Add files via upload

README.md

@@ -1,114 +1,114 @@
-## Introduction
-
-This assignment uses data from
-the <a href="http://archive.ics.uci.edu/ml/">UC Irvine Machine
-Learning Repository</a>, 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:
-
-
-* <b>Dataset</b>: <a href="https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip">Electric power consumption</a> [20Mb]
-
-* <b>Description</b>: 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 <a href="https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption">UCI
-web site</a>:
-
-<ol>
-<li><b>Date</b>: Date in format dd/mm/yyyy </li>
-<li><b>Time</b>: time in format hh:mm:ss </li>
-<li><b>Global_active_power</b>: household global minute-averaged active power (in kilowatt) </li>
-<li><b>Global_reactive_power</b>: household global minute-averaged reactive power (in kilowatt) </li>
-<li><b>Voltage</b>: minute-averaged voltage (in volt) </li>
-<li><b>Global_intensity</b>: household global minute-averaged current intensity (in ampere) </li>
-<li><b>Sub_metering_1</b>: 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). </li>
-<li><b>Sub_metering_2</b>: 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. </li>
-<li><b>Sub_metering_3</b>: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.</li>
-</ol>
-
-## Loading the data
-
-
-
-
-
-When loading the dataset into R, please consider the following:
-
-* The dataset has 2,075,259 rows and 9 columns. First
-calculate a rough estimate of how much memory the dataset will require
-in memory before reading into R. Make sure your computer has 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.
-
-* You may find it useful to convert the Date and Time variables to
-Date/Time classes in R using the `strptime()` and `as.Date()`
-functions.
-
-* Note that 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. Your task is to
-reconstruct the following plots below, all of which were constructed
-using the base plotting system.
-
-First you will need to fork and clone the following GitHub repository:
-[https://github.com/rdpeng/ExData_Plotting1](https://github.com/rdpeng/ExData_Plotting1)
-
-
-For each plot you should
-
-* Construct the plot and save it to a PNG file with a width of 480
-pixels and a height of 480 pixels.
-
-* Name each of the plot files as `plot1.png`, `plot2.png`, etc.
-
-* Create a separate R code file (`plot1.R`, `plot2.R`, etc.) that
-constructs the corresponding plot, i.e. code in `plot1.R` constructs
-the `plot1.png` plot. Your code file **should include code for reading
-the data** so that the plot can be fully reproduced. You should also
-include the code that creates the PNG file.
-
-* Add the PNG file and R code file to your git repository
-
-When you are finished with the assignment, push your git repository to
-GitHub so that the GitHub version of your repository is up to
-date. There should be four PNG files and four R code files.
-
-
-The four plots that you will need to construct are shown below. 
-
-
-### Plot 1
-
-
-![plot of chunk unnamed-chunk-2](figure/unnamed-chunk-2.png) 
-
-
-### Plot 2
-
-![plot of chunk unnamed-chunk-3](figure/unnamed-chunk-3.png) 
-
-
-### Plot 3
-
-![plot of chunk unnamed-chunk-4](figure/unnamed-chunk-4.png) 
-
-
-### Plot 4
-
-![plot of chunk unnamed-chunk-5](figure/unnamed-chunk-5.png) 
-
+## Introduction
+
+This assignment uses data from
+the <a href="http://archive.ics.uci.edu/ml/">UC Irvine Machine
+Learning Repository</a>, 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:
+
+
+* <b>Dataset</b>: <a href="https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip">Electric power consumption</a> [20Mb]
+
+* <b>Description</b>: 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 <a href="https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption">UCI
+web site</a>:
+
+<ol>
+<li><b>Date</b>: Date in format dd/mm/yyyy </li>
+<li><b>Time</b>: time in format hh:mm:ss </li>
+<li><b>Global_active_power</b>: household global minute-averaged active power (in kilowatt) </li>
+<li><b>Global_reactive_power</b>: household global minute-averaged reactive power (in kilowatt) </li>
+<li><b>Voltage</b>: minute-averaged voltage (in volt) </li>
+<li><b>Global_intensity</b>: household global minute-averaged current intensity (in ampere) </li>
+<li><b>Sub_metering_1</b>: 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). </li>
+<li><b>Sub_metering_2</b>: 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. </li>
+<li><b>Sub_metering_3</b>: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.</li>
+</ol>
+
+## Loading the data
+
+
+
+
+
+When loading the dataset into R, please consider the following:
+
+* The dataset has 2,075,259 rows and 9 columns. First
+calculate a rough estimate of how much memory the dataset will require
+in memory before reading into R. Make sure your computer has 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.
+
+* You may find it useful to convert the Date and Time variables to
+Date/Time classes in R using the `strptime()` and `as.Date()`
+functions.
+
+* Note that 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. Your task is to
+reconstruct the following plots below, all of which were constructed
+using the base plotting system.
+
+First you will need to fork and clone the following GitHub repository:
+[https://github.com/rdpeng/ExData_Plotting1](https://github.com/rdpeng/ExData_Plotting1)
+
+
+For each plot you should
+
+* Construct the plot and save it to a PNG file with a width of 480
+pixels and a height of 480 pixels.
+
+* Name each of the plot files as `plot1.png`, `plot2.png`, etc.
+
+* Create a separate R code file (`plot1.R`, `plot2.R`, etc.) that
+constructs the corresponding plot, i.e. code in `plot1.R` constructs
+the `plot1.png` plot. Your code file **should include code for reading
+the data** so that the plot can be fully reproduced. You should also
+include the code that creates the PNG file.
+
+* Add the PNG file and R code file to your git repository
+
+When you are finished with the assignment, push your git repository to
+GitHub so that the GitHub version of your repository is up to
+date. There should be four PNG files and four R code files.
+
+
+The four plots that you will need to construct are shown below. 
+
+
+### Plot 1
+
+
+![plot of chunk unnamed-chunk-2](figure/unnamed-chunk-2.png) 
+
+
+### Plot 2
+
+![plot of chunk unnamed-chunk-3](figure/unnamed-chunk-3.png) 
+
+
+### Plot 3
+
+![plot of chunk unnamed-chunk-4](figure/unnamed-chunk-4.png) 
+
+
+### Plot 4
+
+![plot of chunk unnamed-chunk-5](figure/unnamed-chunk-5.png) 
+

plot1.R

@@ -0,0 +1,25 @@
+temp <- tempfile()
+download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",temp)
+power <- read.table(unz(temp,"household_power_consumption.txt"), 
+                    sep=";", 
+                    header = T, 
+                    na="?", 
+                    colClasses = c("character",
+                                   'character',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric'))
+
+unlink(temp)
+power <- power[which(power$Date == '2/2/2007' | power$Date=='1/2/2007'),]
+
+power$POSIX <-as.POSIXlt.character(paste(power$Date,power$Time),format = "%d/%m/%Y %H:%M:%S")
+
+#plot.1
+png(filename="plot1.png",width=480, height=480)
+hist(power$Global_active_power, col = 'red', main = 'Global Active Power', xlab = 'Global Active Power (kilowatts)')
+dev.off()

plot2.R

@@ -0,0 +1,25 @@
+temp <- tempfile()
+download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",temp)
+power <- read.table(unz(temp,"household_power_consumption.txt"), 
+                    sep=";", 
+                    header = T, 
+                    na="?", 
+                    colClasses = c("character",
+                                   'character',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric'))
+
+unlink(temp)
+power <- power[which(power$Date == '2/2/2007' | power$Date=='1/2/2007'),]
+
+power$POSIX <-as.POSIXlt.character(paste(power$Date,power$Time),format = "%d/%m/%Y %H:%M:%S")
+
+#plot2
+png(filename="plot2.png",width=480, height=480)
+plot(x=power$POSIX ,y=power$Global_active_power, type = 'l', xlab='',ylab = 'Global Active Power (kilowatts)')
+dev.off()

plot3.R

@@ -0,0 +1,29 @@
+temp <- tempfile()
+download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",temp)
+power <- read.table(unz(temp,"household_power_consumption.txt"), 
+                    sep=";", 
+                    header = T, 
+                    na="?", 
+                    colClasses = c("character",
+                                   'character',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric'))
+
+unlink(temp)
+power <- power[which(power$Date == '2/2/2007' | power$Date=='1/2/2007'),]
+
+power$POSIX <-as.POSIXlt.character(paste(power$Date,power$Time),format = "%d/%m/%Y %H:%M:%S")
+
+
+#plot3
+png(filename="plot3.png",width=480, height=480)
+plot(x=power$POSIX,y=power$Sub_metering_1, type='l', col = 'black', ylab = 'Energy sub metering', xlab = '')
+lines(x=power$POSIX,y=power$Sub_metering_2, col='red')
+lines(x=power$POSIX,y=power$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

@@ -0,0 +1,33 @@
+temp <- tempfile()
+download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",temp)
+power <- read.table(unz(temp,"household_power_consumption.txt"), 
+                    sep=";", 
+                    header = T, 
+                    na="?", 
+                    colClasses = c("character",
+                                   'character',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric'))
+
+unlink(temp)
+power <- power[which(power$Date == '2/2/2007' | power$Date=='1/2/2007'),]
+
+power$POSIX <-as.POSIXlt.character(paste(power$Date,power$Time),format = "%d/%m/%Y %H:%M:%S")
+
+
+#plot4
+png(filename="plot4.png",width=480, height=480)
+par(mfrow=c(2,2))
+plot(x=power$POSIX ,y=power$Global_active_power, type = 'l', xlab='',ylab = 'Global Active Power')
+plot(x=power$POSIX ,y=power$Voltage, type = 'l', xlab='datetime',ylab = 'Voltage')
+plot(x=power$POSIX,y=power$Sub_metering_1, type='l', col = 'black', ylab = 'Energy sub metering', xlab = '')
+lines(x=power$POSIX,y=power$Sub_metering_2, col='red')
+lines(x=power$POSIX,y=power$Sub_metering_3, col='blue')
+legend('topright', legend = c('Sub_metering_1',"Sub_metering_2","Sub_metering_3"), col = c('black','red','blue'), lty = 1, bty = "n")
+plot(x=power$POSIX ,y=power$Global_reactive_power, type = 'l', xlab='datetime',ylab = 'Global_reactive_power')
+dev.off()