fototo

belief	y2015	y2014
Improves the security posture of my organization	0.75	0.71
Improves the security posture of the nations critical infrastructure	0.63	0.64
Reduces the cost of detecting and preventing cyber attacks	0.22	0.21
Improves situational awareness	0.60	0.54
Fosters collaboration among peers and industry groups	0.48	0.51
Enhances the timeliness of threat data	0.11	0.16
Makes threat data more actionable	0.21	0.24

	###############################################
	# working with complete set of 2015 play-by-play data
	# collected using the getData function in the openWAR
	# package (retrieves MLBAM GameDay files)
	# currently have this saved as a Rdata file
	###############################################
	load("alldata2015.Rdata")

	# computes the run values of all plate appearances

	#' The single-file, 'source'-able version of https://github.com/hrbrmstr/zellingenach
	#' More info here: http://bit.ly/zellingenach
	#' By @hrbrmstr (2016)
	#' MIT LICENSE

	# NOTE THAT YOU NEED INTERNET ACCESS TO RUN THIS SCRIPT
	# it pulls the three necessary files (only once) from my web site

	library(V8)
	library(dplyr)

	## source: https://github.com/toddwschneider/nyc-taxi-data/blob/master/analysis/analysis.R
	dropoffs = query("SELECT * FROM dropoff_by_lat_long_cab_type ORDER BY count")
	dropoffs = mutate(dropoffs, cab_type_id = factor(cab_type_id))

	p = ggplot() +
	geom_polygon(data = ex_staten_island_map,
	aes(x = long, y = lat, group = group),
	fill = "#080808", color = "#080808") +
	geom_point(data = dropoffs,
	aes(x = dropoff_long, y = dropoff_lat, alpha = count, size = count, color = cab_type_id)) +

	#Load library mass and set seed
	library(MASS)
	set.seed(100)

	# We are going to use 3 random variables
	m <- 3

	# Number of samples to be drawn
	n <- 2000

	import pandas as pd
	# http://blog.yhathq.com/static/misc/data/WineKMC.xlsx
	df_offers = pd.read_excel("./WineKMC.xlsx", sheetname=0)
	df_offers.columns = ["offer_id", "campaign", "varietal", "min_qty", "discount", "origin", "past_peak"]
	df_offers.head()

	df_transactions = pd.read_excel("./WineKMC.xlsx", sheetname=1)
	df_transactions.columns = ["customer_name", "offer_id"]
	df_transactions['n'] = 1
	df_transactions.head()