Displayr vs. Shiny

Displayr is perfectly suited for any insights or data people that understand the type of analysis they want to do, but don't know R code - or just want to get to results more quickly than coding themselves. It's probably not the best learning ground, if you've never done any quantitative analysis before, but then neither are traditional tools like SPSS or Q.

Incentivized

Shiny is very good for developing dashboards or web applications with specific functionalities. But it is not so easy to use to develop from scratch, it is always better to use another tool to have a general idea of ​​what is expected of a dashboard and then develop the most specific functionalities in Shiny. It is much more flexible than other tools and that is why I consider it to be better for most cases, only that it is more complex to develop or has a longer learning curve.

Incentivized

• The intuitive interface and menus make it easy to quickly learn Displayr and find the types of data transformation or analysis that we're looking to do.
• The support level from Displayr's team is FIRST CLASS. Where othe platforms force you to an FAQ or AI chat bot, Displayr's team will jump in first hand, into our data, or on a live call, and help us run a new type of analysis or troubleshoot a problem.
• The ability to work collaboratively, asynchronously and remotely, on the same data set and report is a really huge plus for us.
• The in-built options for multivariate analysis cover 99.9% of anything we have - or will - ever need to run.

Incentivized

• Data tables are appealing to look at.
• Enables us to create trend indexes in an effective way.
• Easy to integrate with the rest of my R syntax.

Incentivized

• The new "glow-up" on the interface has helped make it a bit easier on the eye, but there are some features of working in the "three pane" browser that are a bit frustrating: especially having to 'rearrange' when resizing the window to look at another app simultaneously.
• Such a small point, but being able to drag and move multiple elements in a table (eg drag two rows to the top) SIMULTANEOUSLY would help a bunch.
• I don't think we take advantage of all the visualisation capabilities in Displayr, and perhaps an AI 'recommendation' engine that sees the data I'm working with and prompts either a specific visualisation, or additional analysis option I might use, would be great.

Incentivized

• Shiny can be really time consuming to create visuals.
• It needs excellent knowledge of R programming and coding skillset.
• It still has a limited set of options to choose from.

Incentivized

It's really quite intuitive, but the visual interface could be made a bit more easy to use (window/pane rescaling etc) and I think there could be more 'proactive prompts' to suggest features we're underutilising.

Incentivized

SPSS (the last version I looked at) still requires much more underlying knowledge and coding ability to get where we want to be. That's not where we add value, so the speed and simplicity with which Displayr allows us to get the data analysis done, and move onto developing insight and delivering value is why I chose Displayr.

Incentivized

Whilst dashboarding may be comparable with some of the other products we evaluated. Nothing compared to the analytical capabilities on offer with Shiny. An added advantage was that we had colleagues knowledgeable in R which meant bringing in Shiny and getting to grips with it was a lot more seamless and welcomed by the end users.

Incentivized

• I think Displayr is quite expensive, but has the biggest impact on our P&L of any of our subscriptions, because it has unlocked our ability to deliver bigger, more complex analytic projects for clients - and hence grow our topline.
• The ability to scale the license between years has also been a god-send as our team has gone up or down to deliver the level of quant work available to us.
• There's also a bottom line efficiency driven by some of the speed of analysis that Displayr enables.

Incentivized

• We saw a good involvement to researchers when showing their models in shiny.
• We can have a quicker review from the user when the model is in production.
• False positives can be found easily and they help the retraining of the model.

Incentivized