khufkens

# Ghent air pollution analysis
# parametric t-tests
# non-parametric Mann-Whitney U test

# download and collate the data
pdf_data <- paste0(pdftools::pdf_text("https://klimaat.stad.gent/sites/default/files/nota_circulatieplangent_3.pdf")[2],
               collapse = " ")

# some regular expression moving about
subset <- gsub(',', ".", pdf_data)

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# Before you start install all the referenced packages below
# including the development release of ecmwfr you will also
# need the rnaturalearthdata in addition to the normal package
# To run the code copy and paste segments as this is still
# rough around the edges and requires user interaction to
# set your CDS API key.

# load libraries
if(!require(devtools)){install.packages("devtools")}
devtools::install_github("khufkens/ecmwfr")

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# Before you start install all the referenced packages below
# including the development release of ecmwfr you will also
# need the rnaturalearthdata in addition to the normal package
# To run the code copy and paste segments as this is still
# rough around the edges and requires user interaction to
# set your CDS API key.

# load libraries
if(!require(devtools)){install.packages("devtools")}
devtools::install_github("khufkens/ecmwfr")

khufkens

# Before you start install all the referenced packages below
# including the development release of ecmwfr you will also
# need the rnaturalearthdata in addition to the normal package
# To run the code copy and paste segments as this is still
# rough around the edges and requires user interaction to
# set your CDS API key.

# load libraries
if(!require(devtools)){install.packages("devtools")}
devtools::install_github("khufkens/ecmwfr")

khufkens

#!/bin/bash

# Insert PDF meta-data into exif header
# for pdf files with format author - year - title.pdf.
# This allows e-readers to query meta-data for proper
# display and sorting of authors and titles
# (note: relies on exiftool)

for filename in *.pdf; do  
  noext=`echo "${filename%.*}"`

khufkens

# map El Nino drought in the Amazon and Africa
library(raster)
library(tidyverse)
library(maps)
library(RColorBrewer)

# set colour theme
colours = brewer.pal(11,"RdBu")

# download CHIRP data

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# load required libraries
if(!require(devtools)){install.package("devtools")}
if(!require(daymetr)){devtools::install_github("khufkens/daymetr")}
library("daymetr")

# no delay calls
no_delay = unlist(lapply(1:100, function(...){
  
  # download data
  error = try(download_daymet(silent = TRUE))

khufkens

// years to process (from start year t0 to end year t1)
var t0 = "2001";
var t1 = "2014";

var LC = ee.ImageCollection('MCD12Q1')
.select('Land_Cover_Type_1')
.filterDate(t0.concat("-01-01"),t1.concat("-12-31"))
.median();

// Create a geometry representing an export region.

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#' @param species: genus species or genus
#' @param quiet: TRUE / FALSE provides verbose output
#' @keywords Tropicos, species distribution
#' @examples
#'
#' # with defaults, outputting a data frame with species distribution
#' # for Clematis
#' df <- tropicos.species.distribution()
#' # returns NA if no data are present
#' [requires the rvest package for post-processing]

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library(raster)
library(maps)
library(maptools)
library(sf)
library(scales)

# set coordinate systems
robinson = CRS(" +proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
latlon = CRS("+init=epsg:4326")