Plotly Chart Studio vs. Shiny

[Plotly Chart Studio is] a great tool to make visualizations on a web based portal. For python developers, it can ease of some burden of writing code again and again for the same thing and also refreshing visualizations on old scripts when data changes. All in all, a simple yet effective tool for your report generation and visualization needs[.]

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

• Connects to almost every kind of data source like SQL, Excel, MS Access, flat files and more.
• Charts get saved to the repository and are re-used by multiple users.
• No need of manual intervention in order to refresh the charts every time the data gets changed.

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

• Lack of customer support
• Sometimes fails to visualize extremely large datasets ( probably also due to computer resource constraints)

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

• Improvement in productivity as charts can be shared with teams and feedback can be collected, consolidated and incorporated in the design and charts.
• Charts are aligned with the organization theme and colors and one doesn't need to put in effort every time to do that.
• No upfront capital expenditure and minimum user criteria helps the organization to keep the costs down.

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