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- Visualization (23)8.888%
- Connect to Multiple Data Sources (22)8.484%
- Extend Existing Data Sources (23)8.383%
- Automatic Data Format Detection (22)7.575%
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RStudio is a modular data science platform, combining open source and commercial products.
The vendor states their open source offerings, such as the RStudio IDE, Shiny, rmarkdown and the many packages in the tidyverse, are used by millions of data scientists around the world to enhance the production and consumption of knowledge by everyone, regardless of economic means.
Their commercial software products, including RStudio Workbench, RStudio Connect, and RStudio Package Manager, are available as a bundle in RStudio Team. These products aim to give organizations the confidence to adopt R, Python and other open-source data science software at scale. This enables data science teams using R and Python to deliver interactive reports and applications to decision makers, leverage large amounts of data, integrate with existing enterprise systems, platforms, and processes, and be compliant with security practices and standards.
The platform is complemented by online services, including RStudio Cloud and shinyapps.io, to make it easier to do, teach and learn data science, and share data science insights with others, over the web.
Together, RStudio’s open-source software and commercial software form a virtuous cycle: The adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone.
- Supported: Connect to Multiple Data Sources
- Supported: Extend Existing Data Sources
- Supported: Automatic Data Format Detection
- Supported: Visualization
- Supported: Interactive Data Analysis
- Supported: Interactive Data Cleaning and Enrichment
- Supported: Data Transformations
- Supported: Multiple Model Development Languages and Tools
- Supported: Single platform for multiple model development
- Supported: Self-Service Model Delivery
- Supported: Flexible Model Publishing Options
- Supported: Security, Governance, and Cost Controls
- Supported: Share Data Science insights in the form of Shiny applications, R Markdown reports, Plumber APIs, dashboards, Jupyter Notebooks, interactive Python content, and more.
- Jupyter Notebook
- Apache Spark
- Databricks Lakehouse Platform (Unified Analytics Platform)
- Dash applications
- VS Code
- SAML Marketplaces
|Deployment Types||On-premise, SaaS|
|Operating Systems||Windows, Linux, Mac|
- The Data Science team extensively used R for Injury coding
- mapping of crash location and road infrastructure data
- Replicating and going beyond what Tibco Spotfire can do
- Automating custom reports beyond what SSRS can provide
- Data integration between API and databases
- Data Animation
- Ability for R-Studio apps to share code
- Single Sign-On
- We use it for scatterplot matrices
- We use it to quickly see the dependencies of various predictors
- We check multicollinearity between our input columns
- Enabling login-screens for Shiny applications thus allowing for use of anonymous users while keeping the content secured
- System command calls through R allows for RDS files refresh through applications thus addressing the data connection latency.
- RStudio Connect has been instrumental in helping us provide analytics to senior management by leveraging shiny and dash apps. We don't have to rely on Data Engineering team to create the same visuals on Looker because it usually takes less time from us to produce the results.
- Through the use of RStudio's professional database drivers, we can seamlessly connect RStudio with our snowflake databases and do many of the data transformation processes within R. Using dplyr let us use code we are familiar with and still get the processing power of the cloud database.
- We have also been trying to leverage reticulate to allow Python and R work together more often. We can do many of the preprocessing in R first then have the data object be used in a Python process (PyMC3 for example).
- developing widely used ranking scores
- Building up surveys
- Integrate data across functions
- Access control the data for partners
- Use RStudio to create web based reports.
- Write script in RStudio and call it from JMP software environment.
- Use RStudio for Python integration
- Using scheduled RMarkdown documents for data ETL
- Deploying a shiny app dashboard to monitor a competing product's performance
- Dashboard capabilities for story telling
- Speed of implementation of new ideas
- Easy interaction with other softwares
- geospatial analysis
- implementing new techniques (Bayesian spatial analysis via the spBayes package) on research data
- process large amounts of data easily
- Build dash boards to facilitate cooperation between groups.
- Use notebooks for interactive training.
- Use notebooks for sharing