Background

This project uses data from the UC Irvine Machine Learning Repository, in particular, the “Individual Household Electric Power Consumption” data set. The data set contains 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 in the data set.

The 9 variables available in the data set are (as described in the UCI website):

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.

Project Goals

Global Active Power vs. Frequency
Global Active Power over Time
Energy Sub-Metering over Time
Global Active Power/Voltage/Energy sub-metering/Global Machine Power over Time in one plot

Data Processing

We try to download the data to a local directory if it doesn’t exist already

fileurl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
if(!file.exists("Power.txt"))
{
    download.file(fileurl, "Power.txt")
}

We then try to load the data from the .txt file to the R global environment.

powerData <- read.csv("Power.txt", stringsAsFactors = FALSE, sep = ";", header = TRUE)

Now, we convert the “Date” column of the “powerData” data set to a Date format and convert the 3rd to 9th columns to numeric formats.

powerData$Date <- as.Date(powerData$Date, format = "%d/%m/%Y")

for (i in 3:9)
    powerData[, i] <- as.numeric(powerData[, i])

We now subset the data set for the particular dates.

powerSub <- subset(powerData, Date >= "2007-02-01" & Date <= "2007-02-02")

We add a new column to the data set which contains both the “Date” and “Time” for the data and we subset the new “Date_Time” along with the other numeric columns.

Date_Time <- paste(powerSub$Date, powerSub$Time)
powerSub$DateTime <- as.POSIXct(Date_Time)
powerData <- powerSub[, c(10, 3:9)]

Plots

Plot 1: Global Active Power vs Frequency of Use

hist(powerData$Global_active_power, main = "Global Active Power", xlab = "Global Active Power (kilowatts)", col = "red")

Plot 2: Global Active Power over Time

plot(powerData$Global_active_power ~ powerData$DateTime, ylab = "Global Active Power (kilowatts)", xlab = "", type = "l")

Plot 3: Energy Sub-Metering over Time

plot(powerData$Sub_metering_1 ~ powerData$DateTime, ylab = "Energy sub metering", xlab = "", type = "l")
lines(powerData$Sub_metering_2 ~ powerData$DateTime, col = 'Red')
lines(powerData$Sub_metering_3 ~ powerData$DateTime, col = 'Blue')
legend("topright", col = c("black", "red", "blue"), legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lwd = 1)

Plot 4: Global Active Power/Voltage/Energy sub-metering/Global Machine Power over Time

par(mfrow = c(2,2))
plot(powerData$Global_active_power ~ powerData$DateTime, ylab = "Global Active Power", xlab = "", type = "l")
plot(powerData$Voltage ~ powerData$DateTime, ylab = "Voltage", xlab = "datetime", type = "l")
plot(powerData$Sub_metering_1 ~ powerData$DateTime, ylab = "Energy sub metering", xlab = "", type = "l")
lines(powerData$Sub_metering_2 ~ powerData$DateTime, col = 'Red')
lines(powerData$Sub_metering_3 ~ powerData$DateTime, col = 'Blue')
legend("topright", col = c("black", "red", "blue"), legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lwd = 1)
plot(powerData$Global_reactive_power ~ powerData$DateTime, ylab = "Global_reactive_power", xlab = "datetime", type = "l")

Individual Household Electric Power Consumption Analysis

Ganesh Kalyansundaram