From 18a084a44f90d44d406f578f071de74cbb7210a7 Mon Sep 17 00:00:00 2001
From: Aniruddha Dhar Chowdhury
 <91791624+Aniruddha775@users.noreply.github.com>
Date: Wed, 22 Dec 2021 20:50:58 +0530
Subject: [PATCH] Add files via upload

---
 README.md | 228 +++++++++++++++++++++++++++---------------------------
 plot1.R   |  25 ++++++
 plot1.png | Bin 0 -> 3889 bytes
 plot2.R   |  25 ++++++
 plot2.png | Bin 0 -> 4504 bytes
 plot3.R   |  29 +++++++
 plot3.png | Bin 0 -> 3959 bytes
 plot4.R   |  33 ++++++++
 plot4.png | Bin 0 -> 7134 bytes
 9 files changed, 226 insertions(+), 114 deletions(-)
 create mode 100644 plot1.R
 create mode 100644 plot1.png
 create mode 100644 plot2.R
 create mode 100644 plot2.png
 create mode 100644 plot3.R
 create mode 100644 plot3.png
 create mode 100644 plot4.R
 create mode 100644 plot4.png

diff --git a/README.md b/README.md
index d4c0d752a9e..f3e94b7e035 100644
--- a/README.md
+++ b/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) 
+
diff --git a/plot1.R b/plot1.R
new file mode 100644
index 00000000000..d7a0c108772
--- /dev/null
+++ b/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()
diff --git a/plot1.png b/plot1.png
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diff --git a/plot2.R b/plot2.R
new file mode 100644
index 00000000000..6ff0efc94e6
--- /dev/null
+++ b/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$Global_active_power, type = 'l', xlab='',ylab = 'Global Active Power (kilowatts)')
+dev.off()
diff --git a/plot2.png b/plot2.png
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diff --git a/plot3.R b/plot3.R
new file mode 100644
index 00000000000..23d50fd658e
--- /dev/null
+++ b/plot3.R
@@ -0,0 +1,29 @@
+temp <- tempfile()
+download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",temp)
+power <- read.table(unz(temp,"household_power_consumption.txt"), 
+                    sep=";", 
+                    header = T, 
+                    na="?", 
+                    colClasses = c("character",
+                                   'character',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric',
+                                   'numeric'))
+
+unlink(temp)
+power <- power[which(power$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()
\ No newline at end of file
diff --git a/plot3.png b/plot3.png
new file mode 100644
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diff --git a/plot4.R b/plot4.R
new file mode 100644
index 00000000000..35dba57393f
--- /dev/null
+++ b/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()
diff --git a/plot4.png b/plot4.png
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