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# voluModel Methods in Ecology and Evolution Code Supplement
# R version: 4.1.2 (2021-11-01)
# Platform: x86_64-apple-darwin17.0
#
# Figure 1 ----
library(voluModel) # Because of course
library(ggplot2) # For fancy plotting
library(rgdal) # For vector stuff. Will eventually be replaced with sf.
library(raster) # For raster stuff. Will eventually be replaced with terra.
library(viridisLite) # For high-contrast plotting palettes

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library(raster)
library(dismo)
library(readr)
library(BioCalc)

workingDirectory <- "USER_WORKING_DIRECTORY/data/inputs/"
setwd(paste0(workingDirectory, "PaleoClimateRaw/"))

# Read in data and process to rasters ----
precipFiles <- list.files(pattern = "precip")

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install.packages(c('ape', 'geiger', 'phytools', 'RColorBrewer', 'plotrix', 'diversitree', 'OUwie', 'fishtree', 'picante', 'caper'))

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#Post-processing complementary script
#========================================================
#author: Hannah Owens
#date: 13 March, 2018

#Set the working directory
#====================================
setwd("~/Dropbox/ENMSeminar/Labs:Homeworks/Lab9/Data/"); #Change to YOUR working directory

library(raster);

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library(raster); #For raster-based loading, calculations, and mapping
library(rgdal); #For reading the M polygon

setwd("~/Dropbox/ENMSeminar/Labs:Homeworks/Lab5/Lab5Data/WorldClimTerrestrial/");
  #Navigate to folder containing environmental data

#Loading a raster stack
envtList <- list.files(pattern = ".asc"); #Gets a list of .asc files
envtStack <- stack(envtList); #Reads in .asc files as a raster stack
crs(envtStack) <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0" #Defines projection of layers

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library(spocc); #For getting georefernces
library(raster); #For loading and manipulating rasters
library(scrubr); #Package introduced for data cleaning
library(spatstat); #Spatial statistics package with method for calculating nearest neighbor distance

setwd("~/Dropbox/ENMSeminar/Labs:Homeworks/Lab4/"); #Tell R where things go.

#Get your data using SPOCC
spoccCod <- occ(query = "Gadus morhua", from = c('gbif', 'bison', 'inat', 'ecoengine', 'vertnet', 'idigbio', 'obis'), has_coords = T, limit = 100000000);
  #Make sure you customize this to take advantage of all of the appropriate databases for your localities.

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library(rgbif);
library(spocc);

setwd("~/Dropbox/ENMSeminar/Labs:Homeworks/Lab3/"); #Tell R where to put things.

#RGBIF
#This package works specifically for querying the GBIF database.
rGBIFSquirrels <- occ_search(scientificName = "Sciurus niger shermani", hasCoordinate = T);
  #Searches all GBIF records for squirrels, only returns occurrences with coordinates
head(rGBIFSquirrels$data);

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#This is the R script for Lab 2 part 1. We will walk you through
#how to do some very basic geospatial operations in R.

#Load the libraries you'll need.
library(raster); 
library(maptools);
library(rgdal);

#Set the working directory to the location of the data you
#downloaded for Lab 2. This tells R where to read and write

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# For niche modeling
# ===================================================
# For loading, calculations, and mapping of spatial data
install.packages("terra")

# For data cleaning
install.packages("dplyr")

# For calculating nearest-neighbor distance
install.packages("spatstat")

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#For comparative phylogenetics
#===================================================
install.packages('ctv');
library('ctv');
install.views('Phylogenetics');
update.views('Phylogenetics');

#For niche modeling
#===================================================
#For raster-based loading, calculations, and mapping