Electric Power consumption
soubhagya Laxmi
4 January 2017
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
data <- read.table("household_power_consumption.txt", header= TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
summary(data)## Date Time Global_active_power
## Length:2075259 Length:2075259 Length:2075259
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Global_reactive_power Voltage Global_intensity
## Length:2075259 Length:2075259 Length:2075259
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Sub_metering_1 Sub_metering_2 Sub_metering_3
## Length:2075259 Length:2075259 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.000
## Mode :character Mode :character Median : 1.000
## Mean : 6.458
## 3rd Qu.:17.000
## Max. :31.000
## NA's :25979Subset the data from the dates 2007-02-01 and 2007-02-02.
subsetdata <- data[data$Date %in% c("1/2/2007","2/2/2007"),]
globalActivePower <- as.numeric(subsetdata$Global_active_power)
globalReactivePower <- as.numeric(subsetdata$Global_reactive_power)
voltage <- as.numeric(subsetdata$Voltage)
subMetering1 <- as.numeric(subsetdata$Sub_metering_1)
subMetering2 <- as.numeric(subsetdata$Sub_metering_2)
subMetering3 <- as.numeric(subsetdata$Sub_metering_3)Created a histogram
hist(globalActivePower, col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)")
Create a Time series
datetime <- strptime(paste(subsetdata$Date, subsetdata$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
plot(datetime, globalActivePower, type="l", xlab="", ylab="Global Active Power (kilowatts)")
Create a plot for sub metering
plot(datetime, subMetering1, type="l", ylab="Energy Submetering", xlab="")
lines(datetime, subMetering2, type="l", col="red")
lines(datetime, subMetering3, type="l", col="blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, lwd=2.5, col=c("black", "red", "blue"))
Create multiple plot
par(mfrow = c(2, 2))
# First plot
plot(datetime, globalActivePower, type="l", xlab="", ylab="Global Active Power", cex=0.2)
# Second plot
plot(datetime, voltage, type="l", xlab="datetime", ylab="Voltage")
# Third plot
plot(datetime, subMetering1, type="l", ylab="Energy Submetering", xlab="")
lines(datetime, subMetering2, type="l", col="red")
lines(datetime, subMetering3, type="l", col="blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=, lwd=2.5, col=c("black", "red", "blue"), bty="o")
# Fourth plot
plot(datetime, globalReactivePower, type="l", xlab="datetime", ylab="Global_reactive_power", cex=0.2)