4 hours. 3 hours lecture and 2 hours lab per week.
Programming and Data Literacy Using R
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
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
This course will introduce students to the statistical software package R and train them in how to use its packages to manipulate and statistically analyze data.
Students who take this course will learn to install R and RStudio on personal computers.
Students who take this course will learn to read data from external files (e.g., Excel files).
Students who take this course will learn to enter data manually.
Students who take this course will learn to identify and use data formats, modes, and attributes.
Students who take this course will learn to manage data objects.
Students who take this course will learn to control flow and execution.
Students who take this course will learn to write functions.
Students who take this course will learn to design programs.
Students who take this course will learn to build statistical graphics.
Students who take this course will learn to implement exploratory data analysis (five-point summary, plots).
Students who take this course will learn to load packages and use functions for statistical modeling and inference.
Students who take this course will learn to understand the role of statistical software in conducting statistical analyses.
Students who take this course will learn to use properly output from statistical software to communicate the results of statistical analyses through oral and written means.
Students who take this course will learn to use R Markdown for report writing and reproducible analyses.
Students will demonstrate competence through individual or group programming projects, labs, and examinations throughout the course.