Course project 2

Course Project 2

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+

Project2/plot1.R

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+#Course Project 2 (plot1)
+#loading packages
+library("ggplot2")
+library("dplyr")
+
+#read the file and take the data
+NEI <- readRDS("summarySCC_PM25.rds")
+SCC <- readRDS("Source_Classification_Code.rds")
+
+#calculate the total emissions
+emission_year <- summarise(group_by(NEI, year), Emissions=sum(Emissions))
+
+#using base plotting system show PM2.5 emission
+plot_bar1 <- barplot(emission_year$Emissions/1000, main = "Total PM2.5 Emissions", 
+                    xlab = "Year", ylab = "PM2.5 Emissions in Kilotons", 
+                    names.arg = emission_year$year, col = "red", 
+                    ylim = c(0,8000))
+text(plot_bar1, round(emission_year$Emissions/1000), 
+                     label = round(emission_year$Emissions/1000), pos = 3, 
+                     cex = 1.2)
+
+#copy the graphic to the file
+dev.copy(png, file = "plot1.png")
+dev.off()

Project2/plot2.R

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+#Course Project 2 (plot2)
+#loading packages
+library("ggplot2")
+library("dplyr")
+
+#read the file and take the data
+NEI <- readRDS("summarySCC_PM25.rds")
+SCC <- readRDS("Source_Classification_Code.rds")
+
+#calculate emissions of Baltimore
+emission_Balt <- summarise(group_by(subset(NEI, fips = "24510"), year), 
+                            Emissions = sum(Emissions))
+
+#using base plotting system show PM2.5 emission of Baltimore
+plot_bar2 <- barplot(emission_Balt$Emissions/1000, 
+                     main = "Total PM2.5 Emissions in Baltimore City", 
+                     xlab = "Year", ylab = "PM2.5 Emissions (Tons)", 
+                     names.arg = emission_Balt$year, col = "darkred", 
+                     ylim = c(0, 8000))
+text(plot_bar2, round(emission_Balt$Emissions/1000), 
+     label = round(emission_Balt$Emissions/1000), pos = 3, 
+     cex = 1.2)
+
+#copy the graphic to the file
+dev.copy(png, file = "plot2.png")
+dev.off()

Project2/plot3.R

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+#Course Project 2 (plot3)
+#loading packages
+library("ggplot2")
+library("dplyr")
+
+#read the file and take the data
+NEI <- readRDS("summarySCC_PM25.rds")
+SCC <- readRDS("Source_Classification_Code.rds")
+
+#calculate emissions of Baltimore
+emission_Balt <- summarise(group_by(subset(NEI, fips = "24510"), type, year), 
+                           Emissions = sum(Emissions))
+
+#using ggplot2 to plot
+ggplot(emission_Balt, aes(x = factor(year), y = Emissions, fill = type, 
+                       label = round(Emissions))) + geom_bar(stat = "identity") + 
+        facet_grid(. ~ type) + 
+        ggtitle("Total PM2.5 Emissions in Baltimore City") + 
+        xlab("Year")+ ylab("PM2.5 Emissions (Tons)") + 
+        theme(plot.title = element_text(hjust = 0.7))
+
+#copy the graphic to the file
+dev.copy(png, file = "plot3.png")
+dev.off()

Project2/plot4.R

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+#Course Project 2 (plot4)
+#loading packages
+library("ggplot2")
+library("dplyr")
+
+#read the file and take the data
+NEI <- readRDS("summarySCC_PM25.rds")
+SCC_data <- readRDS("Source_Classification_Code.rds")
+
+#calculate emissions from coal
+coal_data <- grepl("Comb.*Coal", SCC_data$EI.Sector)
+combustion_coal <- SCC_data[coal_data, ]
+emissions_combustion <- NEI[(NEI$SCC %in% combustion_coal$SCC), ]
+emissions_coal <- summarise(group_by(emissions_combustion, year), 
+                            Emissions=sum(Emissions))
+
+#using ggplot2 to plot
+ggplot(emissions_coal, aes(x=factor(year), y=Emissions/1000,fill=year, 
+                           label = round(Emissions/1000,2))) + 
+        geom_bar(stat="identity") +
+        xlab("year") +
+        ylab(expression("total PM2.5 emissions (kilotons)")) +
+        ggtitle("Emissions from coal combustion-related sources (kilotons)")+
+        geom_label(aes(fill = year),colour = "red")
+
+#copy the graphic to the file
+dev.copy(png, file = "plot4.png")
+dev.off()

Project2/plot5.R

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+#Course Project 2 (plot5)
+#loading packages
+library("ggplot2")
+library("dplyr")
+
+#read the file and take the data
+NEI <- readRDS("summarySCC_PM25.rds")
+SCC_data <- readRDS("Source_Classification_Code.rds")
+
+#calculate emissions from motor
+data_motor <- SCC_data[grepl("Vehicle", SCC_data$SCC.Level.Two), ]
+ssc_motor <- unique(data_motor$SCC)
+emission_motor <- NEI[(NEI$SCC %in% ssc_motor), ]
+motor_y <- emission_motor %>% filter(fips == "24510") %>% group_by(year) %>% 
+        summarise(Emissions = sum(Emissions))
+
+#using ggplot2 to plot
+ggplot(motor_y, aes(factor(year), Emissions, label = round(Emissions))) + 
+        geom_bar(stat = "identity", fill = "red") + 
+        ggtitle("Total Motor Emissions in Baltimore City") + 
+        xlab("Year") + ylab("PM2.5 Emissions (Tones)") +
+        ylim(c(0, 450)) + theme_classic()+ geom_text(size = 5, vjust = -1) + 
+        theme(plot.title = element_text(hjust = 0.5))
+
+#copy the graphic to the file
+dev.copy(png, file = "plot5.png")
+dev.off()

Project2/plot6.R

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+#Course Project 2 (plot6)
+#loading packages
+library("ggplot2")
+library("dplyr")
+
+#read the file and take the data
+NEI <- readRDS("summarySCC_PM25.rds")
+SCC_data <- readRDS("Source_Classification_Code.rds")
+data_motor <- SCC_data[grepl("Vehicle", SCC_data$SCC.Level.Two), ]
+ssc_motor <- unique(data_motor$SCC)
+emission_motor <- NEI[(NEI$SCC %in% ssc_motor), ]
+
+#compare emmisions from motor
+compare_year <- emission_motor %>% filter(fips == "24510" | fips == "06037") %>% 
+        group_by(fips, year) %>% summarise(Emissions = sum(Emissions))
+compare_year <- mutate(compare_year, 
+                       Unit = ifelse(fips == "24510", "Baltimore City", 
+                                     ifelse(fips == "06037", "Los Angeles County")))
+
+#using ggplot2 to plot
+ggplot(compare_year, aes(factor(year), Emissions, 
+                          fill = Unit, label = round(Emissions))) + 
+        geom_bar(stat = "identity") + facet_grid(. ~ Unit) + 
+        ggtitle("Total Motor Vehicle Emissions") +
+        xlab("Year") + ylab("PM 2.5 Emissions in Tons") +
+        theme(plot.title = element_text(hjust = 0.5)) + ylim(c(0, 8000)) +
+        theme_classic() + geom_text(size = 4, vjust = -1)
+
+#copy the graphic to the file
+dev.copy(png, file = "plot6.png")
+dev.off()

plot1.R

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+#Course Project 1
+#loading and getting the data
+data_raw <- read.table("household_power_consumption.txt", skip = 1,sep = ";", 
+                       na.strings = "?")
+names(data_raw) <- c("Date","Time", "Global_active_power", 
+                     "Global_reactive_power", "Voltage", "Global_intensity", 
+                     "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
+data_raw <- subset(data_raw, data_raw$Date=="1/2/2007" | data_raw$Date =="2/2/2007")
+
+#plotting(1)
+hist(data_raw[, 3], col = "red", main = "Global Active Power", xlab = "Global Active Power (kilowatts)")
+
+#copy the graphic to the file
+dev.copy(png, file = "plot1.png")
+dev.off()

plot2.R

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+#Course Project 1
+#loading and getting the data
+data_raw <- read.table("household_power_consumption.txt", skip = 1,sep = ";", 
+                       na.strings = "?")
+names(data_raw) <- c("Date","Time", "Global_active_power", 
+                     "Global_reactive_power", "Voltage", "Global_intensity", 
+                     "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
+data_raw <- subset(data_raw, data_raw$Date=="1/2/2007" | data_raw$Date =="2/2/2007")
+
+#change format of time data
+Sys.setlocale("LC_TIME", "English")
+date_format <- as.Date(data_raw$Date, format = "%d/%m/%Y")
+time_format <- strptime(data_raw$Time, format = "%H:%M:%S")
+time_final <- as.POSIXct(paste(date_format, data_raw$Time))
+
+#plotting(2)
+plot(time_final, data_raw$Global_active_power, xlab = "", ylab = "Global Active Power (kilowatts)", type = "l")
+
+#copy the graphic to the file
+dev.copy(png, file = "plot2.png")
+dev.off()

plot3.R

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+#Course Project 1
+#loading and getting the data
+data_raw <- read.table("household_power_consumption.txt", skip = 1,sep = ";", 
+                       na.strings = "?")
+names(data_raw) <- c("Date","Time", "Global_active_power", 
+                     "Global_reactive_power", "Voltage", "Global_intensity", 
+                     "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
+data_raw <- subset(data_raw, data_raw$Date=="1/2/2007" | data_raw$Date =="2/2/2007")
+
+#change format of time data
+Sys.setlocale("LC_TIME", "English")
+date_format <- as.Date(data_raw$Date, format = "%d/%m/%Y")
+time_format <- strptime(data_raw$Time, format = "%H:%M:%S")
+time_final <- as.POSIXct(paste(date_format, data_raw$Time))
+
+#plotting(3)
+plot(time_final, data_raw$Sub_metering_1, xlab = "", ylab = "Energy sub metering", 
+     type = "l")
+lines(time_final, data_raw$Sub_metering_2, col = "red")
+lines(time_final, data_raw$Sub_metering_3, col = "blue")
+
+legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), 
+       col = c("black","red","blue"), lty = 1)
+
+#copy the graphic to the file
+dev.copy(png, file = "plot3.png")
+dev.off()

plot4.R

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+#Course Project 1
+#loading and getting the data
+data_raw <- read.table("household_power_consumption.txt", skip = 1,sep = ";", 
+                       na.strings = "?")
+names(data_raw) <- c("Date","Time", "Global_active_power", 
+                     "Global_reactive_power", "Voltage", "Global_intensity", 
+                     "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
+data_raw <- subset(data_raw, data_raw$Date=="1/2/2007" | data_raw$Date =="2/2/2007")
+
+#change format of time data
+Sys.setlocale("LC_TIME", "English")
+date_format <- as.Date(data_raw$Date, format = "%d/%m/%Y")
+time_format <- strptime(data_raw$Time, format = "%H:%M:%S")
+time_final <- as.POSIXct(paste(date_format, data_raw$Time))
+
+
+#plotting(4)
+par(mfrow = c(2,2))
+plot(time_final, data_raw$Global_active_power, xlab = "", 
+     ylab = "Global Active Power (kilowatts)", 
+     type = "l")
+plot(time_final, data_raw$Voltage, xlab = "datetime", ylab = "Voltage", 
+     type = "l")
+plot(time_final, data_raw$Sub_metering_1, xlab = "", 
+     ylab = "Energy sub metering", type = "l")
+lines(time_final, data_raw$Sub_metering_2, col = "red")
+lines(time_final, data_raw$Sub_metering_3, col = "blue")
+legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), 
+       col = c("black","red","blue"), cex = 0.8, lty = 1 , bty = "n")
+plot(time_final, data_raw$Global_reactive_power, xlab = "datetime", 
+     ylab = "Global_reactive_power", type = "l")
+
+#copy the graphic to the file
+dev.copy(png, file = "plot4.png")
+dev.off()