h-a-graham

# An R script showing how to create a 3D night-time scene of the 'City of London' borough with {EAlidaR } and {Rayshader}

# If you don't already have the {EAlidaR package} Run:
# devtools::install_github('h-a-graham/EAlidaR')

library(EAlidaR)
library(raster)
library(sf)
library(tidyverse)
library(here)

h-a-graham

library(rstac)
library(sf)
library(raytrix)
library(rayshader)

# set region and search stac!
region <- st_bbox(st_buffer(st_point(c(-151.739,-16.501)), 0.063)) %>%
  st_as_sfc() %>%
  st_set_crs(4326)

h-a-graham

#' Buffer a point along a terrain surface.
#'
#' @param dtm A SpatRaster Digital Terrain Model
#' @param pnts A SpatVector of points
#' @param .dist Numeric - the buffer distance.
#' @param .res Numeric - approximate resampling resolution of the (internally
#' calculated) cost raster.
#' @param .smooth Logical default FALSE. Should the output buffer be smoothed using `smoothr::smooth`?
#' @param .method The smoothing method to use - see `smoothr::smooth` for details.
#' @param ... Passed to `smoothr::smooth` for eg. to control smoothness.

h-a-graham

#' @title get administritive outlines for a country
#' @description using the geoBoundaires API,
#' download the geojson outline of a country
#' @param country character vector of country names
#' @param admin_level character vector of admin levels to download
#' @return sf object of the outlines
#' @details check out the documentation for the geoboundaries API at:
#' geoBoundaries.org
#'
geo_bounds <- function(country, admin_level = c("ADM0", "ADM1", "ADM2")) {

h-a-graham

#' download drive files from dribble with path column
#'
#' @param x a dribble with path column
#' @param .overwrite logical. Should files be overwritten
#' 
#' @return The original input dribble
#' @noRd
drive_down_files <- function(x, .overwrite = TRUE) {
  

h-a-graham

#https://twitter.com/AnalyticalEdge/status/1582206425585324034

library(ggplot2)
library(stars)
library(sf)
library(dplyr)
library(plotly)

.d <- dim(volcano)

h-a-graham

library(tibble)
library(purrr)

t1 <- tibble(`C1 Template` = sample(c(166:180), 10),
             `C2 Template` = sample(c(120:130), 10))

t1
#> # A tibble: 10 × 2
#>    `C1 Template` `C2 Template`

h-a-graham

library(vapour)
library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.3, PROJ 8.2.0; sf_use_s2() is TRUE
#> WARNING: different compile-time and runtime versions for GEOS found:
#> Linked against: 3.10.2-CAPI-1.16.0 compiled against: 3.10.1-CAPI-1.16.0
#> It is probably a good idea to reinstall sf, and maybe rgeos and rgdal too
library(rstac)

mpc_dem <- function(aoi, src = c("cop-dem-glo-30", "alos-dem")) {

h-a-graham

 library(raster)
#> Loading required package: sp
#> Warning: no function found corresponding to methods exports from 'raster' for:
#> 'area'
 
 circ_matrix <- function(n){
   if (!n %% 2){
     stop("n must be an odd number.")
   } 

h-a-graham

library(sf)
library(dplyr)
library(mapview)
mapviewOptions(fgb = FALSE)
plts <- read_sf('data/PlotData/KuamutLocations.shp') 
pnts <- read_sf("data/Kuamut_2022_NewGPS_Points_Shapefiles/PlotCentre.shp")

pnts_clean <- pnts %>% 
  filter(is.na(NUMBER)|NUMBER!=100) %>%
  mutate(nearest = st_nearest_feature(., plts),