Tibbles (Modern Data Frames)

A tibble is the tidyverse's modern reimagining of the data frame — a rectangular table that prints compactly, never silently changes your data's type, and never partial-matches a column name.

Learn Tibbles (Modern Data Frames) in our free R course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a quick…

Part of the free R course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

In this lesson you'll build tibbles with tibble() , see how they differ from data.frame() , convert existing data with as_tibble() , and meet list columns — the feature that powers advanced tidyverse workflows.

What You'll Learn in This Lesson

1️⃣ Building a Tibble

You create a tibble with tibble() , passing one named argument per column. Each column is an ordinary vector, and all columns must be the same length (length-1 values are recycled). When you print it, a tibble shows the dimensions, the column names, and — crucially — the type of each column ( <chr> for character, <dbl> for double).

A neat bonus: tibble() evaluates its arguments in order, so you can build a column from columns defined earlier in the same call.

2️⃣ No Surprises: Types & Names

Two long-standing data-frame gotchas disappear with tibbles. First, tibbles never coerce a character column into a factor — what you type is what you get. (Old R turned strings into factors unless you set stringsAsFactors = FALSE ; this was the default until R 4.0.) Second, tibbles disable partial matching of column names, so a typo returns NULL with a warning instead of silently handing you the wrong column.

These guarantees matter most in scripts you run unattended: a silent factor conversion or a partial-match typo can corrupt an analysis without raising an error. Tibbles fail loudly so you catch problems early.

3️⃣ Converting & List Columns

You rarely have to start from scratch. as_tibble() upgrades any existing data frame, and because tibbles don't use row names, you can capture them into a real column with the rownames = argument.

The most distinctive tibble feature is the list column : a column whose cells each hold a list element — a vector, a data frame, even a model. Tibbles print these cleanly, which is why they underpin nested, "many models" workflows.

Your turn. Fill in the # TODO blank, run it, and confirm the column types print.

Build the same dataset two ways and compare. This is the fastest way to feel the difference in printing, type handling, and strictness for yourself.

📋 Quick Reference — Tibbles

Practice quiz

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