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# Customer Check-in Script
import requests


def init():
    checkin_type = raw_input('Enter 1 for "points" or 2 for "punches": ')
    new_customer_data = create_customer()

    if checkin_type == '1':
        checkin_customer('points', new_customer_data)

evanemolo

var express = require('express'); 
var ejs = require('ejs'); //embedded javascript template engine

var app = express.createServer(express.logger());

var mongoose = require('mongoose'); // include Mongoose MongoDB library
var schema = mongoose.Schema; 

var requestURL = require('request');

evanemolo

//Twitter-Stepper Test

#include <SPI.h>
#include <Ethernet.h>
#include <TextFinder.h>

#include <Stepper.h>
#define STEPS 200

// MAC address and server DNS:

evanemolo

//Twitter-Stepper Test

#include <SPI.h>
#include <Ethernet.h>
#include <TextFinder.h>

#include <Stepper.h>
#define STEPS 200

// interval declarations

evanemolo

snf <- read.csv("http://www.jakeporway.com/teaching/data/snf_2.csv", as.is=TRUE)

# Part 1


# Write code to return the percentage of people who were frisked for each race.

race.code = c(-1,1,2,3,4,5,6)

for(i in race.code) {

evanemolo

snf <- read.csv("http://www.jakeporway.com/teaching/data/snf_2.csv", as.is=TRUE)

crime.abbr = substr(snf$crime.suspected, 1, 3)
snf$crime.abbr = crime.abbr

race.code = c(-1,1,2,3,4,5,6)

for(i in race.code) {
  # create subset
  which.race = which(snf$race == i)

evanemolo

[1] "racecode -1 results:"
subset.race.crime.abbr
FEL MIS CPW 
566 229 202 
[1] "racecode 1 results:"
subset.race.crime.abbr
  FEL   MIS   CPW 
10744  4487  4009 
[1] "racecode 2 results:"
subset.race.crime.abbr

evanemolo

snf <- read.csv("http://www.jakeporway.com/teaching/data/snf_2.csv", as.is=TRUE)

# First create a subset of the data only consisting of “good” weights and heights.
clean.subset = snf[snf$height > 40 & snf$weight > 90 & snf$weight < 400, ]
# Add a BMI variable to our dataset, where BMI is computed as (weight)*703/(height*height).
bmi = (clean.subset$weight)*703/(clean.subset$height*clean.subset$height)
# What percentage of people with BMI’s greater than or equal to 30 who were stopped were 
# ultimately arrested?
obese.subset = clean.subset[bmi >= 30, ]
obese.arrested.subset = which(obese.subset$arrested == 1)

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# custom command prompt
export PS1="\W > "

# bash aliases
alias la='ls -la'
alias lah='ls -lah'
alias move='mv -i'
alias rename='mv'
alias remove='rm -i'
alias rmdir='rm -rf'

evanemolo

# Write code to find the 5 most popular words used in the descriptions of our users

# use strsplit to seperate the words in the description
split.description = strsplit(tweets$description, split = "[^a-zA-Z0-9]+")
# use unlist() to turn the list into a vector
split.description = unlist(strsplit(tweets$description, split = "[^a-zA-Z0-9]+"))
# use tolower() to convert all words to lower case to reduce duplicates
split.description = tolower(unlist(strsplit(tweets$description, split = "[^a-zA-Z0-9]+")))
# count the number of occurances of each word, and pull the top five
rev(sort(table(split.description)))[1:5]