Most organizations struggle with the complexity of multiple analytics tools, and fail to get full value out of their data.

There are many reasons for this:

R and Python

Data Science teams using R and Python struggle to collaborate and share their work consistently with their stakeholders. Read More

BI and Data Science

BI and Data Science teams are stuck in silos, failing to collaborate, or even competing for resources and executive mindshare. Read More

RStudio in the Cloud

Data Science work is stuck on local hardware, and not integrated into your organization's cloud strategy. Read More

Leverage your data

Analytics teams struggle to access and combine all their data in reproducible data pipelines. This data can be spread across Excel files, corporate data sources, and the cloud, in the form of structured, unstructured and nontraditional data sources. Read More

Kubernetes

Data Science teams find it difficult to utilize the job management systems that DevOps/IT provides. These systems, built using frameworks like Kubernetes and Slurm, may sit idle, while data science teams struggle to find the compute power they need to create insights. Read More

Integrate via API's

Data Science insights are stuck on laptops, instead of supporting the human and automated workflows that drive an organization's decision making. Read More

Want to learn more?

RStudio's Modular Platform complements your other analytic investments, and helps you maximize the value of your data

By adopting Serious Data Science, open source, code-first, scaled and managed on RStudio's professional products, your organization can fulfill the promised value of your analytic investments.

Eric Nantz, a Research Scientist at Eli Lilly and Company, spoke at rstudio::conf 2020 about the importance of interoperability in R.

Strength through leveraged data

Leverage all your data
Access, transform and combine all your data, tailored to your specific applications

According to a recent Forrester report, "Anecdotal evidence shows that no more than 20% of all enterprise data that could be used to drive actionable insights is leveraged for that purpose." (Forrester report by Boris Evelson and Cinny Little).

Analytics teams struggle to access and combine all their data in reproducible data pipelines, especially when this data is spread across Excel files, corporate data sources, and the cloud, in the form of structured, unstructured and nontraditional data sources (like web scraping).

RStudio's open source, code-first approach gives you the access and flexibility you need to leverage all your data, and build reproducible data pipelines to serve all your analytic requirements.

  • Data access: RStudio's Professional Database Drivers are ODBC data connectors for the most popular data soruces. These drivers will help you explore your data, deploy data-driven interactive applications to your stakeholders, and build data pipelines in production using R. Learn more
  • Best practices for working with databases: RStudio makes it easy to work with databases, both through our products, and providing advice on best practices. Learn more at db.rstudio.com
  • Native and extensible R interface to Spark: Sparklyr allows you to easily filter and aggregate Spark datasets and streams to bring them into R for analysis and visualization, to train models at scale, and productionize machine learning pipelines in Spark. All this can be done using familiar R tools such as dplyr, DBI, broom, and parsnip. Sparklyr is extensible, allowing functionality to be extended to specific domains, such as time series and geospatial analysis. Learn more at spark.rstudio.com
  • The Power of Open Source: The tidyverse collection of packages provide a consistent, intuitive, low code way to access, combine, and transform your data, making data science easier to learn and do. With the dplyr package, you can use the same syntax to transform data in memory, in databases, in Spark, and more.
  • Reproducible data pipelines: Once you have created your data pipeline, you can document it using a reproducible RMarkdown document, and schedule it to run regularly using RStudio Connect. Learn more about scheduling data science tasks
Scale your work

Scale with Kubernetes, Slurm and more
Use all your available compute resources, from your familiar development tools

Analytic infrastructures such as Spark or Kubernetes require considerable resources to set up and maintain. If data scientists have to leave their native tools to access this infrastructure, they have to switch contexts and remember how to use systems they might only rarely touch. Often, this means they won’t fully exploit the resources available, leading to these resources being underutilized.

By providing native access to these tools from the languages and development environments data scientists use everyday, data scientists can leverage these tools without having to switch contexts. The data scientists get the compute power they need, while making better use of IT resources. This higher utilization helps the organization achieve the expected ROI from these analytic investments.

  • RStudio Workbench allows a data scientist to use their preferred development environment (the RStudio IDE, Jupyter, or VS Code), while leveraging the IT-managed computing resources in Kubernetes or Slurm clusters via the RStudio Job Launcher. Learn more about RStudio Workbench, and our support for Kubernetes and Slurm.
  • Native and extensible R interface to Spark: Sparklyr allows you to easily filter and aggregate Spark datasets and streams to bring them into R for analysis and visualization, to train models at scale, and productionize machine learning pipelines in Spark. All this can be done using familiar R tools such as dplyr, DBI, bloom, and parsnip. Sparklyr is extensible, allowing functionality to be extended to specific domains, such as time series and geospatial analysis. Learn more at spark.rstudio.com
  • Docker Deployment: RStudio products can be run inside containers and in Kubernetes, so they can be integrated into an organizations standard DevOps frameworks. Learn more
  • Integrate with your own job scheduling systems: with the RStudio Launcher Plugin SDK. If your requirements extend beyond Slurm and Kubernetes, this SDK enables developers to write Plugins for the RStudio Job Launcher for a custom Job Scheduling System in C/C++. Learn More
API's

Integrate your Data Science via APIs
Deliver your data science insights directly where they add value

Once a predictive model or other analysis has been created, there are many different ways to share that analysis so that it can have an impact. Data scientists may

  • Prepare and present a presentation to business stakeholders.
  • Create a reproducible report that is widely shared and distributed.
  • Develop and share an interactive dashboard or application to provide others with self-service access to the analysis results and findings at their convenience.

However, one of the most scalable ways to share an analysis is to create an API. APIs can empower real time interaction with statistical models and analysis outcomes. This enables other developers either inside or outside of an organization to integrate directly with and build upon work that’s already been done without the need for costly re-implementation.

  • RStudio Connect allows your data scientists to share reports, dashboards, applications and APIs, built on R or Python, with your decision-makers. Learn more
  • Introduction to APIs: APIs are one of several ways your data scientists can increase the impact of their analysis. Learn more
  • R APIs with Plumber: The plumber package allows you to easily create a web API from your existing R source code. Watch the webinar, or see examples
  • Python APIs with Flask, FastAPI, Quart, Falcon, Sanic: Similarly, there are multiple frameworks that allow you to create web APIs from your existing Python models, which can be deployed on RStudio Connect. Learn More
  • TensorFlow Model APIs: Saved TensorFlow models can also be deployed as APIs on RStudio Connect. Learn more
  • Integrate APIs with your analytic applications: Once you have an API, you can easily integrate these APIs into your existing applications. RStudio provides sample code for integration APIs into R, Python, Java, or other languages and systems. Learn more

Even more possibilities
Integrate with other modeling environments, Git, CI/CD workflows and more

One of the great benefits of using open source R and Python is the vast array of integration options these environments provide. New integrations are continually added, either directly by the community, or by vendors like RStudio.

Because of this, no list of integration points can ever be truly comprehensive, but you can be confident that if you need to integrate R or Python into another system, chances are someone else has already solved the problem for you.

  • Git: The RStudio IDE allows you to work directly with code that is stored in remote Git repositories. You can also publish content to RStudio Connect directly from a remote Git repository, and use RStudio Package Manager to build R packages that are stored in remote Git repositories. Learn more
  • Integration with CI/CD processes: Push button publishing, Git deployment, and API-backed publishing provide multiple options to align with your organization's existing CI/CD processes. Learn more
  • Deep learning: RStudio also provides native R interfaces to TensorFlow, Keras, and torch, allowing R users to leverage these deep learning frameworks from their preferred R and Python.
  • Reproducible R Markdown Documents with multiple languages: In addition to R and Python, R Markdown allows you to combine multiple analytic languages in a single notebook, including SQL code for accessing databases, BASH code for shell scripts, C and C++ code using the Rcpp package, STAN code with rstan for Bayesian modeling, Javascript for doing web programming, and many more languages. Learn more
  • Solutions site: The RStudio Solutions engineering team maintains the solutions.rstudio.com site, with extensive articles including reference architectures, product integrations and tips on model management. Learn more

The tools inherent in RStudio allowed our statisticians and data scientists to turn into application developers and data engineers without learning any new languages or computer science skills.”

Paul Ditterline, Brown-Forman
Read the full story here.