report in r package

• Get started
• Example 1: Listing
• Example 2: Table
• Example 3: Table Stub
• Example 4: Table and Text
• Example 5: Spanning Headers
• Example 6: Page Wrap
• Example 7: Page By
• Example 8: Title Header
• Example 9: RTF, PDF, HTML, and DOCX
• Example 10: Fonts and Borders
• Example 11: Figure
• Example 12: Styles
• Example 13: Superscripts, Subscripts, and Symbols
• Example 14: Page Break
• More Examples
• Frequently Asked Questions

Introduction to reporter

Historically, R has not been very strong on reporting. The reporter package aims to fill that gap.

Using reporter , you can create a report in just a few lines of code. Not only is it easy to create a report, but the reporter package can handle all sorts of situations that other packages struggle with.

For example, unlike other packages, the reporter package creates the entire report : page header and footer, titles, footnotes, tables - everything. The end result of a reporter call is a complete, printable report.

In addition, reporter can handle page breaking, page wrapping, and automatic sizing of column widths. The package offers a choice of output file types. And it supports the inclusion of tables, text and graphics into a report.

What is more, the package does not expect you to know R Markdown, knitr, or pandoc. You do not need to learn Latex, HTML, or any other intermediate language. With reporter , you send your data into a create function, assign titles and footnotes, and write the report. That’s it!

If you are familiar with SAS® software, you may notice some similarity between reporter functions and proc report . This similarity, however, is only on the surface. The implementation of reporter is done entirely in R, and, internally, is modeled in a different way. However, SAS® users will find the reporter functions very convenient and easy to understand compared to the alternatives.

Installation

The reporter package can be installed from the console. Simply run the following command:

Or if you want the latest development version, you can install it directly from github:

Then put the following line at the top of your program or script:

The reporter package will give you access to a number of functions to help create, lay out, and write your report to the file system. For examples and usage information, visit the reporter documentation site here .

Getting Help

If you need help, the first place to turn to is the reporter web site. The web site has full documentation on all reporter functions.

If you want to look at the code for the reporter package, visit the github page here .

If you encounter a bug or have a feature request, please submit an issue here .

The reporter package is part of the sassy meta-package. The sassy meta-package includes several packages that help make R easier for SAS® programmers. You can read more about the sassy package here .

• View on CRAN
• Browse source code
• Report a bug
• Full license
• Citing reporter
• David Bosak Author, maintainer
• More about authors...

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The Ultimate Guide to the report Package in R

• February 7, 2024

Package details

• Title: Automated Reporting of Results and Statistical Models
• Description: The aim of the 'report' package is to bridge the gap between R’s output and the formatted results contained in your manuscript. This package converts statistical models and data frames into textual reports suited for publication, ensuring standardization and quality in results reporting.
• Author: Dominique Makowski [aut] (, @Dom_Makowski), Daniel Lüdecke [aut] (, @strengejacke), Indrajeet Patil [aut] (, @patilindrajeets), Rémi Thériault [aut, cre] (, @rempsyc), Mattan S. Ben-Shachar [aut] (, @mattansb), Brenton M. Wiernik [aut] (, @bmwiernik), Rudolf Siegel [ctb] ()
• Maintainer: Rémi Thériault

Find package documentation

View technical details

Learn how to install, load, and use the report package

Overview of the report package in R

The report package in R is designed to automate the process of creating descriptive and inferential statistics reports. It simplifies the process of generating and formatting tables, figures, and text for reporting statistical analyses.

Basic information about the report package

Package title.

The title of an R package on CRAN provides a concise representation of the package’s purpose. It serves as a brief summary, capturing the essence of what the package offers.

Package description

The description of an R package on CRAN provides an overview of the package’s key features, allowing you to quickly determine if the package meets your requirements.

Package author

In R programming, the author refers to the person or group of people who created the package. Authors play a crucial role in the R community because they develop packages with specific functionalities that enhance the capabilities of R. Their contributions make it easier for users to effectively solve problems.

Package maintainer

Maintainers refer to the person or group of people who are responsible for managing an R package. They keep packages functional, up-to-date, and compatible with new versions of R and other dependencies. They also actively interact with users and address technical issues.

Technical details of the report package

Vignettes and examples.

You can view any available vignettes for the report package in the Vignette section on its CRAN page . Please note that some packages do not have vignettes. In those cases, you can refer to the package’s documentation, user guides, and other available resources to gain a better understanding of its capabilities.

Package data

Package website.

Package websites can offer detailed documentation, examples, tutorials, and other materials that can help you master the package’s functionalities. GitHub pages, for example, allow you to access the latest development version, contribute to the project, and report issues directly to the developers.

In R, a version refers to a specific iteration or release of the package. A version number typically consists of three parts: major version, minor version, and patch level. The major version represents updates with significant changes, the minor version indicates smaller updates or added features, and the patch level typically signifies bug fixes.

Versions are crucial for tracking changes made to a package. Specifying a version helps guarantee that your code remains reproducible, even if there are future updates to the package. This is particularly beneficial when collaborating with others.

When using an R package, it’s crucial to know and understand its license. A license is a legal agreement that governs how a package can be used, modified, and distributed. It outlines the permissions and restrictions placed on the package’s code and associated resources. We suggest you take some time to learn more about R package licenses .

Dependencies

In R programming, dependencies are components that a package relies on to work correctly. They can include other packages that provide specific functions.

It’s important to note that some R packages list required dependencies, some list suggested dependencies, and some do not have any dependencies.

Required dependencies

A required dependency refers to the components or other packages that are essential for the functioning of a particular package. Without these required dependencies, the package may not work at all or may not work as intended.  By listing the required dependencies, package developers can ensure that users have the necessary tools and resources to use the package effectively and help users avoid any conflicts or errors.

Suggested dependencies

A suggested dependency is a component or package that adds extra features to the main package, but the main package can still work without it.

External sources

In R programming, any external sources that need to be linked to a specific package are referred to as “LinkingTo” dependencies. These external sources are typically other packages that the main package depends on for linking at compile time, enabling seamless integration of additional functionalities.

Enhancements

In R programming, enhancements extend a specific package’s functionalities. They help developers expand the capabilities of their packages, add new features, and improve performance without starting from scratch.

Imported packages

In R programming, external packages used within a specific package are called imported packages. Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel, saving time and effort. It also helps ensure that the required dependencies are available during runtime.

Compilation requirements

Some R packages include internal code that must be compiled for them to function correctly. This code ensures that the package works as intended.

Citing the report package

Citing R packages in your publications and research papers is important as it recognizes the effort and contribution of their developers. It also promotes reproducibility, enabling others to reference and build upon your work.

Getting help with the report package

If you are seeking comprehensive guidance on the report package, please read our beginner’s guide . It provides instructions on how to install, load, and use the package. We also provide examples and useful tips to help you master the package.

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