[This article was first published on R | r4stats.com, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Graphical user interfaces for the R language are easy to use and getting more powerful all the time. Here is my updated comparison of jamovi, JASP, BlueSky Statistics (free & Pro), Rattle, RKWard, R-Instat, R AnalyticFlow, and R Commander.With so many detailed reviews of Graphical User Interfaces (GUIs) for R available, which should you choose? It’s not too difficult to rate 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 UseGeneral UsabilityGraphicsAnalyticsReproducibilityThis data is trickier to collect than you might think. Some software has fewer menu choices, depending instead on more detailed dialog boxes. Studying every menu and dialog box is very time-consuming, but that is what I’ve tried to do to keep this comparison trustworthy. 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 and 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. Thomas Langkamp did an excellent comparison focused on teaching statistics. You can read it here.Ease of UseFor 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 1 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 15, so there is room for improvement.The ease of use features are:Installs without the use of RStarts without the use of RRemembers recent filesHides R code by defaultUses its full capability without needing R codeData editor includedPub-quality tables w/out R code stepsSimple menus that grow as neededTable of Contents to ease navigationVariable labels ease identification in the outputEasy to move blocks of outputEase reading columns by freezing headers of long tablesAccepts data pasted from the clipboardEasy to move header row of pasted data into the variable name fieldIncludes AI interface to ease interpretation of outputFigure 1. The number of ease-of-use features offered by each R GUI.General UsabilityThis category is dominated by data-wrangling capabilities, which data scientists and statisticians spend most of their time on. 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. The general usability features are: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 onceMultiple output windowsMultiple code windowsVariable metadata viewVariable types (how many)Variable search/filter in dialogsVariable sort by nameVariable sort by typeVariable move manuallyModel Builder (how many effect types)Magnify GUI for teachingR code editorComment/uncomment blocks of codePackage management (comes with R and all packages)Output: word processing featuresOutput: R MarkdownOutput: LaTeXData 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 onceProject saves/loads data, dialogs, and notes in one fileFigure 2. The number of general usability features in each R GUI.GraphicsThis category consists mainly of the number of plot types 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. jamovi has a solid lead in this category. R-Instat’s ability to layer any geometric object, or “geom,” on top of another could substantially increase its count, but I don’t include that since it requires knowledge of R code.When studying all the graphs in this article, 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 might be fine for your needs.The graphics features are:Types of graphs (how many)Small multiples (i.e., facets)Large multiples (i.e., by group / split-file)Export graphics formats (how many)Interactive Plot BuilderInteractive Plot Editor (e.g., add annotations, arrows, etc.)Able to combine any graphs into a single imageContinued…The post R GUI Comparison Update first appeared on r4stats.com.To leave a comment for the author, please follow the link and comment on their blog: R | r4stats.com.R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Continue reading: R GUI Comparison Update