Updated Comparison of R Graphical User Interfaces
This article is originally published at https://r4stats.com
I have just updated my detailed reviews of Graphical User Interfaces (GUIs) for R, so let’s compare them again. It’s not too difficult to rank them based on the number of features they offer, so let’s start there. I’m basing the counts on the number of dialog boxes in each category of four categories:
- Ease of Use
- General Usability
- Graphics
- Analytics
This is trickier data 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. I’m putting the details of each measure in the appendix so you can adjust the figures and create your own categories. If you decide to make your own graphs, I’d love to hear from you in the comments below.
Figure 1 shows how the various GUIs compare on the average rank of the four categories. R Commander is abbreviated Rcmdr, and R AnalyticFlow is abbreviated RAF. We see that BlueSky is in the lead with R-Instat close behind. As my detailed reviews of those two point out, they are extremely different pieces of software! Rather than spend more time on this summary plot, let’s examine the four categories separately.
For the category of ease-of-use, I’ve defined it mostly by how well each GUI does what GUI users are looking for: avoiding code. They get one point each for 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 two shows the result. JASP comes out on top here, with jamovi and BlueSky right behind.
Figure 3 shows the general usability features each GUI offers. This category is dominated by data-wrangling capabilities, where data scientists and statisticians spend most of their time. This category also includes various types of data input and output. BlueSky and R-Instat come out on top not just due to their excellent selection of data wrangling features but also due to 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 40 points. JASP shows up at the bottom of this plot due to its philosophy of encouraging users to prepare the data elsewhere before importing it into JASP.
Figure 4 shows the number of graphics features offered by each GUI. R-Instat has a solid lead in this category. In fact, this underestimates R-Instat’s ability if you…
Thanks for visiting r-craft.org
This article is originally published at https://r4stats.com
Please visit source website for post related comments.
