Name: Shiny
Rating: 8 (12 reviews)
Author: Posit (formerly RStudio)

Call Center Workforce Optimization

Help Desk

Web and Video Conferencing

Customer Support

Mobile App Development

Development

Business Intelligence (BI)

Collaboration

Data Discovery and Visualization

Document Management

Dropshipping

Integrated Workplace Management (IWMS)

Enterprise

Accounting

Payment Gateway

Spend Management

Finance and Accounting

Applicant Tracking

Corporate Learning Management

Course Authoring

Employee Monitoring

Employee Performance Management

HR Management

Payroll

Professional Employer Organizations (PEO)

Recruitment Agency

Recruitment Automation

Succession Planning

Talent Intelligence

Talent Management

Video Interview

Workforce Analytics

Workforce Management

eLearning Content

Human Resources

C/C++ Integrated Development Environments

Cloud Storage

Compliance Consulting

Configuration Management Databases

Data Quality

Distributed Denial of Service (DDoS) Protection

Fraud Detection

IT Alert Management

In-Memory Databases

Infrastructure-as-Code (IaC)

Integration Platform as a Service (iPaaS)

Network Performance Monitoring

Remote Monitoring and Management

Tower Servers

Virtual Private Network (VPN)

Zero Trust Security Solutions

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Competitive Intelligence

Consumer Ratings & Reviews

Content Management

Email Marketing

Email Testing

Market Intelligence

Marketing Automation

Presentation

Social Media Advertising

Social Media Management

Survey & Forms Building

Web Analytics

Marketing

Project Management

Professional Services

Customer Relationship Management (CRM)

Field Sales Software

Multichannel Retail

Sales

Antivirus

Certificate Lifecycle Management

Secure Access Service Edge (SASE)

User and Entity Behavior Analytics (UEBA)

Security

Car Rental

Home Health Care

Insurance Agency Management

Legal Research

Library Management

Vertical-Specific

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### Data Visualization and Analysis
Reviewers generally appreciate Shiny's data visualization and analysis features, highlighting its effectiveness in creating dynamic and interactive visualizations for various audiences. Users find Shiny particularly useful for sharing analysis results in a clear and interactive manner, especially when comparing different scenarios or showcasing trends. While some users mention that creating visuals in Shiny can be time-consuming and requires a strong understanding of R programming, the overall consensus is positive regarding its ability to enhance data visualization and analysis tasks within organizations. (Source Reviews: [1](https://www.trustradius.com/reviews/shiny-2022-06-13-03-16-27), [2](https://www.trustradius.com/reviews/shiny-2022-05-26-13-13-55), [3](https://www.trustradius.com/reviews/shiny-2022-04-28-04-03-11), [4](https://www.trustradius.com/reviews/shiny-2022-04-25-14-15-18), [5](https://www.trustradius.com/reviews/shiny-2022-04-26-17-17-36))

### Dashboard Creation and Management
Users across various industries and departments have shared their experiences with Shiny's dashboard creation and management features. Reviewers generally praise Shiny for its ability to develop visually appealing and interactive dashboards that enhance data visibility within their organizations. While some users find Shiny's dashboard creation process to be slightly complex and requiring a learning curve, the flexibility and customization options it offers are often highlighted as significant advantages. Additionally, the availability of extensive documentation and a supportive community contribute to the overall positive sentiment towards Shiny's dashboard creation and management capabilities. (Source Reviews: [1](https://www.trustradius.com/reviews/shiny-2022-04-26-17-17-36), [2](https://www.trustradius.com/reviews/shiny-2022-04-25-09-10-29), [3](https://www.trustradius.com/reviews/shiny-2022-06-13-03-16-27), [4](https://www.trustradius.com/reviews/shiny-2022-05-26-13-13-55))

### Data Transformation and Management
Users generally praise Shiny for its data transformation and visualization capabilities. Reviewers highlight Shiny's effectiveness in collating data from diverse sources, transforming it, and creating insightful visualizations for different audiences. The tool's ability to handle historical data, model various diseases, and work with different data structures like text, columnar, time series, and images is well-received by users. Shiny's data transformation and management functionalities seem to be a strong suit, enabling users to streamline their analytical pipelines and deliver valuable insights to their target audience. (Source Reviews: [1](https://www.trustradius.com/reviews/shiny-2022-06-13-03-16-27), [2](https://www.trustradius.com/reviews/shiny-2022-05-26-13-13-55), [3](https://www.trustradius.com/reviews/shiny-2022-04-25-09-10-29))

### Advanced Analytics and Modeling
Users generally appreciate Shiny's advanced analytics and modeling capabilities, particularly its ability to interact with ML models and deliver online services. However, some reviewers note that Shiny can be complex for non-technical profiles and may require a high level of coding skills and R programming knowledge. While Shiny is well-suited for developing dashboards with specific functionalities, it may pose challenges for non-programmer business stakeholders due to its complexity. Despite its strengths in modeling different diseases and working with diverse data structures, Shiny's advanced analytics and modeling features may require a learning curve for optimal utilization. (Source Reviews: [1](https://www.trustradius.com/reviews/shiny-2022-06-13-03-16-27), [2](https://www.trustradius.com/reviews/shiny-2022-04-25-09-10-29), [3](https://www.trustradius.com/reviews/shiny-2022-04-28-04-03-11), [4](https://www.trustradius.com/reviews/shiny-2022-04-25-14-15-18), [5](https://www.trustradius.com/reviews/shiny-2022-04-26-17-17-36))

### Programming and Development Tools
Users commonly praise Shiny's flexibility and ease of development with R. While some find it time-consuming and challenging due to the high level of coding skills required, others appreciate its documentation and code reuse capabilities. The tool is seen as well-suited for empowering analysts to deploy repeatable analytical pipelines and add value through advanced analytical techniques, especially in settings where IT support is available for deploying R Shiny dashboards. Despite some users mentioning a learning curve and the need for additional skills beyond R programming, Shiny's programming and development tools are generally regarded as effective for creating data visualizations and dashboards for various business needs. (Source Reviews: [1](https://www.trustradius.com/reviews/shiny-2022-04-26-17-17-36), [2](https://www.trustradius.com/reviews/shiny-2022-04-25-14-15-18), [3](https://www.trustradius.com/reviews/shiny-2022-04-25-09-10-29), [4](https://www.trustradius.com/reviews/shiny-2022-06-13-03-16-27), [5](https://www.trustradius.com/reviews/shiny-2022-05-26-13-13-55))

### Custom Application and Web Development
Users generally appreciate Shiny's flexibility in developing web applications and its ability to streamline model deployment and production. Reviewers highlight the tool's effectiveness in interacting with machine learning models and delivering online services. They find Shiny particularly well-suited for classical ML models and note its capability to enhance model speed in production environments. Additionally, users value Shiny for its operability and its usefulness for researchers working in R. The platform's custom application and web development functionalities seem to be a strong point for users, especially when it comes to deploying models efficiently and receiving quick feedback from end-users. (Source Reviews: [1](https://www.trustradius.com/reviews/shiny-2022-04-26-17-17-36), [2](https://www.trustradius.com/reviews/shiny-2022-04-25-09-10-29))

Shiny, an interactive data visualization tool, has found its place among data experts who aim to make complex analysis accessible to a lay audience. By collating data from various sources and leveraging Shiny dashboards and R Markdown documents, users have been able to generate advanced analysis and dynamic visualizations. This flexibility is further enhanced by the continuous support and development of packages within the Shiny ecosystem, allowing users to harness the latest improvements.

One key use case of Shiny lies in the commercial sector, where it has been employed to develop dashboards for monitoring indicators in different areas of business. These web dashboards have greatly improved visibility across the organization, ensuring that relevant stakeholders have access to essential business insights. Moreover, Shiny's ability to share analyses and facilitate interaction with the data has proven invaluable in scenarios where multiple analyses or what-if scenarios need to be explored.

Researchers in the Research and Development department have also benefited from Shiny's capabilities. It has served as a platform for delivering dashboards based on specific models generated in R. Notably, these models encompass disease modeling, which often involves working with diverse patient data such as medical notes, Rx records, fluids, and various data structures like text, columns, time series, and images. Shiny's interactive nature allows researchers to delve into the intricacies of their models while empowering them to effectively communicate their findings.

Dynamic Visualization: Some users have found the dynamic visualization feature of the product to be particularly helpful, allowing them to showcase interactive drop downs and sliders for analyzing data in an engaging and interactive manner.

Effective Trend Tracking: Reviewers have mentioned that the product enables them to create trend indexes effectively. This feature allows for easy tracking and analysis of trends over time, providing valuable insights for analytical work.

Data Transformation and Automation: Users appreciate the ability to build repeatable analytical pipelines with the product, automating and streamlining their data analysis processes. They find this feature useful for data transformation and visualization tasks.

Some users have found Shiny to be time-consuming and challenging due to the need for excellent knowledge of R programming and coding skills. They feel that it takes a significant amount of time and expertise to work with the platform effectively.

Several reviewers have mentioned that there is a steep learning curve associated with Shiny, making it difficult for beginners to get started. They have expressed that it can be overwhelming to navigate the platform initially.

Users have expressed their frustration with the lack of easy ways to connect to data sources in Shiny. Additionally, they have mentioned the need for better access control features that allow different roles within their organizations to have appropriate levels of access.

Shiny allows users to create data visualization apps, and is designed to be easy to write with. These apps let users interact with data and analyses with R or Python.

Grafana is a data visualization tool developed by Grafana Labs in New York. It is available open source, managed (Grafana Cloud), or via an enterprise edition with enhanced features. Grafana has pluggable data source model and comes bundled with support for popular time series databases like Graphite. It also has built-in support for cloud monitoring vendors like Amazon Cloudwatch, Microsoft Azure and SQL databases like MySQL. Grafana can combine data from many places into a single dashboard.

Grafana Cloud - Free

Grafana Cloud - Pro

Grafana Cloud - Advanced

Grafana - Enterprise Stack

Looker Studio is a data visualization platform that transforms data into meaningful presentations and dashboards with customized reporting tools.

Looker Studio Pro

Shiny

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Likelihood to Recommend

At Posit, our goal is to make data science more open, intuitive, accessible, and collaborative. We provide tools that make it easy for individuals, teams, and enterprises to leverage powerful analytics and gain insights they need to make a lasting impact.

Posit (formerly RStudio)

Data Discovery and Visualization is the analysis of multiple data sources in a search for patterns and outliers and the ability to represent the data visually.

Pre-built visualization formats (heatmaps, scatter plots etc.)

Pre-built visualization formats are canned visualization types that can be selected to visualize different kinds of data.

Location Analytics / Geographic Visualization

Location analytics is the visualization of geographical or spatial data.

Predictive Analytics

Predictive Analytics is the ability to build forecasting models based on existing data sets.

Support for Machine Learning models

Machine learning refers to computer programs that can learn when exposed to new data.

Pattern Recognition and Data Mining

Pattern recognition and data mining mean the ability to recognize hidden patterns in large quantities of data.

Integration with R or other statistical packages

BI Standard Reporting

Standard reporting means pre-built or canned reports available to users without having to create them.

Customizable dashboards

Customizable dashboards are dashboards providing the builder some degree of control over the look and feel and display options.

Report Formatting Templates

Pixel Perfect reports

Pixel Perfect reports are highly-formatted reports with graphics and ability to preview the report before printing.

Ad-hoc Reporting

Ad-Hoc Reports are reports built by the user to meet highly specific requirements.

Drill-down analysis

Drill down analysis is the ability to get to a further level of detail by going deeper into the hierarchy.

Formatting capabilities

Ability to format output e.g. conditional formatting, lines, headers, footers.

Predictive modeling

Predictive modeling is the ability to build future forecasting models.

Integration with the open-source R predictive modeling environment.

Report sharing and collaboration

Report sharing and collaboration is the ability to easily share reports with others.

Report Output and Scheduling

Ability to schedule and manager report output.

Publish to Web

Publish to PDF

Output Raw Supporting Data

Report Versioning

Report versioning is the assignment of version numbers to each version of a report to help in tracking.

Report Delivery Scheduling

Report Delivery Schedule is the ability to have reports delivered to a destination at a specific data and time.

Delivery to Remote Servers

Ability to deliver reports to remote servers

Aravo

Data Science and Analytics Lead

Shiny 2022-04-25 14:15:18

PostgreSQL

Research Assistant

Shiny 2022-05-26 13:13:55

Shiny 2022-06-13 03:16:27

Shiny 2022-04-26 17:17:36

Shiny 2022-04-28 04:03:11

TrustRadius is a technology and business research firm based in Austin, Texas. The company is known as a review platform for verified B2B technology and software reviews through a proprietary algorithm and human verification.

Shiny

Overview

What is Shiny?