R Basics

Lecture 1

Dr. Eric Friedlander

College of Idaho
CSCI 2025 - Winter 2026

Before we start…

Make sure you have done the following:
- Downloaded and installed R
- Downloaded and installed Positron (or R Studio)
- Created a Gemini Pro AI account (free for students)

R is a statistical programming language
- Inspired by the S programming language developed at Bell Labs in the 1970s
- Created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand in the early 1990s for teaching statistics
- Now one of the most popular programming languages for data science and statistics
R is free and open source
- Anyone can download and use R for free
- Anyone can contribute to the development of R and its packages

R is the programming language
R Studio (now Positron) is an integrated development environment (IDE) for R
- Provides a user-friendly interface for writing and running R code

Let’s open up a script and run some R code!
- Running R from the console
- Running R from a script file

R has a rich ecosystem of packages that extend its functionality
- You can install packages using install.packages("package_name") (only do this once)
- Load packages into your R session using library(package_name) (do this every time you start a new R session)
An important package for this class:
- tidyverse: A collection of packages for data manipulation, visualization, and modeling
  - ggplot2: A package for creating graphics using the Grammar of Graphics
  - dplyr: A package for data manipulation
Let’s install and load tidyverse

Install the palmerpenguins package
Contains dataset on the famous Palmer Penguins: penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.
Explore more with ?penguins

Data frame: A table-like structure in R that holds data (penguins)
- In the tidyverse data frames are referred to as tibbles
Variable: is a quantity, quality, or property that you can measure (e.g. species, bill_length_mm, island)
Observation or Data point: a single object that has been measured (e.g. one of the penguins in the data set)
Value: the entry for a particular variable and observation (e.g. for the first penguin, the value of species is “Adelie”, the value of bill_length_mm is 39.1)
Tabular Data: data organized in rows and columns (like a spreadsheet or data frame)
Tidy Data: tabular data where each observation is a row, each variable is a column, and each type of observational unit forms a table