Visualizing protein data sets in R through a student peer-review rubric.

Area of Science:

• Life Science Education
• Bioinformatics
• Data Visualization

Background:

• The R programming language is widely used by life science researchers for big data analysis.
• R's visualization capabilities are increasingly popular due to numerous available packages.
• Effective methods are needed to engage students with complex biological data, such as protein analysis.

Purpose of the Study:

• To investigate how R-based visualizations can promote student engagement in protein analysis within a classroom setting.
• To present a teaching strategy utilizing R for visualizing protein data.
• To evaluate student interaction and learning outcomes from R-based data analysis.

Main Methods:

• A case study methodology was employed.
• Student peer comments on protein analyses conducted in R were reviewed.
• The study focused on an "Introduction to R for Professional Data Science" class during a "Teaching Science with Big Data" workshop.

Main Results:

• Students actively participated in the peer-review process.
• Students demonstrated an improved ability to critically evaluate data visualizations.
• The use of R for protein data visualization facilitated engagement.

Conclusions:

• R-based visualizations are an effective tool for promoting student engagement in protein analysis.
• This teaching strategy enhances critical thinking about data visualization in life sciences.
• Integrating R into the curriculum can improve data science skills for students.