest, which can relate to the student's field and research interests.
Class Materials (Suggested)
Required: Each student should bring a personal laptop to work in class.
A textbook available online (open source):
R for Data Science: https://r4ds.had.co.nz/
During the course we will conduct in-class exercises on the personal computer (under any Mac, Linux, or Windows OS). Students will need to install R and RStudio on the computer (not required before the first class). Instructions of installment are available on Canvas.
LAB - Technological Institute F285: Wed 11:00AM - 12:50PM