timriffe

# instructions:

#1) Register for the Human Fertility Database (http://www.humanfertility.org)
#2) Download the complete zip file from this page: http://www.humanfertility.org/cgi-bin/zipfiles.php (it's the #one labeled "All types of HFD data"
#3) make a folder whereever you keep your data, and call it something memorable, like, 'HFD'
#4) unpack the HFD zip file, which will have a couple nested folders, the last of which contains the text files #for each variable/series.- move these into your HFD directory.

# then the following function ought to work just fine for now:

HFDget <- function(abbrev = TRUE, countries = FALSE, years = FALSE, NoCohortNAs = TRUE, path) {

timriffe

SqLexis <- function(Rates, 
		value = "ASFR", 
		col, 
		breaks, 
		guides = TRUE, 
		guide.int = 5, 
		legend = TRUE, 
		lab.breaks = NULL, 
		legend.args = NULL, 
		axis.args = NULL, 

timriffe

# the code executes cellular automata 2^(2^3) experiments under 2^3 neighbor rules. Where each rule determines whether a cell turns on or off (TRUE-FALSE). Code is semi-efficient in turns of vectorizing processes, but there is plenty of room for improvement. 

# first the rule matrix 8 rows (rules) by 3 columns (positions relative to middle)
ruleMatrix <- matrix(0,ncol=3,nrow=8)
ruleMatrix[2,3] <- 1
ruleMatrix[3,2] <- 1
ruleMatrix[4,2:3] <- 1
ruleMatrix[5,1] <- 1
ruleMatrix[6,c(1,3)] <- 1
ruleMatrix[7,1:2] <- 1

timriffe

# define a set of functions for iterating through the Game of Life, as described (err, as I understood it) by Scott Page in the 2012 Model Thinking course:

# queenNeighbors() 
# given the status of the cell located and index 'ind'
# in matrix 'M' with 'n' columns, what are the statuses 
# of its neighbors 
# *neighborhoods to NOT wrap left and right but they DO wrap up and down...
queenNeighbors <- function(ind,M,n){
	inds <- ind+outer(c(-1,0,1),c(-n,0,n),"+")
	M[inds[inds > 0 & inds <= length(M) & inds != ind]]

timriffe

# here's my hacky code for a Schelling segregation model. 

# assumptions I've made in order to make life easier:
# 1) vacant cells have no influence. If a person happens to be totally surrounded by vacant cells, 
#    they don't move
# 2) when a person moves, they have no info about where they're going. This saves tons of computations
#    about the preferability of all the vacant plots
# 3) a person knows perfectly well if their threshold is met or not (threshold is here is the 
#    minimum proportion same)
# 4) all movers have the same chances of landing in any of the vacant plots.

timriffe

# Author: Tim Riffe: tim.riffe@gmail.com

# A basic implementation of the standing ovation model in R.
# The first part walks through the components, explaining the 
# inputs and what's going on. It's a full example, start to finish
# this is follwed by a function StandingOvationSimple(), that just takes 3 inputs 
# (with some other optional parameters) and will animate the process for you
# you may wish to modify the function to return a series of statistics taken
# along the progression of the model. Enjoy!

timriffe

# Game of Life, by Vadim Vinichenko  https://github.com/vvinichenko
# I've pasted his 2 functions here with no experiment at the bottom in order to be able to source this code
# straight from R like this:
# source("http://raw.github.com/gist/1899814/881e5f92c9413a5e1800c8a274d177982d796a8c/life2.r")
# that line loads the 2 functions automatically. Experiment Gist to follow

shiftMatrix <- function(mx, dr, dc) {
	#Shift the matrix by dr (delta r) rows and dc columns
	#by adding e.g. dr rows of zeros and removing dr rows from the other side 
	

timriffe

# this code sources 2 functions from Vadim Vinichenko for the game of life. 
# His functions were more convenient because they scale well. I'll make a 1000x1000 starting matrix, with
# 30% of cells initially on, remove the plotting and just get this one diagnostic:

# this line loads the 2 functions from an abdridged github gist:
source("http://raw.github.com/gist/1899814/881e5f92c9413a5e1800c8a274d177982d796a8c/life2.r")

# set parameters:
n_rows <- 1000
n_cols <- 1000

timriffe

# this is an internal function just to make up block-like neighborhoods on a matrix with no natural borders.
# no need to tinker with it unless you're curious
DecideBLocks <- function(A,hoodsize){
	nhood_side <- floor(sqrt(hoodsize))
	Neighborhoods <- matrix(paste(ceiling(col(A)/nhood_side), ceiling(row(A)/nhood_side), sep="-"), nc=ncol(A))	
	nhoods.out <- length(unique(c(Neighborhoods)))
	return(Neighborhoods)
}

timriffe

# these functions were already presented in gist 1885003, but are copied here
# so that they can be sourced directly from the R console.
# to do so, click on the 'raw' button, get rid of the 's' in https, and change the .R to .r, like this:
# source("http://raw.github.com/gist/1910183/cbc854b9f1de4495db6fbd7eba43c337a1a8922d/SchellingProcessFunctions.r")

queenNeighbors <- function(ind, M, n){
	inds <- ind+outer(c(-1, 0, 1), c(-n, 0, n), "+")
	M[inds[inds > 0 & inds <= length(M) & inds != ind]]
}