Stat 427: R Programming
Home
Course Logistics
Syllabus
Contact
Dates
Links
Lectures
Schedule
Module 1: Getting Started
Module 2: R Scripts
Module 3: Functions
Module 4: Basic Graphs
Module 5: Data Input and Output
Module 6: Iterative Data Processing
Module 7: Logic and Control
Module 8: R Markdown and Packages
Module 9: Mathematical Functions
Module 10: Data Validation, Cleaning, Combining Datasets
Module 11: Matrix Arithmetic
Module 12: Systems of Linear Equations
Module 13: Advanced Graphs
Module 14: Probability and Simulation
Module 15: Introductory Inferential Methods
Supplemental Topics
>
Module 16: Real World Examples
Module 17: Fitting Models to Data
Review
>
Intro Class lectures
Coursework
Instructions
Labs
Project
R
Code
Information
Lab Submission Instructions
Failure to follow instructions will result in loss of up to all possible points
Labs submitted as per following instructions
Clearly label all problems
Keep problems in order
Only one (1) document can be uploaded per submission
Only the most recent submission will be graded
Copy and paste R script and output into document
All pasted scripts and output MUST be clean (no errors, etc.; clean your code before running final time and then submit)
Word or PDF only (
all other file types will be ignored
)
Project Submission Instructions
Failure to follow instructions will result in loss of up to all possible points
Projects submitted as per following instructions
If applicable, clearly label all problems
If applicable, k
eep problems in order
Only one (1) document can be uploaded per submission
Only the most recent submission will be graded
Projects will require R scripts, and output pasted into document
All pasted scripts and output MUST be clean (no errors, etc.; clean your code before running final time and then submit)
Word, PDF, or PPT only (
all other file types will be ignored
)
Home
Course Logistics
Syllabus
Contact
Dates
Links
Lectures
Schedule
Module 1: Getting Started
Module 2: R Scripts
Module 3: Functions
Module 4: Basic Graphs
Module 5: Data Input and Output
Module 6: Iterative Data Processing
Module 7: Logic and Control
Module 8: R Markdown and Packages
Module 9: Mathematical Functions
Module 10: Data Validation, Cleaning, Combining Datasets
Module 11: Matrix Arithmetic
Module 12: Systems of Linear Equations
Module 13: Advanced Graphs
Module 14: Probability and Simulation
Module 15: Introductory Inferential Methods
Supplemental Topics
>
Module 16: Real World Examples
Module 17: Fitting Models to Data
Review
>
Intro Class lectures
Coursework
Instructions
Labs
Project
R
Code
Information