Learn R for Data Science! This first course in data science with the R language complements statistical knowledge with the practical skills to clean, prepare, and visualize data before analyses are run, as well as the skills to tabulate, plot, and export statistical results. The core of the course will cover modules from the online book: R for Data Science. (http://r4ds.had.co.nz/). Prior programming or statistical experience is not required, but a general understanding of computers and basic statistics such as mean, variance, and correlation is helpful. Experience with SAS, SPSS, or Stata can also be helpful.
Modules from the book that will be extensively covered include: Explore (import, visualize, and describe data) Wrangle (transform/recode variables, select data subsets, aggregate, reshape, and merge datasets), and Programming (looping, creating functions). The module on Modeling will have less emphasis because this what statistics courses teach; however we will code several regression and SEM models. Visualization will cover both pre-model exploratory plots, as well as post-model plots of results and diagnostics.
Other course topics will introduce R’s data objects, R’s files and website I/O capabilities, conditional IF/ELSE logic, and an introduction to text processing functions. Primary focus throughout the course will be on the latest generation of R libraries called the TidyVerse, but elements of base-R will also be covered. Where possible, demonstrations of how to do some exercises in SPSS or SAS will be provided.
56985 - EPSY 6349 – 006. Thursdays, 2:00 - 4:50pm
Rm#210 Institute for Measurement, Methodology, Analysis, and Policy IMMAP). Located on the 2nd floor of the National Wind Institute Building
For more information contact the instructor, Dr. Daniel Bontempo, daniel.bontempo@ttu.edu.