Guten Tag! 👋 

Many greetings from Munich, Germany. I have this one rule: “When I advertise for my paid courses, I have to make sure that there’s free learnings for people who don’t want to or can’t buy the course yet.” 

Unfortunately, my last e-mail didn’t fit my standards. It had too little specific code that readers could benefit from. So today, during the last day where you can still use the “FRIDAY20” coupon to get 20%-off for all my courses, I’m giving you an extensive tutorial that

• takes you from Excel file

• to cleaned up data set

• to nice visualization

in no time. So let’s dive in.

Loading data into R

Today’s data is a fake data set that I’ve generated with fakery.dev. Let’s have a look at that Excel file.

With the {openxlsx} package, we can read the Excel file into R. Unfortunately, {openxlsx} works with data.frames but I like tibbles more so we’ll convert it immediately after loading.

Clean up names

Those column names are terrible. I prefer all lower caps and underscores between words. We can easily achieve that with the {janitor} package.

Convert texts to dates

Look at those dates! If you look closely, you’ll realize that they are not actually encoded as datetimes but instead are characters. That’s super annoying to work with. But the {lubridate} package has fantastic functions to convert texts to dates.

Extract days of the week

With that, another {lubridate} function lets us easily turn those dates into a week day.

Extract transaction types

By combining regular expressions and str_match() we can also process the type of transaction.

And by using title-case on our new column and removing superfluous columns, we now have a nice data set that we can visualize.

Showing the distributions

Now, let’s focus on the distribution of deposits and withdrawals. With stat_halfeye() we can quickly generate a chart that shows that.

Themes and labels

With basic theme changes and some meaningful labels we can make the chart much nicer.

Some more theme changes

Now, let’s get rid of the color legend and let’s also apply some small tweaks to the theme of the chart. All of that can be done in the theme() layer.

Use meaningful axes labels

Finally, it might be nice to reflect dollar amounts on the x-axis. We can easily achieve that with the {scales} package and the scale_x_continuous() layer.

And with that, we have a nice lookin’ chart which would probably show some nice trends if the data were not completely made up.

I hope you enjoyed this little tutorial. Of course, this was a whirlwind tutorial that goes over many techniques quickly. In my courses, I do things a lot more carefully. But I hate praising my own content, so I’ll just let this lovely feedback I’ve received this week do that:

Now, if you’re interested in leveling up your skills, you can still use the promo code “FRIDAY20” to get 20-off all courses. You can find them here:

Best,
Albert