R GUI Reviews Updated

I have just finished updating my reviews of graphical user interfaces for the R language. These include BlueSky Statistics, jamovi, JASP, R AnalyticFlow, R Commander, R-Instat, Rattle, and RKward. The permanent link to the article that summarizes it all is https://r4stats.com/articles/software-reviews/r-gui-comparison/.
I list the highlights below as this post to reach all the blog aggregators. If you have suggestions for improving any of the reviews, please let me know at muenchen.bob@gmail.com.

With so many detailed reviews of Graphical User Interfaces (GUIs) for R available, which should you choose? It’s not too difficult to rank them based on the number of features they offer, so I’ll start there. Then, I’ll follow with a brief overview of each.

I’m basing the counts on the number of dialog boxes in each category of the following categories:

• Ease of Use
• General Usability
• Graphics
• Analytics
• Reproducibility

This data is trickier to collect than you might think. Some software has fewer menu choices, depending on more detailed dialog boxes instead. Studying every menu and dialog box is very time-consuming, but that is what I’ve tried to do to keep this comparison trustworthy. Each development team has had a chance to look the data over and correct errors.

Perhaps the biggest flaw in this methodology is that every feature adds only one point to each GUI’s total score. I encourage you to download the full dataset to consider which features are most important to you. If you decide to make your own graphs with a different weighting system, I’d love to hear from you in the comments below.

Ease of Use

For ease of use, I’ve defined it primarily by how well each GUI meets its primary goal: avoiding code. They get one point for each of the following abilities, which include being able to install, start, and use the GUI to its maximum effect, including publication-quality output, without knowing anything about the R language itself. Figure one shows the result. R Commander is abbreviated Rcmdr, and R AnalyticFlow is abbreviated RAF. The commercial BlueSky Pro comes out on top by a slim margin, followed closely by JASP and RKWard. None of the GUIs achieved the highest possible score of 14, so there is room for improvement.

• Installs without the use of R
• Starts without the use of R
• Remembers recent files
• Hides R code by default
• Use its full capability without using R
• Data editor included
• Pub-quality tables w/out R code steps
• Simple menus that grow as needed
• Table of Contents to ease navigation
• Variable labels ease identification in the output
• Easy to move blocks of output
• Ease reading columns by freezing headers of long tables
• Accepts data pasted from the clipboard
• Easy to move header row of pasted data into the variable name field

General Usability

This category is dominated by data-wrangling capabilities, where data scientists and statisticians spend most of their time. It also includes various types of data input and output. We see in Figure 2 that both BlueSky versions and R-Instat come out on top not just due to their excellent selection of data-wrangling features but also for their use of the rio package for importing and exporting files. The rio package combines the import/export capabilities of many other packages, and it is easy to use. I expect the other GUIs will eventually adopt it, raising their scores by around 20 points.

• Operating systems (how many)
• Import data file types (how many)
• Import from databases (how many)
• Export data file types (how many)
• Languages displayable in UI (how many, besides English)
• Easy to repeat any step by groups (split-file)
• Multiple data files open at once
• Multiple output windows
• Multiple code windows
• Variable metadata view
• Variable types (how many)
• Variable search/filter in dialogs
• Variable sort by name
• Variable sort by type
• Variable move manually
• Model Builder (how many effect types)
• Magnify GUI for teaching
• R code editor
• Comment/uncomment blocks of code
• Package management (comes with R and all packages)
• Output: word processing features
• Output: R Markdown
• Output: LaTeX
• Data wrangling (how many)
• Transform across many variables at once (e.g., row mean)
• Transform down many variables at once (e.g., log, sqrt)
• Assign factor labels across many variables at once
• Project saves/loads data, dialogs, and notes in one file

Graphics

This category consists mainly of the number of graphics each software offers. However, the other items can be very important to completing your work. They should add more than one point to the graphics score, but I scored them one point since some will view them as very important while others might not need them at all. Be sure to see the full reviews or download the Excel file if those features are important to you. Figure 3 shows the total graphics score for each GUI. R-Instat has a solid lead in this category. In fact, this underestimates R-Instat’s ability if you include its options to layer any “geom” on top of another graph. However, that requires knowing the geoms and how to use them. That’s knowledge of R code, of course.

When studying these graphs, it’s important to consider the difference between the relative and absolute performance. For example, relatively speaking, R Commander is not doing well here, but it does offer over 25 types of plots! That absolute figure might be fine for your needs.

Continued…