Topic 12 A mapview Map Demo
Since the each individual file size cannot be bigger that 25 MB, we only show a simple ’mapview` map so we can we can interact with this type of maps.
mapview provides functions to very quickly and conveniently create interactive visualizations of spatial data. Its main goal is to fill the gap of quick (not presentation grade) interactive plotting to examine and visually investigate both aspects of spatial data, the geometries, and their attributes.
It can also be considered a data-driven application programming interface (API) for the leaflet package as it will automatically render correct map types, depending on the type of the data (points, lines, polygons, raster).
In addition, it makes use of some advanced rendering functionality that will enable the viewing of much larger data than is possible with leaflet.
mapview() - view (multiple) spatial objects on a set of background maps
viewExtent() - view extent / bounding box of spatial objects
viewRGB() - view RGB true- or false-color images of raster objects
mapshot() - easily save maps (including leaflet maps) as HTML and/or
png (or other image formats).
mapview() function can work for a quick view of the data, providing choropleths, background maps, and attribute popups. Performance varies on the object and customization can be tricky.
In this note, we will use three data sets to illustrate how to make maps with mapview(): state population, state population (50 state), and state capitals.
Choropleth Map with mapview - Demo
library(tigris)
library(mapview)
library(dplyr)
library(sf)
#invisible({capture.output({
## Download shapefile for all states into R in tigris
## states() is a function in library {tigris}
us_geo <- states(cb = F, resolution = '20m')
#})
#})## caution: need to tell R that GEOIS should be a character variable since
## the same GEOID is character variable in the shape file us_geo with
## some leading zeros!
pop_data <- read.csv("https://raw.githubusercontent.com/pengdsci/sta553/main/data/state_population_data.csv", colClasses = c(GEOID = "character"))
## merger two data use the primary key: GEOID.
all_data <- inner_join(us_geo, pop_data, by = c("GEOID" = "GEOID"))
##
mapview(all_data, zcol = "PctChange10_20")