This document compares several graphical user interfaces (GUIs) for R, including BlueSky, Deducer, jamovi, JASP, R AnalyticFlow, Rattle, RKWard, and R-Instat. It ranks them based on features related to ease of use, general usability, graphics capabilities, and analytics. BlueSky and R-Instat score highest for their data wrangling features. JASP is strongest for Bayesian analysis and machine learning. Rattle focuses on machine learning/AI. RKWard provides an advanced integrated development environment. The document also notes strengths and limitations of each GUI.

1. R Graphical User Interface Comparison
RAMAKRISHNA REDDY B
(MCA, MTECH)
AVANTHI PG COLLEGE
HYDERABAD

2. R Graphical User Interface
Comparison
Having completed several detailed reviews of Graphical
User Interfaces (GUIs) for R, let’s compare them. 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:
1. Ease of Use
2. General Usability
3. Graphics
4. Analytics

3. • Some software has fewer menu choices,
depending instead on more detailed dialog
boxes.
• Studying every menu and dialog box is very
time-consuming.
• 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.

5. • 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 2 shows the result.
• JASP comes out on top here, with jamovi and
BlueSky right behind.

7. • 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.

8. • 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.

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

12. • Many important details are buried in these
simple counts.
• For example, I enjoy using jamovi for
statistical analyses, but it currently lacks
machine learning and artificial intelligence.
• I like BlueSky too, but it doesn’t yet do any
Bayesian statistics (jamovi and JASP do).
• Rattle comes out near the bottom due to its
focus on machine learning, but it does an
excellent job of introducing students to that
area.

14. • BlueSky Statistics –
• This software was created by former SPSS employees, and it shares
many of SPSS’ features.
• BlueSky is only a few years old, and it converted from commercial to
open source mid-way through 2018.
• Its developers have been adding features at a rapid rate.
• When using BlueSky, it’s not initially apparent that R is involved at all.
• Unless you click the code button “</>” included in every dialog box,
you’ll never see the R code. BlueSky saves the dialog settings for every
step, providing GUI-based reproducibility.
• BlueSky’s output is in publication-quality tables which follow the
popular style of the American Psychological Association.
• It’s stronger than most of the others in AI/ML and psychometrics.
• It is now available for Windows and Mac (previous versions were
Windows-only).
• BlueSky is the only R GUI with a company supporting it, though that
support is not free.

15. Deducer –
• This has a very nice-looking interface, and it’s probably the
first R GUI to offer output in true APA-style word processing
tables.
• Being able to just cut and paste a table into your word
processor saves a lot of time, and it’s a feature that has been
copied by several others.
• Deducer was released in 2008, and when I first saw it, I
thought it would quickly gain developers.
• It got a few, but development seems to have halted.
• Deducer’s installation is quite complex, and it depends on
the troublesome Java software.
• It also uses JGR, which never became as popular as the
similar RStudio.
• The main developer, Ian Fellows, has moved on to another
interesting GUI project called Vivid.

16. Jamovi
• The developers who form the core of the jamovi project used
to be part of the JASP team.
• Even though they started a couple of years later, they’re
ahead of JASP in several ways at the moment.
• Its developers decided that the R code it used should be
visible and any R code should be executable, features that
differentiate it from JASP.
• jamovi has an extremely interactive interface that immediately
shows you the result of every selection in each dialog box
(JASP does too).
• It also saves the settings in every dialog box and lets you re-
use every step on a new dataset by saving a “template.” That’s
extremely useful since GUI users often prefer to avoid learning
R code.
• jamovi’s biggest weakness is its dearth of data management
features, though there are plans to address that.

17. JASP
• The biggest advantage JASP offers is its emphasis
on Bayesian analysis.
• If that’s your preference, this might be the one for
you. Another strength is JASP’s Machine Learning
module.
• Currently, JASP is very different from all the other
GUIs reviewed here because it can’t show you the
R code it’s writing.
• The development team plans to address that issue,
but it has been planned for a couple of years now,
so it must not be an easy thing to add.

18. R AnalyticFlow
• This is unique among R GUIs as it is the only one
that lets you organize your analyses using
flowchart-like workflow diagrams.
• That approach makes it easy to visualize what a
complex analysis is doing and to rerun it.
• It writes very clean base R code and provides easy
access to the powerful lattice graphics package.
• It also supports the ggplot2 graphics package, but
only through its more limited quickplot function.
• R AnalyticFlow also lets you extend its capability
making it easier for R power users to interact with
non-programmers.

19. Rattle
• If your work involves ML/AI (a.k.a. data
mining) instead of standard statistical
methods, Rattle may be the GUI for you.
• It’s focused on ML/AI, and its tabbed-based
interface makes quick work of it.
• However, it’s the weakest of them all
regarding statistical analysis.
• It also lacks many standard data management
features.

20. RKWard
• This GUI blends a nice point-and-click interface with
an integrated development environment (IDE) that
is the most advanced of all the other GUIs reviewed
here.
• It’s easy to install and start, and it saves all your
dialog box settings, allowing you to rerun them.
• However, that’s done step-by-step, not all at once
as jamovi’s templates allow.
• The code RKWard creates is classic R, with no
tidyverse at all.
• RKWard is one of only three R GUIs that support R
Markdown (the others: BlueSky and jamovi).

21. R-Instat
• This offers one of the most extensive collections of
data wrangling, graphics, and statistical analysis
methods of any R GUI.
• At a basic level, its graphics dialogs are easy to use,
and it offers powerful multi-layer support for
people who are familiar with the ggplot2 package’s
geom functions.
• To use its full modeling capabilities, you need to
know what R’s packages (e.g. MASS) are and what
each one’s functions