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RKWard
RKWard screenshot
Developer(s)	RKWard community
Stable release	0.8.1  / 22 April 2025 (22 April 2025)
Repository	invent.kde.org/education/rkward
Written in	C++, ECMAScript
Operating system	BSD, Linux, macOS, Unix, Windows
Type	Statistical software
License	GNU General Public License
Website	rkward.kde.org

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RKWard is a transparent front-end to the R programming language, a scripting-language with a strong focus on statistics functions. RKWard tries to combine the power of the R language with the ease of use of commercial statistical packages.

RKWard is written in C++ and although it can run in numerous environments, it was designed for and integrates the KDE desktop environment with the Qt (software) libraries.

Features

RKWard's features include

• Spreadsheet-like data editor
• Syntax highlighting, code folding and code completion
• Data import (e.g. SPSS, Stata, CSV and Excel through package `rio`)
• Plot preview and browsable history
• R package management
• Workspace browser
• GUI dialogs for all kinds of statistics and plots

Interface

RKWard aims to be easy to use, both for people with deep knowledge of R, and for users who, although they have experience in statistics, are not familiar with the language. The application design offers the possibility of using the graphic tools as well as ignoring many of them and using the program as integrated development environment.

It includes a workspace viewer, which gives access to packages, functions and variables loaded by R or imported from other sources. It also has a file viewer, and data set editing windows, display of the contents of the variables, help, command log and HTML output.

Furthermore, it also offers components that help in code editing and direct order execution, such as the script window and the R console, where one can enter complete commands or programs as one would in the original R text interface. Likewise, it provides additional help such as syntax coloring documentation of functions while writing, and includes the feature of capturing graphs or emerging dialogs produced by offering additional options for handling, saving and exporting them.

In recent versions, RKWard includes integration with RMarkdown such as highlighting, preview and exporting. It has also included an integration with git for projects version control.

Package Management

The R package management is carried out through a configuration dialog that allows one to, either automatically (because a plug-in requires it) or manually, install new packages from the repository's official project, update existing ones, delete them or upload / download them from the workspace.

Add-ons system

Thanks to its add-ons system RKWard constantly expands the number of functions that can be accessed without writing the code directly. These components allow, from a graphical user interface, instructions to be generated in R for the most common or complex statistical operations. In this way, even without having deep knowledge about the language it is possible to perform advanced data analysis or elaborated graphs. The results of the computations are formatted and presented as HTML, making it possible, with a single click and drag, to export tables and graphs to, for example, office suites.

The list of current add-ons available through CRAN and GitHub on rkward-community is:

• cocor for Comparing Correlations.
• cocron for Statistical Comparisons of Two or more Alpha Coefficients.
• klausuR for Multiple Choice Test Evaluation.
• koRpus for Text Analysis with Emphasis on POS Tagging, Readability, and Lexical Diversity.
• rk.ANOVA a GUI to conduct ANOVAs (using the ez package), pairwise t-Tests and plot interactions.
• rk.ClusterAnalysis to conduct k-means, model based and hierarchical cluster analyses.
• rk.CohenKappa to calculate Cohen's Kappa.
• rk.FactorAnalysis to conduct principal component and factor analysis.
• rk.gitInstall to install R packages directly from git or subversion repositories.
• rk.MPT for multinomial processing tree (MPT) models.
• rk.MultidimensionalScaling for multidimensional scaling.
• rk.power to perform power analysis and sample size estimation.
• rk.subset to define subsets of data objects.
• rk.Teaching a plugin with basic utilities for teaching Statistics, and gives access to most common commands for data manipulation, probabilities, distributions, descriptive statistics (tables, measures and graphics), parametric tests, non-parametric tests, correlations and regressions.

See also

• Comparison of statistical packages
• R interfaces

External links

• RKWard homepage