R Homework 1

Questions

You must address the following questions and tasks in your exploratory analysis. For each question/task you will need to make a single plot. Unless specified, you must use ggplot2 to make your 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.

library(ggplot2)
library(RColorBrewer)
# Download and unzip the file:
dir.create("./air_pollution")

## Warning in dir.create("./air_pollution"): '.\air_pollution' already exists

urlzip <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(urlzip, destfile = "./air_pollution.zip" )
unzip("./air_pollution.zip", exdir = "./air_pollution" )
# Load the data:
NEI <- readRDS("./air_pollution/summarySCC_PM25.rds")
SCC <- readRDS("./air_pollution/Source_Classification_Code.rds")
# Check NEI data
str(NEI)

## 'data.frame':    6497651 obs. of  6 variables:
##  $ fips     : chr  "09001" "09001" "09001" "09001" ...
##  $ SCC      : chr  "10100401" "10100404" "10100501" "10200401" ...
##  $ Pollutant: chr  "PM25-PRI" "PM25-PRI" "PM25-PRI" "PM25-PRI" ...
##  $ Emissions: num  15.714 234.178 0.128 2.036 0.388 ...
##  $ type     : chr  "POINT" "POINT" "POINT" "POINT" ...
##  $ year     : int  1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 ...

# Check SCC data
str(SCC)

## 'data.frame':    11717 obs. of  15 variables:
##  $ SCC                : Factor w/ 11717 levels "10100101","10100102",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ Data.Category      : Factor w/ 6 levels "Biogenic","Event",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Short.Name         : Factor w/ 11238 levels "","2,4-D Salts and Esters Prod /Process Vents, 2,4-D Recovery: Filtration",..: 3283 3284 3293 3291 3290 3294 3295 3296 3292 3289 ...
##  $ EI.Sector          : Factor w/ 59 levels "Agriculture - Crops & Livestock Dust",..: 18 18 18 18 18 18 18 18 18 18 ...
##  $ Option.Group       : Factor w/ 25 levels "","C/I Kerosene",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Option.Set         : Factor w/ 18 levels "","A","B","B1A",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ SCC.Level.One      : Factor w/ 17 levels "Brick Kilns",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ SCC.Level.Two      : Factor w/ 146 levels "","Agricultural Chemicals Production",..: 32 32 32 32 32 32 32 32 32 32 ...
##  $ SCC.Level.Three    : Factor w/ 1061 levels "","100% Biosolids (e.g., sewage sludge, manure, mixtures of these matls)",..: 88 88 156 156 156 156 156 156 156 156 ...
##  $ SCC.Level.Four     : Factor w/ 6084 levels "","(NH4)2 SO4 Acid Bath System and Evaporator",..: 4455 5583 4466 4458 1341 5246 5584 5983 4461 776 ...
##  $ Map.To             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Last.Inventory.Year: int  NA NA NA NA NA NA NA NA NA NA ...
##  $ Created_Date       : Factor w/ 57 levels "","1/27/2000 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Revised_Date       : Factor w/ 44 levels "","1/27/2000 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Usage.Notes        : Factor w/ 21 levels ""," ","includes bleaching towers, washer hoods, filtrate tanks, vacuum pump exhausts",..: 1 1 1 1 1 1 1 1 1 1 ...

totalNEI <- aggregate(Emissions ~ year, NEI, sum)

plot(totalNEI$year, totalNEI$Emissions, type = "o", col = "steelblue3", main = expression("Total US "~ PM[2.5]~ "Emissions by Year"), ylab = expression("Total US "~   PM[2.5] ~ "Emissions"), xlab = "Year")

2. Have total emissions from PM2.5 decreased in San Antonio, Texas (fips==“48029”) from 1999 to 2008?

SanAntonio <- subset(NEI, NEI$fips == "48029")

totalSanAntonio <- aggregate(Emissions ~ year, SanAntonio, sum)

plot(totalSanAntonio$year, totalSanAntonio$Emissions, type = "o", main = expression("Total San Antonio" ~ PM[2.5] ~ "Emissions by Year"), xlab = "Year", ylab = expression("Total San Antonio "~ PM[2.5] ~ "Emissions"), col = "steelblue3")

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 San Antonio? Which have seen increases in emissions from 1999–2008?

SanAntonio <- subset(NEI, NEI$fips == "48029")
SanAntonioType <- aggregate(Emissions ~ year + type, SanAntonio, sum)

ggplot(SanAntonioType, aes(year, Emissions, col = type)) +
      geom_line() +
      geom_point() +
      ggtitle(expression("Total San Antonio " ~ PM[2.5] ~ "Emissions by Type and Year")) +
      ylab(expression("Total San Antonio " ~ PM[2.5] ~ "Emissions")) +
      xlab("Year") +
      scale_colour_discrete(name = "Type of sources") +
      theme(legend.title = element_text(face = "bold"))

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

SCCcoal <- SCC[grepl("coal", SCC$Short.Name, ignore.case = T),]
NEIcoal <- NEI[NEI$SCC %in% SCCcoal$SCC,]
totalCoal <- aggregate(Emissions ~ year + type, NEIcoal, sum)

ggplot(totalCoal, aes(year, Emissions, col = type)) +
      geom_line() +
      geom_point() +
      ggtitle(expression("Total US" ~ PM[2.5] ~ "Coal Emission by Type and Year")) +
      xlab("Year") +
      ylab(expression("US " ~ PM[2.5] ~ "Coal Emission")) +
      scale_colour_discrete(name = "Type of sources") +
      theme(legend.title = element_text(face = "bold"))

5. How have emissions from motor vehicle sources changed from 1999–2008 in San Antonio?

SanAntonioMotor <- subset(NEI, NEI$fips == "48029" & NEI$type == "ON-ROAD")
SanAntonioMotorAGG <- aggregate(Emissions ~ year, SanAntonioMotor, sum)

ggplot(SanAntonioMotorAGG, aes(year, Emissions)) +
      geom_line(col = "steelblue3") +
      geom_point(col = "steelblue3") +
      ggtitle(expression("San Antonio " ~ PM[2.5] ~ "Motor Vehicle Emissions by Year")) +
      xlab("Year") +
      ylab(expression(~PM[2.5]~ "Motor Vehicle Emissions"))

6. Compare emissions from motor vehicle sources in San Antonio 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?

SanAnLosAngelesMotors <- subset(NEI, NEI$fips %in% c("48029","06037") & NEI$type == "ON-ROAD")
SanAnLosAngelesMotorsAGG <- aggregate(Emissions ~ year + fips, SanAnLosAngelesMotors, sum)

ggplot(SanAnLosAngelesMotorsAGG, aes(year, Emissions, col = fips)) +
      geom_line() +
      geom_point() +
      ggtitle(expression("San Antonio and Los Angeles" ~ PM[2.5] ~ "Motor Vehicle Emissions by Year")) +
      labs(x = "Year", y = expression(~PM[2.5]~ "Motor Vehicle Emissions") ) +
      scale_colour_discrete(name = "City", labels = c("Los Angeles", "San Antonio")) +
      theme(legend.title = element_text(face = "bold"))

R Homework 1

Savanna Torres

6/6/2020

Assignment

Questions