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# -----------------------------------------------------------------------------
# Example to check some taxa names with Taxonstand & wikitaxa
# -----------------------------------------------------------------------------
my_sp <- fread("Output/taxa_to_check_VS.csv")
my_sp
# taxa
# Thereianthus_spicatus
# Eriocnema_fulva
# Hebe_macrocarpa
# Aspalathus_cymbriformis

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library(ggplot2)
library(ggsci) # for cool palettes

# read data
my_data <- read.csv("data/chickengrowthdiet.csv")
str(my_data)
# Diet should not be numeric but factor (or categorical)
my_data$Diet <- factor(my_data$Diet)

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

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#####################################################
# CI-s for linear model with ggplot
#####################################################

library(ggplot2)

# -------------------------------------
# Use simulated data from Mick Wu
# -------------------------------------
set.seed(1)

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setwd("share/")
getwd()
my_data <- read.table("data/drunkwalks_data.txt", header = TRUE)

plot(my_data)

aggreg <- table(my_data$SEX, my_data$DOMINANT_SIDE)
rownames(aggreg) <- c("female", "male")
colnames(aggreg) <- c("left-handed", "right-handed")

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# Example of using foreach parallel loop
# This should work both on Linux and Windows without any modifications.

# for an interesting intro, check:
# http://bgc.yale.edu/sites/default/files/ParallelR.html

library(foreach)
library(doParallel)

# Prepare cluster

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require(rgeos)
require(sp)
require(plotKML)

# Generate a random grid-points for a (almost) global bounding box
b.box <- as(raster::extent(120, -120, -60, 60), "SpatialPolygons")
proj4string(b.box) <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
set.seed(2017)
pts <- sp::spsample(b.box, n=100, type="regular")
plot(pts@coords)

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make_GeodesicBuffer <- function(XY.dg, dg.step=5, dst.m, crs){
    #######################################################################################################
    ## Function to make a circle-buffer around given points (long-lat coordinates)
    ## Is different from rgeos::gBuffer() by the fact that it allows the user to create 
    ## a geodesic buffer with a width expressed in metric units.
    ## Otherwise the user needs to project and apply an Euclidian buffer with rgeos::gBuffer(),
    ## which will introduce distortions that vary greatly with latitude and the radius of the circle buffer. 
    ## See also my question addressed here:
    ## https://gis.stackexchange.com/questions/250389/euclidean-and-geodesic-buffering-in-r
    ##

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# Read <table> HTML tag with {XML} library
# ====================================================

# Load library
library(XML)

# =======================
# Read the web page [accessed 08-Apr-2017]
# =======================
link <- "http://www.theplantlist.org/1.1/statistics/"

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# Read <table> HTML tag with {rvest} using CSS selectors
# ====================================================

# Load library
library(rvest)

# =======================
# Read the web page [accessed 08-Apr-2017]
# =======================
link <- "http://www.theplantlist.org/1.1/statistics/"

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# Read <div> HTML tag with {rvest} using CSS selector
# ====================================================

# Load library
library(rvest)

# -----------------------
# Read the web page
# -----------------------
link <- "http://www.adirondacklakessurvey.org/alscrpt.inc.php?alscpond=020225B&pname=ALLEGANY%20BROOK%20POND"