diff --git a/plot1.R b/plot1.R
new file mode 100644
index 00000000000..468b3ef185e
--- /dev/null
+++ b/plot1.R
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+library(dplyr)
+
+## PLotting function
+plot1 <- function(){
+    ## Build the environment
+    build_environment()
+    ## Read data
+    data <- read_dataset()
+    ## Set the png device
+    png("plot1.png")
+    ## Draw the Global_active_power histagram
+    with(data, hist(Global_active_power, col="red",xlab = "Global Active Power (kilowatts)", 
+                    main = "Global Active Power"))
+    ## Close the device
+    dev.off()
+}
+
+## This function downloads and unzips the file needed by the project
+build_environment <- function(){
+    ## Directory to store the data
+    data_dir <- "data"
+    ## The actual date
+    date <- Sys.Date()
+    ## The URL containing the data
+    download_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
+    ## The file containing the data
+    data_file <- file.path(data_dir,"household_power_consumption.txt")
+    ## The name og the zip file
+    zip_file <- file.path(data_dir,paste0("project_",date,".zip"))
+    ## Creation of the working environment
+    if(!file.exists(data_dir)){dir.create(data_dir)}
+    if(!file.exists(zip_file)){
+        download.file(download_url, zip_file)
+        unzip(zip_file,exdir = data_dir)    
+    }
+}
+
+## This function reads the dataset and filter by the requested date range. 
+## Il casts the Date variable to Date class and the Time variable to Time class
+read_dataset <- function(){
+    ## Read the dataset
+    data <- read.delim("data/household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?")
+    ## Convert the Time column to Time class
+    data$Time <- strptime(paste(data$Date, data$Time), format="%d/%m/%Y %H:%M:%S")
+    ## Convert the Date column to Date class
+    data$Date <- as.Date(data$Date, format="%d/%m/%Y")
+    ## Filter the rows by date
+    data <- data[data$Date <= "2007-02-02" & data$Date >= "2007-02-01",]
+}
+
diff --git a/plot1.png b/plot1.png
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diff --git a/plot2.R b/plot2.R
new file mode 100644
index 00000000000..57b43950fdb
--- /dev/null
+++ b/plot2.R
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+library(dplyr)
+
+## PLotting function
+plot2 <- function(){
+    ## Build the environment
+    build_environment()
+    ## Read data
+    data <- read_dataset()
+    ## Set the png device
+    png("plot2.png")
+    ## Draw the Global_active_power graph
+    plot(x = data$Time, y=data$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power (kilowatts)")
+    ## Close the device
+    dev.off()
+}
+
+## This function downloads and unzips the file needed by the project
+build_environment <- function(){
+    ## Directory to store the data
+    data_dir <- "data"
+    ## The actual date
+    date <- Sys.Date()
+    ## The URL containing the data
+    download_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
+    ## The file containing the data
+    data_file <- file.path(data_dir,"household_power_consumption.txt")
+    ## The name og the zip file
+    zip_file <- file.path(data_dir,paste0("project_",date,".zip"))
+    ## Creation of the working environment
+    if(!file.exists(data_dir)){dir.create(data_dir)}
+    if(!file.exists(zip_file)){
+        download.file(download_url, zip_file)
+        unzip(zip_file,exdir = data_dir)    
+    }
+}
+
+## This function reads the dataset and filter by the requested date range. 
+## Il casts the Date variable to Date class and the Time variable to Time class
+read_dataset <- function(){
+    ## Read the dataset
+    data <- read.delim("data/household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?")
+    ## Convert the Time column to Time class
+    data$Time <- strptime(paste(data$Date, data$Time), format="%d/%m/%Y %H:%M:%S")
+    ## Convert the Date column to Date class
+    data$Date <- as.Date(data$Date, format="%d/%m/%Y")
+    ## Filter the rows by date
+    data <- data[data$Date <= "2007-02-02" & data$Date >= "2007-02-01",]
+}
+
diff --git a/plot2.png b/plot2.png
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index 00000000000..753d61e1387
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diff --git a/plot3.R b/plot3.R
new file mode 100644
index 00000000000..3e848cff087
--- /dev/null
+++ b/plot3.R
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+library(dplyr)
+
+## PLotting function
+plot3 <- function(){
+    ## Build the environment
+    build_environment()
+    ## Read data
+    data <- read_dataset()
+    ## Set the png device
+    png("plot3.png")
+    ## Draw the Energy sub metering graphs
+    plot(x = data$Time, y=data$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering", col="black")
+    with(data, lines(x=Time, y=Sub_metering_2, col="red"))
+    with(data, lines(x=Time, y=Sub_metering_3, col="blue"))
+    ## Add the legend
+    legend("topright",lwd=c(2.5,2.5), col=c("black","red","blue"), 
+           legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
+    ## Close the device
+    dev.off()
+}
+
+## This function downloads and unzips the file needed by the project
+build_environment <- function(){
+    ## Directory to store the data
+    data_dir <- "data"
+    ## The actual date
+    date <- Sys.Date()
+    ## The URL containing the data
+    download_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
+    ## The file containing the data
+    data_file <- file.path(data_dir,"household_power_consumption.txt")
+    ## The name og the zip file
+    zip_file <- file.path(data_dir,paste0("project_",date,".zip"))
+    ## Creation of the working environment
+    if(!file.exists(data_dir)){dir.create(data_dir)}
+    if(!file.exists(zip_file)){
+        download.file(download_url, zip_file)
+        unzip(zip_file,exdir = data_dir)    
+    }
+}
+
+## This function reads the dataset and filter by the requested date range. 
+## Il casts the Date variable to Date class and the Time variable to Time class
+read_dataset <- function(){
+    ## Read the dataset
+    data <- read.delim("data/household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?")
+    ## Convert the Time column to Time class
+    data$Time <- strptime(paste(data$Date, data$Time), format="%d/%m/%Y %H:%M:%S")
+    ## Convert the Date column to Date class
+    data$Date <- as.Date(data$Date, format="%d/%m/%Y")
+    ## Filter the rows by date
+    data <- data[data$Date <= "2007-02-02" & data$Date >= "2007-02-01",]
+}
+
diff --git a/plot3.png b/plot3.png
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index 00000000000..eb97ad8b870
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diff --git a/plot4.R b/plot4.R
new file mode 100644
index 00000000000..8acf4587a0a
--- /dev/null
+++ b/plot4.R
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+library(dplyr)
+
+## PLotting function
+plot4 <- function(){
+    ## Build the environment
+    build_environment()
+    ## Read data
+    data <- read_dataset()
+    ## Set the png device
+    png("plot4.png")
+    ## Prepare to draw graphs in a 2+2 grid
+    par(mfcol=c(2,2))
+    ## Draw the Global_active_power histagram
+    plot(x = data$Time, y=data$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power")
+    ## Draw the Energy sub metering graphs    
+    plot(x = data$Time, y=data$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering", col="black")
+    with(data, lines(x=Time, y=Sub_metering_2, col="red"))
+    with(data, lines(x=Time, y=Sub_metering_3, col="blue"))
+    ## Add the legend
+    legend("topright",lwd=c(2.5,2.5), bty = "n", col=c("black","red","blue"),
+           legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
+    ## Draw the Voltage graph
+    plot(x = data$Time, y=data$Voltage, type = "l", xlab="datetime", ylab = "Voltage")
+    ## Draw the Global reactive power graph
+    plot(x = data$Time, y=data$Global_reactive_power, type = "l", xlab="datetime", ylab = "Global_reactive_power")
+    ## Close the device
+    dev.off()
+}
+
+## This function downloads and unzips the file needed by the project
+build_environment <- function(){
+    ## Directory to store the data
+    data_dir <- "data"
+    ## The actual date
+    date <- Sys.Date()
+    ## The URL containing the data
+    download_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
+    ## The file containing the data
+    data_file <- file.path(data_dir,"household_power_consumption.txt")
+    ## The name og the zip file
+    zip_file <- file.path(data_dir,paste0("project_",date,".zip"))
+    ## Creation of the working environment
+    if(!file.exists(data_dir)){dir.create(data_dir)}
+    if(!file.exists(zip_file)){
+        download.file(download_url, zip_file)
+        unzip(zip_file,exdir = data_dir)    
+    }
+}
+
+## This function reads the dataset and filter by the requested date range. 
+## Il casts the Date variable to Date class and the Time variable to Time class
+read_dataset <- function(){
+    ## Read the dataset
+    data <- read.delim("data/household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?")
+    ## Convert the Time column to Time class
+    data$Time <- strptime(paste(data$Date, data$Time), format="%d/%m/%Y %H:%M:%S")
+    ## Convert the Date column to Date class
+    data$Date <- as.Date(data$Date, format="%d/%m/%Y")
+    ## Filter the rows by date
+    data <- data[data$Date <= "2007-02-02" & data$Date >= "2007-02-01",]
+}
+
diff --git a/plot4.png b/plot4.png
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