STAT 184Introduction to R
R is a powerful, open-source programming language used widely for applications in statistics and data science. It is easily extendable, and thousands of user-created packages are publicly available to extend its capabilities. This course will introduce students to data computing fundamentals and a reproducible workflow using the R programming language and related tools. Students will be expected to access, join, wrangle, clean, and visualize real data from various sources (e.g. CSV, HTML scraping, web URL, R packages). The course will emphasize the use of "tidyverse" R packages (e.g. dplyr, ggplot2), although students will also be exposed to Base R and other packages. In addition, students will be exposed to one or more integrated development environments (e.g. RStudio) and will be expected to write well-documented code using a reproducible workflow (e.g. RMarkdown, Git/GitHub). The course focuses on descriptive and graphical summary techniques rather than inferential statistical techniques
Prerequisites
From the catalog: “Placement into MATH 21 or higher”
Where it sits in degree plans
Suggested plans most often put it in Year 1 · Fall (3 programs).
Required in (9)
- Data Sciences, B.S. (Abington)
- Data Sciences, B.S. (Engineering)
- Data Sciences, B.S. (Information Sciences and Technology)
- Data Sciences, B.S. (Science)
- Functional Data Analytics, B.S.
- Social Data Analytics, B.S.
- Sociology, B.S. (Liberal Arts)
- Statistics, B.S.
- Systems Neuroscience, B.S.