Microsoft offers Visual Studio Code, an open source text editor that supports code editing, debugging, IntelliSense syntax highlighting, and other features.
$0
Posit
Score 10.0 out of 10
N/A
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
[Microsoft Visual Studio Code] builds on the simplicity and speed of Notepad++ yet remains very customizable and offers features that are comparable and often go beyond other paid offerings such as Dreamweaver CC. It also requires no upfront/subscription so there's no risk in …
Posit is way way way more reliable than Excel for anything more involved than a quick spreadsheet. Faster speeds, greater charting abilities, flexible functionality and more efficient memory usage. Python is still my go-to for anything that needs integration, but Posit beats …
We have used Visual Studio Code in some circumstances. It is a more full featured IDE for all coding, not just R. RStudio does some things easily--just run the code, load the IDE, and go do your work. Visual Studio Code requires a ton of hand holding and extension stuff before …
I've been pitched a few different data science notebook tools that tend to be more expensive and less suited to R development. I don't think I've actually seen another product that really compares to RStudio Connect for publishing Shiny Apps. I think the alternative there is …
Rodeo, jupyter and other editors RStudio like for both R and Python are simply not at the level of RStudio and they do not provide the same range of features that comes with it.
As a general workhorse IDE, Microsoft Visual Studio Codee is unmatched. Building on the early success of applications such as Atom, it has long been the standard for electron based IDEs. It can be outshone using IDEs that are dedicated to particular platforms, such as Microsoft Visual Studio Code for .net and the Jetbrains IDEs for Java, Python and others. For remote collaborative development, something like Zed is ahead of VSCode live share, which can be quite flakey.
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.
The customization of key combinations should be more accessible and easier to change
The auxiliary panels could be minimized or as floating tabs which are displayed when you click on them
A monitoring panel of resources used by Microsoft Visual Studio Code or plugins and extensions would help a lot to be able to detect any malfunction of these
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.
Solid tool that provides everything you need to develop most types of applications. The only reason not a 10 is that if you are doing large distributed teams on Enterprise level, Professional does provide more tools to support that and would be worth the cost.
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.
Microsoft Visual Studio Code earns a 10 for its exceptional balance of power and simplicity. Its intuitive interface, robust extension ecosystem, and integrated terminal streamline development. With seamless Git integration and highly customizable settings, it adapts perfectly to any workflow, making complex coding tasks feel effortless for beginners and experts alike.
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
Overall, Microsoft Visual Studio Code is pretty reliable. Every so often, though, the app will experience an unexplained crash. Since it is a stand-alone app, connectivity or service issues don't occur in my experience. Restarting the app seems to always get around the problem, but I do make sure to save and backup current work.
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
Microsoft Visual Studio Code is pretty snappy in performance terms. It launches quickly, and tasks are performed quickly. I don't have a lot of integrations other than CoPilot, but I suspect that if the integration partner is provisioned appropriately that any performance impact would be pretty minimal. It doesn't have a lot of bells and whistles (unless you start adding plugins left and right).
Active development means filing a bug on the GitHub repo typically gets you a response within 4 days. There are plugins for almost everything you need, whether it be linting, Vim emulation, even language servers (which I use to code in Scala). There is well-maintained official documentation. The only thing missing is forums. The closest thing is GitHub issues, which typically has the answers but is hard to sift through -- there are currently 78k issues.
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.
Visual Studio Code stacks up nicely against Visual Studio because of the price and because it can be installed without admin rights. We don't exclusively use Visual Studio Code, but rather use Visual Studio and Visual Studio code depending on the project and which version of source control the given project is wired up to.
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.
It is easily deployed with our Jamf Pro instance. There is actually very little setup involved in getting the app deployed, and it is fairly well self-contained and does not deploy a large amount of associated files. However, it is not particularly conducive to large project, multi-developer/department projects that involve some form of central integration.
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).
