Category: Graphical User Interfaces

The Popularity of Point-and-Click GUIs for R

 

Point-and-click graphical user interfaces (GUIs) for R allow people to analyze data using the R software, without having to learn how to program in the R language. This is a brief look at how popular each one is. Knowing that a GUI is popular doesn’t mean it will meet your needs, but it does mean that it’s meeting the needs of many others. This may be helpful information when selecting the appropriate GUI for you, if programming is not your primary interest. For detailed information regarding what each GUI can do for you, and how it works, see my series of comparative reviews, which is currently in progress.

There are many ways to estimate the popularity of data science software, but one of the most accurate is by counting the number of downloads (see appendix for details). Figure 1 shows the monthly downloads of four of the six R GUIs that I’m reviewing (i.e. all that exist as far as I know).  We can see that the R Commander (Rcmdr) is the most popular GUI, and it has had steady growth since its introduction. Next comes Rattle, which is more oriented towards machine learning tasks. It too, has shown high popularity and steady growth.

The three lines at the bottom could use more “breathing room” so let’s look at them in their own plot.

Figure 1. Number of times each software was downloaded by month.

 

Figure 2 shows the same data as Figure 1, but with the two most popular GUIs removed to make room to study the remaining data. From it we can see that Deducer has been around for many more years than the other two. Downloads for Deducer grew steadily for a couple of years, then they leveled off. Its downloads appear to be declining slightly in recent years. jamovi (its name is not capitalized) has only been around for a brief period, and its growth has been very rapid. As you can see from my recent review, jamovi has many useful features.

Figure 2. Number of times the less popular GUIs were downloaded. (Same as Fig. 1, with the R Commander and rattle removed).

The lowest (blue) line shows downloads for the jmv package, that contains all the functions used by the jamovi GUI. It allows programmers to write code instead of using the jamovi GUI. People who point-and-click their way through an analysis in jamovi can send their code to any R user, who would then use the jmv package to run it. Since most jamovi users would prefer to point-and-click their way through analyses, it makes sense that the jmv package has been downloaded many fewer times than jamovi itself.

Two GUIs are missing from this plot: RKWard and BlueSky Statistics. Neither of those are downloaded from CRAN, and I was unable to obtain data from the developers of those GUIs. However, knowing that RKWard has a similar number of point-and-click features as Deducer, one can deduce (heh!) that it might have a similar level of popularity. The BlueSky software has only recently appeared on the scene, especially with its current level of features, so I expect it too will be towards the bottom, but growing rapidly.

I’m nearly done with all my reviews, so stay tuned to see what the other GUIs offer.

Acknowledgements

Thanks to Guangchuang Yu for making the dlstats package which allowed me to collect data so easily. Thanks also to Jonathon Love, who provided the download data for jamovi, and to Josh Price for his helpful editorial advice.

Appendix: Where the Data Came From

I used R’s dlstats package, which makes quick work of gathering counts of monthly downloads of R packages from the Comprehensive R Archive Network (CRAN). CRAN consists of sites around the world called “mirrors” from which people can download R packages. When starting the download process, R asks you to choose a mirror that is close to your location. In the popular RStudio development environment for R, the default mirror is set to their own server, which is actually a worldwide network of mirrors. Since it’s the default download location in a very popular tool for R, its download data will give us a good idea of the relative popularity of each GUI. The absolute popularity will be greater, but to get that data I would have to gather data from all the other servers around the world. If you have time to do that, please send me the results!

A Comparative Review of the RKWard GUI for R

Introduction

RKWard is a free and open source Graphical User Interface for the R software, one that supports beginners looking to point-and-click their way through analyses, as well as advanced programmers. You can think of it as a blend of the menus and dialog boxes that R Commander offers combined with the programming support that RStudio provides. RKWard is available on Windows, Mac, and Linux.

This review is one of a series which aims to help non-programmers choose the Graphical User Interface (GUI) that is best for them. However, I do include a cursory overview of how RKWard helps you work with code. In most sections, I’ll begin with a brief description of the topic’s functionality and how GUIs differ in implementing it. Then I’ll cover how RKWard does it.

Figure 1. RKWard’s main control screen containing an open data editor window (big one), an open dialog box (right) and its output window (lower left).

 

Terminology

There are various definitions of user interface types, so here’s how I’ll be using these terms:

GUI = Graphical User Interface specifically using menus and dialog boxes to avoid having to type programming code. I do not include any assistance for programming in this definition. So GUI users are people who prefer using a GUI to perform their analyses. They often don’t have the time required to become good programmers.

IDE = Integrated Development Environment which helps programmers write code. I do not include point-and-click style menus and dialog boxes when using this term. IDE users are people who prefer to write R code to perform their analyses.

 

Installation

The various user interfaces available for R differ quite a lot in how they’re installed. Some, such as jamovi or BlueSky Statistics, install in a single step. Others install in multiple steps, such as R Commander and Deducer. Advanced computer users often don’t appreciate how lost beginners can become while attempting even a single-step installation. I work at the University of Tennessee, and our HelpDesk is flooded with such calls at the beginning of each semester!

Installing RKWard on Windows is done in a single step since its installation file contains both R and RKWard. However, Mac and Linux users have a two-step process, installing R first, then download RKWard which links up to the most recent version of R that it finds. Regardless of their operating system, RKWard users never need to learn how to start R, then execute the install.packages function, and then load a library.  Installers for all three operating systems are available here.

The RKWard installer obtains the appropriate version of R, simplifying the installation and ensuring complete compatibility. However, if you already had a copy of R installed, depending on its version, you could end up with a second copy.

RKWard minimizes the size of its download by waiting to install some R packages until you actually try to use them for the first time. Then it prompts you, offering default settings that will get the package you need.

On Windows, the installation file is 136 megabytes in size.

 

Plug-ins

When choosing a GUI, one of the most fundamental questions is: what can it do for you? What the initial software installation of each GUI gets you is covered in the Graphics, Analysis, and Modeling section of this series of articles. Regardless of what comes built-in, it’s good to know how active the development community is. They contribute “plug-ins” which add new menus and dialog boxes to the GUI. This level of activity ranges from very low (RKWard, BlueSky, Deducer) through moderate (jamovi) to very active (R Commander).

Currently all plug-ins are included with the initial installation.  You can see them using the menu selection Settings> Configure Packages> Manage RKWard Plugins. There are only brief descriptions of what they do, but once installed, you can access the help files with a single click.

RKWard add-on modules are part of standard R packages and are distributed on CRAN. Their package descriptions include a field labeled, “enhances: rkward”. You can sort packages by that field in RKWard’s package installation dialog where they are displayed with the RKWard icon.

Continued here…