michelleboisson

<form method="post">
	<dl>
	<% for(i=0; i<=itemTypes.length-1; i++) { %>                               
                <dt><input type="checkbox" name="itemChoices" value="<%= itemTypes[i].itemTypeName %>"/> <strong><%= itemTypes[i].itemTypeName %></strong></dt>
                <dd>Dominence Points: <%= itemTypes[i].domPts %><dd>
		<dd>Picture: <%= itemTypes[i].picture %><dd>
		<select>
			<% for (r=rooms.length-1; r>=0; r--) { %>
			<option value="<%= rooms[r].name %>"><%= rooms[r].name %></option>
			<% } %>

michelleboisson

#Read in the data, save it to variable 'data'
data = read.csv("http://www.jakeporway.com/teaching/data/snf_11_2011_1.csv", header=TRUE, as.is=TRUE)

#How many women were stopped?  
sum(data$sex == "F")
#[1] 3927

#What percentage of the stops is this?
sum(data$sex == "F") / length(data$sex) * 100
#[1] 6.760316

michelleboisson

# Write code to return the percentage of people who were frisked for each 
# race.  In other words, count up the number of people who were frisked for a given race 
# divided by the number of people of that race stopped.  Which race leads to the highest 
# percentage of frisks?  Which one the lowest? 
 
#read in the data
snf <- read.csv("http://www.jakeporway.com/teaching/data/snf_2.csv", as.is=TRUE)

#race1 is black
race1.in.total = table(snf$race ==1)

michelleboisson

#How many unique users have more than 100000 followers?  What are their screen names?

tweets <- read.csv("/Users/michelleboisson/Documents/ITP/* Data without Borders/hw3/libya_tweets.csv", as.is=TRUE)

unique(tweets$screen_name[which(as.numeric(tweets$followers) >= 100000)])
# [1] "detikcom"       "DonLemonCNN"    "HuffingtonPost" "Dputamadre"     "WorldRss"       "AlMasryAlYoum" 
# [7] "theobscurant"   "fadjroeL"       "TPO_Hisself"    "CAPAMAG"        "TwittyAlgeria"  "foxandfriends" 
# [13] "PranayGupte"

michelleboisson

michelleboisson

#include <Servo.h>      // include the servo library

Servo servoMotor;       // creates an instance of the servo object to control a servo
int servoPin = 2;       // Control pin for servo motor
int pos = 0;    // variable to store the servo position 

int now, previousTime, interval = 0;


//for smoothing of values

michelleboisson

# Let’s breakdown our tweets around a certain topic.  How about, oh, say, Iran?  
# So how do we pull tweets out that have a certain word in them?
# grep() to the rescue!  If you’ve used the grep function on the command-line, this 
# should look familiar.  grep() takes as arguments a phrase you’re searching for, a 
# set of text to look through, and optional arguments about how to search.  It will 
# then return the row numbers of any rows that match your search.  To pull out Iran 
# tweets, we can use the code:

iran.tweets <- tweets[grep(“iran”, ignore.case=TRUE, tweets$text), ]

michelleboisson

# CRIME MAPPING
#We’ve done some basic analysis of our Stop and Frisk data this semester, looking at 
#basic statistics (e.g. number of stops by race or most common crimes by race) and 
#timeseries information.  We haven’t yet touched geography though.  Let’s spend this 
#week’s assignment answering the question:  Where are Stop n Frisks happening in New 
#York?

snf = read.csv("/Users/michelleboisson/Documents/ITP/* Data without Borders/snf_3.csv", as.is=TRUE)
geo = read.csv("/Users/michelleboisson/Documents/ITP/* Data without Borders/geo.csv", as.is=TRUE)

michelleboisson

#Describing Distributions
snf = read.csv("http://jakeporway.com/teaching/data/snf_4.csv", head=T, as.is=T)

#Make a “height” column for your data that is the total number of inches tall each person is.
heights = apply(snf,1, function(x) { as.numeric(x['feet'])*12 + as.numeric(x['inches']) })
snf$fullheight = heights

mean(heights) 
#[1] 68.57884

michelleboisson

/**
 * <p>This uses the Ketai Sensor Library for Android: http://ketaiMotion.org</p>
 *
 *
 */

import ketai.sensors.*;


KetaiSensor sensor;