search

codyevers / envisionr

EnvisionR is a package for assembling Envision delta array output into vector data cubes and then viewing that data using a basic GIS interface.
MIT License
0 stars 0 forks source link

readme

EnvisionR

Cody Evers 2024-04-30

EnvisionR is a shiny application for exploring and extracting simulation data from Envision using a simple map interface.

Create R project

  1. Create a folder on computer in a easy to find location (e.g., documents)
  2. Open RStudio: Launch RStudio on your computer.
  3. Go to the File menu at the top left corner of RStudio, then select New Project
  4. Select Existing Directory and navigate to the folder you want to use.

This will create a new RStudio project with an .Rproj file in the specified directory. This file helps manage paths and settings specific to your project.

Install required packages

This script requires several packages: pacman, devtools, dplyr, tidyr, purr, glue, stars, terra, sf, data.table, xml2, shiny, shinyjs, and shinyWidgets. Type the following line into the R console to first install the package pacman, then use pacman to automate the installation of the other require packages.

if (!require("pacman")) install.packages("pacman")
pacman::p_load(devtools, dplyr, tidyr, purrr, glue) # data maniuplation
pacman::p_load(stars, terra, sf, data.table, xml2) # data structure
pacman::p_load(shiny, shinyjs, shinyWidgets) # shiny

Install EnvisionR

Download and install the EnvisionR package. Before we do, we’ll make sure we delete any version of EnvisionR that is already installed. Then we’ll use the install_github() from the devtools package to install the EnvisionR package from github. Final, we load EnvisionR using library(envisionr)

if(!require(envisionr)){
  devtools::install_github(
    repo = 'https://github.com/codyevers/envisionr', 
    build = FALSE)
}
library(envisionr)

Download demonstration datacubes

Load some of the datacubes that we’d like to explore. These are large files so are not included in the EnvisionR package itself. Rather, you’ll want to create a folder in your working directory called datacubes then download the following files from box and then move them to the datacubes directory. Download the 7 files in this box folder and place them in a folder called datacubes in your working directory. You can create a new folder if this folder doesn’t exist.

Load required reference data from the EnvisionR data

Load required reference data into memory includes the IDU data as well as boundaries, roads, and cities.

data('ref_data', package = 'envisionr')

Load datacubes into memory

Read datacubes saved in your datacubes directory into memory using load_datacube and assign each to its own object (e.g., dc1, dc2, etc).

dc1 <- load_datacube('datacubes/FTW-FF-HC_GFDL_Run0.datacube')
dc2 <- load_datacube('datacubes/FTW-FF-HD_GFDL_Run0.datacube')
dc3 <- load_datacube('datacubes/FTW-FF-LD_GFDL_Run0.datacube')
dc4 <- load_datacube('datacubes/FTW-NM-NO-GFDL_Run0.datacube')

Run EnvisionR

Run EnvisionR using run_envisionr by providing the datacubes we just read into memory.

run_envisionr(dc1, dc2, dc3, dc4)

Loading new data

Build datacubes from scratch. You’ve run Envision and taken the delta array csv file, placed it in a directory called deltaarrays in your working directory and renamed it to DeltaArra_example.csv. Read the csv into memory then convert the csv into a datacube called dc for 4 selected fields. Save the datacube to your datacubes directory for later use.

# read delta_array csv into memory (1 minute)
da <- read_delta_csv('/deltaarrays/DeltaArray_example.csv')

# list attributes contained within the delta array
unique(da$field)

# select fields to include in the datacube
fields = c('LULC_B','SIZE','CANOPY','LAYERS')

# build datacube (1 minute) - save as stars object
dc <- build_datacube(
  delta_array = da,
  fields = fields,
  run_name = 'test_run')

# save datacube object to the datacubes folder
save_datacube(dc, filename = 'datacubes/dc_test.datacube')

# load the datacube (same as above)
dc1 <- load_datacube('datacubes/dc_test.datacube')

# show datacube info
print(dc1)

Required data

This script requires several reference files which are saved in the data/misc folder .

Functions

Functions are organized in a series of R scripts