Coursera RStudio Ozone Example

Leanpubs

The Elements of Data Analytic Style
Jeff Leek
https://leanpub.com/datastyle/

R Programming for Data Science
Roger Peng
https://leanpub.com/rprogramming

The Art of Data Science
Roger Peng and Elizabeth Matsui
https://leanpub.com/artofdatascience

ozone_example.R

# Name: ozone_example.R
# Author: David F. Severski
# Purpose: Demo of ozone data analysis, lifted from Roger Peng's 
#         "The Art of Data Science"

library(readr)
library(dplyr)
library(ggplot2)

# Fetch Data ------------------------------

url <- "http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/hourly_44201_2014.zip"

if (!dir.exists("./data")) { dir.create("./data") }

if (!file.exists("./data/hourly_44201_2014.zip")) {
  download.file(url, "./data/hourly_44201_2014.zip", quiet = TRUE)
}

unzip("./data/hourly_44201_2014.zip", exdir = "./data")

# Read data ------------------------------
# col types derived from http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/FileFormats.html


ozone <- read_csv("data/hourly_44201_2014.csv", 
                  col_types = "ccccinnccccccnccnccccccc")


# clean up names
names(ozone) <- make.names(names(ozone))

# Fri Sep 18 08:12:58 2015 ------------------------------
# Verify data

nrow(ozone)

ncol(ozone)

str(ozone)

glimpse(ozone)

head(ozone[, c(6:7, 10)])

tail(ozone[, c(6:7, 10)])


# Examine data  ------------------------------
head(table(ozone$Time.Local))

filter(ozone, Time.Local =="02:00") %>% 
  select(State.Name, County.Name, Date.Local, Time.Local, Sample.Measurement)

ozone %>% 
  filter(County.Name=="King" & State.Name=="Washington" & Date.Local=="2014-09-30") %>% 
  select(Date.Local, Time.Local, Sample.Measurement) %>% 
  as.data.frame

# More Examination ------------------------------
select(ozone, State.Name) %>% unique %>% nrow

unique(ozone$State.Name)

#interesting, Mexico, Puerto Rico, and DC are included

summary(ozone$Sample.Measurement)

quantile(ozone$Sample.Measurement, seq(0, 1, by=0.1))


# look at WA
ozone <- ozone %>% mutate(Is.Washington = State.Name == "Washington") 

# Visualize Data ------------------------------

gg <- ggplot(ozone, aes(x=State.Name, y=Sample.Measurement))
gg <- gg + geom_boxplot()
gg <- gg + ylab("Ozone Level (ppm)")
gg <- gg + ggtitle("Boxplot of Ozone Values by State")
gg <- gg + theme_minimal()
gg <- gg + theme(axis.text.x = element_text(angle=30, hjust=1, vjust=1))
gg


#Let's highlight WA only

gg + geom_boxplot(mapping=aes(fill=Is.Washington))


# Compare East vs West ------------------------------
ozone$region <- factor(ifelse(ozone$Longitude < -100, "west", "east"))

ozone %>% group_by(region, State.Name) %>% tally %>% as.data.frame

us_only_ozone <- ozone %>% 
  filter(State.Name != "Country of Mexico") %>% 
  group_by(region) %>% 
  summarise(mean = mean(Sample.Measurement, na.rm = TRUE), 
            median = median(Sample.Measurement, na.rm = TRUE))

gg <- ozone %>% filter(State.Name != "Country of Mexico") %>% ggplot(aes(x=region, y=Sample.Measurement))
gg <- gg + geom_boxplot()
gg <- gg + theme_minimal()
gg <- gg + ylab("Ozone Level (ppm)")
gg <- gg + ggtitle("Boxplot of Ozone Values by Region")
gg