Original dataset could not be upload due to GH constraints to large files in free accounts.
Download the dataset from the link below:
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Dataset: Electric power consumption [20Mb]
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Save it locally and the unzip it
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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:
- 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.
hpc <- read.csv("household_power_consumption.txt", sep=";", na.strings = "?")
When loading the dataset into R, please consider the following:
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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).
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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.
##Subsetting ss_dates <- as.Date(hpc$Date, "%d/%m/%Y") ss_hpc <- data.frame(hpc[which(ss_dates>="2007-02-01" & ss_dates<="2007-02-02"),])
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Convert the Date and Time variables to Date/Time classes
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Converted in the subsetting process
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Missing values were remarked during the file reading process
For each plot you should (DONE)
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Construct the plot and save it to a PNG file with a width of 480 pixels and a height of 480 pixels.
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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 inplot1.R
constructs theplot1.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.
- DONE