Martin-Jung

# Test of Significance, takes the same arguments as t.test() .
signif.test <- function(x, ...) {
  p <- t.test(x, ...)$p.value
  
  # List of p excuses retrieved from http://mchankins.wordpress.com/2013/04/21/still-not-significant-2/
  p_excuses <- c(
    "(barely) not statistically significant <p>",
    "a barely detectable statistically significant difference <p>",
    "a borderline significant trend <p>",
    "a certain trend toward significance <p>",

Martin-Jung

---
title: "Reproducible R coding"
author: "Martin Jung"
date: "12.02.2015"
output:
  ioslides_presentation:
    highlight: zenburn
    highlighter: highlight.js
    hitheme: zenburn
  slidy_presentation:

Martin-Jung

Some ggplot tutorials available online:

• H. Wickham: Official ggplot2 documentation
• H. Wickham: ggplot2 book
• W. Chang: R graphics cookbook and Cookbook for R
• Z. Ross: Beautiful plotting in R: A ggplot2 cheatsheet
• D. Koffman: Introduction to ggplot2
• R. Saccilotto: Tutorial: ggplot2
• R. Hartman: How to format plots for publication using ggplot2
• G. Williams: Visualising data with ggplot2

Martin-Jung

# By Martin Jung (2015)
# http://conservationecology.wordpress.com/
# Takes a IUCN species id code as input, which
# can ether be queried from the api (http://api.iucnredlist.org/)
# or directly from the full database downloadable here:
# http://api.iucnredlist.org/index/all.csv

isForestSpecialist <- function (inputID) 
{
  print(paste("Now processing ID",inputID))

Martin-Jung

  [1] "Key farm variables: area, livestock (LSU), labour force and standard output (SO) by agricultural size of farm (UAA), legal status of holding and NUTS 2 regions"      
  [2] "Key variables: area, livestock (LSU), labour force and standard output (SO) by economic size of farm (SO in Euro), legal status of holding and NUTS 2 regions"        
  [3] "Key variables: area, livestock (LSU), labour force and standard output (SO) by type of farming (2-digit) and NUTS 2 regions"                                          
  [4] "Land use: number of farms and areas of different crops by agricultural size of farm (UAA) and NUTS 2 regions"                                                         
  [5] "Land use: number of farms and areas of different crops by economic size of farm (SO in Euro) and NUTS 2 regions"                                                      
  [6] "Permanent crops: number of farms and areas by size of permanent crop area and NUTS 2 regions"                                                      

Martin-Jung

# Include BAAD data
#http://www.esajournals.org/doi/abs/10.1890/14-1889.1
library(dplyr)
library(ggplot2)
fd <- tempfile()
download.file("http://www.esapubs.org/archive/ecol/E096/128/baad_data.zip",destfile = fd)
b <- unzip(fd)
data <- read.csv("baad_data/baad_data.csv",header=T) # the data
meta <- read.csv("baad_data/baad_dictionary.csv",header=T) # the column explanations
# Filter those out without an age

Martin-Jung

import os
import sys

import requests

 
basefname = 'Hansen_GFC2013_lossyear_%d%s_%03d%s.tif'
baseurl = 'http://commondatastorage.googleapis.com/earthenginepartners-hansen/GFC2013/%s'

def download(url, output):

Martin-Jung

# MJ 14/12/2015
#### Load or install packages ####
if(!require(devtools))install.packages("devtools");library(devtools)
if(!require(rAltmetric))install.packages("rAltmetric");library(rAltmetric)
if(!require(plyr))install.packages("plyr");library(plyr)
if(!require(ggplot2))install.packages("ggplot2");library(ggplot2)

# This script expects a csv file with title / year / journal / doi
refs <- read.csv("ScharlemannlabPublications.csv",header = T,stringsAsFactors = F)

Martin-Jung

# Arguments:
#  - model = a clmm model
#  - newdata = a dataframe of new data to apply the model to
# Returns a dataframe of predicted probabilities for each row and response level
fake.predict.clmm <- function(model, newdata) {
  # Actual prediction function
  pred <- function(eta, theta, cat = 1:(length(theta) + 1), inv.link = plogis) {
    Theta <- c(-1000, theta, 1000)
    sapply(cat, function(j) inv.link(Theta[j + 1] - eta) - inv.link(Theta[j] - eta))
  }

Martin-Jung

stat_smooth_func <- function(mapping = NULL, data = NULL,
                        geom = "smooth", position = "identity",
                        ...,
                        method = "auto",
                        formula = y ~ x,
                        se = TRUE,
                        n = 80,
                        span = 0.75,
                        fullrange = FALSE,
                        level = 0.95,