exploratory_analysis_final

This is an exploratory analysis on fine particulate matter (PM2.5), which is an ambient air pollutant with strong evidence that it is harmful to humans. The assignment is part of Coursera’s Exploratory Data Analysis course through the data science specialization offered by Johns Hopkins University. The figures are just meant to provide insights about potential relationships that can be further analyzed. First, the data need to be downloaded and read into R.

#setwd before running code
data1 <- "pm25_data.zip"

#download the file
if(!file.exists(data1)){
                fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
                download.file(fileURL, data1, method = "curl")
}
#unzip the file
if(!file.exists("pm25_data.txt")){
                unzip(data1)
}
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")

Next, load in the necessary R packages.

library(dplyr)
library(ggplot2)

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.

NEI$year <- as.character(NEI$year)

total_emissions <- with(NEI, tapply(Emissions, year, sum, na.rm = TRUE))

plot(total_emissions/10^6,xaxt ='n', type = "l", xlab = "Year", 
                ylab = "Total PM2.5 Emissions in Megatons",
                main = "PM2.5 Trend in the USA from 1999-2008")
axis(1, 1:4, labels = c("1999", "2002", "2005", "2008"))

It appears PM2.5 has been decreasing in the United States for the specified period.

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.

NEI$year <- as.character(NEI$year)

baltimore_emissions <- NEI %>%
                filter(fips == "24510")%>%
                group_by(year)%>%
                summarize(sum_emissions = sum(Emissions, na.rm = T))

with(baltimore_emissions, plot(year, sum_emissions,
                type = "l", ylab = "Total PM2.5 Emissions in tons", main = 
                "Total PM2.5 Emissions by Year in Baltimore, USA"))
with(baltimore_emissions, points(year, sum_emissions, pch = 20, col = "blue"))

Total emissions have decreased in Baltimore, but there was an increase in 2005.

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.

NEI$type <- as.factor(NEI$type)
NEI$year <- as.integer(NEI$year)
baltimore_emissions <- NEI %>%
                  filter(fips == "24510")%>%
                  group_by(type, year) %>%
                  summarize(sum_emissions = sum(Emissions, na.rm = T))

ggplot(baltimore_emissions, aes(year, sum_emissions, col = type)) + 
                  ylab("Total PM2.5 Emissions (tons)") +
                  ggtitle("PM 2.5 Levels by Source Type in Baltimore from 1998-2008") + 
                  geom_point() +
                  geom_smooth(method = "lm", se = FALSE)

Of the four types, the only type that had a downward trend was “nonpoint” sources.

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

SCC_coal <- SCC[grepl("Coal", SCC$Short.Name), ]
joined_SCC <- inner_join(NEI, SCC_coal, by = "SCC")

## Warning: Column `SCC` joining character vector and factor, coercing into
## character vector

coal_emissions <- joined_SCC %>%
                  group_by(year) %>%
                  summarise(emissions = sum(Emissions))

ggplot(coal_emissions, aes(year, emissions)) +
                  geom_point(color = "red", size = 3) + 
                  geom_line() +
                  ylab("PM2.5 Emissions (tons)") +
                  ggtitle("PM2.5 Emissions from Coal Combustion in the USA")

Emissions from coal combustion-related sources have decreased over the period.

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

baltimore_motor_emissions <- NEI %>%
                  filter(fips == "24510" & type == "ON-ROAD") %>%
                  group_by(year)%>%
                  summarize(emissions = sum(Emissions, na.rm = T))

ggplot(baltimore_motor_emissions, aes(year, emissions)) +
                  geom_point(color = "blue", size = 3) +
                  geom_line(color = "blue") +
                  ylab("PM2.5 Emissions (tons)") +
                  ggtitle("PM2.5 Emissions from Motor Vehicles in Baltimore (1998-2008)")

Motor emissions have decreased in Baltimore.

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?

NEI$fips[NEI$fips == "24510"] <- "Baltimore"
NEI$fips[NEI$fips == "06037"] <- "LA"

baltimore_vs_LA_motor_emissions <- NEI %>%
                  filter(fips == "Baltimore" & type == "ON-ROAD" | fips == "LA" & type == "ON-ROAD") %>%
                  group_by(year, fips)%>%
                  summarize(emissions = sum(Emissions, na.rm = T))

ggplot(baltimore_vs_LA_motor_emissions, aes(year, emissions, col = fips)) +
                  geom_point(pch = 20, size = 3) +
                  geom_smooth(method = "lm", se = FALSE) +
                  ylab("PM2.5 Emissions (tons)") +
                  ggtitle("PM2.5 Emissions from Motor Vehicles in Baltimore vs LA (1998-2008)")

Emissions have trended upward for LA, and the emissions are much higher compared to Baltimore.