RStudio Desktop

As a statistician, I have been an active user of the R statistical programming language since 2007, and I had insisted to use the legacy R GUI all these years until about two years ago. Then I started to use RStudio and It was really a big step that makes me more productive.
RStudio is an advanced Integrated Development Environment (IDE) for the R language. It has a very nice user interface with advanced code highlighting, viewing data environment, help, and graphs in the same window. Moreover, It is integrated with GIT for easy version controlling inside of the Rstudio. Furthermore, It facilitates creating R shiny apps and developing R packages. Last but not least, it let to easily develop an R notebook to integrate code, graph, table, and documentation with the ability to export it to HTML, PDF, and Word formats. So it is a complete R environment for both Statisticians and Data scientists.

Rstudio does not provide menu-driven support for data analysis. In fact, a user can make it him/herself by using Shiny apps but the nature of the Rstudio is code-driven and the software graphical user interface is intended to help programmers and data scientists and not ordinary pople that like a menu-driven data analysis framework.

Programming in R is available for free and without restrictions. So, there are no requirements for employment, including fees or licenses.

R is a state-of-the-art program that gets updates whenever new features are published.
R has a renowned set of graphics libraries. These libraries provide assistance and improve the R development environment. The libraries that R provides are numerous and have many different uses.

Being able to open the code editor in a separate window is a wonderful feature.

With R, creating impressive graphs and charts is simple.

R is not able to be used in online applications, for example, which is just one of many restrictions it has.

R lacks sufficient security.

MATLAB and Python are both considerably faster than R as a programming language.

There are a ton of packages that can be loaded into R for many purposes (e.g. tables, graphs). For example, GGPlot makes very professional looking graphs that are great for publications. Once you have a basic understanding of the code processes, R can be very useful for a variety of statistics, from simple to quite complex (e.g. MLM, HLM). Also, R is free, which is so amazing considering some of its competitors cost thousands of dollars.

This is very very difficult to learn as a beginning. It takes quite some time to feel remotely comfortable with the syntax. There are free tutorials on youtube, etc, but it can still be very discouraging as a beginning with coding experience to get started on this platform.

It is great that it has a interfase that helps you to be familiar with the terminal, variables and libraries.

It can be helpful a tutorial for each feature, helping to understand all the things that you can do.

It gives the analysis of each question and distractor in detail, so it is great to see the results and try to optimize the assessment items.

There is nothing which can be considered as a con.