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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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- Setup fee optional
- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
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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|
- mapping of crash data to road asset data
- deep dive to find out more about crash data to support road safety
- Text analytics of the survey data and meeting notes were collected from all the council visits in Victoria.
- Web app development
- API development
- Automating custom reports
- Market Basket Analysis
- Data Visualization
- Industrialisation of "small" apps that would never be approved for a full size dev team
- Proof of concepts
- Data validation
- Technical research
- Running quick predictions based on the data at hand
- Representing data using graphs and charts
- Exploratory data analysis using RStudio
- Data Cleaning
- Model APIs
- Dashboards for change review of systems and alerts
- Report generations
- Create Actuarial Reserving processes and inform senior stakeholders financial results on a monthly basis
- Create Underwriting and Pricing models to drive profitable growth and appropriately price small commercial risks
- Create Marketing data analytics to help target customers who are good risks and more likely to switch to or buy their commercial insurance with us.
- developing predictive models
- creating forecast models
- classification based on NLP
- Data integration
- Data review and checking
- Data analyses
- Statistical modeling
- Data visualization
- Yield monitoring
- Learning R
- Carrying out empirical research work
- Scheduling jobs
- Deploying web apps
- Deploying APIs
- Modeling new traffic congestion initiatives
- Historical traffic data
- Monitoring live traffic congestion data
- analyzing data
- plotting data and results
- data processing/formatting
- Biostatistic modeling - regression analysis etc
- Bioinformatics analysis - microbiome, RNA-seq, etc
- Machine Learning through caret