gisr

Geospatial Analytics Utilities

Installation

gisr is not on CRAN, so you will have to install it directly from rOpenSci or GitHub using the code found below.

## SETUP
  
  # Pre-requisits - gisr user rnaturalearthdata and rnaturalearthhires 
    remotes::install_github("ropensci/rnaturalearth")
    remotes::install_github("ropensci/rnaturalearthhires")

  #install from rOpenSci
    install.packages('gisr', repos = c('https://usaid-oha-si.r-universe.dev', 'https://cloud.r-project.org'))
    
  #alt: install from GitHub using pak
    #install.packages("pak")
    #pak::pak("USAID-OHA-SI/gisr")
    
  #load the package
    library(gisr)

## LIST TYPES OF STYLES INCLUDED WITH PACKAGE
  ls("package:gisr")

Get administrative boundaries

Admin boundaries + neighbor countries data from Natural Earth Data

library(tidyverse)
library(sf)
library(gisr) 

zambia0 <- get_admin0("Zambia") 
zambia1 <- get_admin1("Zambia") 

zambia_neighbors <- geo_neighbors("Zambia") 

ggplot(data = zambia_neighbors) +
        geom_sf(fill = NA) +
        geom_sf(data = zambia1, fill = gray(.92), lty = "dashed") +
        geom_sf(data = zambia0, fill = NA) +
        geom_sf_text(aes(label = sovereignt), size = 3) +
        theme_void()

Plot a terrain map

Create a terrain map with vector + raster data from local DEM Tiff file

library(tidyverse)
library(sf)
library(gisr)

dem_dir <- "./GIS"

z_map1 <- terrain_map("Zambia", terr_path = dir_terr)

print(z_map1)

z_map2 <- terrain_map("Zambia", terr_path = dir_terr, add_neighbors = TRUE) 
    
print(z_map2)

Plot an admin map

Create an administrative map with vector data from RNaturalEarth

library(systemfonts)
library(tidyverse)
library(sf)
library(glitr)
library(gisr)

z_map1 <- admins_map("Zambia")

print(z_map1)

z_map2 <- admins_map("Zambia", add_neighbors = TRUE) 
    
print(z_map2)

Apply SI Style Guide to maps

Create an administrative map with vector data from RNaturalEarth and apply SI Style

library(systemfonts)
library(tidyverse)
library(gisr)
library(sf)
library(glitr)

sfdf <- gisr::get_admin1("Nigeria") %>%
 select(name) %>%
 mutate(value = runif(nrow(.), 0, 1))

ggplot() +
   geom_sf(data = sfdf,
           aes(fill = value),
           color = grey10k,
           size = .1) +
   scale_fill_si(palette = "genoas",
                 discrete = FALSE,
                 limits = c(0, 1),
                 labels = scales::percent) +
   labs(title = "NIGERIA - % OF PLHIV BY STATE",
        subtitle = "States from XYZ Region are the most hit by HIV/AIDS",
        caption = base::paste0("Produced by OHA/SIEI/SI, ON ", base::Sys.Date())) +
  si_style_map()
  

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Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development. All errors remain our own.