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Albert Rapp

@rappa753

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…

The key is to reduce the amount of colors and leverage the `alpha` aesthetic as well.

Read 11 tweets

Albert Rapp

@rappa753

Jun 7

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.

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

Albert Rapp

@rappa753

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

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.

Read 8 tweets

Albert Rapp

@rappa753

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.

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

@rappa753

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.

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!

Read 24 tweets

Albert Rapp

@rappa753

May 5

@infobeautiful

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

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.

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.

Read 16 tweets

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