Q Research Software vs. Shiny

We use Q for quantitative data. If you know what you are doing it can still take a bit of time to manipulate your data into the most suitable format for the software to help you. But it is time well spent because once it's set up, Q makes the analysis a breeze. We use it for producing data tables, word clouds, significance testing, audience segmentation and coding of open-responses.

Incentivized

Shiny is well suited where an organisation is looking to empower their analysts to minimise time spent on repetitive analysis by deploying repeatable analytical pipelines, but also looking for them to add greater value to the organisation by utilising more advanced analytical techniques. Ideally it is well suited where IT are on board and supportive of some of the more advanced features such as deploying R Shiny dashboards.

Incentivized

• Produces really easy to view tables
• Automatically applies significance testing to data, helping the user spot trends
• Create and insert your own variables and filters to help manipulate the data

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 pricing model is a little restrictive for smaller teams that only really need one license but have to buy a 2nd to help out modest users/users learning the ropes.
• Learning the basics can take quite a bit of time but they offer plenty of free resources that help you through it step-by-step
• Too be honest, I don't have too many complaints

Incentivized

• Easier ways to connect to data sources
• Better access control for different roles in the organization
• Video material that allows a better learning experience

Incentivized

We still use Excel in order to use Q, but all the analysis happens in Q. No need to learn formulas or reformat spreadsheets. Q does all the heavy lifting.

Incentivized

- Faster response working with a large amount of data. - R Studio connection and flexibility. - Scenarios modelling.

Incentivized

• Time saving - not exaggerating when I say we can do at least 10x the amount of analysis than we could without it
• More thorough insights obtained from our data sets
• Makes data engaging to other stakeholders

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