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| ## Introduction | ||
| ## Exploratory Data Analysis | ||
| ### Project 1 | ||
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| This assignment uses data from | ||
| the <a href="http://archive.ics.uci.edu/ml/">UC Irvine Machine | ||
| Learning Repository</a>, 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: | ||
| This is my repository for the Coursera Course "Exploratory Data Analysis". | ||
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| To run this script, please do the following: | ||
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| * <b>Dataset</b>: <a href="https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip">Electric power consumption</a> [20Mb] | ||
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| * <b>Description</b>: 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. | ||
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| The following descriptions of the 9 variables in the dataset are taken | ||
| from | ||
| the <a href="https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption">UCI | ||
| web site</a>: | ||
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| <ol> | ||
| <li><b>Date</b>: Date in format dd/mm/yyyy </li> | ||
| <li><b>Time</b>: time in format hh:mm:ss </li> | ||
| <li><b>Global_active_power</b>: household global minute-averaged active power (in kilowatt) </li> | ||
| <li><b>Global_reactive_power</b>: household global minute-averaged reactive power (in kilowatt) </li> | ||
| <li><b>Voltage</b>: minute-averaged voltage (in volt) </li> | ||
| <li><b>Global_intensity</b>: household global minute-averaged current intensity (in ampere) </li> | ||
| <li><b>Sub_metering_1</b>: 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). </li> | ||
| <li><b>Sub_metering_2</b>: 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. </li> | ||
| <li><b>Sub_metering_3</b>: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.</li> | ||
| </ol> | ||
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| ## Loading the data | ||
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| When loading the dataset into R, please consider the following: | ||
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| * The dataset has 2,075,259 rows and 9 columns. First | ||
| calculate a rough estimate of how much memory the dataset will require | ||
| in memory before reading into R. Make sure your computer has enough | ||
| memory (most modern computers should be fine). | ||
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| * We will only be using data from the dates 2007-02-01 and | ||
| 2007-02-02. One alternative is to read the data from just those dates | ||
| rather than reading in the entire dataset and subsetting to those | ||
| dates. | ||
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| * You may find it useful to convert the Date and Time variables to | ||
| Date/Time classes in R using the `strptime()` and `as.Date()` | ||
| functions. | ||
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| * Note that in this dataset missing values are coded as `?`. | ||
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| ## Making Plots | ||
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| Our overall goal here is simply to examine how household energy usage | ||
| varies over a 2-day period in February, 2007. Your task is to | ||
| reconstruct the following plots below, all of which were constructed | ||
| using the base plotting system. | ||
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| First you will need to fork and clone the following GitHub repository: | ||
| [https://github.com/rdpeng/ExData_Plotting1](https://github.com/rdpeng/ExData_Plotting1) | ||
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| For each plot you should | ||
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| * Construct the plot and save it to a PNG file with a width of 480 | ||
| pixels and a height of 480 pixels. | ||
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| * Name each of the plot files as `plot1.png`, `plot2.png`, etc. | ||
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| * Create a separate R code file (`plot1.R`, `plot2.R`, etc.) that | ||
| constructs the corresponding plot, i.e. code in `plot1.R` constructs | ||
| the `plot1.png` plot. Your code file **should include code for reading | ||
| the data** so that the plot can be fully reproduced. You should also | ||
| include the code that creates the PNG file. | ||
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| * Add the PNG file and R code file to your git repository | ||
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| When you are finished with the assignment, push your git repository to | ||
| GitHub so that the GitHub version of your repository is up to | ||
| date. There should be four PNG files and four R code files. | ||
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| The four plots that you will need to construct are shown below. | ||
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| ### Plot 1 | ||
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| ### Plot 2 | ||
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| ### Plot 3 | ||
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| ### Plot 4 | ||
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|  | ||
| * Download the scripts (plit1.R, plot2.R, plot3.R, plot4.R) to a local directory | ||
| * Download the data from https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip | ||
| * Extract the data into the same directory in which you are working in | ||
| * Run the scripts | ||
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