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-ExData_Plotting1
-================
+## Introduction
+
+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:
+
+
+- Date: Date in format dd/mm/yyyy
+- Time: time in format hh:mm:ss
+- Global_active_power: household global minute-averaged active power (in kilowatt)
+- Global_reactive_power: household global minute-averaged reactive power (in kilowatt)
+- Voltage: minute-averaged voltage (in volt)
+- Global_intensity: household global minute-averaged current intensity (in ampere)
+- 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).
+- 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.
+- 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
+
+
+
+
+
+When loading the dataset into R, please consider the following:
+
+* 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).
+
+* 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.
+
+* 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.
+
+* Note that in this dataset missing values are coded as `?`.
+
+
+## Making Plots
+
+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.
+
+First you will need to fork and clone the following GitHub repository:
+[https://github.com/rdpeng/ExData_Plotting1](https://github.com/rdpeng/ExData_Plotting1)
+
+
+For each plot you should
+
+* Construct the plot and save it to a PNG file with a width of 480
+pixels and a height of 480 pixels.
+
+* Name each of the plot files as `plot1.png`, `plot2.png`, etc.
+
+* 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.
+
+* Add the PNG file and R code file to your git repository
+
+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.
+
+
+The four plots that you will need to construct are shown below.
+
+
+### Plot 1
+
+
+
+
+
+### Plot 2
+
+
+
+
+### Plot 3
+
+
+
+
+### Plot 4
+
+
-Plotting Assignment 1 for Exploratory Data Analysis