In januaryadvisors/jaspatial: Make Spatial Analysis Easier For JA Employees

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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jaspatial

knitr::include_graphics("jaspatial_logo.png")

The goal of jaspatial is to set up your environment to conduct geospatial analyses at January Advisors and provide a repository of common functions we have developed.

Installation

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("januaryadvisors/jaspatial")

Set up your environment

When you start a geospatial analysis, load the jaspatial library and then load all the other common geospatial packages and set the correct options:

library(jaspatial)
load_geo_packages() 
#This will load: sf, tigris, tidycensus, mapview, 
  #leaflet, leaflet.extras, leaflet.extras2, rmapshaper,
set_geo_options() #Set the correct options for tigris downloads

Clean your data

You can also quickly clean and transform the projection of an sf object using clean_shape. The default projection that comes pre-loaded is wgs84. But you can use others, too.

clean_tx_counties <- clean_shape(tx_counties) #The default 

#Or change projection
utm14n <- st_crs("+proj=utm +zone=14 +ellps=GRS80 +datum=NAD83 +units=m +no_defs +towgs84=0,0,0")
clean_tx_counties_utm14n <- clean_shape(tx_counties, utm14n)

Using leaflet

This package allows you to get up and running in leaflet really quickly with ja_base_map. The one trick is that you need to add a mapPane option (='polygons') to any layers you want to appear underneath the map label names.

ja_base_map(.zoom_level = 9) %>% 
  addPolygons(
    data = clean_tx_counties,
    options = leafletOptions(pane = "polygons")
  )

januaryadvisors/jaspatial documentation built on Sept. 3, 2023, 4:22 p.m.