The gt package in R is a
powerful tool for creating elegant and customizable
tables for data visualization and reporting. It offers a
user-friendly way to design and style tables in
RMarkdown documents and Shiny applications.
{gt}
The gt package in R is a powerful tool for creating
elegant and customizable tables for data visualization
and reporting.
It offers options for formatting, styling, and theming tables, as well as support for handling complex data structures and creating publication-ready tables with ease.
✍️ author → Richard Iannone
📘 documentation → github
⭐️ more than 1000 stars on github
| Country | Population | GDP |
|---|---|---|
| USA | 331.00 | 21.43 |
| China | 1.44 M | 14.34 |
| India | 1.39 M | 2.87 |
| Brazil | 212.00 | 1.49 |
To get started with gt, you can install it directly from
CRAN using the install.packages function:
We generally use the dplyr package in combination of
gt for better code readability.
Here’s a basic example with default rendering:
library(gt)
library(dplyr)
# Create a simple data frame
data = data.frame(
Country = c("USA", "China", "India", "Brazil"),
Capitals = c("Washington D.C.", "Beijing", "New Delhi", "Brasília"),
Population = c(331, 1441, 1393, 212),
GDP = c(21.43, 14.34, 2.87, 1.49)
)
# Alternatively you can do (same output):
#gt(data)
# Use the gt function
data %>%
gt()| Country | Capitals | Population | GDP |
|---|---|---|---|
| USA | Washington D.C. | 331 | 21.43 |
| China | Beijing | 1441 | 14.34 |
| India | New Delhi | 1393 | 2.87 |
| Brazil | Brasília | 212 | 1.49 |
You can add and customize title and subtitle with
the tab_header() function. Moreover, by using the
md() function we can insert some markdown
styling inside the texts.
Example:
data %>%
gt() %>%
tab_header(title = md("What a **nice title**"),
subtitle = md("Pretty *cool subtitle* too, `isn't it?`"))| What a nice title | |||
Pretty cool subtitle too, isn't it? |
|||
| Country | Capitals | Population | GDP |
|---|---|---|---|
| USA | Washington D.C. | 331 | 21.43 |
| China | Beijing | 1441 | 14.34 |
| India | New Delhi | 1393 | 2.87 |
| Brazil | Brasília | 212 | 1.49 |
The tab_spanner() function lets you group
columns into categories.
Example:
data %>%
gt() %>%
tab_spanner(
label = "Number",
columns = c(GDP, Population)) %>%
tab_spanner(
label = "Label",
columns = c(Country, Capitals)
)| Label | Number | ||
|---|---|---|---|
| Country | Capitals | GDP | Population |
| USA | Washington D.C. | 21.43 | 331 |
| China | Beijing | 14.34 | 1441 |
| India | New Delhi | 2.87 | 1393 |
| Brazil | Brasília | 1.49 | 212 |
