Analysis of German Fuel Prices with R

Keywords: Analytics, Marketing, tidyverse, purrr, ggplot2, rgdal, sp and more
Webpages: https://creativecommons.tankerkoenig.de (sic), https://www.openstreetmap.org/
We present an R-based analysis to measure the impact of different market drivers on fuel prices in Germany. The analysis is based on the open dataset on German fuel prices, bringing in many additional open data sets along the way.

• Overview of the dataset
  1. History, Legal framework and data collection
  2. Current uses in "price-finder apps"
  3. Structure of the dataset
  4. Preparation of the data
  5. A first graphical analysis
  • price levels
  • weekly and daily pricing patterns

• Overview of potential price drivers and corresponding data sources
  1. A Purrr workflow for preparing regional data from Destatis
  • Number of registered cars
  • Number of fuel stations
  • Number of inhabitants
  • Mean income, etc.
  2. Determining geographical market drivers with OSM data using sp, rgdal, geosphere
  • Branded vs independent
  • Location: higwhway, close to highway exit ("Autohof") etc.
  • Proximity to competitors, etc.
  3. Cost drivers
  • Market prices for crude oil
  • Distance of fuel station to fuel depot
  • Land lease and property-prices
  4. Outlook:
  • Weather
  • Traffic density

Based on this data, we will present different modelling approaches to quantify the impact of the above drivers on average price levels. We will also give an outlook and first results on temporal pricing patterns and indicators for competitive or anti-competitive behaviour.
This talk is a condensed version of an online R-workshop that I am currently preparing and which I expect to be fully available at the time of UseR 2017.