This summer I crossed the alps with my road bike. I’ve recorded the whole ride and as a nice memory, I would like to visualise this ride. A short time ago I’ve discovered the awesome R package drake. The use of this package transformed the way I do my analysis and it helps me to make my post more reproducible. The following blog post describes the underlying workflow, after which I’ve developed the underlying package transalp for this post.
Recently I came across an excellent talk about the gganimate package. In order to try it out, I’ve decided to collect and analyze some data from one of my favorite sporting events: Le Tour de France. In this post, I will describe how to get the data using the rvest package. After collecting the data, I will describe how to create animations using the gganimate package. At first, load all important libraries.
There is a new package in the tidyverse ecosystem for modeling. Like the tidyverse, the new tidymodels package is a collection of packages. It follows the same basic underlying principles of the tidyverse package, but the central topic of this collection of packages is modeling. Since it’s not around for long, I decided to give it a go on my own datasets. As a rough guideline I followed the great blog posts from Julia Silge’s blog.
In this post, I want to explore my hardest Strava rides. By ‘hard’ rides, I am talking about the activities with the highest positive altitude, that I had to overcome. As a visualisation technique, I wanted to try out a so called ‘Ridge Plot’. These type of plots resamble the iconic cover art for Joy Division’s album Unknown Pleasures. First load all my strava activities from a private Github repository.
The TidyTuesday project is a weekly social data project in R, where participants get the chance to analyse a new dataset every week. The task is to wrangle and explore the data with the tools that R provides. This weeks TidyTuesday dataset is about Tour de France results. As a big Fan of the Tour, this is a good opportunity to create some interesting analysis. Data First load the needed libraries:
In this post, I want to visualise my bike ride to the Tour de France this summer. The Tour visited Alsace and the Vosges this summer, which is near my home town. I’ve tracked my trip with a GPS device and uploaded it to Strava. Data Load libraries and general settings. Already define the start and end date of my trip, to make it easier to find the relevant Strava activities later.
I am a vivid runner and cyclist. Since a few years, I’m recording almost all my activities with some kind of GPS device. I record my runs with a Garmin device and my bike rides with a Wahoo device. Both accounts get synchronized with my Strava account. I figured that it would be nice to directly access my data from my Strava account. In the following text, I will describe the progress to get the data into R.
In this post, I want to visualise some hiking paths near my hometown. The so called ‘Traufgänge’ are some panoramic tours, that pass through spectacular natural scenery along a steeply declining Albtrauf. This post will describe the progress to scrap, preprocess and visualise the data. The whole process is put together in one big drake plan. I will describe the most important functions, data frames and plots in more detail: