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.
PowerApps is well suited for "quick-wins" and fast prototypes of business solutions. It also is beneficial for situations where business partners and developers work together - it allows the business folks to provide a "quick-and-dirty" prototype which is then fleshed-out by developers that are trained experts on the platform. The interactive and easy to understand representation of the solution allows business partners to "see" the solution and add, remove, or correct aspects of it themselves. It provides a common view and understanding of the actual solution across business units and tech teams. PowerApps, being a low-code\no-code platform is not well suited for business processes that require many complex computations or large amounts of custom code - such as solutions that are better architected as Web Site or "full-blown" desktop solutions. There are solutions that are just not easy or quick to accomplish in a low-code\no-code platform. Enterprise Architects should know the difference, however business partners often try to create a solution and only when stuck because it becomes too complex do they engage a tech team for assistance - at which point there are sunk-costs involved and hinderences to re-platforming the solution
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.
Power Apps has formats that are pre-built that don't require any coding which makes it easier to achieve your vision. This does become a challenge if your App needs don't fit into that format.
We deal with a ton of data so the fact that you can connect to any data source in addition to their pre-stablished data connections makes the process a breeze.
The online learning resources and tutorials are helpful as well for those who are tech savvy.
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.
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
PowerApps is a great solution and I have spent the last year familiarizing myself with the platform and building custom applications to complete a whole range of tasks such as asset management, custom invoice generation, and item restriction tracking. We as a company have barely begun to scratch the surface of what can be achieved with PowerApps.
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
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.
The community forums are extremely responsive to questions asked, there is a good body of online documentation and many community posts to draw from. Although the platform has changed, which means some of the posts are out of date and the solutions provided aren't relevant. Of relevance, I read over 400 articles plus documentation to get this first app built in SharePoint, move it to SQL and make it work exactly the way it should.
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.
Much cheaper, much more customizable, and easier to use. There is not much of a learning curve and the licensing cost is much cheaper. PowerApps does one thing very well, whereas other platforms are mediocre. There is much more customization possible for your in-house workflows that you can build yourself vs using NetSuite engineers to build it for you.
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.
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).
It has given us a focal point for development. We now have the possibility of connecting to mobile and the default SharePoint online interface isn't always easy to manipulate. PowerApps has given us an opportunity to improve our user experience.
An improved user experience has given us a better shot at compliance. When users don't fight the environment, they don't gravitate towards workarounds or non-compliance.
As lists and libraries change, the platform scales pretty well.
Having users with the capability to create their own forms and tools has dialed back the app dev need (there is a balance though) and distributed power to the process architects and people who actually need the solutions in the first place—much more efficient model of service delivery: self-service.
