Introduction

This repository is a submission for Project 1 of the "Exploratory Data Analysis" course offered via Coursera by John Hopkins University.

A total of 4 PNG format images are supplied along with the necessary R code to recreate them fom the source data file. The period covered is Feb 1 - Feb 2, 2007.

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.

The following descriptions of the 9 variables in the dataset are taken from the UCI web site:

1. Date: Date in format dd/mm/yyyy
2. Time: time in format hh:mm:ss
3. Global_active_power: household global minute-averaged active power (in kilowatt)
4. Global_reactive_power: household global minute-averaged reactive power (in kilowatt)
5. Voltage: minute-averaged voltage (in volt)
6. Global_intensity: household global minute-averaged current intensity (in ampere)
7. Sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered).
8. Sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light.
9. Sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.

Loading the data

Please download and extract the ZIP file into your working directory for R or RStudio. The supplied R code will read directly from the CSV file.

Recreating the plots

To regenerate the plot, simply load the appropriate R file and call the named function as below. A PNG file will be created i your working directory.

Plot	R File	Function
Plot 1	plot1.R	plot1()
Plot 2	plot2.R	plot2()
Plot 3	plot3.R	plot3()
Plot 4	plot4.R	plot4()

The plots created by the code are shown below.

Plot 1

Plot 2

Plot 3

Plot 4