Shawn Mollie Taylor mollietaylor

## sectors.R
sectors <- read.table("sectors.csv", header=TRUE, sep=",")
library(maps)
library(RColorBrewer)
library(classInt)
names(sectors)
plotvar <- sectors$secnum #change this line to change data
plotclr <- brewer.pal(5,"Set1")
class <- classIntervals(plotvar, 5, 'pretty')#or pretty
colcode <- findColours(class, plotclr)
map("state", ylim=range(50, 22), col=8)

## histdens.R
png("beaverhist.png")
layout(matrix(c(1:2), 2, 1,
	byrow = TRUE))
hist(beaver1$temp, # histogram
	col = "peachpuff", # column color
	border = "black",
	prob = TRUE, # show densities instead of frequencies
	xlim = c(36,38.5),
	xlab = "temp",
	main = "Beaver #1")

## reality.R
# recreating realitytvgenerator.com

# dictionary:
# define our two vectors with the names we'd like to use
first <- c("Fear", "Frontier", "Nanny", "Job", "Yard", "Airport", "Half Pint", "Commando", "Fast Food", "Basketball", "Bachelorette", "Diva", "Baggage", "College", "Octane", "Clean", "Sister", "Army", "Drama", "Backyard", "Pirate", "Shark", "Project", "Model", "Survival", "Justice", "Mom", "New York", "Jersey", "Ax", "Warrior", "Ancient", "Pawn", "Throttle", "The Great American", "Knight", "American", "Outback", "Celebrity", "Air", "Restaurant", "Bachelor", "Family", "Royal", "Surf", "Ulitmate", "Date", "Operation", "Fish Tank", "Logging", "Hollywood", "Amateur", "Craft", "Mystery", "Intervention", "Dog", "Human", "Rock", "Ice Road", "Shipping", "Modern", "Crocodile", "Farm", "Amish", "Single", "Tool", "Boot Camp", "Pioneer", "Kid", "Action", "Bounty", "Paradise", "Mega", "Love", "Style", "Teen", "Pop", "Wedding", "An American", "Treasure", "Myth", "Empire", "Motorway", "Room", "Casino", "Comedy", "Unde

## palettes-in-R.R
palette()

# it's fine for your vector to include a mixture of hex triplets and R color names:
colors <- c("#A7A7A7",
	"dodgerblue",
	"firebrick",
	"forestgreen",
	"gold")
hist(discoveries,
	col = colors)

## sort.R
library(lattice)
colors = c("#1B9E77", "#D95F02", "#7570B3")

dotplot(rownames(mtcars) ~ mpg,
	data = mtcars,
	col = colors[1],
	pch = 1)

dotplot(reorder(rownames(mtcars), cyl) ~ mpg,
	data = mtcars,

## gini.R
# generate random data:
city <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
income <- sample(1:100000,
  100,
	replace = TRUE)
cities <- data.frame(city, income)

# graph our data:
library(ggplot2)
ggplot(cities,

## quickStart.html
<!DOCTYPE html>
<meta charset="utf-8">
<html>
<head>
  <title>Quick Start</title>
  <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.6.4/leaflet.css" />
  <!--[if lte IE 8]>
      <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.6.4/leaflet.ie.css" />
  <![endif]-->
  <script src="http://cdn.leafletjs.com/leaflet-0.6.4/leaflet.js"></script>

## presentations.txt
bus tracker
trash collection 404-620-5025
budget app http://public.tableausoftware.com/views/Expenses_2_0/DepartmentDashboard#1
innovations app
APS parent portal
form flow
votified http://votified.com/
geoincentives wordpress plugin
vendor application
municiplanner http://municiplanner.org/

## cities-coords.csv

          
            City
            State
            Latitude
            Longitude

            
              San Francisco
              CA
              37.7782251
              -122.4424955

            
              New York
              NY
              40.7142691
              -74.0059729

            
              Los Angeles
              CA
              34.0522342
              -118.2436849

            
              Chicago
              IL
              41.850033
              -87.6500523

            
              Dallas
              TX
              32.7830556
              -96.8066667

            
              Columbus
              GA
              32.4609764
              -84.9877094

            
              Columbus
              OH
              39.9611755
              -82.9987942

## callan.R
gps <- read.csv("callan.csv",
  header = TRUE)

# calculate moving average: (this section is optional)
library(TTR)
movingN <- 5 # define the n for the moving average calculations
gps$Altitude <- gps$Altitude * 3.281 # convert m to ft
gps$SMA <- SMA(gps$Altitude,
  n = movingN)
gps <- gps[movingN:length(gps$SMA), ] # remove first n-1 points
	sectors <- read.table("sectors.csv", header=TRUE, sep=",")
	library(maps)
	library(RColorBrewer)
	library(classInt)
	names(sectors)
	plotvar <- sectors$secnum #change this line to change data
	plotclr <- brewer.pal(5,"Set1")
	class <- classIntervals(plotvar, 5, 'pretty')#or pretty
	colcode <- findColours(class, plotclr)
	map("state", ylim=range(50, 22), col=8)
	png("beaverhist.png")
	layout(matrix(c(1:2), 2, 1,
	byrow = TRUE))
	hist(beaver1$temp, # histogram
	col = "peachpuff", # column color
	border = "black",
	prob = TRUE, # show densities instead of frequencies
	xlim = c(36,38.5),
	xlab = "temp",
	main = "Beaver #1")
	# recreating realitytvgenerator.com

	# dictionary:
	# define our two vectors with the names we'd like to use
	first <- c("Fear", "Frontier", "Nanny", "Job", "Yard", "Airport", "Half Pint", "Commando", "Fast Food", "Basketball", "Bachelorette", "Diva", "Baggage", "College", "Octane", "Clean", "Sister", "Army", "Drama", "Backyard", "Pirate", "Shark", "Project", "Model", "Survival", "Justice", "Mom", "New York", "Jersey", "Ax", "Warrior", "Ancient", "Pawn", "Throttle", "The Great American", "Knight", "American", "Outback", "Celebrity", "Air", "Restaurant", "Bachelor", "Family", "Royal", "Surf", "Ulitmate", "Date", "Operation", "Fish Tank", "Logging", "Hollywood", "Amateur", "Craft", "Mystery", "Intervention", "Dog", "Human", "Rock", "Ice Road", "Shipping", "Modern", "Crocodile", "Farm", "Amish", "Single", "Tool", "Boot Camp", "Pioneer", "Kid", "Action", "Bounty", "Paradise", "Mega", "Love", "Style", "Teen", "Pop", "Wedding", "An American", "Treasure", "Myth", "Empire", "Motorway", "Room", "Casino", "Comedy", "Unde
	palette()

	# it's fine for your vector to include a mixture of hex triplets and R color names:
	colors <- c("#A7A7A7",
	"dodgerblue",
	"firebrick",
	"forestgreen",
	"gold")
	hist(discoveries,
	col = colors)
	library(lattice)
	colors = c("#1B9E77", "#D95F02", "#7570B3")

	dotplot(rownames(mtcars) ~ mpg,
	data = mtcars,
	col = colors[1],
	pch = 1)

	dotplot(reorder(rownames(mtcars), cyl) ~ mpg,
	data = mtcars,
	# generate random data:
	city <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
	income <- sample(1:100000,
	100,
	replace = TRUE)
	cities <- data.frame(city, income)

	# graph our data:
	library(ggplot2)
	ggplot(cities,
	<!DOCTYPE html>
	<meta charset="utf-8">
	<html>
	<head>
	<title>Quick Start</title>
	<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.6.4/leaflet.css" />
	<!--[if lte IE 8]>
	<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.6.4/leaflet.ie.css" />
	<![endif]-->
	<script src="http://cdn.leafletjs.com/leaflet-0.6.4/leaflet.js"></script>
	bus tracker
	trash collection 404-620-5025
	budget app http://public.tableausoftware.com/views/Expenses_2_0/DepartmentDashboard#1
	innovations app
	APS parent portal
	form flow
	votified http://votified.com/
	geoincentives wordpress plugin
	vendor application
	municiplanner http://municiplanner.org/
City	State	Latitude	Longitude
San Francisco	CA	37.7782251	-122.4424955
New York	NY	40.7142691	-74.0059729
Los Angeles	CA	34.0522342	-118.2436849
Chicago	IL	41.850033	-87.6500523
Dallas	TX	32.7830556	-96.8066667
Columbus	GA	32.4609764	-84.9877094
Columbus	OH	39.9611755	-82.9987942
	gps <- read.csv("callan.csv",
	header = TRUE)

	# calculate moving average: (this section is optional)
	library(TTR)
	movingN <- 5 # define the n for the moving average calculations
	gps$Altitude <- gps$Altitude * 3.281 # convert m to ft
	gps$SMA <- SMA(gps$Altitude,
	n = movingN)
	gps <- gps[movingN:length(gps$SMA), ] # remove first n-1 points