Quarto is the new cool kid on the block

Hello there!

Welcome to this week's edition of my newsletter. Every other week, I share a few thoughts and ideas about data visualization, statistics and web app development. Here's an overview of what I will share with you today:

1. Quarto is the new cool kid on the block

2. Hidden gems in dataviz packages

3. Visualizing correlations

Last week, RStudio::conf(2022) finally happened. And one thing that created a HUGE amount of buzz was Quarto. It is a supremely powerful publishing system and you can think of it as RMarkdown on steroids.

But this does not mean that RMarkdown will go away though.  In fact, during the conference it was repeatedly stressed that RMarkdown will still receive support from the developers. However, the focus will now lie on Quarto.

Rightfully so! At this stage, Quarto is already excellent and it comes with one massive advantage that RMarkdown never had: It is programming language agnostic.

This means that you can use Quarto with Python, R, Julia, and Observable. It doesn't have to be just R. And what's really cool: you can even mix programming languages.

Quarto really is a game changer and I like to think that it has never been easier to create a manifold of different document types. Also, Quarto comes with many cool features like tabset panels that I couldn't previously use in my blog which was built on Rmarkdown.

That's why I've migrated my blog to Quarto over the last couple of weeks. And you can do the same. And if you don't have a blog, you should think about starting one. It's easy now. I've even put together a detailed guide for you.

This guide is an in-depth tutorial on how to create a blog with Quarto. It is the result from hours of working with Quarto's amazing docs. And I've poured in everything I've struggled with myself while creating my blog. This includes

• Custom themes with HTML/CSS

• Making your blog posts robust with {renv}

• Quarto extensions

• Building a personal landing page

I've already had some pretty nice feedback from happy readers. Maybe this guide will help you too. Also, there's always room for improvement. Let me know if my guide misses a crucial step.

The {ggplot2}-ecosystem is MASSIVE. By now, there are many, many extension packages. Most of them come with a name like {gg-something-something}. The variety of packages is great because this can make your dataviz life easy. But it is also hard to keep track of them all.

Even worse, many packages are only remembered for one or two geoms. But the truth is that many packages come with super awesome helper functions. However, these helpers - or hidden gems as I like to call them - rarely get any spotlight.

But not today! Today, we let hidden gems shine!

To do so, I've compiled a small list of unknown helpers from well-known packages. Hopefully, this gives them the spotlight they deserve. You can find the list either on Twitter or on my blog.

Recently, I saw a cool idea on Twitter to visualize correlations with bars instead of colored matrices. This is what it was supposed to look like.

And as I was recreating this idea myself with {ggplot2} I noticed that I have some ideas of my own. Here's what I came up with.

🍭 Use lollipops instead of bars🎨 Avoid color gradient📌 Pin labels to lollipops🚨 Highlight selected pairs

This could look something like this.

If you want to see more details on how this is constructed with {ggplot2}, you can check out my newest blog post.

That's it for today. Thank you for reading this week's issue. As always, I am happy to hear your feedback. If you have some, just send me a quick message via Twitter or mail.

Enjoy the rest of your day and see you next time!Albert