Martin-Jung

# load packages
library(prioritizr)
library(prioritizrdata)
library(terra)
library(microbenchmark)

# import planning unit data
wa_pu <- get_wa_pu()

# preview data

Martin-Jung

# load packages
library(prioritizr)
library(prioritizrdata)
library(terra)

# import planning unit data
wa_pu <- get_wa_pu()

# preview data
print(wa_pu)

Martin-Jung

countryname	code	biogeoregion
Albania	Alpine	Alpine Bio-geographical Region
Albania	Mediterranean	Mediterranean Bio-geographical Region
Andorra	Alpine	Alpine Bio-geographical Region
Andorra	Mediterranean	Mediterranean Bio-geographical Region
Austria	Alpine	Alpine Bio-geographical Region
Austria	Continental	Continental Bio-geographical Region
Austria	Pannonian	Pannonian Bio-geographical Region
Belgium	Continental	Continental Bio-geographical Region
Belgium	Atlantic	Atlantic Bio-geographical Region

Martin-Jung

################# other format ##################
#' @param x [`Raster-class`],
#'   [sf::st_sf()],
#'   [`SpatialPolygonsDataFrame-class`],
#'   [`SpatialLinesDataFrame-class`],
#'   [`SpatialPointsDataFrame-class`],
#'   [data.frame()] object,
#'   [numeric()] vector, or
#'   [matrix()] specifying the planning units to use in the reserve
#'   design exercise and their corresponding cost. It may be desirable to

Martin-Jung

#### 1. Sign up at GitHub.com ################################################

## If you do not have a GitHub account, sign up here:
## https://github.com/join

# ----------------------------------------------------------------------------

#### 2. Install git ##########################################################

## If you do not have git installed, please do so: 

Martin-Jung

Table of Contents

1. Table of Contents
2. General Usage
3. File Spacing
4. Numbering and Calculations
5. Conversion and Substitution
6. Selective Printing
7. Selective Deletion

Martin-Jung

import os
import subprocess
import rasterio


def extract_bands(file, bands=None):
    dataset = rasterio.open(file)
    if bands is None:
        bands = range(1, dataset.count + 1)
    elif isinstance(bands, int):

Martin-Jung

as.data.table.raster <- function(x, row.names = NULL, optional = FALSE, 
                                 xy = FALSE, inmem = canProcessInMemory(x, 2), 
                                 ...) {
  stopifnot(require("data.table"))
  if(inmem) {
    v <- as.data.table(raster::as.data.frame(x, row.names=row.names, 
                                             optional=optional, xy=xy, ...))
    coln <- names(x)
    if(xy) coln <- c("x", "y", coln)

Martin-Jung

---
title: "R Notebook"
output: html_document
---

```{r echo=FALSE, warning=FALSE, message=FALSE}
library(tidyverse)
library(leaflet)
library(mapview)
library(sf)

Martin-Jung

SS,SSB,SSBS,Sample_start_earliest,Sample_end_latest,Max_linear_extent,Landcover_sampling,Landcover_postsampling,Years_before_change
AD1_2001__Liow 1,AD1_2001__Liow 1 ,AD1_2001__Liow 1  5,1999-03-01,1999-07-03,2e3,Forest,Urban,13
AD1_2001__Liow 2,AD1_2001__Liow 2 ,AD1_2001__Liow 2  5,1999-03-01,1999-07-03,2e3,Forest,Urban,13
AD1_2006__Blanche 2,AD1_2006__Blanche 2 ,AD1_2006__Blanche 2  3,2005-01-01,2005-12-31,222.80710269545455,Forest,Agriculture,3
AD1_2006__Blanche 2,AD1_2006__Blanche 2 ,AD1_2006__Blanche 2  5,2005-01-01,2005-12-31,222.80710269545455,Shrubland,Forest,3
AD1_2006__Marshall 1,AD1_2006__Marshall 1 B,AD1_2006__Marshall 1 B 23,2003-06-04,2003-07-08,97.46794345,SparseVegetation,Grassland,4
AD1_2006__Marshall 1,AD1_2006__Marshall 1 E,AD1_2006__Marshall 1 E 26,2003-06-04,2003-07-08,154.11035005,SparseVegetation,Grassland,4
AD1_2006__Marshall 1,AD1_2006__Marshall 1 E,AD1_2006__Marshall 1 E 5,2003-06-04,2003-07-08,145.77379735,Agriculture,Grassland,4
AD1_2006__Marshall 1,AD1_2006__Marshall 1 L,AD1_2006_