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
History, Legal framework and data collection
Current uses in "price-finder apps"
Structure of the dataset
Preparation of the data
A first graphical analysis
price levels
weekly and daily pricing patterns
Overview of potential price drivers and corresponding data sources
A Purrr workflow for preparing regional data from Destatis
Number of registered cars
Number of fuel stations
Number of inhabitants
Mean income, etc.
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.
Cost drivers
Market prices for crude oil
Distance of fuel station to fuel depot
Land lease and property-prices
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.