Caspio vs RStudio Desktop: Which is a better fit?

Speaker 1: Hi, my name is John. I'm the communications chair for a not-for-profit organization. We use Caspio to track member data as well as signups for various services that we provide throughout the summer season. I rate Caspio a five out of five. I evaluated Airtable as a potential solution, but found the data limits to be a bit more severe than what we were looking for, and Caspio provided much better flexibility. The reason I chose Caspio was its overall ease of use, the data views, which provided a great amount of flexibility so that I can create data views, put them behind a password protected page on our association's website, and have people who need to get to the information, get to it. I found that super easy. I also like the loading of the product from Excel spreadsheets. That provides a built-in backup for us. And overall, I found the product to be very intuitive. The onboarding process was pretty straightforward. I was able to define tables that I wanted. I already had a basic design for how the database would be structured. I read some of the onboarding materials and really just played around with it, and I was up and running probably in a couple of days. I think if you are an organization with low transaction volumes and you just want to get your feet wet, Caspio is perfect. The free version supports low, but manageable volumes. Super easy. You can get up and running in a short amount of time, and it really provides a tremendous amount of flexibility. I'm very happy with the product.

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.