animation

Revisited: Animating 2020 running goals - and compare them with previous years

Picking up the pieces Recently, I’ve animated a specific running goal for one year with data from the Strava API. I thought it would also be nice to compare different years within the same animation - in a way you could say that you’re racing yourself over several years. This only needs some minor modifications to the code I posted before. Data collection stays basically the same, so I’m just re-posting this here.
'Running through the fields, lying flat on the ground': Animating 2020 running goals

'Running through the fields, lying flat on the ground': Animating 2020 running goals

Full disclosure: I did not even have any running goals for 2020. But as it turned out: It has been a good year for running - at least one thing this year was good at. In this post, I want to run you through my R script which creates an animated graph that looks like I had the goal of running 500 kilometres this year (spoiler: I didn’t). This is what the finished graph looks like (for 2020-12-23):
Let the snail crawl: Animated density curves

Let the snail crawl: Animated density curves

Previously, I’ve plotted a ridgeline based on a variable’s density through time. It might look nice but it’s quite obvious that time can be visualized in a more fitting way - by time itself, in an animated plot that is. So, let’s fire up the {gganimate} package again. My goal is to show a moving kernel density curve as it moves through time, based on a moving window of 30 days sliding from the past to the present.

‚Arrest this man, he talks in maths‘ - Animating ten years of listening history on Last.FM

Previously, when Rcrastinate was still on blogspot.com, I had a first look at ten years of my playback history on Last.FM. But there is still a lot one can do with this dataset. I wanted to try {gganimate} for a long time and this nice longitudinal dataset gives me the opportunity. Loading and preparing the data First, I am loading the dataset. I already did some preparations like extracting the top five tags for each track and some other stuff I used in my previous entry.