Basic Data Structures

EE BIOL C177/C234

Chuliang Song

Today’s Menu 🎯

1. Understand vectors — the building block
2. Understand tibbles — tidy tables
3. Meet the penguins dataset 🐧

Vectors

What is a Vector?

A one-dimensional array — like a row of seats that only allows one type

c() means “combine” — you’ll type it a lot!

Common Data Types

⚠️ Everything in a vector must be the same type

Always verify with class():

Always Check Data Types

Horror story: Excel auto-converts gene name SEPT4 → 4-Sept 😱

Read the news on Science

Extracting Elements

Use square brackets [] — indexing starts at 1 (not 0!):

Exercise

Create a vector of numbers from -1 to -3, then extract the second element:

Tibbles

What is a Tibble?

Vectors bound together in columns — a tidy table

Tidy Data Principles

Each variable is a column, each observation is a row, each type of observational unit is a table. — Hadley Wickham

From https://r4ds.hadley.nz/data-tidy.html

• Tibble tracks data types explicitly (dbl, chr, lgl)
• Enforces tidy structure automatically
• Much better than base R data.frame

Exercise

Create a tibble with name, age, and is_student columns:

The Penguins Dataset 🐧

Meet the Penguins

Three species from Palmer Station, Antarctica:

Artwork by allison_horst

Always Look at Your Data!

First rule of data analysis: always inspect your data

Summary

• Vectors: one-dimensional, single type → c()
• Tibbles: tidy tables, multiple columns → tibble()
• Always check types with class()
• Always inspect data before analyzing it