Data Wrangling with the tidyverse

Welcome

Welcome to the “Data Wrangling with tidyverse workshop!

Hi everyone! Welcome to this workshop on Data Wrangling with the tidyverse. I’m really happy to have you all here today. In this session, we’ll learn how to clean, transform, and explore data using some of the most useful tools in R.

About me

• Postdoctoral researcher
• Behavioral ecologist / Cognitive scientist
• Also teach Quarto, PKM, and Open Science

A quick intro about me before we begin. My name is Cecilia Baldoni. I’m a postdoctoral researcher working at the intersection of behavioral ecology and cognitive science. I also love teaching topics like Quarto, Personal Knowledge Management, and Open Science. If you’re curious about my work or want to connect after the workshop, these links are a good place to start.

What is Data Wrangling?

• Cleaning, reshaping, transforming data
• Examples:
  • Renaming columns
  • Filtering rows
  • Creating new variables
  • Summarizing or reshaping tables

So, what do we mean by ‘data wrangling’? It refers to all the steps we take to prepare raw data for analysis. That includes cleaning messy entries, filtering, renaming columns, and even transforming or summarizing tables.

What is the tidyverse?

• A collection of R packages for data science
• Key packages:
  • dplyr – manipulate data
  • ggplot2 – visualize data
  • readr – import data
  • tidyr – reshape data
  • forcats – helps with factor levels (like categories)
  • stringr – makes string/text handling easier
• Unified grammar and syntax

The tidyverse is a collection of R packages designed for data science. These packages work really well together and follow a unified grammar. Some core packages you’ll encounter are: dplyr for data manipulation, ggplot2 for visualization, readr for importing, and tidyr for reshaping. Even though we won’t use every single package today, you’ll get a feel for how they all share the same structure and syntax.

Why use the tidyverse?

• Human-readable syntax
• Consistent verbs: filter(), select(), mutate()…
• Chain commands using pipes: %>%
• Well-documented and community-supported

The tidyverse is popular for good reasons. It uses a very readable syntax — instead of cryptic commands, you get verbs like filter() and mutate(). You can chain multiple steps together using the pipe operator %>%, which we’ll practice together.” “Even if you’ve used base R before, I think you’ll find this way of writing code more intuitive and enjoyable.

Meet our dataset: gapminder

• Countries × years
• Variables:
  • Country and Continent
  • life expectancy lifeExp
  • GDP per capita gdpPercap
  • population pop
• Clean and well-structured ~ perfect for practice!

# A tibble: 6 × 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.
4 Afghanistan Asia       1967    34.0 11537966      836.
5 Afghanistan Asia       1972    36.1 13079460      740.
6 Afghanistan Asia       1977    38.4 14880372      786.

We’ll use the gapminder dataset today — a great starter dataset. It contains information about countries over time, including life expectancy, GDP per capita, and population.

Tidy Data principles

• Each variable = one column
• Each observation = one row
• Each type of observation = one table

Visual from tidyr documentation:

Before diving into wrangling, let’s talk about tidy data. This structure is crucial because most tidyverse functions assume it. In tidy data, each variable gets a column, each observation is a row, and each type of observation goes in a separate table.

Workshop Goals

You’ll learn how to:

• Explore: glimpse(), summary()
• Filter/Select: filter(), select()
• Transform: mutate(), case_when()
• Summarize: group_by(), summarise()
• Visualize: ggplot2

Here’s what we’ll accomplish together. We’ll start by exploring the data using functions like glimpse() and summary(). Then we’ll move on to selecting and filtering, creating new variables, summarizing data, and finally visualizing it using ggplot2. This will be a hands-on workshop, so you’ll have time to try out each step with me.

What You’ll Need

• RStudio or RStudio Cloud
• tidyverse package installed
• A copy of the .R script we’ll use live
• .Rmd for follow-up practice Here is the GitHub repo where you can download all!

Before we begin, please make sure you’ve got RStudio open. You should have the tidyverse package installed. We’ll be using a .R script together, and I’ll share a more polished .Rmd file afterward so you can practice on your own.

Let’s Begin!

• Open your .R script now
• I’ll walk you through each chunk step by step
• Ask questions anytime in the chat or unmute

Ready? Let’s wrangle

Let’s get started! Open the .R script now. I’ll guide you through it step by step. From here on, I’ll switch to live coding. We’ll work with the gapminder dataset together.