Module 10 assignment

Nathan’s Hot Dog Eating Contest

# Load data
hotdogs <- read_csv("http://datasets.flowingdata.com/hot-dog-contest-winners.csv")
head(hotdogs)

# Load ggplot2
library(ggplot2)

# Create color scheme for new records
colors <- ifelse(hotdogs$New.record == 1, "darkred", "grey")

# Base R plot
barplot(hotdogs$Dogs.eaten, 
        names.arg = hotdogs$Year, 
        col = colors, 
        border = NA,
        main = "Nathan's Hot Dog Eating Contest Results, 1980-2010",  
        xlab = "Year", 
        ylab = "Hot dogs and buns (HDBs) eaten")

A base R bar chart showing the number of hot dogs eaten each year with new records in dark red.

ggplot2 for the same data:

ggplot(hotdogs) + 
  geom_bar(aes(x = Year, y = Dogs.eaten, fill = factor(New.record)), stat = "identity") +
  labs(title = "Nathan's Hot Dog Eating Contest Results, 1980-2010", fill = "New Record") +
  xlab("Year") + 
  ylab("Hot dogs and buns (HDBs) eaten")

A more polished ggplot2 bar chart that automatically includes a legend and modern styling.

Stacked Bar Plot:

hotdog_places <- as.matrix(hotdog_places)
colnames(hotdog_places) <- lapply(2000:2010, as.character)
barplot(hotdog_places, border = NA, 
        main = "Hot Dog Eating Contest Results, 1980-2010", 
        xlab = "Year", 
        ylab = "Hot dogs and buns (HDBs) eaten")

Part 2: Time Series Visualization Using the Economics Dataset

The economics dataset comes preloaded in ggplot2 and provides U.S. economic data over time. 

library(ggplot2)
head(economics)

# Add year column
year <- function(x) as.POSIXlt(x)$year + 1900
economics$year <- year(economics$date)

# Plot unemployment rate over time
plot1 <- qplot(date, unemploy / pop, data = economics, geom = "line")
plot1

A simple line plot showing unemployment rate trends over time.

We can also visualize the median unemployment duration and compare it to unemployment rate:

plot2 <- qplot(date, uempmed, data = economics, geom = "line")
grid.arrange(plot1, plot2, ncol = 2)

And add color by year to make trends more visible

plot1 <- qplot(unemploy / pop, uempmed, data = economics, geom = c("point", "path"))
plot2 <- qplot(unemploy / pop, uempmed, data = economics, geom = c("point", "path"), color = year)
grid.arrange(plot1, plot2, ncol = 2)

Visualization plays a major role in understanding time series data. It allows us to:

• Quickly identify trends, patterns, and seasonal variations.

• Spot anomalies or turning points in data.

• Compare multiple variables over time.

• Communicate findings clearly to others.

Using ggplot2 makes it easier to produce clear, professional-quality time series plots. The ability to add layers, colors, and annotations helps us explore and interpret data more effectively.

http://datasets.flowingdata.com/hot-dog-contest-winners.csv