This document provides a comprehensive guide to creating a wide variety of tables in R, from simple static tables to complex, interactive, and publication-ready displays. We will explore several powerful packages, each with its own strengths.
1 1. gt: For Beautiful, Presentation-Ready Tables
The gt package is designed for creating beautiful and highly customizable tables. It follows a “grammar of tables” philosophy, allowing you to build a table layer by layer, from the data to the header, footer, and cell formatting. This makes it ideal for tables in reports, presentations, and publications where appearance and clarity are paramount.
This example demonstrates how to create a rich, informative table from the sp500 dataset included with gt.
Code
library(gt)library(tidyverse)library(glue)# Define the start and end dates for the data rangestart_date <-"2010-06-07"end_date <-"2010-06-14"# Create a gt table from preprocessed sp500 datasp500 |>filter(date >= start_date & date <= end_date) |>select(-adj_close) |>gt() |># Add a title and subtitle using markdown for formattingtab_header(title =md("**S&P 500 Daily Prices**"),subtitle =glue("Data from {start_date} to {end_date}") ) |># Format columns for currency, dates, and large numbersfmt_currency(columns =c(open, high, low, close)) |>fmt_date(columns = date, date_style ="wd_m_day_year") |>fmt_number(columns = volume, suffixing =TRUE) |># Add a source note with a linktab_source_note(source_note =html("Source: <a href='https://www.spglobal.com/'>S&P Global</a>") ) |># Center-align all columnscols_align(align ="center",columns =everything() ) |># Add a color scale to the `close` column to highlight valuesdata_color(columns = close,colors = scales::col_numeric(palette =c("#FEEBDB", "#FDD8B0", "#FDC686", "#FDB35B", "#FD9F2C"),domain =range(sp500$close) ) )
The DT package provides an R interface to the powerful JavaScript library DataTables. It turns a standard R data frame into an interactive HTML table that supports live filtering, pagination, sorting, and exporting. It is perfect for data exploration in R Markdown documents and Shiny apps.
Code
library(DT)
This is a basic example of an interactive table using the iris dataset.
DT allows for extensive styling using the format*() family of functions. You can add color bars, conditional formatting, and more.
Code
# Take a sample of the iris dataset for a cleaner looksample_iris <- iris[c(1:5, 51:55, 101:105), ]sample_iris |>datatable(options =list(pageLength =5)) %>%# Bold the Sepal.Length for values > 5formatStyle('Sepal.Length', fontWeight =styleInterval(5, c('normal', 'bold'))) %>%# Apply conditional colors to Sepal.WidthformatStyle('Sepal.Width',color =styleInterval(c(3.0, 3.5), c('red', 'orange', 'green')),backgroundColor =styleInterval(3.0, c('lightgray', 'lightyellow')) ) %>%# Add a background color bar to Petal.LengthformatStyle('Petal.Length',background =styleColorBar(range(iris$Petal.Length), 'steelblue'),backgroundSize ='100% 90%',backgroundRepeat ='no-repeat',backgroundPosition ='center' ) %>%# Style the Species columnformatStyle('Species',backgroundColor =styleEqual(unique(iris$Species), c('lightblue', 'lightgreen', 'lightpink') ) )
2.0.2 Table Export Buttons
The Buttons extension adds buttons to the table for exporting the data to formats like CSV, Excel, and PDF, or for copying to the clipboard.
Code
iris |>datatable(extensions ='Buttons', options =list(dom ='Bfrtip', # Defines the layout, B is for buttonsbuttons =c('copy', 'csv', 'excel', 'pdf', 'print'),pageLength =5 ))
3 3. reactable: For Modern and Feature-Rich Interactive Tables
reactable is another excellent package for creating interactive tables, known for its modern look, rich feature set, and ease of use. It supports sorting, filtering, grouping, and aggregation out of the box.
Code
library(reactable)library(reactablefmtr)mtcars %>%rownames_to_column("car_name") %>%select(car_name, mpg, cyl, hp, gear) %>%reactable(filterable =TRUE, # Add column filterssearchable =TRUE, # Add a global search boxgroupBy ="cyl", # Group by number of cylinderscolumns =list(hp =colDef(aggregate ="mean", format =colFormat(digits =1)), # Show mean HP for each groupgear =colDef(aggregate ="max"), # Show max gears for each group# Custom cell rendering to add a bar chart for mpgmpg =colDef(cell =data_bars(., text_position ="above", box_shadow =TRUE)) ),defaultPageSize =10 ) %>%add_title(title ='MTCars Dataset Summary') %>%add_subtitle(subtitle ='Grouped by Cylinders', font_weight ='normal')
MTCars Dataset Summary
Grouped by Cylinders
4 4. knitr::kable: For Simple, Static Tables
knitr::kable() is a simple and lightweight function for creating clean, static tables in various formats like HTML, LaTeX, and Markdown. It’s perfect for quick reports and documents where interactivity is not needed. The kableExtra package adds many styling options.
For longer tables, rmarkdown::paged_table() creates a scrollable, paged version, which is more user-friendly than a very long static table.
Code
library(rmarkdown)mtcars %>%paged_table()
5 5. flextable: For Publication-Ready Tables in Office Documents
The flextable package is designed to create tables for reporting, with a focus on producing high-quality output for Microsoft Word, PowerPoint, and HTML documents. It offers a high degree of control over formatting, including cell merging, borders, and fonts.
Code
library(flextable)# Create a basic flextableft <-flextable(head(mtcars))# Apply formattingft <- ft %>%set_header_labels(mpg ="Miles per Gallon", cyl ="Cylinders") %>%theme_booktabs() %>%autofit() %>%set_caption("A Publication-Ready Table with flextable") %>%color(j =~ mpg, color ="red") %>%bold(j =~ cyl, bold =TRUE)ft
Miles per Gallon
Cylinders
disp
hp
drat
wt
qsec
vs
am
gear
carb
21.0
6
160
110
3.90
2.620
16.46
0
1
4
4
21.0
6
160
110
3.90
2.875
17.02
0
1
4
4
22.8
4
108
93
3.85
2.320
18.61
1
1
4
1
21.4
6
258
110
3.08
3.215
19.44
1
0
3
1
18.7
8
360
175
3.15
3.440
17.02
0
0
3
2
18.1
6
225
105
2.76
3.460
20.22
1
0
3
1
6 6. Conclusion: Which Package Should You Use?
For beautiful, static reports and publications: Use gt.
For interactive data exploration in R Markdown or Shiny: Use DT or reactable. reactable has a more modern feel and powerful grouping features, while DT is very mature and has a rich ecosystem of extensions.
For quick, simple static tables: Use knitr::kable with kableExtra for styling.
For reports destined for Microsoft Office (Word, PowerPoint): Use flextable.
---title: "Data Tables in R"execute: warning: false error: falseformat: html: toc: true toc-location: right code-fold: show code-tools: true number-sections: true code-block-bg: true code-block-border-left: "#31BAE9"---This document provides a comprehensive guide to creating a wide variety of tables in R, from simple static tables to complex, interactive, and publication-ready displays. We will explore several powerful packages, each with its own strengths.# 1. `gt`: For Beautiful, Presentation-Ready Tables{width="200"}The `gt` package is designed for creating beautiful and highly customizable tables. It follows a "grammar of tables" philosophy, allowing you to build a table layer by layer, from the data to the header, footer, and cell formatting. This makes it ideal for tables in reports, presentations, and publications where appearance and clarity are paramount.This example demonstrates how to create a rich, informative table from the `sp500` dataset included with `gt`.```{r}library(gt)library(tidyverse)library(glue)# Define the start and end dates for the data rangestart_date <-"2010-06-07"end_date <-"2010-06-14"# Create a gt table from preprocessed sp500 datasp500 |>filter(date >= start_date & date <= end_date) |>select(-adj_close) |>gt() |># Add a title and subtitle using markdown for formattingtab_header(title =md("**S&P 500 Daily Prices**"),subtitle =glue("Data from {start_date} to {end_date}") ) |># Format columns for currency, dates, and large numbersfmt_currency(columns =c(open, high, low, close)) |>fmt_date(columns = date, date_style ="wd_m_day_year") |>fmt_number(columns = volume, suffixing =TRUE) |># Add a source note with a linktab_source_note(source_note =html("Source: <a href='https://www.spglobal.com/'>S&P Global</a>") ) |># Center-align all columnscols_align(align ="center",columns =everything() ) |># Add a color scale to the `close` column to highlight valuesdata_color(columns = close,colors = scales::col_numeric(palette =c("#FEEBDB", "#FDD8B0", "#FDC686", "#FDB35B", "#FD9F2C"),domain =range(sp500$close) ) )```# 2. `DT`: For Interactive Data ExplorationThe `DT` package provides an R interface to the powerful JavaScript library DataTables. It turns a standard R data frame into an interactive HTML table that supports live filtering, pagination, sorting, and exporting. It is perfect for data exploration in R Markdown documents and Shiny apps.{width="212"}```{r}library(DT)```This is a basic example of an interactive table using the `iris` dataset.```{r}iris |>datatable(options =list(pageLength =5, scrollX =TRUE))```### Advanced Formatting and Styling`DT` allows for extensive styling using the `format*()` family of functions. You can add color bars, conditional formatting, and more.```{r}# Take a sample of the iris dataset for a cleaner looksample_iris <- iris[c(1:5, 51:55, 101:105), ]sample_iris |>datatable(options =list(pageLength =5)) %>%# Bold the Sepal.Length for values > 5formatStyle('Sepal.Length', fontWeight =styleInterval(5, c('normal', 'bold'))) %>%# Apply conditional colors to Sepal.WidthformatStyle('Sepal.Width',color =styleInterval(c(3.0, 3.5), c('red', 'orange', 'green')),backgroundColor =styleInterval(3.0, c('lightgray', 'lightyellow')) ) %>%# Add a background color bar to Petal.LengthformatStyle('Petal.Length',background =styleColorBar(range(iris$Petal.Length), 'steelblue'),backgroundSize ='100% 90%',backgroundRepeat ='no-repeat',backgroundPosition ='center' ) %>%# Style the Species columnformatStyle('Species',backgroundColor =styleEqual(unique(iris$Species), c('lightblue', 'lightgreen', 'lightpink') ) )```### Table Export ButtonsThe `Buttons` extension adds buttons to the table for exporting the data to formats like CSV, Excel, and PDF, or for copying to the clipboard.```{r}iris |>datatable(extensions ='Buttons', options =list(dom ='Bfrtip', # Defines the layout, B is for buttonsbuttons =c('copy', 'csv', 'excel', 'pdf', 'print'),pageLength =5 ))```# 3. `reactable`: For Modern and Feature-Rich Interactive Tables`reactable` is another excellent package for creating interactive tables, known for its modern look, rich feature set, and ease of use. It supports sorting, filtering, grouping, and aggregation out of the box.```{r}library(reactable)library(reactablefmtr)mtcars %>%rownames_to_column("car_name") %>%select(car_name, mpg, cyl, hp, gear) %>%reactable(filterable =TRUE, # Add column filterssearchable =TRUE, # Add a global search boxgroupBy ="cyl", # Group by number of cylinderscolumns =list(hp =colDef(aggregate ="mean", format =colFormat(digits =1)), # Show mean HP for each groupgear =colDef(aggregate ="max"), # Show max gears for each group# Custom cell rendering to add a bar chart for mpgmpg =colDef(cell =data_bars(., text_position ="above", box_shadow =TRUE)) ),defaultPageSize =10 ) %>%add_title(title ='MTCars Dataset Summary') %>%add_subtitle(subtitle ='Grouped by Cylinders', font_weight ='normal')```# 4. `knitr::kable`: For Simple, Static Tables`knitr::kable()` is a simple and lightweight function for creating clean, static tables in various formats like HTML, LaTeX, and Markdown. It's perfect for quick reports and documents where interactivity is not needed. The `kableExtra` package adds many styling options.```{r}library(knitr)library(kableExtra)mtcars %>%head() %>%kable(caption ="A Simple Static Table with kable") %>%kable_styling(bootstrap_options =c("striped", "hover"), full_width = F)```For longer tables, `rmarkdown::paged_table()` creates a scrollable, paged version, which is more user-friendly than a very long static table.```{r}library(rmarkdown)mtcars %>%paged_table()```# 5. `flextable`: For Publication-Ready Tables in Office DocumentsThe `flextable` package is designed to create tables for reporting, with a focus on producing high-quality output for Microsoft Word, PowerPoint, and HTML documents. It offers a high degree of control over formatting, including cell merging, borders, and fonts.```{r}library(flextable)# Create a basic flextableft <-flextable(head(mtcars))# Apply formattingft <- ft %>%set_header_labels(mpg ="Miles per Gallon", cyl ="Cylinders") %>%theme_booktabs() %>%autofit() %>%set_caption("A Publication-Ready Table with flextable") %>%color(j =~ mpg, color ="red") %>%bold(j =~ cyl, bold =TRUE)ft```# 6. Conclusion: Which Package Should You Use?- **For beautiful, static reports and publications:** Use `gt`.- **For interactive data exploration in R Markdown or Shiny:** Use `DT` or `reactable`. `reactable` has a more modern feel and powerful grouping features, while `DT` is very mature and has a rich ecosystem of extensions.- **For quick, simple static tables:** Use `knitr::kable` with `kableExtra` for styling.- **For reports destined for Microsoft Office (Word, PowerPoint):** Use `flextable`.# 7. References- [gt Package](https://gt.rstudio.com/)- [DT Package](https://rstudio.github.io/DT/)- [reactable Package](https://glin.github.io/reactable/)- [flextable Package](https://davidgohel.github.io/flextable/)
