Top Highlights

• book: the emergence of tidy and lazy data formats and structures for spatial and non-spatial data, to improve data manipulations, data wrangling and data handling supporting cleaner data science.
• tmap package for thematic mapping (Tennekes, 2015) and the sf package that includes both new data structures and tools for handling spatial data (Pebesma et al., 2016).
• although standard GIS packages and software provide tools for the visualisation of spatial data, their analytical capabilities are relatively limited, inflexible and cannot represent the state of the art.
• In geographical data, features are typically represented as points, lines or areas.
• This chapter uses a number of packages: raster, OpenStreetMap, RgoogleMaps, grid, rgdal, tidyverse, reshape2, ggmosaic, GISTools, sf and tmap.