#' Species distribution
#'
#' @description
#' Methods for analysing species distributions on a global scale
#'
#' @details The package provides usefull functions for species analysis on a
#' global scale.
#'
#' @name speciesdist
#'
NULL
# Function for processing data (just as test) ----------------------------------
#'
#' @export speciesdist
#' @rdname speciesdist
#'
speciesdist <- function(){
library(data.table)
inpath <- "D:/active/juergen/"
datapath <- paste0(inpath, "data/")
datapath_gbif <- paste0(datapath, "gbif/")
datapath_rdata <- paste0(datapath, "rdata/")
# Read GBIF for the first time -------------------------------------------------
# Split GBIF into chunks and load them into a data.table afterwards.
# Splitting is done because there was an error once regarding the correct column
# numbers.
infile <- paste0(datapath_gbif, "gbif_PTERIDOPHYTA.txt")
splitLTF(infile, sep = "\t")
infiles <- list.files(datapath_gbif, pattern = glob2rx("gbif_chunk*.txt"),
full.names = TRUE)
relevant_cols <- c(1, 63, 70, 71, 72, 78, 79, 93, 100, 157, 164, 173, 182,
210, 213, 214, 215, 216, 217, 218, 219, 220)
gbif <- readLTF(infiles, sep = "\t", rlvt_cols = relevant_cols)
for(i in seq(40)){
print(dim(gbif[[i]]))
}
save(gbif, file = paste0(datapath_rdata, "gibf_01_initial_input.Rdata"))
# Clean data set ---------------------------------------------------------------
preprocess <- FALSE
modulepath <- paste0(inpath, "scripts/specdist/src/")
# datapath_processed <- paste0(datapath, "processed/")
# datapath_climate <- paste0(datapath, "climate/")
# datapath_world <- paste0(datapath, "world/")
src <- list.files(modulepath, full.name = TRUE)
src <- src[-grep("src/specdist.R", src)]
sapply(src, function(x){source(x)})
library(countrycode)
library(maps)
library(maptools)
library(geosphere)
library(raster)
library(rgdal)
library(rgeos)
library(sp)
# Preprocessing of GBIF and other data -----------------------------------------
}
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