Adam H. Sparks adamhsparks

This gist shows in two steps how to tilt and stack maps using ggplot2 in order to create an image like this one: [![enter image description here][1]][1]

Let's load the necessary libraries and data to use a reproducible example:

# load libraries
  library(rgeos)
  library(UScensus2000tract)
  library(ggplot2)

	# 05/01/2017
	# This script has been superceded by the getCRUCL2.0 R package which offers extended functionality
	# see the GitHub repository for this package for more:
	# https://github.com/adamhsparks/getCRUCL2.0

	##############################################################################
	# title : Download and create raster stack CRU CL2.0 Data.R;
	# purpose : create R raster stack of CRU CL 2.0 data;
	# producer : prepared by A. Sparks;
	# last update : in Los Baños, Laguna, PI May 2014;

	##############################################################################
	# title : Compare_EPIRICE_Outputs.R;
	# purpose : Compare output from EPIRICE when using 1º and 0.25º data;
	# producer : prepared by A. Sparks;
	# last update : in Los Baños, Laguna, Jun. 2014;
	# inputs : EPIRICE output from 2001-2008;
	# outputs : histograms and graphs of comparison for leaf blast at 1º and 0.25º;
	# remarks 1 : ;
	# Licence: : GPL2;
	##############################################################################

	# Create some sample data
	CV_1 <- 0.2
	CV_2 <- 0.3
	Mean <- 65
	sigma_1 <- sqrt(log(1 + CV_1^2))
	mu_1 <- log(Mean) - sigma_1^2 / 2
	sigma_2 <- sqrt(log(1 + CV_2^2))
	mu_2 <- log(Mean) - sigma_2^2 / 2
	q <- c(0.25, 0.5, 0.75, 0.9, 0.95)
	Q <- data.frame(Quantile=paste0(100*q,"%ile"),

	# data from http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat
	# Originally seen at http://spatial.ly/2014/08/population-lines/

	# So, this blew up on both Reddit and Twitter. Two bugs fixed (southern Spain was a mess,
	# and some countries where missing -- measure twice, submit once, damnit), and two silly superflous lines removed after
	# @hadleywickham pointed that out. The result of the code below can be seen at http://imgur.com/nhVJqwk

	library(tidyverse)

	read_csv('../data/geostat-2011/GEOSTAT_grid_POP_1K_2011_V2_0_1.csv') %>%


	# cropsim
	# https://r-forge.r-project.org/scm/?group_id=335
	#
	# Potato late blight forecast models
	# Model name: Hyre
	# Hyre, R. A. 1954. Progress in forecasting late blight of potato and tomato. Plant Disease Reporter. 38: 245-253.

	# R implementation by Robert Hijmans, [email protected]
	# October 2009

	scatter_violin <- ggplot(data = dat, aes(x = condition, y = value)) +
	geom_violin(aes(fill = condition, color = condition)) +
	geom_crossbar(stat = "summary", fun.y = mean, fun.ymax = mean, fun.ymin = mean, fatten = 2, width = .5) +
	geom_point(color = "black", size = 1, position = position_jitter(w = 0.05)) +
	theme_minimal()


	# original: https://inattentionalcoffee.wordpress.com/2017/02/14/data-in-the-raw-violin-plots/