Several R packages provide an interface to query map services (Google Maps, Stamen Maps or OpenStreetMap) to obtain raster images …
I’m a big fan of open-source software for research. For example, R-statistics, Qgis, and Grass GIS are awesome programs. R can do any statistical tests and numerical modeling you can imagine; if there’s not a built-in function you can write … Continue reading →
Our 5th Cologne R user group meeting was the best attended meeting so far, with 20 members finding their way to the Institute of Sociology for two talks by Diego de Castillo on shiny and Stephan Holtmeier on cluster analysis, followed by beer and schnitzel at the Lux, a gastropub nearby.
Still going through the book Veterinary Epidemiologic Research and today it’s chapter 18, modelling count and rate data. I’ll have a look at Poisson and negative binomial regressions in R. We use count regression when the outcome we are measuring is a count of number of times an event occurs in an individual or group 
Thanks to a helpful SO-Answer I was able to download all CLC vector data (43 zip-files) programmatically:
require(XML)
path_to_files dir.create(path_to_files)
setwd(path_to_files)
doc urls
# function to get zip file names
get_zip_name
# function to plug into sapply
dl_urls
# download all zip-files
sapply(urls, dl_urls)
# function for unzipping
try_unzip
# unzip all files in dir and delete them afterwards
sapply(list.files(pattern = "*.zip"),...
I was Inspired from Ben Frys all Streets project. There he plotted all streets of the United States of America (about 240 million segments). I tried this first for the countries in Europe, France has about 22 million segments, with the goal to get an all street map of Europe. My data source originate from