NetBeans is extremely user friendly and easy to start developing complex applications. Adding and configuring external libraries is much simpler than in Eclipse. It is highly cost effective and most of the latest framework based libraries required are automatically downloaded to the projects. The overall tool is also light weight and consumes less memory as compared to other competitor tools.
In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.
The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.
NetBeans [should] work smoothly with systems having less RAM. Systems with less RAM face trouble with NetBeans.
File open history also requires improvement. Once NetBeans is restarted, all files are closed automatically and there is no shortcut to open last opened files.
Python integration is newer and still can be rough, especially with when using virtual environments.
RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.
There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.
Netbeans enhances my coding work, shows me where I have errors and helps find variable instances. I would be lost without find/replace in projects functionality as I use projects as templates for new projects. Occasionally the code hints aggravate me, but I understand that it is actually making me a better coder, working to get the 'green light' of a clean file with no errors or clumsy code.
For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when
RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses
NetBeans has a very strong user community. We can find solutions here for almost all the problems we face. In addition, we can forward NetBeans Support teams the problems we cannot solve. We can get quick feedback from the support teams, but I generally try to solve my problems by following the forums.
Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.
It works very smoothly as compared to other tools . The problem of restarting and reimporting the projects is not in the netbeans IDE . The front end development features are good . Netbeans connector is one of the best thing which enables us to deeply integrate netbeans IDE with google chrome browser
RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.
RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.
By working on Netbeans I just learned one more tool and can teach others about it. One should learn every tool so that it might help someday if another editor is not available and you have to use different software for your work.
Compiling code became easy as it is not a feature of normal text editors. Only IDE can do this.
Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).
