Jillian Jarrett mas-dse-jejarret

## run_analysis.R
# Getting and Cleaning Data Project John Hopkins Coursera
# Author: Michael Galarnyk

# 1. Merges the training and the test sets to create one data set.
# 2. Extracts only the measurements on the mean and standard deviation for each measurement.
# 3. Uses descriptive activity names to name the activities in the data set
# 4. Appropriately labels the data set with descriptive variable names.
# 5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

# Load Packages and get the Data

## LinearRegression.R
# Linear Regression predicts linear relationship between two variables

# Set path to Desktop
setwd("~/Desktop")

download.file(url = 'https://raw.githubusercontent.com/mGalarnyk/Python_Tutorials/master/Python_Basics/Linear_Regression/linear.csv'
              , destfile = 'linear.csv')
rawData=read.csv("linear.csv", header=T)

# Show first n entries of data.frame, notice NA values

## DataTwitterAPIusingR.R
#install.packages("twitteR")
library(twitteR)

# Change the next four lines based on your own consumer_key, consume_secret, access_token, and access_secret.
consumer_key <- "OQMbUsBfWQ1mVUGASpSArbG33"
consumer_secret <- "GQ5kc0BlwJZE2FYyvv8cxn845z32ES6HsID87cawkQ075jwyIy"
access_token <- "4338966852-lBmLvEg9mADHIdjK2hT4W5mtHmI9jRKxcV4PTrB"
access_secret <- "AwKRZw9AvTMvMrb2jouX5JHTjDASI3zeceVsemgQa1SSq"

setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)

## R_Github_Api.R
#install.packages("jsonlite")
library(jsonlite)
#install.packages("httpuv")
library(httpuv)
#install.packages("httr")
library(httr)

# Can be github, linkedin etc depending on application
oauth_endpoints("github")

## LinearRegression.py
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Read in csv file
# File: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Python_Basics/Linear_Regression/linear.csv
raw_data = pd.read_csv("linear.csv")

# Removes rows with NaN in them

## Install_Cuda_7.5_Ubuntu_14_04
# http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_7.5-18_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb
rm cuda-repo-ubuntu1404_7.5-18_amd64.deb
echo 'export CUDA_HOME=/usr/local/cuda
export CUDA_ROOT=/usr/local/cuda
export PATH=$PATH:$CUDA_ROOT/bin:$HOME/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64
' >> ~/.bashrc

## RStudioUbuntu1404.sh
# Install R + RStudio on Ubuntu 14.04

sudo apt-key adv –keyserver keyserver.ubuntu.com –recv-keys E084DAB9

# Ubuntu 12.04: precise
# Ubuntu 14.04: trusty
# Ubuntu 16.04: xenial
# Basic format of next line deb https://<my.favorite.cran.mirror>/bin/linux/ubuntu <enter your ubuntu version>/
sudo add-apt-repository 'deb https://ftp.ussg.iu.edu/CRAN/bin/linux/ubuntu trusty/'
sudo apt-get update
	# Getting and Cleaning Data Project John Hopkins Coursera
	# Author: Michael Galarnyk

	# 1. Merges the training and the test sets to create one data set.
	# 2. Extracts only the measurements on the mean and standard deviation for each measurement.
	# 3. Uses descriptive activity names to name the activities in the data set
	# 4. Appropriately labels the data set with descriptive variable names.
	# 5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

	# Load Packages and get the Data
	# Linear Regression predicts linear relationship between two variables

	# Set path to Desktop
	setwd("~/Desktop")

	download.file(url = 'https://raw.githubusercontent.com/mGalarnyk/Python_Tutorials/master/Python_Basics/Linear_Regression/linear.csv'
	, destfile = 'linear.csv')
	rawData=read.csv("linear.csv", header=T)

	# Show first n entries of data.frame, notice NA values
	#install.packages("twitteR")
	library(twitteR)

	# Change the next four lines based on your own consumer_key, consume_secret, access_token, and access_secret.
	consumer_key <- "OQMbUsBfWQ1mVUGASpSArbG33"
	consumer_secret <- "GQ5kc0BlwJZE2FYyvv8cxn845z32ES6HsID87cawkQ075jwyIy"
	access_token <- "4338966852-lBmLvEg9mADHIdjK2hT4W5mtHmI9jRKxcV4PTrB"
	access_secret <- "AwKRZw9AvTMvMrb2jouX5JHTjDASI3zeceVsemgQa1SSq"

	setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)
	#install.packages("jsonlite")
	library(jsonlite)
	#install.packages("httpuv")
	library(httpuv)
	#install.packages("httr")
	library(httr)

	# Can be github, linkedin etc depending on application
	oauth_endpoints("github")
	import numpy as np
	import pandas as pd
	from sklearn.linear_model import LinearRegression
	import matplotlib.pyplot as plt

	# Read in csv file
	# File: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Python_Basics/Linear_Regression/linear.csv
	raw_data = pd.read_csv("linear.csv")

	# Removes rows with NaN in them
	# http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/
	wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_7.5-18_amd64.deb
	sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb
	rm cuda-repo-ubuntu1404_7.5-18_amd64.deb
	echo 'export CUDA_HOME=/usr/local/cuda
	export CUDA_ROOT=/usr/local/cuda
	export PATH=$PATH:$CUDA_ROOT/bin:$HOME/bin
	export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64
	' >> ~/.bashrc
	# Install R + RStudio on Ubuntu 14.04

	sudo apt-key adv –keyserver keyserver.ubuntu.com –recv-keys E084DAB9

	# Ubuntu 12.04: precise
	# Ubuntu 14.04: trusty
	# Ubuntu 16.04: xenial
	# Basic format of next line deb https://<my.favorite.cran.mirror>/bin/linux/ubuntu <enter your ubuntu version>/
	sudo add-apt-repository 'deb https://ftp.ussg.iu.edu/CRAN/bin/linux/ubuntu trusty/'
	sudo apt-get update