Albert Rapp Profile picture
May 31, 2022 10 tweets 6 min read Read on X
I am rebuilding my #rstats blog from the bottom up with #quarto. This will let me use quarto's cool new tricks like tabs and easy columns.

I've already spent hours using quarto's great docs to build a custom blog. If you want to do the same, let me show you what I did. ImageImage
Today, I will show you the first of many steps to your own quarto blog. First, create a new quarto blog project via RStudio.

Make sure to create a git repo as well. This lets you revert changes when you break your blog. You can follow along my repo at github.com/AlbertRapp/qua…
You can render your blog with `Render Website` from RStudio's `Build` tab.

The first easy changes happen in the `_quarto.yml` file.

1⃣ Set `theme: default`
2⃣ Name your blog via `title`
3⃣ Link your GitHub profile etc.

This will change the navbar at the top of your blog. ImageImage
Now, make the about page into a landing page.

This let's you introduce your blog's highlights to your readers before they are flooded by a list of your blog posts.

Rename 
`index.qmd` -> `blog.qmd` 
`about.qmd` -> `index.qmd`

& change reference of `about.qmd` in `_quarto.yml` ImageImage
Once again, adjust your blog's navbar. Go to `blog.qmd` and change `title` .

You see, in `_quarto.yml` you reference to websites or other quarto docs. These will show up in the navbar. In this case, the title of the quarto doc decides the title in the navbar. ImageImage
Create a new blog posts by creating a blank `.qmd`-file in the directory `posts/new`.

Here, I have populated the file's YAML header with a few basic properties. And I filled the file with a bit of text, code chunks, math parts etc.

You can find the file in my repo. ImageImageImage
The new post was rendered by clicking `Render Website` in RStudio's `Build` tab.

This will render all `.qmd`-files that have not been rendered before. If you make changes to a blog post later on, then you will have to render the respective `.qmd`-file manually.
During your active writing process, you can activate `Render on Save` in RStudio. Then, on every Ctrl+S your post will be rerendered.

Remember, all other blog posts remain unchanged by this. Let's take a look into the `posts/_metadata.yml` file to see why.
You can see that this file sets `freeze: true`. This means that all `.qmd`-files within the `posts` directory will only be RErendered if done explicitly.

In `_metadata.yml` we can define more YAML options for all blog posts. For example, we can generate tables of content. ImageImageImage
These were the first steps to build a #quarto blog from the bottom up. There is lots more to come.

We'll cover
- Custom colors/fonts/landing pages
- Comments
- Newsletter signups
- Build robust #rstats posts with {renv}
- More, more, more

Follow @rappa753 to not miss out.

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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
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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
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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 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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