Time Series and Visualizations in GGPlot2
1) This week we look over some graphs in RStudio and then discuss the input of visualization on time series analysis.
> ### Hot Dog Graph Regular ###
> hotdogs <- read.csv("CENCORED/hot-dog-contest-winners.csv")
> head(hotdogs) Year Winner Dogs.eaten Country New.record
1 1980 Paul Siederman & Joe Baldini 9.10 United States 0
2 1981 Thomas DeBerry 11.00 United States 0
3 1982 Steven Abrams 11.00 United States 0
4 1983 Luis Llamas 19.50 Mexico 0
5 1984 Birgit Felden 9.50 Germany 0
6 1985 Oscar Rodriguez 11.75 United States 0
> library("ggplot2")
>
> # Adjust colors
> colors <- ifelse(hotdogs$New.record == 1, "green", "purple") # Changed to a more vibrant color
> barplot(hotdogs$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",
+ cex.main=1.5, # Increase title size
+ cex.lab=1.2) # Increase axis label size
> 
2) The bar plot above shows the results of Nathan's Hot Dog Eating Contest from 1980 to 2010, showcasing the number of hot dogs and buns consumed by winners each year. The use of complimentary colors—green for new records and purple for others—enhances the visual appeal and clearly distinguishes between record-breaking performances. The axis labels and title are enlarged for better readability, creating an engaging representation of this competitive eating event.
> ### Hot Dog Graph GGPlot2 ###
> 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") +
+ scale_fill_manual(values = c("lightblue", "lightyellow")) + # Match colors to barplot
+ theme_minimal() + # Add a minimal theme
+ theme(text = element_text(size=12, family = "Comic Sans MS"), # Fun font for hot dog graphs
+ plot.title = element_text(size=16, face="bold", family = "Comic Sans MS"), # Title style
+ axis.title = element_text(size=14, family = "Comic Sans MS")) # Axis title style
> 
3) The ggplot2 version of the hot dog contest data shows the same results in a bar graph format with a much cleaner look. Light blue and light yellow fill colors differentiate new records from other performances. A minimal theme and playful font, I tried to make it in comic sans but I didn't have enough time to look up what package would allow me to do that. This version is visually appealing and easily readable.
> ### Economics Data GGPlot ###
> head(economics)
# A tibble: 6 × 8
date pce pop psavert uempmed unemploy year decade
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1967-07-01 507. 198712 12.6 4.5 2944 1967 1960
2 1967-08-01 510. 198911 12.6 4.7 2945 1967 1960
3 1967-09-01 516. 199113 11.9 4.6 2958 1967 1960
4 1967-10-01 512. 199311 12.9 4.9 3143 1967 1960
5 1967-11-01 517. 199498 12.8 4.7 3066 1967 1960
6 1967-12-01 525. 199657 11.8 4.8 3018 1967 1960
> year <- function(x) as.POSIXlt(x)$year + 1900
> economics$year <- year(economics$date) # Setting up our analysis
> head(economics)
# A tibble: 6 × 8
date pce pop psavert uempmed unemploy year decade
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1967-07-01 507. 198712 12.6 4.5 2944 1967 1960
2 1967-08-01 510. 198911 12.6 4.7 2945 1967 1960
3 1967-09-01 516. 199113 11.9 4.6 2958 1967 1960
4 1967-10-01 512. 199311 12.9 4.9 3143 1967 1960
5 1967-11-01 517. 199498 12.8 4.7 3066 1967 1960
6 1967-12-01 525. 199657 11.8 4.8 3018 1967 1960
>
> plot1 <- qplot(date, unemploy / pop, data = economics, geom = "line",
+ main = "Unemployment Rate Over Time",
+ xlab = "Date", ylab = "Unemployment Rate") +
+ geom_line(color = "blue") + # Custom color for line
+ theme_minimal() +
+ theme(text = element_text(size=12, family = "Arial"), # Professional font for economics
+ plot.title = element_text(size=16, face="bold", family = "Arial"), # Title style
+ axis.title = element_text(size=14, family = "Arial")) # Axis title style
> 
4) This plot depicts the unemployment rate over time, utilizing a line graph to illustrate trends. Dates are on the x-axis and the unemployment rate is on the y-axis. The blue and yellow lines highlights significant fluctuations. The minimal theme keeps the focus on the data, allowing for easy interpretation of economic trends against the clear background.
> ### Economics Data GGPlot2 ### > library(gridExtra) > > plot2 <- qplot(date, uempmed, data = economics, geom = "line", + main = "Median Unemployment Duration Over Time", + xlab = "Date", ylab = "Median Duration (Months)") + + geom_line(color = "orange") + # Custom color for line + theme_minimal() + + theme(text = element_text(size=12, family = "Arial"), # Professional font for economics + plot.title = element_text(size=16, face="bold", family = "Arial"), # Title style + axis.title = element_text(size=14, family = "Arial")) # Axis title style > > grid.arrange(plot1, plot2, ncol=2) > > # Convert year to decade for grouping > economics$decade <- floor(economics$year / 10) * 10 > > # Combined plot with adjusted aesthetics > plot_combined <- qplot(unemploy / pop, uempmed, data = economics, + geom = c("point", "path"), + color = factor(decade)) + # Color by decade + labs(title = "Unemployment Rate vs Median Duration", + x = "Unemployment Rate", y = "Median Duration (Months)") + + scale_color_viridis_d() + # Use a discrete viridis color scale + theme_minimal() + + theme(text = element_text(size=12, family = "Arial"), # Professional font for economics + plot.title = element_text(size=16, face="bold", family = "Arial"), # Title style + axis.title = element_text(size=14, family = "Arial"), # Axis title style + legend.title = element_blank()) # Remove legend title > > grid.arrange(plot_combined, ncol=1)>
5) This plot analyzes the relationship between unemployment rate and median duration of unemployment, highlighting data across decades. Points and lines represent the data, while decade-based coloring helps categorize time periods. The discrete color scale improves visibility, and the clear labeling promotes effective understanding of the economic data, facilitating a comprehensive view of employment trends. Much better than a fluctuation of the same color.
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