It’s About Dang Time: Visualizing Time Series Data

R
Data Viz
Time Series
Author

Lydia Gibson

Published

May 28, 2023

In this blog post, which will be my first to include R code, I’d like to share some of the time series visualizations I’ve seen as I’ve been reading Data Visualization with R by Rob Kabacoff.

“TIME is not a line, but a SERIES of now-points.” -Taisen Deshimaru

When I first told my Data Visualization Society (DVS) data viz mentor, Ilija Stojić, and others that I would be naming my blog Once Upon a Time Series, naturally their first instinct was to ask whether my blog would be about time series analysis. To that I answered with a resounding no. Although, I had been fostering a general love of data visualization several months prior to creating my blog, I was just a sucker for a pun and hadn’t yet gained experience dealing with or visualizing time series data.

In this blog post, I’d like to share some of the time series visualizations in the Data Visualization with R book, which I am currently reading as a member and co-facilitator of the #book_club-datavisr book club within the R for Data Science (R4DS) Online Learning Community. To find out more about what the book club has been up to, you can check out the notes about the book we’re creating as we read the book, the GitHub repo for these notes, and the YouTube playlist containing our weekly discussions of each chapters’ notes. Whatever way you prefer, I highly encourage you to get involved.

Simplified time series with modified date axis

The following plot can be found in section 7.1 Time-dependent graphs: Time series of the book. It uses the economics dataset of US monthly economic data collected from January 1967 thru January 2015 and the geom_line function which come with the ggplot2 package to plot personal savings rate (psavert).

library(ggplot2)
library(scales)
ggplot(economics, aes(x = date, y = psavert)) +
  geom_line(color = "indianred3", 
            size=1 ) +
  geom_smooth() +
  scale_x_date(date_breaks = '5 years', 
               labels = date_format("%b-%y")) +
  labs(title = "Personal Savings Rate",
       subtitle = "1967 to 2015",
       x = "",
       y = "Personal Savings Rate") +
  theme_minimal()

Sorted, colored dumbell chart

The following plot can be found in section 7.2 Time-dependent graphs: Dumbbell Charts of the book. Here, the geom_dumbbell function from the ggalt package is used, along with the gapminderdataset, to plot the change in life expectancy from 1952 to 2007 in the Americas.

library(ggplot2)
library(ggalt)
library(tidyr)
library(dplyr)

# load data
data(gapminder, package = "gapminder")

# subset data
plotdata_long <- filter(gapminder,
                        continent == "Americas" &
                        year %in% c(1952, 2007)) %>%
  select(country, year, lifeExp)

# convert data to wide format
plotdata_wide <- spread(plotdata_long, year, lifeExp)
names(plotdata_wide) <- c("country", "y1952", "y2007")

# create dumbbell plot
ggplot(plotdata_wide, 
       aes(y = reorder(country, y1952),
           x = y1952,
           xend = y2007)) +  
  geom_dumbbell(size = 1.2,
                size_x = 3, 
                size_xend = 3,
                colour = "grey", 
                colour_x = "blue", 
                colour_xend = "red") +
  theme_minimal() + 
  labs(title = "Change in Life Expectancy",
       subtitle = "1952 to 2007",
       x = "Life Expectancy (years)",
       y = "")

Slope graph

The following plot can be found in section 7.3 Time-dependent graphs: Slope graphs of the book. It uses the the newggslopegraph function from the CGPfunctions package and the gapminder data to plot life expectancy for six Central American countries in 1992, 1997, 2002, and 2007.

library(CGPfunctions)

# Select Central American countries data 
# for 1992, 1997, 2002, and 2007

df <- gapminder %>%
  filter(year %in% c(1992, 1997, 2002, 2007) &
           country %in% c("Panama", "Costa Rica", 
                          "Nicaragua", "Honduras", 
                          "El Salvador", "Guatemala",
                          "Belize")) %>%
  mutate(year = factor(year),
         lifeExp = round(lifeExp)) 

# create slope graph

newggslopegraph(df, year, lifeExp, country) +
  labs(title="Life Expectancy by Country", 
       subtitle="Central America", 
       caption="source: gapminder")

Stacked area chart with simplified scale

The following plot can be found in section 7.4 Time-dependent graphs: Area Charts of the book. It uses the ggplot2 package geom_area function and the uspopage dataset from the gcookbook package to plot the age distribution of the US population from 1900 and 2002.

library(gcookbook)

# stacked area chart
data(uspopage, package = "gcookbook")
ggplot(uspopage, aes(x = Year,
                     y = Thousands/1000, 
                     fill = forcats::fct_rev(AgeGroup))) +
  geom_area(color = "black") +
  labs(title = "US Population by age",
       subtitle = "1900 to 2002",
       caption = "source: U.S. Census Bureau, 2003, HS-3",
       x = "Year",
       y = "Population in Millions",
       fill = "Age Group") +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal()

Heatmap for time series

The following plot can be found in section 9.9 Other Graphs: Heatmaps of the book. It uses the superheat function from the superheat package and the gapminder dataset to display changes in life expectancy over time for Asian countries.

# create heatmap for gapminder data (Asia)
library(tidyr)
library(dplyr)
library(superheat)

# load data
data(gapminder, package="gapminder")

# subset Asian countries
asia <- gapminder %>%
  filter(continent == "Asia") %>%
  select(year, country, lifeExp)

# convert to long to wide format
plotdata <- spread(asia, year, lifeExp)

# save country as row names
plotdata <- as.data.frame(plotdata)
row.names(plotdata) <- plotdata$country
plotdata$country <- NULL

# row order
sort.order <- order(plotdata$"2007")

# color scheme
library(RColorBrewer)
colors <- rev(brewer.pal(5, "Blues"))

# create the heat map
superheat(plotdata,
          scale = FALSE,
          left.label.text.size=3,
          bottom.label.text.size=3,
          bottom.label.size = .05,
          heat.pal = colors,
          order.rows = sort.order,
          title = "Life Expectancy in Asia")

Closing Words

I’m really excited to finish up reading Data Visualization with R and continue my exploration of the art and science of data visualization. I hope to continue chronicling this journey in a series of blog posts I intend to call “What R you doing?” . Stay tuned!

Resources

Below are some FREE online books to help you learn more about visualizing time series data and/or data in general.

You may also find these websites useful in your own data viz journey