Using EDA to compare emissions from motor vehicle sources in Baltimore City with emissions from motor vehicle sources in Los Angeles, CA. Graph shows which city has seen greater changes over time in motor vehicle emissions.

plot6.R

# read in data 
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")

# load packages
library(tidyverse)

# convert to proper class
SCC$SCC <- as.character(SCC$SCC)
SCC$EI.Sector <- as.character(SCC$EI.Sector)
NEI$type <- as.factor(NEI$type)

# use regex to index observations related to motor vehicle sources
ind <- grep(pattern = "Vehicle", x = SCC$EI.Sector)
newSCC <- SCC[ind,]

# get vector of related SCC's
vSCC <- newSCC$SCC

# filter data for emissions from motor vehicle sources in 
# Baltimore City, MD and Los Angeles County, CA
my_df <- NEI %>%
  filter(SCC %in% vSCC & (fips == "24510" | fips == "06037")) %>%
  mutate(county = if_else(fips == "24510", "Baltimore City", "Los Angeles")) %>%
  group_by(year, county) %>%
  summarize(total = sum(Emissions))

# plot
ggplot(my_df, aes(year, total)) +
  geom_point(shape = 20, size = 3, alpha = 0.5) + 
  geom_smooth(aes(color = county)) + 
  theme_bw() +
  labs(title = "Emissions from Motor Vehicles",
       subtitle = "Baltimore City vs Los Angeles",
       x = "Year", y = "PM2.5 Emissions (by tons)")