plot4.R

download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
              "household_power_consumption.zip",
              "curl")
unzip("household_power_consumption.zip")
df <- read.table(
  "household_power_consumption.txt",
  sep=";",
  na.strings="?",
  col.names=c("Date",
              "Time",
              "Global_active_power",
              "Global_reactive_power",
              "Voltage",
              "Global_intensity",
              "Sub_metering_1",
              "Sub_metering_2",
              "Sub_metering_3"),
  colClasses=c("character",
               "character",
               rep("numeric",7)),
  comment.char="",
  nrows=3000,
  # skip forward to the start of the data of interest
  skip=tail(grep("31/1/2007",
                 readLines("household_power_consumption.txt")),1)
)

# convert date column from character class to date class
df[, "Date"] <- as.Date(df[, "Date"],"%d/%m/%Y")
# remove rows outside of relevant date range
df <- subset(df,Date < "2007-02-03")
# create a new column dt that combines the date and time elements
dt <- strptime(paste(df$Date,df$Time),"%Y-%m-%d %T")
df <- cbind(df,dt)

png("plot4.png")
par(mfrow=c(2,2))
with(df, {
  plot(dt,
       Global_active_power,
       type="l",
       ylab="Global Active Power",
       xlab="")
  plot(dt,
       Voltage,
       type="l",
       ylab="Voltage",
       xlab="datetime")
  plot(dt,
       Sub_metering_1,
       type="l",
       ylab="Energy sub metering",
       xlab="")
  lines(dt,
        Sub_metering_2,
        type="l",
        xlab="",
        col="red")
  lines(dt,
        Sub_metering_3,
        type="l",
        xlab="",
        col="blue")
  legend("topright",
         col=c("black","red","blue"),
         legend=c("Sub_metering_1",
                  "Sub_metering_2",
                  "Sub_metering_3"),
         lty=1,
         lwd=1,
         bty="n")
  plot(dt,
       Global_reactive_power,
       type="l",
       xlab="datetime")
})
dev.off()