Plotting Power Consumption

About

This was the first project for the Exploratory Data Analysis course in Coursera’s Data Science specialization track. The objective was to practice creating plots by reproducing a series of four plots, and also to practice using github by forking and cloning an existing repository.

Sypnosis

The goal here was simply to examine how household energy usage varies over a 2-day period in February, 2007 by reproducing a series of four plots. Due to specific image size requirements of the new plots, they will look slightly different than the originals.

Data

This project used data from the UC Irvine Machine Learning Repository, a popular repository for machine learning datasets. The “Individual household electric power consumption Data Set” was used and made available on the course web site:

Electric power consumption Dataset (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.

Data Loading and Preprocessing

if(!file.exists("exdata-data-household_power_consumption.zip")) {
        temp <- tempfile()
        download.file("http://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",temp)
        file <- unzip(temp)
        unlink(temp)
}
power <- read.table(file, header=T, sep=";")
power$Date <- as.Date(power$Date, format="%d/%m/%Y")
df <- power[(power$Date=="2007-02-01") | (power$Date=="2007-02-02"),]
df$Global_active_power <- as.numeric(as.character(df$Global_active_power))
df$Global_reactive_power <- as.numeric(as.character(df$Global_reactive_power))
df$Voltage <- as.numeric(as.character(df$Voltage))
df <- transform(df, timestamp=as.POSIXct(paste(Date, Time)), "%d/%m/%Y %H:%M:%S")
df$Sub_metering_1 <- as.numeric(as.character(df$Sub_metering_1))
df$Sub_metering_2 <- as.numeric(as.character(df$Sub_metering_2))
df$Sub_metering_3 <- as.numeric(as.character(df$Sub_metering_3))

Plot 1

alt text

My Plot 1

plot1 <- function() {
        hist(df$Global_active_power, main = paste("Global Active Power"), col="red", xlab="Global Active Power (kilowatts)")
        dev.copy(png, file="plot1.png", width=480, height=480)
        dev.off()
        cat("Plot1.png has been saved in", getwd())
}
plot1()

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## Plot1.png has been saved in /Users/dmaurath/Documents/JHDS/datasciencecoursera

Plot 2

My Plot 2

plot1 <- function() {
        plot(df$timestamp,df$Global_active_power, type="l", xlab="", ylab="Global Active Power (kilowatts)")
        dev.copy(png, file="plot2.png", width=480, height=480)
        dev.off()
        cat("plot2.png has been saved in", getwd())
}
plot1()

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## plot2.png has been saved in /Users/dmaurath/Documents/JHDS/datasciencecoursera

Plot 3

My Plot 3

plot3 <- function() {
        plot(df$timestamp,df$Sub_metering_1, type="l", xlab="", ylab="Energy sub metering")
        lines(df$timestamp,df$Sub_metering_2,col="red")
        lines(df$timestamp,df$Sub_metering_3,col="blue")
        legend("topright", col=c("black","red","blue"), c("Sub_metering_1  ","Sub_metering_2  ", "Sub_metering_3  "),lty=c(1,1), lwd=c(1,1))
        dev.copy(png, file="plot3.png", width=480, height=480)
        dev.off()
        cat("plot3.png has been saved in", getwd())
}
plot3()

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## plot3.png has been saved in /Users/dmaurath/Documents/JHDS/datasciencecoursera

Plot 4

My Plot 4

plot4 <- function() {
        par(mfrow=c(2,2))
        
        ##PLOT 1
        plot(df$timestamp,df$Global_active_power, type="l", xlab="", ylab="Global Active Power")
        ##PLOT 2
        plot(df$timestamp,df$Voltage, type="l", xlab="datetime", ylab="Voltage")
        
        ##PLOT 3
        plot(df$timestamp,df$Sub_metering_1, type="l", xlab="", ylab="Energy sub metering")
        lines(df$timestamp,df$Sub_metering_2,col="red")
        lines(df$timestamp,df$Sub_metering_3,col="blue")
        legend("topright", col=c("black","red","blue"), c("Sub_metering_1  ","Sub_metering_2  ", "Sub_metering_3  "),lty=c(1,1), bty="n", cex=.5) #bty removes the box, cex shrinks the text, spacing added after labels so it renders correctly
        
        #PLOT 4
        plot(df$timestamp,df$Global_reactive_power, type="l", xlab="datetime", ylab="Global_reactive_power")
        
        #OUTPUT
        dev.copy(png, file="plot4.png", width=480, height=480)
        dev.off()
        cat("plot4.png has been saved in", getwd())
}
plot4()

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## plot4.png has been saved in /Users/dmaurath/Documents/JHDS/datasciencecoursera