michelleboisson

//Compass Bracelet GPS

//This reads the heading from HMC6352, reads your current location (hardcoded),
//and your destination (hardcoded), and turns the pin high pointing to 
//your destination

//HMC6352 compass is hooked up this way: SDA is on analog input pin 4, and SCL is on analog pin 5
//To customize, change destinationLat, destinationLon, currentLat, currentLon

#include <SoftwareSerial.h>

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//This reads the heading from HMC6352, spit it out via serial and lights up 1-2 lights to show North
//HMC6352 compass is hooked up this way: SDA is on analog input pin 4, and SCL is on analog pin 5

#include <Wire.h>
int HMC6352SlaveAddress = 0x42;
int HMC6352ReadAddress = 0x41; //"A" in hex, A command is: 

int headingValue;

int EPin = 13;//east

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#Final Data Exploration
#Grameen Foundation, CKWs

ix.data = read.csv("/Users/michelleboisson/Documents/ITP/* Data without Borders/final/search_logs.csv", header=TRUE, as.is=TRUE)
head(ix.data)
dim(ix.data)
#[1] 334718     11

ckws = read.csv("/Users/michelleboisson/Documents/ITP/* Data without Borders/final/ckw.csv", header=TRUE, as.is=TRUE)
head(ckws)

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

import ketai.sensors.*;


KetaiSensor sensor;

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#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

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# 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)

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# 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), ]

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#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

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#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"