plot.R

# $Id$
# plot.R
# ======
#
#     Introduction
#     ------------
#         This assignment uses data from the UC Irvine Machine Learning 
#         Repository (<http://archive.ics.uci.edu/ml/>), 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:
#
#             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 (<https://archive.ics.uci.edu/ml/datasets/
#         Individual+household+electric+power+consumption>):
#
#             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.
#
#     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
#
#         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 must also include the code that creates the 
#               PNG file.
#
#             * Add the PNG file and R code file to the top-level folder of 
#               your git repository (no need for separate sub-folders)
#
#         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, a total 
#         of eight files in the top-level folder of the repo.
#
#         The four plots that you will need to construct are shown below.
#
#             * Plot 1
#
#             * Plot 2
#
#             * Plot 3
#
#             * Plot 4
#
#     Reviewing the Assignments
#     -------------------------
#         Keep in mind this course is about exploratory graphs, understanding 
#         the data, and developing strategies. Here's a good quote from a 
#         swirl lesson about exploratory graphs: "They help us find patterns 
#         in data and understand its properties. They suggest modeling 
#         strategies and help to debug analyses. We DON' use exploratory 
#         graphs to communicate results."
#
#         The rubrics should always be interpreted in that context.
#
#         As you do your evaluation, please keep an open mind and focus on the 
#         positive. The goal is not to deduct points over small deviations from 
#         the requirements or for legitimate differences in implementation 
#         styles, etc. Look for ways to give points when it's clear that the 
#         submitter has given a good faith effort to do the project, and when 
#         it's likely that they'e succeeded. Most importantly, it's okay if a 
#         person did something differently from the way that you did it. The 
#         point is not to see if someone managed to match your way of doing 
#         things, but to see if someone objectively accomplished the task at 
#         hand.
#
#         To that end, keep the following things in mind:
#
#         DO
#             * Review the source code.
#             * Keep an open mind and focus on the positive.
#             * When in doubt, err on the side of giving too many points, 
#               rather than giving too few.
#             * Ask yourself if a plot might answer a question for the person 
#               who created it.
#             * Remember that not everyone has the same statistical background 
#               and knowledge.
#
#         DON'T:
#             * Deduct just because you disagree with someone's statistical 
#               methods.
#             * Deduct just because you disagree with someone' plotting 
#               methods.
#             * Deduct based on aesthetics.
#
# Copyright (C) Martin HEIN (m#)/October 2015
#
#     $Log$
#

######################################################################
######################################################################
### Section #01 -- libraries & packages.                           ###
######################################################################
######################################################################
library(R.utils) 
library(RCurl)
library(httr)
library(XML)
library(rvest)
library(gdata)
library(data.table)
library(dplyr)
library(tidyr)
library(lubridate)
library(ISOcodes)
library(microbenchmark)

data("ISO_3166_1")

######################################################################
######################################################################
### Section #03 -- functions.                                      ###
######################################################################
######################################################################

source("lib/setusragnt.R")
source("lib/bldurl.R")
source("lib/chkurl.R")
source("lib/chkdir.R")
source("lib/dldat.R")
source("lib/estuciepc.R")
source("lib/rduciepc.R")
source("lib/sstuciepc.R")
source("lib/plot1.R")
source("lib/plot2.R")
source("lib/plot3.R")
source("lib/plot4.R")

######################################################################
######################################################################
### Section #02 -- global constants & variables.                   ###
######################################################################
######################################################################
Locale <- "UTF-8"
origLocale <- Sys.getlocale()

cmdWC <- (if(.Platform$OS.type != "windows") "wc" 
          else file.path("c:", "Program Files", "R", "Rtools", "bin", "wc.exe"))

iso31661 <- NULL

extCSV <- "csv"
extTSV <- "tsv"
extTXT <- "txt"
extZIP <- "zip"

inDir <- "data"
outDir <- "publish"
docDir <- "doc"

inDatRmtZip <- BldURL("https://d396qusza40orc.cloudfront.net",
                      "exdata%2Fdata%2Fhousehold_power_consumption.zip",
                      chkURL=TRUE)
inDatLoc <- "household_power_consumption.txt"

outPlot <- c("plot1.png", "plot2.png", "plot3.png", "plot4.png")
outSize <- rep("480x480x150", length(outPlot))

szInDat <- NULL
dtInDat <- data.table(NULL)

######################################################################
######################################################################
### Section #04 -- main entry point.                               ###
######################################################################
######################################################################

## initialisation
if(.Platform$OS.type != "windows") Sys.setlocale(locale=Locale)
iso31661 <- data.table(ISO_3166_1, key=c("Alpha_2", "Alpha_3", "Name"))
if (length(iso31661) > 0) rm(ISO_3166_1)

## check for required directories
if (chkDir(c(docDir, inDir, outDir)) && (!is.na(inDatRmtZip))) {
    
    ## download raw data
    if (dlDat(inDatRmtZip, inDir, basename(inDatRmtZip), exp=TRUE, redl=FALSE)) {
        
        ## estimate memory size of raw data to load
        szInDat <- estUCIepc(inDir, inDatLoc, unts="m")

        ## load raw data
        print(paste("About to load approximately", szInDat, 
                    "Mbytes of data into the main memory..."))
        dtInDat <- rdUCIepc(inDir, inDatLoc)
        
        ## extract period of interest from raw data
        if (!is.null(dtInDat)) {
            dtInDat <- subsetUCIepc(dtInDat, "20070201", "20070202")
        } # if
        
        ## plot data
        if (!is.null(dtInDat)) {
            plot1(dtInDat, outPlot[1], outSize[1])
            plot2(dtInDat, outPlot[2], outSize[2])
            plot3(dtInDat, outPlot[3], outSize[3])
            plot4(dtInDat, outPlot[4], outSize[4])
        } # if
    } # if
} # if

#
# end of file
#