JMP Pro offers all the capabilities of JMP, plus advanced features for more sophisticated analysis including predictive modeling and cross-validation techniques.
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Posit
Score 10.0 out of 10
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Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
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Pricing
JMP Pro
Posit
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Pricing Offerings
JMP Pro
Posit
Free Trial
No
Yes
Free/Freemium Version
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Yes
Premium Consulting/Integration Services
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No
Entry-level Setup Fee
No setup fee
Optional
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Community Pulse
JMP Pro
Posit
Considered Both Products
JMP Pro
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Posit
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Chose Posit
Coding in RStudio is more convenient and plot display in RStudio can be more user-defined than JMP Pro.
RStudio offers less out-of-the-box point and click solutions than other products, but it allows for custom solution development and its integration with the Shiny package in particular allows for the custom development of point and click solutions.
JMP Pro is perfectly suited for statistical analysis but users should have some statistical knowledge before using it since there may be some terms/functions in the software that are not widely used in other fields. No prior coding experience is needed to use JMP Pro. However, most people doing data processing would prefer to code their analysis.
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.
JMP Pro is a really powerful tool for doing statistical analysis. Although the click environment does not require coding experience, new learners will still need to take a long time to know the parameters in the function before performing any analysis.
The output from JMP Pro analysis (regression analysis) is not always easy to understand, especially when the parameters are programmed differently with the other similar software.
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
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.
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.
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).
