Microsoft Visual Studio vs RStudio Desktop: Which is a better fit?

Kevin: Hi, this is Kevin. I'm a product owner and I give Microsoft Visual Studios a four out of five, and for more reviews like this, please click below. I use Microsoft Visual Studio with Python Code. Microsoft Visual Studios allows multiple different softwares to run within it, and I use it for Python, which is a programming language that we use for spreadsheets and other analytical tools. What I like about Microsoft Visual Studios is it's a Microsoft product, so it's included with our other Microsoft Suites. It has very similar menus across the top in the ribbon, and it's fairly reliable, and I feel secure using it. What I don't like about Microsoft Visual Studios is because it's a one-size-fits-many, it's not tailored to the software that we use it with currently, so your mileage may vary.

Christopher P.: Hi, I'm Chris, Chief Data Scientist. And I give RStudio five stars out of five. More reviews click below. So the softwares that we looked at, we looked at, obviously, things like Tableau for visualization. We looked at the raw IDEs for Python and R. We looked at very expensive packages, like [inaudible 00:00:19] for example, and pretty much tried everything on the market when it comes to an integrated IDE for data science. So, RStudio, we chose for a few reasons. One, cost. It is open source software, which is awfully nice to be able to have that be zero cost. Two, is the environment itself is very friendly in terms of the ability for you to do your work. So you have a lot of configurability, but it does have a lot of conveniences built in that. Obviously, some of which are integrated into the R language itself, but a lot of them, like package management for example, is built right in. And it's optimized for data science and analytics, where you can take a look at what variables are in your environment and things, be able to manipulate data frames and use some of the gooey as well as all the power of command lines and scripts within one environment. So it's this really nice all in one Swiss army knife for data science, that's very, very difficult to find competing packages. I constantly wish I could find something like it for Python, because a lot of stuff is in the Python language. But for anything in R, it is the default by far. So integration is actually handled by our enterprise package management software. So we use Homebrew, because we're a Mac OS shop, and it's super easy to get started. You simply install it through Homebrew and then it's automatically maintained when you just run your maintenance cycles. Getting started with R itself, again, very straightforward. You have your R environment, your environmental variables and all the things that you would expect to be in enterprise level software and your package match maintainment we use the package manager. So again, it keeps things up to date, keeps things organized, and you don't have to worry too much about dependencies in your packages because the software handles that. And RStudio also does a really good job of identifying when you're using commands or functions that aren't in packages that are installed, what package they're from, and then offers to install it for you. So from a getting started perspective, it's really made it very easy. For other people considering RStudio, it really depends on the skill level of the person that you're working with. It is still a programming environment, programming language. So if you have users who are looking for a no code environment, RStudio is not the right choice, right? It is a coding environment. They'd be better served by a tool like, for example, a Watson studio or Tableau something a little easier. For experienced users who are coders, make sure that they spend time configuring the environment to what's comfortable so that it works for them. Again it's a very configurable piece of software and what comes out of the box may not be right for you. And there are all these little add-ons here and there that I think are worth spending some time with the software. What theme you use, for example, it has great support for dark mode. There are certain fonts like Fira code that make it even friendlier and easier to use. Fira font code, for example, it uses ligatures. So when you're looking at your code in RStudio, it's easier to read, it's easier to interpret, because the ligatures look coherent. So there's lots of little niceties, but all that come from experience from time working with the software, from talking to other users and user groups and all those things I think would be make it an even better choice for when you're working with any kind of data science in R language.