4 hours. 3 hours lecture and 2 hours lab per week.
Programming and Data Literacy Using R
Analytical Thinking
Communication
Course Description
Elementary statistical analysis and data manipulation in R. Topics
include algorithms, programs, and computing in R. Fundamental
techniques of program development in R. Programming projects and
applications. Hands-on experience of data input/output and
formatting, brief introduction to object-oriented programming,
introduction to statistical computing and very elementary data
analysis and graphics.
Athena Title
Program and Data Lit Using R
Equivalent Courses
Not open to students with credit in STAT 2360E
Prerequisite
STAT 2000 or STAT 2000E or STAT 2010 or STAT 2100H or BIOS 2010 or BIOS 2010E
Semester Course Offered
Offered fall and spring
Grading System
A - F (Traditional)
Student learning Outcomes
Students will write and debug R scripts to perform data operations, classify different data types (numeric, character, factor), and describe fundamental data structures (vectors, matrices, data frames) in R.
Students will import and clean datasets from various formats (CSV, Excel) using RStudio, and apply data manipulation techniques (e.g., filtering, sorting, summarizing) using packages like dplyr.
Students will analyze and interpret datasets, creating reproducible reports using R Markdown while utilizing visualization (ggplot2) and documentation tools (knitr).
Students will communicate statistical findings effectively by generating interpretable outputs in R, delivering clear written reports, and presenting results to technical and non-technical audiences, using instructor feedback to improve future communication.
Students will work collaboratively to solve problems using R, dividing tasks such as data manipulation, visualization, and analysis, ensuring effective teamwork in a collaborative environment that emphasizes the shared responsibility of completing tasks and communicating their work effectively with their peers.
Students will experiment with different approaches, debug scripts, and implement operations such as filtering, sorting, and summarizing data, as they synthesize complex information, develop original approaches, and communicate insights effectively, transforming raw data into meaningful analyses and well-documented reports.
Topical Outline
Data input/output in R
Data formats and types
Data management
Vectors and matrices and arrays in R
Loop structure
Introduction to algorithms
Writing functions in R
Plotting in R
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
Communication
The ability to effectively develop, express, and exchange ideas in written, oral, interpersonal, or visual form.
