Brock Tibert Btibert3

	first	pvs
	A	1
	B	2
	C	3
	D	4
	E	5

Raspberry Pi Emulator Setup

Resources

http://39digits.com/build-a-nintendo-emulator-using-a-raspberry-pi-2/
http://www.emuparadise.me/ (for roms)
https://github.com/RetroPie/RetroPie-Setup/wiki
https://github.com/RetroPie/RetroPie-Setup/wiki/scraper (on how to scrape games, can go through setup or ssh into pi)

Shortest Path Question

Problem Statement

In my use-case, I am modeling a marketing campaign by which I want to find the shortest path to a click. Simply, the first outreach that generated a click through.

Email Marketing Analytics App

Problem Statement

I want to summarize the email engagement for a particular contact in my database. The summary stats that I am looking for are:

	{
	"swagger": "2.0",
	"info": {
	"description": "API Description",
	"title": "API Title",
	"version": "1.0.0"
	},
	"host": "localhost:8000",
	"schemes": [
	"http"

	import requests
	import base64
	import json
	from simple_salesforce import Salesforce

	userName = ''
	password = ''
	securityToken = ''
	instance = ''


	# Write R data.frame to a Tableau data extract file (.tde) by building and executing
	# a python script which utilizes the Tableau data extract API (a hack, yes).
	#
	# This, naturally, has a hard dependency on the TDE API, so is only available for
	# Windows and Linux systems (unfortunately)
	#
	# Devin Riley
	# October, 2014

	## read the file in using read_delim from readr package
	chegg2 = read_delim("data/Bentley University-export10-17-2016_16628777.txt", delim="\t")

	## hopefully there are patterns where the bad characters exist
	## for example
	chegg2$first_name = str_replace_all(chegg2$first_name, "'", "")

	## what it comes down to are the options on the various functions
	## the readr package is pretty handy, and gets the data in without issues
	## from there, you can clean the data, but like I said, hopefully there are some standard columns where the bad data exist

	options(stringsAsFactors = F)
	library(jsonlite)
	library(stringr)
	library(data.table)


	##################### high schools

	dfRaw = readLines("hs.json")

	options(stringsAsFactors = F)
	library(jsonlite)
	setwd("~/Downloads/")
	x = readLines("school-codes.txt")
	library(stringr)
	## fix the keys to add quotes around them
	tmp = str_sub(x, 1, 121)
	y = str_replace_all(tmp, 'id:', '\\"id\\":')
	y = str_replace_all(y, 'Cb:', '\\"Cb\\":')
	y = str_replace_all(y, 'N:', '\\"N\\":')