Exploratory Data Analysis Project 1

This assignment uses data from the UC Irvine Machine Learning Repository, a popular repository for machine learning datasets. In particular, we will be using the “Individual household electric power consumption Data Set” which I have made available on the course web site:

Dataset: Electric power consumption [20Mb]
Description: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.

library("data.table")

mGalarnyk / quiz2.md

Created March 5, 2017 23:10

Exploratory Data Analysis Quiz 2 (Week 2) for the John Hopkins Data Science Specialization github repo https://github.com/mGalarnyk/datasciencecoursera/tree/master/4_Exploratory_Data_Analysis

Exploratory Data Analysis Quiz 2 (JHU) Coursera

Question 1

Under the lattice graphics system, what do the primary plotting functions like xyplot() and bwplot() return?

nothing; only a plot is made
an object of class "lattice"

mGalarnyk / quiz1.md

Created March 5, 2017 22:55

Exploratory Data Analysis Quiz 1 (Week 1) JHU Coursera for github repo https://github.com/mGalarnyk/datasciencecoursera

Exploratory Data Analysis Quiz 1 (JHU) Coursera

Question 1

Which of the following is a principle of analytic graphics?

Make judicious use of color in your scatterplots (NO)
Don't plot more than two variables at at time (NO)

mGalarnyk / helloWorld.html

Created February 25, 2017 19:28

Hello World HTML for my blog post https://medium.com/@GalarnykMichael

	<html>
	<header><title>This is title</title></header>
	<body>
	Hello world
	</body>
	</html>

mGalarnyk / run_analysis.R

Last active March 15, 2022 20:35

Getting and Cleaning Data Project John Hopkins Coursera for the blog post https://medium.com/@GalarnykMichael/getting-and-cleaning-data-jhu-coursera-course-3-c3635747858b#.270anhem0

	# 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

mGalarnyk / quiz_week4_datatable.R

Last active February 15, 2017 20:39

Getting and Clean Data Quiz 4 from John Hopkins Coursera for the blog post https://medium.com/@GalarnykMichael/getting-and-cleaning-data-jhu-coursera-course-3-c3635747858b#.270anhem0

	# Getting and Cleaning Data, JHU Coursera

	#1.
	#The American Community Survey distributes downloadable data about United States communities. Download the 2006 microdata survey about housing for the state of Idaho using download.file() from here:

	# https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv

	# and load the data into R. The code book, describing the variable names is here:

	# https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FPUMSDataDict06.pdf

mGalarnyk / GettingAndCleanDataWeek3Quiz.R

Last active February 21, 2024 09:30

Getting and Clean Data Quiz 3 from John Hopkins Coursera for the blog post https://medium.com/@GalarnykMichael/getting-and-cleaning-data-jhu-coursera-course-3-c3635747858b#.270anhem0

	# Getting and Cleaning Data, Quiz 3 JHU Coursera

	# 1. Register an application with the Github API here https://github.com/settings/applications.
	#The American Community Survey distributes downloadable data about United States communities. Download the 2006 microdata survey about housing for the state of Idaho using download.file() from here:

	#https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv

	#and load the data into R. The code book, describing the variable names is here:

	#https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FPUMSDataDict06.pdf

mGalarnyk / GettingAndCleanDataWeek2Quiz.R

Last active February 1, 2025 10:14

Getting and Clean Data Quiz 2 from John Hopkins Coursera for the blog post https://medium.com/@GalarnykMichael/getting-and-cleaning-data-jhu-coursera-course-3-c3635747858b#.270anhem0

	# 1. Register an application with the Github API here https://github.com/settings/applications.
	# Access the API to get information on your instructors repositories (hint: this is the url you want "https://api.github.com/users/jtleek/repos").
	# Use this data to find the time that the datasharing repo was created. What time was it created?
	# see https://medium.com/@GalarnykMichael/accessing-data-from-github-api-using-r-3633fb62cb08#.z0z07ph5h for more details.

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

mGalarnyk / GettingAndCleanDataWeek1Quiz.R

Last active February 15, 2017 20:40

Getting and Clean Data Quiz 1 from John Hopkins Coursera for the blog post https://medium.com/@GalarnykMichael/getting-and-cleaning-data-jhu-coursera-course-3-c3635747858b#.270anhem0

	# Quiz data.table code Week 1

	# 1.
	# fread url requires curl package on mac
	# install.packages("curl")
	# Reading in data
	housing <- data.table::fread("https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv")

	# VAL attribute says how much property is worth, .N is the number of rows
	housing[VAL == 24, .N]

mGalarnyk / LinearRegression.R

Last active December 5, 2020 13:40

Univariate Linear Regression (relationship between the dependent variable y and the independent variable x is linear) using R programming Language for the blog post https://medium.com/@GalarnykMichael/univariate-linear-regression-using-r-programming-3db499bdd1e3#.kcm3t9rl3

	# 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

Michael Galarnyk mGalarnyk

Exploratory Data Analysis Project 1

Exploratory Data Analysis Quiz 2 (JHU) Coursera

Question 1

Exploratory Data Analysis Quiz 1 (JHU) Coursera

Question 1