Stat 427: R Programming
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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
Learning Outcomes
Use an R script to edit, save, debug, and many more tasks. Practice more program writing.
Key Concepts
R scripts are available in the versions of R for Mac OSX and Windows; Unix and Linux do not
Why use a script?
Lecture
pdf
Links
Download R
Download RStudio
(free desktop version)
Quick-R
Shady practices: 1st chapter of
Merchants of Doubt
, Oreskes and Conway
How peer review works
from How Stuff Works
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