rk.tinytable: Beautiful, Publication-Ready Tables for RKWard

A Powerful GUI Wrapper for the tinytable Package Bringing Elegant Data Tables to RKWard

rk.tinytable provides a seamless, point-and-click interface for creating, styling, and exporting beautiful, publication-ready tables directly within RKWard. This plugin leverages the highly versatile tinytable package, allowing users to generate complex cross-tabulations or format raw data into stunning tables with zero R code.

🌟 Key Features

Zero-Code Table Generation: Create professional tables directly from RKWard's GUI without writing a single line of R script.
Intuitive Interface: Cleanly organized tabbooks for data selection, content (titles/notes), numeric formatting, styling, and file export.
Advanced Cross-Tabulations: Easily calculate Counts, Means, or Sums across multiple variables. Includes automatic Sturges-rule binning for numeric variables.
Automatic Margins (Totals): Easily append Row and Column totals to your cross-tabulations. Totals are automatically calculated via dplyr and intelligently formatted with bold text using tinytable styling. (Note: Automatically disabled when calculating Means to prevent invalid math).
Interactive HTML Previews: Features an isolated HTML rendering engine with injected Bootstrap CSS to preview tables accurately inside RKWard before exporting.
Comprehensive Customization: Control column alignments, numeric formats (Scientific, Percentage), decimal places, themes (Grid, Striped, Empty), and custom NA replacements.
Universal Export: Save tables seamlessly into multiple formats including Word (.docx), HTML, PDF, PNG, Markdown, LaTeX, and Typst.
Multilingual Ready: Fully prepared for translation into English, Spanish, French, German, and Portuguese (Brazil).

⚠️ Note on HTML Previews vs. Exported Files: Advanced styling features—like Striped or Grid themes and specific column alignments—are heavily optimized for compiled backend engines (pandoc, webshot2, typst). While the live HTML preview inside RKWard uses a generalized Bootstrap layout to ensure rendering stability (which may override some inline CSS), your final exported .docx, .pdf, and .png files will beautifully retain all specific tinytable formatting and themes.

What's New in Version 0.0.1

Initial Release: Introduces the foundational GUI for generating customized tables using the tinytable package.
Dual Capabilities: Includes two core modules: "Raw Data" (for filtering, selecting, and presenting data subsets) and "Cross-Tabulation" (for aggregating, binning, and summarizing).
Safe HTML Injection: Bypasses RKWard's default static graphical device to render robust, responsive HTML tables via srcdoc iframes.
Overwrite Safety: Built-in safeguards to properly overwrite exported files without raising internal R package errors.

🛠️ Dependencies

This plugin relies on the following R packages. RKWard will attempt to load them automatically, but you may need to install them if they are missing from your system:

tinytable, dplyr, tidyr
webshot2 (Required for PNG exports)
pandoc (Required for Word/Markdown exports)

install.packages(c("tinytable", "dplyr", "tidyr", "pandoc", "webshot2"))

(Note: A local installation of Pandoc is required by R to export tables to formats like .docx and .pdf).

🚀 Installation

You can install this plugin directly from GitHub using remotes or devtools inside your RKWard console:

# Install the plugin
library("remotes")
# install_github("AlfCano/rk.tinytable")
install.packages("rk.tinytable", repos = c('https://alfcano.r-universe.dev', 'https://cloud.r-project.org'))

Once installed, restart RKWard to ensure the new menu entries and translations appear correctly.

🛠️ Usage Workflow

This plugin adds its core components under the Analysis > Tables (tinytable) menu in RKWard:

1. Raw Data

Format and display subsets of your datasets directly.

Data & Filtering: Select your data frame, subset by condition, drop missing values (drop_na), select specific columns, or aggregate basic numeric means.
Table Content: Add a formal Title (Caption) and Footer notes. Control the relative width of the table.
Formatting & Theme: Set decimal places, handle how NAs are displayed, apply global text alignment, and choose a visual theme (Default, Striped, Grid).
Export: Save the table back into the R Workspace or export it directly to your hard drive (Word, HTML, PDF, etc.).

2. Cross-Tabulation

Aggregate and summarize relationships between multiple variables.

Data & Layout: Assign Row and Column variables. Select a "Measurement" variable and a statistic to calculate (Count, Mean, Sum).
Smart Binning: Check a single box to automatically bin numeric row/column variables into categorical ranges using the Sturges rule.
Formatting & Export: Apply all the same powerful theming, formatting, and export options available in the Raw Data module.
Data & Layout: Assign Row and Column variables. Select a statistic to calculate (Count, Mean, Sum). You can also toggle Row Totals and Column Totals with a single click to automatically generate bolded margin sums.

3. Universal Export (The "Write Once, Export Anywhere" Engine)

Export your exact table natively to:
- 🌐 HTML (.html)
- 📝 Word (.docx)
- 📄 PDF (.pdf)
- 🖼️ PNG (.png)
- 🖨️ LaTeX (.tex) & Typst (.typ)

(Note: Advanced features like stripes and specific alignments are heavily optimized for compiled outputs like Word, PDF, and PNG. The live HTML preview in RKWard utilizes a generalized Bootstrap layout for speed and compatibility).

💻 Usage

Once installed, the tools are organized under:

Analysis -> Tables (tinytable)

🎓 Learning Exercises

Follow these step-by-step examples to master the table generation tools.

1. The Summary Table (Raw Data)

Scenario: We want a clean, publication-ready table showing the average characteristics of cars based on their cylinder count.

A. Data Preparation (Run in Console):

# Load built-in dataset and make cylinders a factor
my_cars <- mtcars
my_cars$cyl <- as.factor(my_cars$cyl)

B. Plugin Settings (tinytable - Raw Data):

Tab: Data & Filtering
- Data Frame: my_cars
- Variables to include: cyl, mpg, hp, wt
- Aggregate data: Checked (Calculates Means of numeric columns)
Tab: Formatting
- Decimals: 1
Tab: Export
- Export as File: /path/to/save/Car_Summary.docx

2. The Pivot Table (Cross-Tabulation)

Scenario: Analyzing the survival rates of Titanic passengers by Class and Sex.

A. Data Preparation (Run in Console):

# Use the built-in Titanic dataset (convert to flat dataframe)
titanic_df <- as.data.frame(Titanic)
# Expand the frequencies back into raw rows for realistic cross-tabulation
titanic_raw <- titanic_df[rep(1:nrow(titanic_df), titanic_df$Freq), 1:4]

B. Plugin Settings (tinytable - Cross-Tabulation):

Tab: Data & Layout
- Data Frame: titanic_raw
- Row Variable(s): Class, Sex
- Column Variable(s): Survived
- Statistic: Count (Frequencies / N)
Tab: Theme & Styling
- Table Theme: Striped
- Global Text Alignment: Center (c)
Tab: Export
- Export as File: /path/to/save/Titanic_Survival.pdf

🌍 Internationalization (i18n)

The graphical interface automatically adapts to your RKWard language settings. Currently supported languages:

🇺🇸 English (Default)
🇪🇸 Spanish (Español)
🇫🇷 French (Français)
🇩🇪 German (Deutsch)
🇧🇷 Portuguese (Português do Brasil)

📝 License and Author

Author: Alfonso Cano (@AlfCano)
Email: alfonso.cano@correo.buap.mx

Assisted by: Gemini, a large language model from Google.
License: GPL (>= 3)

This project is licensed under the GPL (>= 3) License.