The mrggt
package is a fork of the gt package focused on adding latex functionality to bring it closer inline to the capabilities of html. The rstudio team has expressed this is eventually on their roadmap, however the timelines are uncertain. The original goal was to sit on-top of gt, however during the course of development, numerous internal refactorings broke our code. As such, a hard fork will keep the functionality locked until we explicitly merge upstream changes.
With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer.
It all begins with preprocessed table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting to a file using gtsave()
. Currently, gt supports HTML output, with LaTeX and RTF planned for the future.
The gt package is designed to be both straightforward yet powerful. The emphasis is on simple functions for the everyday display table needs. Here is a brief example of how to use gt to create a table from the included sp500
dataset:
library(mrggt) library(tidyverse) library(glue) # Define the start and end dates for the data range start_date <- "2010-06-07" end_date <- "2010-06-14" # Create a gt table based on preprocessed # `sp500` table data sp500 %>% dplyr::filter(date >= start_date & date <= end_date) %>% dplyr::select(-adj_close) %>% gt() %>% tab_header( title = "S&P 500", subtitle = glue::glue("{start_date} to {end_date}") ) %>% fmt_date( columns = vars(date), date_style = 3 ) %>% fmt_currency( columns = vars(open, high, low, close), currency = "USD" ) %>% fmt_number( columns = vars(volume), suffixing = TRUE )
There are six datasets included in gt: countrypops
, sza
, gtcars
, sp500
, pizzaplace
, and exibble
. All of them are useful for experimenting with gt’s functions.
Beyond this simple example, there are many functions available in gt for creating super-customized tables.
Want to try this out? Then, take the gt Test Drive on RStudio Cloud. It’s full of ready-to-run examples.
The gt package can be installed from CRAN with:
install.packages("gt")
You can also choose to install the development version of gt from GitHub:
devtools::install_github("rstudio/gt")
If you encounter a bug, have usage questions, or want to share ideas to make this package better, please feel free to file an issue.
The gt package joins a burgeoning collection of packages for display table generation. Why another? We feel that there is enough room in this space to innovate further. Here are some of the ways that gt contributes to this ecosystem:
While gt is trying to do something different with its own interface, it may not suit your specific needs. Here is a listing of leading table-making R packages, with links to their respective project pages:
knitr (GITHUB, WEBSITE) — kableExtra (GITHUB, WEBSITE) — formattable (GITHUB, WEBSITE) — DT (GITHUB, WEBSITE) — pander (GITHUB, WEBSITE) — huxtable (GITHUB, WEBSITE) — reactable (GITHUB, WEBSITE) — flextable (GITHUB, WEBSITE) — pixiedust (GITHUB) — tangram (GITHUB) — ztable (GITHUB) — condformat (GITHUB) — stargazer (CRAN) — xtable (CRAN)
Please note that this project is released with a Contributor Code of Conduct.
By participating in this project you agree to abide by its terms.