mapview - interactive viewing of spatial data in R

Installation

The easiest way to install mapview is to use library("devtools"). With devtools installed and loaded type the following:

For the stable release of mapview:

install_github("environmentalinformatics-marburg/mapview")

For the devolpment version:

install_github("environmentalinformatics-marburg/mapview", ref = "develop")

Introduction

Working with spatial data in R I find myself quite often in the need to quickly visually check whether a certain analysis has produced reasonable results. There are two ways I usually do this. Either I:

1. (sp)plot the data in R and then toggle back and forth between the static plots (I use RStudio) or
2. save the data to the disk and then open in QGIS or similar to interactively examine the results.

Both these approaches are semi-optimal. Where option 1. is fine for a quick glance at a coarse patterns, it lacks the possibility to have a closer look into the results via zooming and panning. While option 2. provides the interactivity, the detour via the hard disk is annoying (at best), especially when fine-tuning and checking regularly.

I attended this years useR2015! conference in Aalborg (which was marvelous!) and attended the session on interactive graphics in R where Joe Cheng from RStudio presented the leaflet package. leaflet is great as it gives you a great deal of control over the individual map components. This, however, also means that in order to get some useful visualization of spatial objects, we need to do a fair bit of coding for the map components to show what we want (e.g. we would need to provide popup-text manually for each object that we want to map). What an interactive functionality would need is some default behaviour for different objects from the spatial universe.

This got me thinking and sparked my enthusiasm to write some wrapper functions for leaflet to provide at least very basic interactive graphing capabilities that are directly accessible within RStudio (or the web browser, if you’re not using RStudio).

The result is a package called mapview. The main workhorse function is mapView() (mind the capital ‘V’) and is currently defined for:

• Raster *objects (Layer, Stack, Brick) and SpatialPixelsDataFrame
• Spatial *objects (Points, Polygons, Lines and their DataFrame version)
• Satellite *objects (from the satellite package for working with satellite data)

Raster data objects

NOTE: similar to spplot() for Raster* objects mapView() has a maxpixels = argument to avoid long rendering times for large rasters. This is currently set to a default value of 500000 (which produces acceptable rendering times on my machine) and a suitable warning is printed for larger Raster objects.

library(mapview)
library(raster)

mapView(poppendorf[[10]])

mapView(poppendorf)

library(sp)

data(meuse.grid)
coordinates(meuse.grid) <- ~x+y
proj4string(meuse.grid) <- CRS("+init=epsg:28992")
gridded(meuse.grid) <- TRUE

mapView(meuse.grid, zcol = "soil")

Vector data objects

For spatial vector objects from the sp-package I would like to highlight the following arguments:

• zcol similar to SpatialPixelsDataFrame you can specify which column of the attribute table to view. If this is set to NULL (the default) all columns will be used.
• burst if set to FALSE (the default) one layer is rendered with information from all columns in the attribute table provided in pop-ups (triggered by clicking on the features). If set to TRUE one layer + legend for each column in the attribute table will be drawn. Again, depending on the number of columns, this may mean that not all legend will be visible.

data(meuse)
coordinates(meuse) <- ~x+y
proj4string(meuse) <- CRS("+init=epsg:28992")

# only one layer, all info in popups
mapView(meuse)