Nicolas

2 minute read

Big dataset (Ok, not so big) df <- tibble::tribble( ~ID, ~city, ~lat, ~lon, 1172, "Zaria", 11.11128, 7.7227, 1173, "Oslo", 59.91273, 10.74609, 1174, "Masqat (Muscat)", 23.61387, 58.5922, 1175, "Bahawalpur", 29.4, 71.68333, 1181,"Islamabad",33.70351,73.059373, 1194,"Rawalpindi",33.6,73.0666667 ) df ## # A tibble: 6 x 4 ## ID city lat lon ## <dbl> <chr> <dbl> <dbl> ## 1 1172 Zaria 11.1 7.72 ## 2 1173 Oslo 59.9 10.7 ## 3 1174 Masqat (Muscat) 23.6 58.6 ## 4 1175 Bahawalpur 29.

Nicolas

8 minute read

Recently, I wanted to create a blogpost about simple mapping in R. It required some data (a shapefile), some spatial librairies and the R packages to use thoses libraries. Unfortunatly, the blogdown image that I was using at that time didn’t provided any of that. It was the generic one provided by the Blogdown guide. First reaction was to make a rendered html file. But the build was still cancelled and the html file was not deployed.

Nicolas

8 minute read

This post is based on a notebook I started about R spatial analysis for the project OSGeoLive It aims to provide a quick introduction to R spatial analysis and cartography and will be extended. R is a language dedicated to statistics and data analysis. It has also a lot of strong packages for spatial analysis. Recent packages like {sf} allows easy Simple Features manipulation. This document aims to complete the R Overview and R Quickstart.