Advanced Table Skills

August 24, 2026

gtExtras

The gtExtras package

• By now we know the difference between a graph and a table
  • Tables are good for comparing individual values
  • Graphs are for visualizing a whole bunch of values
• Usually we keep our graphs and tables separate
• But are there times when we might want to combine them?
• gtExtras lets us include sparklines, histograms, etc. in our tables

Democracy Example

library(vdemlite)

# Grab the polyarchy scores for the United States between 2000 and 2020
brics_dem <- fetchdem(indicators = "v2x_polyarchy",
                         start_year = 2000, end_year = 2020,
                         countries = c("BRA", "RUS", "IND", "CHN", "ZAF"))

Democracy Example

library(dplyr)

dem_summary <- brics_dem|>
  group_by(country_id) |>
  summarize(
    max = max(v2x_polyarchy, na.rm = TRUE),
    mean = mean(v2x_polyarchy, na.rm = TRUE),
    sd = sd(v2x_polyarchy, na.rm = TRUE),
    dem_data = list(v2x_polyarchy),
    .groups = "drop"
  )

Democracy Example

library(gtExtras)

dem_summary |>
  gt() |>
  gt_plt_sparkline(dem_data) #from gtExtras

country_id	max	mean	sd	dem_data
8	0.773	0.75280952	0.01862423
11	0.405	0.30133333	0.04091251
19	0.878	0.83461905	0.05923046
39	0.736	0.63866667	0.11018182
110	0.103	0.09042857	0.00944760

Your Turn!

• Download democracy data for one year
• Indicator is your choice
• Add region codes
• Group and summarize by region
• Add an appropriate inline plot to your table

15:00

When Not to Use a Table

Not a Good Table

name	estimate	lower_90	upper_90
Barnstable	40442	39554	41330
Berkshire	33040	32074	34006
Bristol	36910	36347	37473
Dukes	40119	34791	45447
Essex	39756	39174	40338
Franklin	34775	33712	35838
Hampden	32262	31707	32817
Hampshire	30795	29868	31722
Middlesex	51808	51337	52279
Nantucket	45717	38260	53174
Norfolk	52591	51991	53191
Plymouth	43684	43014	44354
Suffolk	39200	38624	39776
Worcester	39009	38454	39564

Use Confidence Intervals Instead

Use Confidence Intervals Instead

library(ggplot2)

mass_med_inc |>
  ggplot(aes(x = estimate, y = reorder(name, estimate))) +
  geom_errorbar(aes(xmin = lower_90, xmax = upper_90)) +
  geom_point(color = "red", size = 2) +
  labs(title = "Household income by county in Massachusetts",
       subtitle = "2017-2021 American Community Survey",
       y = "",
       x = "Median Income", 
       caption = "ACS estimate (bars represent 90% confidence intervals)") +
  theme_minimal()

Your Turn!

• Download data on median income for another state
• Plot the income estimates for that state

15:00

Time Permitting

reactable

• reactable is an R package that lets you create interactive tables
• You can sort, filter, and search the table
• Based on the React Table library
• Let’s explore it together!