R Programming for Data Science II

2.0 credits · Undergraduate

Statistics courses usually use clean and well-behaved data, this leaves many unprepared for the messiness and chaos of data in the real world. This course will follow on from STSCI 2120 and cover more advanced data wrangling topics including how to tidy data using the tidyverse R packages to better facilitate data analysis. This includes string processing with regular expressions, manipulating date and time data, web scraping, and text mining. Data visualization topics will cover visualization principles, the use of ggplot2 to create custom plots, and how to communicate data-driven findings.