Introduction to R
Basics of Data

BUAN 327
Yegin Genc

Agenda

  • Course overview and mechanics
  • Built-in data types
  • Built-in functions and operators
  • First data structures: Vectors and arrays

Why good statisticians learn to program

  • Independence: Otherwise, you rely on someone else having given you exactly the right tool

  • Honesty: Otherwise, you end up distorting your problem to match the tools you have

  • Clarity: Making your method something a machine can do disciplines your thinking and makes it public; that's science

How this class will work

  • No programming knowledge presumed
  • Some stats. knowledge presumed
  • General programming mixed with data-manipulation and statistical inference
  • Class will be very cumulative

Mechanics

  • Lectures: concepts, methods, examples
  • In-class Assignments to try stuff out and get fast feedback
  • HW weekly to do longer and more complex things
  • Projects:
    • Project 1: Descriptive statistics
    • Project 2: Inferencial Statistics
  • Midterm and Final: in class, hands-on

R as statistical programming environment

Download and review at https://www.r-project.org/