Albert Rapp Profile picture
Jun 10, 2023 11 tweets 5 min read Read on X
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
Once you have fewer colors. You can concentrate on a better choice. How about the color-blind safe okabe ito palette?

While you're at it. How about a better range of `alpha` values so that top and bottom blocks have the same transparency? Image
Now comes the hard part. Consolidate the two legends into one.

Overwriting the aesthetics in a guides() layer will do the trick. Image
There you go. Same information as in the beginning but less of a rainbow mess. 🌈

Still it would be nice to make adjacent blocks stand out.

No problem. Use the `color` aesthetic to add lines.

But wait. There is more 🥳 Image
Spaghetti plots are easy to create but hard to decipher.

This is a chance to shine for the {gghighlight} package. Image
{gghighlight} makes it dead-simple to emphasize parts of your plot.

And with a small annotation, we can immediately tell a story. ImageImage
Of course, this also works with more than just one color.

Once we get rid of too many colors, why not also get rid of other clutter like the legend? ImageImage
That's some efficient use of space and colors right there!

Finally, let's come full circle and apply the same labeling techniques to our initial bar plot. Image
That's a wrap 🥳

For the full code and more explanations, check out my blog post at albert-rapp.de/posts/ggplot2-…

Let me know if you enjoyed this thread, by hitting the like button on the start of this thread below. ☺️

For more posts like this, follow @rappa753

See you next time 👋

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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.
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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:
Image
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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
Image
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
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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 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
May 31, 2023
Data cleaning is tedious.

But it's much easier with the {janitor} package. Especially if you work with Excel files.

Here are 5 underrated features from {janitor}. #rstats
1 // Create clean names

This is absolutely the best function. It transforms column names such that they are easier to use for programming.

Left: Bad for programming
Right: Good for programming ImageImage
2 // Remove empty or constant cells from Excel files

Excel files can be messy to read in R. Lots of weird column names and empty cells.

{janitor} takes care of that for us. ImageImage
Read 8 tweets

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