Oscar Baruffa started the great initiative of collecting useful resources around R as the Big Book of R. In particular I enjoyed reading and working my way through the book Geocomputation with R by Robin Lovelace, Jakub Nowosad and Jannes Muenchow. In this blog post I would like to adapt their case study in Chapter 13 on Geomarketing from finding potential bike shop locations to wine shops.

In a recent article I read about the “Kiosk-Kultur” in Germany and the impact COVID-19 has on the sector. That motivated me to have a closer look on where in Germany the in the article mentioned 23.500 “Kioske” are located and how the relation of population to number of kiosks spreads across Germany. I will use {osmdata} and the {eurostat} R package to retrieve the Kiosk locations and NUTS3 boundaries as well as the population density.

Since the early start of the Corona pandemic German public broadcaster NDR Info airs the popular podcast with the virologist Christian Drosten. In addtion to the podcast itself the transcript of each episode is published on the broadcaster’s website. This allows to easily do some text processing and analysis on the podcasts’ content. First I wrote a script to scrape and transform the transcript of all podcast episodes to get a dataframe with the columns episode title, date, link to the transcript, episode no, interviewer, speaker of the transcript section and transcript section.

I thought the Beer Production data would be my favorite dataset for TidyTuesday, but this week’s Tour de France data from the excellent {tdf} package by Alastair Rushworth is going straigth to the top spot! The data mainly comes from Wikipedia and has all sort of data on TdF winners and stages. Being quite familiar with the Tour de France and its stats I’m less interested in stage lengths and winning margins, but I would like to analyse the development of internalisation of the Tour de France and cycling as a sport.

I have to say as a homebrewer this week’s #tidytuesday got me quite excited. 🍺 This is the first time I use the {ggdark} package to convert regular ggplot2 themes into a dark version. The data was mainly gathered from pdf report using {pdftools} and some {stringr} magic, therefore the data includes total rows and some missing and duplicate values. Before working on the visualization I will spend some time on cleaning the dataset to then answer the question “How did the total beer production per year develop over the last 10 years by brewery size?

Inspired by this question on Quora I wanted to dig into marathon results and in particular the “DNFs” (participants who started the marathon but didn’t make it to the finish line). I would have liked to do my little analysis on the results from the Berlin Marathon, but unfortunately I couldn’t find any results including the DNFs (if you have a hint where to find them please let me know 😄).

A bit late to the party but here it is: My contibution to week 7 #TidyTuesday. This week’s dataset is about hotel booking demand for two hotels located in Portugal published by Antonio, Almeida and Nunes, 2019. I would like to focus on visualizations and analysis between the two hotels: The city hotel is located in Lisbon, while the resort hotel is located in the region of Algarve. hotels %>% ggplot(aes(x=hotel, fill=hotel))+ geom_bar()+ labs(title = "Bookings in the dataset per hotel (Arrival date: 01.