Our Go-to Analytics Tools for Data Visualization
We use advanced analytics tools like R Shiny and Spotfire to create interactive visualizations that provide actionable insights for clinical trial data. Our commitment to utilize the latest technologies ensures cost-effective and efficient solutions for data visualization needs.
R Shiny
We have extensive experience with R Shiny to create interactive dashboards, reports, and data exploration tools, which allows real-time data analysis and visualization for clients.
Spotfire
Our Spotfire services provide a powerful analytics platform for creating interactive visualizations and dashboards, allowing clients to gain quick insights from their data and make informed decisions.
Other Analytics tools in Our Arsenal
We have experience with a variety of other analytics tools and platforms, including Tableau, Power BI, and Python-based tools such as Plotly and Bokeh.
Tableau
Years of full clinical data services
Power BI
Provides a centralized repository for TFL standards and templates
Plotly
Microsoft’s data visualization and business intelligence platform.
Bokeh
Automates TFL shell generation and provides machine-readable metadata
We also leverage the CDISC Open-Source Alliance (COSA) project directory and pharmaverse.org to stay up-to-date with the latest open-source solutions and integrate them into our offerings.
“A picture is worth a thousand words”
The traditional approach was to “look” at the static output without giving end users an ability to explore and drill-down further. With the availability of our data visualization services and analytic tools such as R Shiny® and TIBCO Spotfire®, our users will be able to “see” their data more interactively, identify trends, visualize the patient profiles and review results at a high-level while still being able to drill-down to get a complete picture.
Got Questions? We've Got Answers
What is Spotfire and R Shiny and how are they different?
Spotfire and R Shiny are both software tools that can be used for data analytics and visualization. Spotfire is a commercial product from TIBCO that offers a powerful and interactive platform for exploring, analyzing, and presenting data from various sources and domains. R Shiny is an open-source package from RStudio that allows users to create web applications or dashboards using the R programming language and HTML/CSS/JavaScript.
What are the benefits of using Spotfire and R Shiny for clinical data analytics and visualization?
Spotfire and R Shiny can offer several benefits for clinical data analytics and
visualization, such as:
-
Speed: Spotfire and R Shiny can enable fast and easy access to
clinical data from different sources and formats, and provide
real-time updates and feedback.
-
Flexibility: Spotfire and R Shiny can allow users to customize their
data analysis and visualization according to their specific needs
and preferences, and integrate with other tools and platforms.
-
Interactivity: Spotfire and R Shiny can provide dynamic and
interactive features that enhance the user experience and
engagement, such as filters, sliders, buttons, tabs, charts, maps,
etc..
-
Collaboration: Spotfire and R Shiny can facilitate communication and
collaboration among different stakeholders, such as researchers,
clinicians, regulators, sponsors, etc., by allowing them to share,
comment, annotate, and export their data analysis
and visualization results.
Speed: Spotfire and R Shiny can enable fast and easy access to clinical data from different sources and formats, and provide real-time updates and feedback.
Flexibility: Spotfire and R Shiny can allow users to customize their data analysis and visualization according to their specific needs and preferences, and integrate with other tools and platforms.
Interactivity: Spotfire and R Shiny can provide dynamic and interactive features that enhance the user experience and engagement, such as filters, sliders, buttons, tabs, charts, maps, etc..
Collaboration: Spotfire and R Shiny can facilitate communication and collaboration among different stakeholders, such as researchers, clinicians, regulators, sponsors, etc., by allowing them to share, comment, annotate, and export their data analysis and visualization results.
What are the challenges of using Spotfire and R Shiny for clinical data analytics and visualization?
Spotfire and R Shiny can also pose some challenges for clinical data analytics
and visualization, such as:
-
Cost: Spotfire is a proprietary software that requires a license fee
to use, while R Shiny is a free software that may incur hosting or
deployment costs.
-
Learning curve: Spotfire may require some training or support to use
its advanced features, while R Shiny may require some programming
skills or knowledge to create web applications or dashboards.
-
Validation: Spotfire and R Shiny may need to undergo rigorous
testing and verification to ensure their functionality, reliability,
and accuracy for clinical data analysis and visualization.
Cost: Spotfire is a proprietary software that requires a license fee to use, while R Shiny is a free software that may incur hosting or deployment costs.
Learning curve: Spotfire may require some training or support to use its advanced features, while R Shiny may require some programming skills or knowledge to create web applications or dashboards.
Validation: Spotfire and R Shiny may need to undergo rigorous testing and verification to ensure their functionality, reliability, and accuracy for clinical data analysis and visualization.
How can I use Spotfire and R Shiny for clinical data analytics and visualization?
The usage of Spotfire and R Shiny for clinical data analytics and visualization
depends on the specific requirements and objectives of the project. However,
some general steps that can be followed are:
-
Define the scope and purpose of the data analysis and visualization
-
Identify the data sources, formats, and quality
-
Select the appropriate tool (Spotfire or R Shiny) or a combination
of both
-
Load, transform, and clean the data
-
Perform exploratory or confirmatory data analysis
-
Create interactive or static data visualizations
-
Share, present, or publish the data analysis and visualization
results
Define the scope and purpose of the data analysis and visualization
Identify the data sources, formats, and quality
Select the appropriate tool (Spotfire or R Shiny) or a combination of both
Load, transform, and clean the data
Perform exploratory or confirmatory data analysis
Create interactive or static data visualizations
Share, present, or publish the data analysis and visualization results
You have different questions?
Dont worry! our team will answer it for you.
