4 Plots (png) with the R Code to Create Those Plots

plot1.R

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+# What's my available memory?
+    system('systeminfo')
+    #...Available Physical Memory: 345 MB
+
+# How much memory does the file require?
+    object.size(url)
+    #184 bytes = 0.000184 MB
+    #plenty of room
+
+# Read in the data (assuming working directory is set)
+    url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
+    #download.file(url, "household_power_consumption.txt")
+        #^ this does not work because the file is zipped
+        #found help here: http://stackoverflow.com/questions/3053833/using-r-to-download-zipped-data-file-extract-and-import-data
+    temp <- tempfile()
+    download.file(url,temp)
+    mydata <- read.table(unz(temp, "household_power_consumption.txt"), header = TRUE, sep = ";", col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
+    mydata_mod <- mydata #create a second df for manipulation
+
+#Fix the class of the Date and Time variables
+    #Date
+        mydata_mod$Date <- as.Date(as.character(mydata_mod$Date), format = "%d/%m/%Y")
+        class(mydata_mod$Date)
+        #[1] "Date"
+    #Time - Attempt 1
+        #mydata_mod$Time <- strptime(as.character(mydata_mod$Time), format = "%H:%M:%S")
+            #^this adds an unwanted (and incorrect) date portion
+        #mydata_mod$Time <- strftime(mydata_mod$Time, format = "%T")
+            #^this removes the unwanted (and incorrect) date portion, but turns the class back to character
+    #Time - Attempt 2
+        library(chron)
+        mydata_mod$Time <- times(mydata_mod$Time)
+        class(mydata_mod$Time)
+        #[1] "times"
+
+# Subset the data to the only 2 dates we're interested in
+    mydata_subset <- mydata_mod[mydata_mod$Date == "2007-02-01"|mydata_mod$Date == "2007-02-02",]
+
+# Plot the data
+    mydata_subset$Global_active_power <- as.numeric(as.character(mydata_subset$Global_active_power))
+        #^initially got an error because the variable was a factor
+    png("plot1.png")
+    hist(mydata_subset$Global_active_power, col = "red", main = "Global Active Power", xlab = "Global Active Power (kilowatts)")
+    dev.off()
+
+

plot2.R

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+# What's my available memory?
+    system('systeminfo')
+    #...Available Physical Memory: 345 MB
+
+# How much memory does the file require?
+    object.size(url)
+    #184 bytes = 0.000184 MB
+    #plenty of room
+
+# Read in the data (assuming working directory is set)
+    url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
+    #download.file(url, "household_power_consumption.txt")
+        #^ this does not work because the file is zipped
+        #found help here: http://stackoverflow.com/questions/3053833/using-r-to-download-zipped-data-file-extract-and-import-data
+    temp <- tempfile()
+    download.file(url,temp)
+    mydata <- read.table(unz(temp, "household_power_consumption.txt"), header = TRUE, sep = ";", col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
+    mydata_mod <- mydata #create a second df for manipulation
+
+#Fix the class of the Date and Time variables
+    #Date
+        mydata_mod$Date <- as.Date(as.character(mydata_mod$Date), format = "%d/%m/%Y")
+        class(mydata_mod$Date)
+        #[1] "Date"
+    #Time - Attempt 1
+        #mydata_mod$Time <- strptime(as.character(mydata_mod$Time), format = "%H:%M:%S")
+        #^this adds an unwanted (and incorrect) date portion
+        #mydata_mod$Time <- strftime(mydata_mod$Time, format = "%T")
+        #^this removes the unwanted (and incorrect) date portion, but turns the class back to character
+    #Time - Attempt 2
+        library(chron)
+        mydata_mod$Time <- times(mydata_mod$Time)
+        class(mydata_mod$Time)
+        #[1] "times"
+
+# Subset the data to the only 2 dates we're interested in
+    mydata_subset <- mydata_mod[mydata_mod$Date == "2007-02-01"|mydata_mod$Date == "2007-02-02",]
+
+# Plot the data
+    #First have to "merge" the Time column data into the Date column, for one date/time variable
+        mydata_subset2 <- mydata_subset
+        mydata_subset2$Date <- strptime(paste(as.character(mydata_subset$Date), as.character(mydata_subset$Time)), format = "%Y-%m-%d %H:%M:%S")
+    #Then plot
+        png("plot2.png")
+        plot(mydata_subset2$Date, mydata_subset2$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power (kilowatts)")
+        dev.off()
+

plot3.R

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+# What's my available memory?
+    system('systeminfo')
+    #...Available Physical Memory: 345 MB
+
+# How much memory does the file require?
+    object.size(url)
+    #184 bytes = 0.000184 MB
+    #plenty of room
+
+# Read in the data (assuming working directory is set)
+    url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
+    #download.file(url, "household_power_consumption.txt")
+        #^ this does not work because the file is zipped
+        #found help here: http://stackoverflow.com/questions/3053833/using-r-to-download-zipped-data-file-extract-and-import-data
+    temp <- tempfile()
+    download.file(url,temp)
+    mydata <- read.table(unz(temp, "household_power_consumption.txt"), header = TRUE, sep = ";", col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
+    mydata_mod <- mydata #create a second df for manipulation
+
+#Fix the class of the Date and Time variables
+    #Date
+        mydata_mod$Date <- as.Date(as.character(mydata_mod$Date), format = "%d/%m/%Y")
+        class(mydata_mod$Date)
+        #[1] "Date"
+    #Time - Attempt 1
+        #mydata_mod$Time <- strptime(as.character(mydata_mod$Time), format = "%H:%M:%S")
+        #^this adds an unwanted (and incorrect) date portion
+        #mydata_mod$Time <- strftime(mydata_mod$Time, format = "%T")
+        #^this removes the unwanted (and incorrect) date portion, but turns the class back to character
+    #Time - Attempt 2
+        library(chron)
+        mydata_mod$Time <- times(mydata_mod$Time)
+        class(mydata_mod$Time)
+        #[1] "times"
+
+# Subset the data to the only 2 dates we're interested in
+    mydata_subset <- mydata_mod[mydata_mod$Date == "2007-02-01"|mydata_mod$Date == "2007-02-02",]
+
+# Plot the data
+    #First have to "merge" the Time column data into the Date column, for one date/time variable
+        mydata_subset2 <- mydata_subset
+        mydata_subset2$Date <- strptime(paste(as.character(mydata_subset$Date), as.character(mydata_subset$Time)), format = "%Y-%m-%d %H:%M:%S")
+    #Then have to get factor variables into numeric form
+        mydata_subset2$Sub_metering_1 <- as.numeric(as.character(mydata_subset2$Sub_metering_1))
+        mydata_subset2$Sub_metering_2 <- as.numeric(as.character(mydata_subset2$Sub_metering_2))
+        #mydata_subset2$Sub_metering_3 is already numeric
+
+    #Then plot
+        png("plot3.png")
+
+        plot(mydata_subset2$Date, mydata_subset2$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering", ylim = c(-1,39), yaxt = "n")
+            #found ylim max and min by manually checking max and min of each y data set
+        axis(side = 2, at = c(0,10,20,30))
+        lines(mydata_subset2$Date, mydata_subset2$Sub_metering_2, col = "red")
+        lines(mydata_subset2$Date, mydata_subset2$Sub_metering_3, col = "blue")
+        legend("topright", legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), col = c("black", "red", "blue"), lty = 1)
+
+        dev.off()
+
+
+
+

plot4.R

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+# What's my available memory?
+    system('systeminfo')
+    #...Available Physical Memory: 345 MB
+
+# How much memory does the file require?
+    object.size(url)
+    #184 bytes = 0.000184 MB
+    #plenty of room
+
+# Read in the data (assuming working directory is set)
+    url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
+    #download.file(url, "household_power_consumption.txt")
+        #^ this does not work because the file is zipped
+        #found help here: http://stackoverflow.com/questions/3053833/using-r-to-download-zipped-data-file-extract-and-import-data
+    temp <- tempfile()
+    download.file(url,temp)
+    mydata <- read.table(unz(temp, "household_power_consumption.txt"), header = TRUE, sep = ";", col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
+    mydata_mod <- mydata #create a second df for manipulation
+
+#Fix the class of the Date and Time variables
+    #Date
+        mydata_mod$Date <- as.Date(as.character(mydata_mod$Date), format = "%d/%m/%Y")
+        class(mydata_mod$Date)
+        #[1] "Date"
+    #Time - Attempt 1
+        #mydata_mod$Time <- strptime(as.character(mydata_mod$Time), format = "%H:%M:%S")
+        #^this adds an unwanted (and incorrect) date portion
+        #mydata_mod$Time <- strftime(mydata_mod$Time, format = "%T")
+        #^this removes the unwanted (and incorrect) date portion, but turns the class back to character
+    #Time - Attempt 2
+        library(chron)
+        mydata_mod$Time <- times(mydata_mod$Time)
+        class(mydata_mod$Time)
+        #[1] "times"
+
+# Subset the data to the only 2 dates we're interested in
+    mydata_subset <- mydata_mod[mydata_mod$Date == "2007-02-01"|mydata_mod$Date == "2007-02-02",]
+
+# Plot the data
+    #First have to "merge" the Time column data into the Date column, for one date/time variable
+        mydata_subset2 <- mydata_subset
+        mydata_subset2$Date <- strptime(paste(as.character(mydata_subset$Date), as.character(mydata_subset$Time)), format = "%Y-%m-%d %H:%M:%S")
+    #Then have to get factor variables into numeric form
+        mydata_subset2$Sub_metering_1 <- as.numeric(as.character(mydata_subset2$Sub_metering_1))
+        mydata_subset2$Sub_metering_2 <- as.numeric(as.character(mydata_subset2$Sub_metering_2))
+        #mydata_subset2$Sub_metering_3 is already numeric
+        mydata_subset2$Global_reactive_power <- as.numeric(as.character(mydata_subset2$Global_reactive_power))
+        mydata_subset2$Voltage <- as.numeric(as.character(mydata_subset2$Voltage))
+
+    #Then plot
+        png("plot4.png")
+
+        par(mfrow = c(2, 2), mar = c(5, 4, 2, 1))
+
+        #Plot 4a (=Plot2)
+            plot(mydata_subset2$Date, mydata_subset2$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power")
+
+        #Plot 4b (new)
+            plot(mydata_subset2$Date, mydata_subset2$Voltage, type = "l", xlab = "datetime", ylab = "Voltage")
+
+        #Plot 4c (=Plot3)    
+            plot(mydata_subset2$Date, mydata_subset2$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering", ylim = c(-1,39), yaxt = "n")
+                #found ylim max and min by manually checking max and min of each y data set
+            axis(side = 2, at = c(0,10,20,30))
+            lines(mydata_subset2$Date, mydata_subset2$Sub_metering_2, col = "red")
+            lines(mydata_subset2$Date, mydata_subset2$Sub_metering_3, col = "blue")
+            legend("topright", legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), col = c("black", "red", "blue"), lty = 1, cex = 0.7)
+
+        #Plot 4d (new)
+            plot(mydata_subset2$Date, mydata_subset2$Global_reactive_power, type = "l", xlab = "", ylab = "Global_reactive_power")
+            #^had to change class to numeric from factor to get y-axis units to match example
+
+        dev.off()
+
+
+