This course provides a practical introduction to data analysis, with an emphasis on visual exploration and presentation using the R programming language. By the end of the course, students will be able to: • Perform data wrangling and transformation with R. • Apply the principles of the Grammar of Graphics to build effective visualizations. • Use visualization to explore data patterns and communicate insights. • Create reproducible and polished data analysis reports using RMarkdown. • Deliver clear and structured visual presentations based on real-world data.
第1週 (09/07~09/13) Introduction and Class regulations
第2週 (09/14~09/20) 1 Introduction 2 First steps (Exercise 1)
第3週 (09/21~09/27) 3 Individual geoms 4 Collective geoms (Exercise 2)
第4週 (09/28~10/04) 5 Statistical summaries (Exercise 3)
第5週 (10/05~10/11) 6 Maps (Exercise 4)
第6週 (10/12~10/18) 7 Networks (Exercise 5)
第7週 (10/19~10/25) 8 Annotations (Exercise 6)
第8週 (10/26~11/01) 9 Arranging plots
第9週 (11/02~11/08) Midterm Presentations
第10週 (11/09~11/15) 10 Position scales and axes (Exercise 7)
第11週 (11/16~11/22) 11 Colour scales and legends (Exercise 8)
第12週 (11/23~11/29) 12 Other aesthetics (Exercise 9)
第13週 (11/30~12/06) 13 Build a plot layer by layer (Exercise 10)
第14週 (12/07~12/13) 14 Scales and guides (Exercise 11)
第15週 (12/14~12/20) 15 Coordinate systems (Exercise 12)
第16週 (12/21~12/27) 16 Faceting 17 Themes
第17週 (12/28~01/03) Final Presentations
第18週 (01/04~01/10) Independent Study Week
• Wickham, H., & Grolemund, G. (2017). R for Data Science. O’Reilly. • Wickham, H. (2023). ggplot2: Elegant Graphics for Data Analysis (3rd ed.). Chapman & Hall/CRC. https://ggplot2-book.org
平時評量 60%： o In-class Exercises: 12 sessions across the semester o Each worth 5 points, totaling 60 points o Submission required by the day. (via TronClass)
期中評量 20%： o In-class Group Presentation: Worth 20 points o Students present an exploratory analysis based on a provided dataset (3 minutes per student; e.g., 3 students = 9 minutes)
期末評量 20%： o In-class Group Presentation: Worth 20 points o Students choose a dataset of interest, analyze it using R, and present results in a structured group talk (3 minutes per student)