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"

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.

• The dataset has 2,075,259 rows and 9 columns.

• We will only be using data from the dates 2007-02-01 and 2007-02-02.

• Missing values are coded as ?. But there are no missing values on the two days of interest for us

Our overall goal here is to examine how household energy usage varies over a 2-day period in February, 2007. The task was to reconstruct the following plots, all of which were constructed using the base plotting system.

• Created a separate R code file (plot1.R, plot2.R, etc.) that constructs the corresponding plot.

• The additional file processData.R reads the original text file, extracts the data for the two dates in February and saves it in a comma separated file 'power_consumption_transformed.csv` for later use. This increases speed and efficiency of the operations.

• Constructed each plot and saved it to a PNG file with a width of 480 pixels and a height of 480 pixels (this is the default size for R).

• The plot files are plot1.png, plot2.png, plot3.png, and plot4.png.

• Added the PNG file and R code file to the student git repository