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Albert Rapp Profile picture
@rappa753
Jun 7 • 11 tweets • 5 min read Twitter logo Read on Twitter
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
3 // TidyTuesday Screencast

There is something magical about seeing data sets getting cleaned in real time. Learning directly from masters, teaches you so much.

An excellent resource for R screencast is rscreencasts.com
4 // A ModernDive into R and the Tidyverse

I've learned a lot about the computer-backed statistical side of data science from this one.

I recommend it as an accessible stats+R intro.

moderndive.com/index.html
5 // Tidy Modeling with R

If you're into machine learning, there's no way around this one.

To me this is like the best {tidymodels} intro that you can find out there.

tmwr.org
6 // tidymodels screencasts

Easiest way to start with {tidymodels} is to see Julia Silge use it.

On her blog she shares code and screencasts of different parts of {tidymodels} in action. juliasilge.com/blog/
7 // Big book of R

Finally, there's the big book of R. It collects a huge amount of resources.

If the previous resources were not your cup of tea, then you'll find something that works for you in the big book of R.

bigbookofr.com
8 // TidyTuesday

This one is not really a resource but a fun weekly challenge.

This concludes my small list of R resources. 🥳

If you found this thread helpful, feel free to leave a like below.

Want to see more R content? Feel free to follow @rappa753. See you next time 👋
If you liked this post, you may enjoy my 3-minute newsletter too.

Every week, my newsletter shares insights on
- R & dataviz,
- Shiny and web dev

Reading time: 3 minutes or less

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alberts-newsletter.beehiiv.com/subscribe

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More from @rappa753

Albert Rapp Profile picture
Jun 10
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
Albert Rapp Profile picture
May 31
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
Albert Rapp Profile picture
May 19
Ever heard of logistic regression? Or Poisson regression? Both are generalized linear models (GLMs).

They're versatile statistical models. And by now, they've probably been reframed as super hot #MachineLearning.

Brush up on their math with this thread. #rstats
Let's start with logistic regression. Assume you want to classify a penguin as male or female based on its

* weight,
* species and
* bill length

Better yet, let's make this specific. Here's a dataviz for this exact scenario. It is based on the {palmerpenguins} data set. Image
As you can see, the male and female penguins form clusters that do not overlap too much.

However, regular linear regression (LR) won't help us to distinguish them. Think about it. Its output is something numerical. Here, we want to find classes.
Read 26 tweets
Albert Rapp Profile picture
May 12
The best way to learn data analysis is to actually practice it.

Each week, the #tidyTuesday challenge gives you plenty of opportunity for this.

Don't know how to get started with the challenge? In case you missed it, I've put together an #rstats guide in January.
First, get the data.

Head over to the tidyTuesday's GitHub repo at github.com/rfordatascienc…

Just copy the code from the "Get the data" section. Image
Next, I suggest that you use the clean_names() function from the {janitor} package.

This will format the column names of your data set so that it's easier to work with.

Huge time saver! Image
Read 24 tweets
Albert Rapp Profile picture
May 5
I used to think tables are boring.

But they can be beautiful & engaging.

Here's a nice example from @infobeautiful.

It uses many eye-catching elements but you don't need them to create a great table.

Just stick to these guidelines 🧵#dataviz A huge table describing wha...
Let's start with a not so great table and improve it.

Here's a table I would have created just a few months ago.

Not so sexy, right? Let's clean that up. Image
1. Avoid vertical lines

The above table uses waaaay to many grid lines.

Without vertical lines, the table will look less cramped.

Have a look for yourself. Image
Read 16 tweets
Albert Rapp Profile picture
Apr 28
I hate code duplication. It's just a sure way to bloat code and do copy-and-paste mistakes 🙈

In Shiny, modules help me to avoid that.

BONUS: They move the app's logic to separate + reusable functions for cleaner code.

Here's how modules work. #rstats
Let's build an app that displays a scatterplot of two variables of a given data set.

Let's imagine that each data set needs its own page in our app. Here's how that could look. ImageImage
Every element of our app will need to get a unique ID. And we will need to repeat that for every data set.

For two data sets a non-modularized Shiny UI could look something like this.

Notice how I have to append "_iris" each time for the second tabPanel / data set. Image
Read 11 tweets

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