David Masad dmasad

Dual axes for ggplot2

Modified from:

https://stackoverflow.com/questions/21026598/ggplot2-adding-secondary-transformed-x-axis-on-top-of-plot

https://rpubs.com/kohske/dual_axis_in_ggplot2

Updated: 2016-07-27

	# Based on tutorial from https://jeffbradberry.com/posts/2015/09/intro-to-monte-carlo-tree-search/
	# Author: Kyle Kastner
	# License: BSD 3-Clause
	from __future__ import print_function
	import random
	import copy
	import numpy as np
	import time
	import argparse
	import sys

	# 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. Also, switched from geom_segment to geom_line.

	# The result of the code below can be seen at http://imgur.com/ob8c8ph

	library(tidyverse)

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	require(grid)
	require(scales)
	require(reshape2)

	# Notes:
	# dat should be a 3 column data.frame with fields in order as per example below
	# wave wavyness of curves defined in terms of x axis data range - experiment to get this right
	# ygap gap between items on each y axis
	# col a single colour or vector of colours for categories when listed in alphabetical order
	# leg.mode if true legend plotted in largest data observation, otherwise custom coordinates (leg.x/y [0,1])

	'''
	Non-parametric computation of entropy and mutual-information

	Adapted by G Varoquaux for code created by R Brette, itself
	from several papers (see in the code).

	This code is maintained at https://github.com/mutualinfo/mutual_info
	Please download the latest code there, to have improvements and
	bug fixes.

	SLIDES := $(patsubst %.md,%.md.slides.pdf,$(wildcard *.md))
	HANDOUTS := $(patsubst %.md,%.md.handout.pdf,$(wildcard *.md))

	all : $(SLIDES) $(HANDOUTS)

	%.md.slides.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -o $@

	%.md.handout.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -V handout -o $@

	{%- extends 'full.tpl' -%}

	{% block input_group -%}
	<div class="input_hidden">
	{{ super() }}
	</div>
	{% endblock input_group %}

	{%- block header -%}
	{{ super() }}

	doInstall <- TRUE
	toInstall <- c("ggplot2")
	if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
	lapply(toInstall, library, character.only = TRUE)

	ANES <- read.csv("http://www.oberlin.edu/faculty/cdesante/assets/downloads/ANES.csv")
	ANES <- ANES[ANES$year == 2008, -c(1, 11, 17)] # Limit to just 2008 respondents,
	head(ANES) # remove some non-helpful variables

	# Fit several models with the same DV: