Plotly Dash vs. Shiny

Applicable for data visualization across disciplines. I have used it for data from buildings, building occupancy, public health, and statistics. It is a useful tool to use for big data. It has nice templates and a number of interesting visualization types. If you are familiar with R and python it is easy to use.

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

• Powerful visualization options.
• Ability to create in-browser interactive visualization apps.
• Ability to create hosted apps.
• Allows you to develop web-based reporting applications without requiring web application development expertise.

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

• Would be good if Dashboard Engine was included in the Enterprise VPC plan
• Would love to see ready made fintech apps

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

Incentivized

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

Incentivized

• A no-cost option as it is open sourced.

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