Exploratory Data Analysis in R - Week 4 Assignment

# Load the raw data files.
# These lines of code will take a little time to execute, so please be patient!

NEI <- readRDS("exdata-data-NEI_data/summarySCC_PM25.rds")
SCC <- readRDS("exdata-data-NEI_data/Source_Classification_Code.rds")
merged_df <- merge(NEI,SCC,by="SCC")

Questions

We will address the following questions and tasks in our exploratory analysis. For each question/task we will need to make a single plot. Unless specified, we can use any plotting system in R to make our plot.

1. Have total emissions from PM2.5 decreased in the United States from 1999 to 2008? Using the base plotting system, make a plot showing the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008.

total_emissions <- aggregate(NEI$Emission, by=list(NEI$year), sum)
plot(total_emissions,pch=16,xlab="Year",ylab="Emissions (tons)",main="Total Emissions by Year")
lines(total_emissions$Group.1,total_emissions$x)
grid(lty="dotted")

2. Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips == "24510") from 1999 to 2008? Use the base plotting system to make a plot answering this question.

# subset NEI for Baltimore City (fips=="24510")
baltimore_city <- subset(NEI,fips=="24510")
balt_emissions <- aggregate(baltimore_city$Emission, by=list(baltimore_city$year), sum)

plot(balt_emissions,pch=16,xlab="Year",ylab="Emissions (tons)",main="Total Emissions by Year: Baltimore-City")
lines(balt_emissions$Group.1,balt_emissions$x)
grid(lty="dotted")

3. Of the four types of sources indicated by the type (point, nonpoint, onroad, nonroad) variable, which of these four sources have seen decreases in emissions from 1999–2008 for Baltimore City? Which have seen increases in emissions from 1999–2008? Use the ggplot2 plotting system to make a plot answer this question.

library(ggplot2)

balt_emissions_type_year <- aggregate(baltimore_city$Emission, by=list(baltimore_city$type,baltimore_city$year), sum)
names(balt_emissions_type_year) <- c("type","Year","Emissions")

ggplot(balt_emissions_type_year, aes(Year,Emissions))+ geom_point(aes(color=type))+geom_line(aes(color=type))+ggtitle("Total Emissions by Type and Year: Baltimore-City")+ylab("Emissions (tons)")+labs(color="Emission Type")

4. Across the United States, how have emissions from coal combustion-related sources changed from 1999–2008?

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)

# Filter the merged df for 'coal combustion-related' sources
coal_combustion <- dplyr::filter(merged_df, grepl('Coal', Short.Name) & grepl('Ext Comb',Short.Name))
emissions_type_year <- aggregate(coal_combustion$Emission, by=list(coal_combustion$type,coal_combustion$year), sum)
names(emissions_type_year) <- c("type","Year","Emissions")

ggplot(emissions_type_year, aes(Year,Emissions))+ geom_point(aes(color=type))+geom_line(aes(color=type))+ggtitle("Total Coal Combustible-Related Emissions by Type and Year")+ylab("Emissions (tons)")+labs(color="Emission Type")

5. How have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City?

library(dplyr)
library(ggplot2)

# Filter the merged_df for 'Baltimore City' and 'Highway Veh'
balt_cars <- dplyr::filter(merged_df, grepl("24510",fips) & grepl('Highway Veh',Short.Name))
balt_car_emissions <- aggregate(balt_cars$Emission, by=list(balt_cars$type,balt_cars$year), sum)
names(balt_car_emissions) <- c("type","Year","Emissions")

ggplot(balt_car_emissions, aes(Year,Emissions))+ geom_point(aes(color=type))+geom_line(aes(color=type))+ggtitle("Total Motor Vehicle Emissions by Type and Year: Baltimore-City")+ylab("Emissions (tons)")+labs(color="Emission Type")

6. Compare emissions from motor vehicle sources in Baltimore City with emissions from motor vehicle sources in Los Angeles County, California (fips == "06037"). Which city has seen greater changes over time in motor vehicle emissions?

library(dplyr)
library(ggplot2)

# Filter the merged_df for fips==24510|fips==06037 and 'Highway Veh'
balt_la_cars <- dplyr::filter(merged_df, grepl("24510|06037",fips) & grepl('Highway Veh',Short.Name))
balt_la_car_emissions <- aggregate(balt_la_cars$Emission, by=list(balt_la_cars$fips,balt_la_cars$year), sum)
names(balt_la_car_emissions) <- c("fips","Year","Emissions")

ggplot(balt_la_car_emissions, aes(Year,Emissions))+ geom_point(aes(color=fips))+geom_line(aes(color=fips))+ggtitle("Total Motor Vehicle Emissions by FIPS Location and Year")+ylab("Emissions (tons)")+labs(color="fips location")