Albert Rapp Profile picture
Mar 30, 2022 17 tweets 7 min read Read on X
From standard output to powerful storytelling.

Here's how you can recreate the storytelling with data look in #ggplot2.

A detailed thread 🧵⬇️
#rstats #dataviz
This thread is based on my newest blog post. Find it at albert-rapp.de/post/2022-03-2…

There, you will find more details and ressources than I can offer in this thread.

The blog post was inspired by a makeover video by storytelling with data. You can find it at
This is our original dataviz. We will improve it to tell a compelling story.
First, use the y-axis for names. This is more legible when names are more than an id.

And make sure that geom_col() understands that your labels are categorical (watch our for numeric IDs).

Also, use the group- rather than the fill-aesthetic. Reserve colors for storytelling.
Add a reference point to put your data into perspective. A simple average can be enough.
Order your bars for a quicker overview:

Important quantities on the left and right for easy comparison.

Order in increasing or decreasing order from top to bottom.

factor() and fct_reorder() get the job done for you
Bring color into play to highlight your story points.

Protip: Save your colors as variable names. You will try out different colors at some point. Copy and pasting the new color into multiple parts of your code is tedious.
Get rid of axes expansion to avoid clutter (the lines from id to bar).

Also, allow drawing outside the grid panel to set the stage for more changes to come.

coord_cartesian() is the place to make these changes.
Move and format axes. Get the x-axis to the top so that the readers see it early on. This is handled with scale_x_continuous().

The axes label does not need to be highlighted. Greying them out works too. theme() and element_*() functions will do that for you.
Align labels.

The SWD look likes to create clear lines through aligning plot elements. Also, the SWD look emphasizes that important things like axes title are prominently visible in the top left corner.

Shuffle the labels with more of theme().
Align manually.

The axes labels can only be set to a single hjust value in theme(). But to create "clear" lines 0% should be left-justified and 100% should be right-justified.

An annotation draws the labels manually. This works because we allow drawing outside the grid.
Label your story elements directly.

Extract the highlighted data and use geom_text() for labelling.
Since we allowed drawing outside the grid, we can also annotate a description of the bars.

This time, we need a richtext annotation from the ggtext package because we want to highlight a single word.
The bar plot is done. Time to add the story texts.

First, create a text-only plot. Using richtext once again, we can format the text in any way we want.
Finally, put together your bar plot and text-only plot with patchwork.

Use the (sub)title of your composed plot to add call to action messages.

Don't forget that with ggtext and theme() you can enable Markdown syntax for easier text formatting.
One final tip to get the text sizes right.

Use the camcorder package at the start of your visualization process. This will create and show plots as png-files in the dimensions you want.

This is not a definite cure for tedious size struggles but I found that it helps a bit.
If you enjoyed this summary, check out the full blog post at albert-rapp.de/post/2022-03-2…

It contains way more details and some further ressources.

And if you want to see more of my content, stay in touch by following @rappa753.

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More from @rappa753

Sep 9, 2023
Three steps to use color in your title instead of wasting space on a huge legend. Image
1 // Wrap your subtitle into <span> tags

These span-tags are HTML notation for inline text. So in principle, adding them should change nothing.

But as you can see, it does have an impact.
Image
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2 // Enable HTML notation

The problem is that ggplot does not know that you want to use HTML notation.

So, enable that with element_markdown() from the {ggtext} package in theme.

This will render the span-tags instead of displaying them as text:
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Read 7 tweets
Aug 26, 2023
Paired bar charts suck at comparing values. The only reason they're used all the time is because they are easy to create.

But there are better alternatives that are just as easy.

Here's how to create 4 better alternatives with #rstats. Image
0 // Where's the code?

The code for all plots can be found at

This thread walks you through the code quickly.albert-rapp.de/posts/ggplot2-…
1 // Dot plot

Instead of using bars next to each other, why not points on the same line?

Makes comparison suuper easy.

And it takes only a geom_point() layer. Dead-simple, right?

I think it's even easier to create than a paired bar chart.
Image
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Read 15 tweets
Aug 19, 2023
R makes it dead-simple to use some of the most effective dataviz principles.

Here are six principles that are so easy that any ggplot beginner’s course should teach them.
1 // Make sure your labels are legible

Too many plots use waaaay too small texts.
With ggplot, it just takes one line to fix this.

Img 1: Way too small fonts & unclear labels
Img 2: Fixed with labs() and theme_gray(base_size = 20)
Img 3: Full code

Image
Image
Image
2 // Use a minimal theme

As a rule of thumb, you should minimize everything that could potentially distract your audience.

That’s why I usually recommend to use a minimal theme: Just use `theme_minimal()` instead of `theme_gray()`. Image
Read 9 tweets
Jun 17, 2023
Need to extract days, months, years or more from time data?

Don't compute them all manually with {lubridate}. That's way too tedious.

The {timetk} package has a nice function that does all the heavy lifting for you.

LEFT: {lubridate} workflow
RIGHT: {timetk} workflow
#rstats ImageImage
BONUS: Maybe you don't want use all of the stuff that {timetk} computes for you.

Here's a simple function that extracts only the parts you want.

All of the code can be found on GitHub at gist.github.com/AlbertRapp/2c9… Image
Also, shoutout to @EatsleepfitJeff for teaching me about this function from {timetk} ☺️
Read 4 tweets
Jun 10, 2023
Everybody loves colors but only few know how to use them well.

With the right guidelines, using colors becomes super easy.

Let me show you how to implement these guidelines with ggplot 🧵
#rstats
Anyone can create a stacked bar chart with ggplot.

But that can end up in a colorful & messy plot.

Let's implement a couple of guidelines from this datawrapper blog post to level up our color game blog.datawrapper.de/10-ways-to-use… Image
The key is to reduce the amount of colors and leverage the `alpha` aesthetic as well. Image
Read 11 tweets
Jun 7, 2023
Sometimes people ask me if I can do one-on-one R tutoring.

Sure I can. But then my hourly rate applies. And there are many amazing *free* resources. Want to try them first?

Here are a few that I recommend. #rstats
1 // Yet Again: R + Data Science

Find it at yards.albert-rapp.de

I'll start with one of my own bc I assume that you like my style (otherwise why ask me?)

Beware though: YARDS is a graduate-level course that I taught for math students w/ a bit of programming experience. Image
2 // R for Data Science

To me this book is like the R bible. It introduced me to the so-called tidyverse and taught me much of what I know.

This one starts out slow and is really beginner-friendly

r4ds.had.co.nz
Read 11 tweets

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