2-days household energy usage pattern

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”.

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:

1. Date: Date in format dd/mm/yyyy
2. Time: time in format hh:mm:ss
3. Global_active_power: household global minute-averaged active power (in kilowatt)
4. Global_reactive_power: household global minute-averaged reactive power (in kilowatt)
5. Voltage: minute-averaged voltage (in volt)
6. Global_intensity: household global minute-averaged current intensity (in ampere)
7. 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).
8. 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.
9. 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, the following to be considered:

• The dataset has 2,075,259 rows and 9 columns. First a rough estimate of how much memory the dataset will require in memory before reading into R should be calculated. Computer should have 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.

• It may be useful to convert the Date and Time variables to Date/Time classes in R using the strptime() and as.Date() functions.

• 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. Our task is to construct the plots using the base plotting system.

fh <- file("household_power_consumption.txt")
ba <- read.table(text = grep("^[1,2]/2/2007", readLines(fh), value = TRUE), col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), sep = ";", header = TRUE)

# Generating Plot 1
hist(ba$Global_active_power, col = "red", main = paste("Global Active Power"), xlab = "Global Active Power (kilowatts)")

## Getting full dataset
data_full <- read.csv("household_power_consumption.txt", header = T, sep = ';', 
                      na.strings = "?", nrows = 2075259, check.names = F, 
                      stringsAsFactors = F, comment.char = "", quote = '\"')
data_full$Date <- as.Date(data_full$Date, format = "%d/%m/%Y")

## Subsetting the data
data <- subset(data_full, subset = (Date >= "2007-02-01" & Date <= "2007-02-02"))
rm(data_full)

## Converting dates
datetime <- paste(as.Date(data$Date), data$Time)
data$Datetime <- as.POSIXct(datetime)

## Generating Plot 2
plot(data$Global_active_power ~ data$Datetime, type = "l",
     ylab = "Global Active Power (kilowatts)", xlab = "")

## Generating Plot 3
with(data, {
    plot(Sub_metering_1 ~ Datetime, type = "l", 
    ylab = "Global Active Power (kilowatts)", xlab = "")
    lines(Sub_metering_2 ~ Datetime, col = 'Red')
    lines(Sub_metering_3 ~ Datetime, col = 'Blue')
})
legend("topright", col = c("black", "red", "blue"), lty = 1, lwd = 2, 
       legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))

## Generating Plot 4
par(mfrow = c(2,2), mar = c(4,4,2,1), oma = c(0,0,2,0))
with(data, {
     plot(Global_active_power ~ Datetime, type = "l", 
     ylab = "Global Active Power", xlab = "")
     plot(Voltage ~ Datetime, type = "l", ylab = "Voltage", xlab = "datetime")
     plot(Sub_metering_1 ~ Datetime, type = "l", ylab = "Energy sub metering",
          xlab = "")
     lines(Sub_metering_2 ~ Datetime, col = 'Red')
     lines(Sub_metering_3 ~ Datetime, col = 'Blue')
     legend("topright", col = c("black", "red", "blue"), lty = 1, lwd = 2, 
             bty = "n",
             legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
     plot(Global_reactive_power ~ Datetime, type = "l", 
          ylab = "Global_rective_power", xlab = "datetime")
})