TrustRadius Insights
Intuitive User Interface: Users have found RStudio to have an intuitive user interface that allows them to quickly test and debug code. …
Overview
What is Posit?
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
Recent Reviews
Awards
Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards
Popular Features
- Visualization (26)8.484%
- Connect to Multiple Data Sources (25)8.181%
- Extend Existing Data Sources (26)7.474%
- Automatic Data Format Detection (25)6.464%
Reviewer Pros & Cons
Video Reviews
2 videos
RStudio Review: It Proves To Be A Reliable Statistical Tool W/ Support Avenues In Place If Needed
02:53
RStudio Review: Works As An Useful Tool But User Finds Free Version Could Be More Competitive
02:13
Pricing
N/A
Unavailable
What is Posit?
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
Entry-level set up fee?
- Setup fee optional
For the latest information on pricing, visithttps://posit.co/pricing
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Would you like us to let the vendor know that you want pricing?
11 people also want pricing
Alternatives Pricing
What is MATLAB?
MatLab is a predictive analytics and computing platform based on a proprietary programming language. MatLab is used across industry and academia.
What is Rational BI?
Rational BI provides analytics, data science and business intelligence in an analytical platform that connects to databases, data files and cloud drives including AWS and Azure data sources, enabling users to explore and visualize data. Users can build real-time notebook-style reports directly in a…
Product Demos
What is Posit Workbench? Build Data Products in R & Python using Jupyter, VSCode, and RStudio.
YouTube
Posit Connect | Host all of the data products you create
YouTube
Features
Return to navigation
Product Details
- About
- Integrations
- Competitors
- Tech Details
- FAQs
What is Posit?
Posit, formerly RStudio, provides a modular data science platform that combines open-source and commercial products.
their open source offerings, such as the RStudio IDE, Shiny Server, rmarkdown and the many packages in the tidyverse, boast users among data scientists around the world to enhance the production and consumption of knowledge by everyone, regardless of economic means.
Their commercial software products, including Posit Workbench, Posit Connect, and Posit Package Manager, are available as a bundle in Posit 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 Posit 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.
Posit’s open-source software and commercial software form what the vendor describes as a virtuous cycle: The adoption of open-source data science software at scale in organizations creates demand for Posit’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone.
Posit Features
Platform Connectivity Features
- Supported: Connect to Multiple Data Sources
- Supported: Extend Existing Data Sources
- Supported: Automatic Data Format Detection
Data Exploration Features
- Supported: Visualization
- Supported: Interactive Data Analysis
Data Preparation Features
- Supported: Interactive Data Cleaning and Enrichment
- Supported: Data Transformations
Platform Data Modeling Features
- Supported: Multiple Model Development Languages and Tools
- Supported: Single platform for multiple model development
- Supported: Self-Service Model Delivery
Model Deployment Features
- Supported: Flexible Model Publishing Options
- Supported: Security, Governance, and Cost Controls
Additional Features
- Supported: Share Data Science insights in the form of Shiny applications, Quarto content, R Markdown reports, Plumber APIs, dashboards, Jupyter Notebooks, and interactive Python content.
Posit Screenshots
Posit Videos
Open Source Software for Data Science - CEO J.J. Allaire provides an overview of Posit's mission, and why Posit has become a Public Benefits Corporation.
Posit Integrations
- Amazon SageMaker
- Kubernetes
- Apache Spark
- Jupyter Notebook
- Streamlit
- Tableau Desktop
- Azure Machine Learning
- Databricks Lakehouse Platform
- Microsoft Visual Studio Code
- Bokeh
- Slurm
- Dash applications
- SAML Marketplaces
Posit Competitors
Posit Technical Details
| Deployment Types | On-premise, Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Windows, Linux, Mac |
| Mobile Application | No |
Frequently Asked Questions
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
Reviewers rate Security, Governance, and Cost Controls highest, with a score of 8.9.
The most common users of Posit are from Enterprises (1,001+ employees).
Comparisons
Compare with
Reviews and Ratings
(237)
Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Pros
- Cons
- Recommendations
Intuitive User Interface: Users have found RStudio to have an intuitive user interface that allows them to quickly test and debug code. This has been mentioned by numerous reviewers, highlighting the ease of use and convenience it offers in coding tasks.
Seamless Integration with Git: The seamless integration of RStudio with Git has been praised by users, making it easy for them to manage version control. Several reviewers have specifically mentioned this as a major advantage of using RStudio for their coding projects.
Powerful Statistical Analysis Tool: Many users appreciate RStudio's capabilities as a powerful tool for statistical analysis and data exploration. They mention its ability to import data from multiple sources, apply machine learning models easily, and export data into various channels.
Confusing and Outdated User Interface: Several users have expressed dissatisfaction with the user interface of RStudio, finding it confusing, unattractive, and outdated compared to other tools. They feel that the interface is too technical for business people.
Frequent Crashes with Large Datasets: Some users have mentioned that RStudio frequently crashes when loading large amounts of data. This can be frustrating and disrupt their workflow.
Lack of Integration with Other Applications: Users have pointed out that RStudio is not as integrated with other applications as Python. This limitation makes it less convenient for users who rely on seamless integration between different software tools.
Users commonly recommend RStudio for beginners in R programming and data analytics. They believe that RStudio is a good tool for learning machine learning and recommend using it for data work, programming R code for machine learning, implementing R software, data analysis, and data science. Users consider RStudio to be a great resource for analyzing data and necessary for anyone who wants to get into R programming. While considering other suites and languages like Python, they still recommend taking a look at RStudio for data analysis. Additionally, users find RStudio to be useful for doing statistics and creating professional plots and figures. They suggest familiarizing oneself with common libraries in the field and doing online tutorials before starting to use RStudio. Users warn about the steep learning curve but believe it is worth investing the time to learn it. Moreover, they recommend using RStudio for big data and epidemiological research.
Attribute Ratings
Reviews
(1-25 of 46)Companies can't remove reviews or game the system. Here's why
August 01, 2023
Posit, the Best ever Data Science Software
I use Posit software RStudio Pro to analyze, modelling and visualize dataset related to healthcare, medical affairs and pharma. There are lots of R packages available mainly dplyr, stringr, ggplot2, tidyr which we usually use in our day-to-day data management, data wrangling, cleaning, pre-processing tasks. Also, we use lots of other machine learning packages such as caret, tidymodels for statistical modelling and prediction. Our client network is integrated with AWS cloud platform so that we can use Posit software seamlessly and efficiently.
Business problems like patient analytics, feasibility studies are done using Posit Workbench. Based on clients' requirements and requests we use RStudio and R packages for data visualization including Bar plots, Line Plots for various kind of statistical analysis viz. Correlation analysis, LASSO regression, Elasso or Network analysis and Graph.
We have used RStudio for parallel computing with the R package VSURF to handle big data like millions of rows and columns (mostly patient churn and history data). We also used ggplot2 and plotly library for stunning graphs and plots.
Last but not the least, we have used Rmarkdown (or now Quarto) for generating PDF, Word reports to clients for data validation and case studies according to business requirements.
Business problems like patient analytics, feasibility studies are done using Posit Workbench. Based on clients' requirements and requests we use RStudio and R packages for data visualization including Bar plots, Line Plots for various kind of statistical analysis viz. Correlation analysis, LASSO regression, Elasso or Network analysis and Graph.
We have used RStudio for parallel computing with the R package VSURF to handle big data like millions of rows and columns (mostly patient churn and history data). We also used ggplot2 and plotly library for stunning graphs and plots.
Last but not the least, we have used Rmarkdown (or now Quarto) for generating PDF, Word reports to clients for data validation and case studies according to business requirements.
- Efficient coding
- Clean IDE
- Help page and large community
- Data view support for all kinds of data formats
- More organized help page
- Installation packages of older version as well as latest one
June 30, 2023
All-in with RStudio
RStudio products are used across multiple departments in our organization, including the research, IT, and data science business units. Our work largely involves analyzing clinical and supply-chain data to improve quality of patient care and to reduce excess cost in the healthcare system. This work can include predicting patient outcomes based on clinical and demographic data, predicting supply shortages, and estimating under- or over-utilization of hospital resources.
- RStudio provides an integrated product suite for both model development and deployment to end users.
- RStudio provides an open-source version of its products to ensure accessibility to all users
- The RStudio Connect (Shiny) platform is an incredible tool for quickly making statistical models, documents, apis, and enriched data available to end users.
- RStudio could consider an archetype tool within the RStudio IDE. This would be similar to a Maven archetype, where the user identifies the type of work they are doing and the tool would generate a series of directories and the project scaffolding (based on best practices) to springboard a larger project.
- It would be nice to see the files in a directory tree similar to Intellij. This would prevent the constant drilling in and out of directories in a larger project.
October 28, 2022
Everything you need in data science
We use Posit to process large amounts of data, both for importing into other systems and after exporting back from them. Other uses cases include performing statistical analysis and creating visualizations. Posit makes it easy to perform complex manipulations on large datasets and automate long complicated processes, saving us a ton of time and removes the potential of human error.
- Data processing
- Statistical analysis
- Libraries for just about every use case
- Improved debugging tools - breakpoints can be clunky
- Better generalisation - creating for loops to handle dynamic data can be a pain
- Garbage collection - when working with large datasets it can be necessary to manage memory yourself
September 01, 2022
Great Product for Data Analysis
RStudio helps our large team of conservation researchers address problems relating to data management, cleaning, and processing. In addition, it also helps our team with database management as we often manage large and historical sets of data. In many cases, our teams are using RStudio for the analysis of field data to assist with international conservation programs.
- Data analysis
- Data sharing
- Graphs
- User interface
- Cleaner file storage in desktop
- Collaboration
April 19, 2022
RStudio is great but needs some improvements
RStudio is used in my organization to build machine learning models, such as linear regression, logistic regression, decision trees, random forest, k-mean clustering, and more. It solves our business problem of having a low-cost, open-source tool for building statistical models and running models for data analysis. We can also use this for data visualization and data cleaning.
- Data cleaning
- Statistical packages
- Machine learning algorithms
- Installation process is a bit confusing
- Steep learning curve for non technical person
- Better UI
April 14, 2022
RStudio from a Grad Student's POV
RStudio is used as a supporting program for graduate-level courses, such as Experimental Research Methods. It helps students understand how to clean, analyze, and visualize quantitative data. The scope of my use case is 10-week courses that have used RStudio in different ways, i.e., information visualization and data transformation.
- Cleaning large datasets
- Automating the data transformation process
- Customizing datasets, plots, etc.
- Unintuitive importing/exporting CSVs and/or datasets
- Console errors are difficult to understand and not informative
- Steep learning curve, especially for those unfamiliar with R and programming
April 13, 2022
RStudio: An all-purpose way to interact with R
Currently, we use RStudio within our group as the primary way to interact with R and particularly R scripts for automated analysis of large datasets. We've also used RStudio to develop Shiny GUIs to provide a user-friendly interface for these R scripts for others in our organization that may be less familiar with running scripts in RStudio.
- Great statistical packages
- Good code visualization (formatting/color coding options)
- Decent integration with other languages
- Documentation and versioning of the packages can be tedious to track and check for compatibility
- Requires startup time from the user to learn to use/setup
- Some features like RStudio Connect are a little buggy/not super smooth
April 11, 2022
RStudio - Very Powerful Statistical Tool
We are using RStudio to develop shiny web applications and develop predictive data models. We perform statistical analysis on the data and try to gain insights from it.
With the shiny apps, we are automating routine excel reports which saves a lot of time for database and business analysts.
We have written numerous algorithms in RStudio like Naive Bayesian Classification, K-Means Clustering and ARIMA modelling.
RStudio is an amazing platform for statistical data analysis.
With the shiny apps, we are automating routine excel reports which saves a lot of time for database and business analysts.
We have written numerous algorithms in RStudio like Naive Bayesian Classification, K-Means Clustering and ARIMA modelling.
RStudio is an amazing platform for statistical data analysis.
- Performing Statistical Analysis is very efficient. With a lot of open source packages available in R programming, data analysis becomes very easy.
- Publishing web applications and deploying predictive data models is very easy if you have R Server in your firm using Shiny. It can handle large sets of data.
- Writing data science algorithms like Clustering, Classification and Apriori Analysis is very efficient. The open source nature of this programming language allows everyone to contribute packages to the environment.
- There are some packages in RStudio which aren't very well known hence its very difficult to get help if you get stuck using them.
- If the dataset size crosses 20 million rows, then you need extremely high RAM otherwise the processing gets very slow. So in such a case R Server is a must. Cloud storage can be a good alternative though.
- The graphs which are plotted in the console aren't very intuitive and labels, colors, axis, etc have to be manually written to make the visuals look more appeasing.
January 25, 2022
RStudio - a cheap and effective statistics program
RStudio is currently used to analyze data. It makes using R much easier for us researchers and allows us to test our hypotheses. It is used by researchers across the department to do quantitative analyses using data we have collected. We use it for social network analyses that include friendship nominations.
- Quantitative analyses.
- Descriptive analyses.
- Graphs.
- The point and click functions of the program could be better.
- Updating the program could be an easier process.
- Other programs make it easier to read in data.
January 19, 2022
RStudio: Best Bang for Your Buck
I use RStudio to produce descriptive and predictive analytics surrounding various business products. My analytics help higher-ups understand the efficiencies and problem areas in business processes and make evidence-based decisions. The data visualizations I generate in [RStudio] are especially instrumental in presenting accurate, easily consumable metrics for lay audiences.
- Visualizing data
- Integration with other programming languages and tools
- Variety of inbuilt functions and packages
- Dashboard publishing
- Processing is slow when working with large datasets
- Description of bugs could be clearer
- More tutorials on capabilities
January 13, 2022
A must have tool for data analysts
I am working with an Australian supermarket giant and helping them analyze data for their e-commerce business. RStudio helps me in getting the raw data from various sources and cleaning them up so that they can be aggregated and visualized in a BI tool for insight generation to improve the business performance.
- It's super quick.
- It has inbuilt functions for most of the analytics procedures.
- It has great visualizations.
- It has lots of libraries and sometimes it shows errors while importing them.
- Its UI can be improved.
- It takes a lot of time exporting files.
December 23, 2021
RStudio is Very Useful but Needs Improvements
I use RStudio to manipulate, munge, analyze data for ad hoc projects, and create visualizations. [My main use for the platform] is for projects that are too big or slow for Excel.
- good visual design environment
- Allows you to quickly see your data set in tabular form
- Manages package list/download well
- When rendering a plot, there are several issues bringing it more smoothly to copy/paste it to a slide deck, etc. It is very frustrating that the aspect ratio, etc. visual quality is not consistent from when you originally render to when copy/paste or downloading as a file to later be put in a slide.
- My resolution changes between laptop mode and desktop mode (plugging into external monitors). In desktop mode I literally have to shut RStudio down and restart/reload/rerun to continue my project. HOW DO I fix this? Is there a way. I've seen a toggle that says it can bounce back and forth depending on device type but it doesn't seem to work!
- When a program hangs, there is a red stop sign ( I think) in console corner to end the process. This requires, however that I need to complete restart RStudio and restart/reload/rerun etc. Can't it just start but keep all packages, datasets, variables in memory?
September 10, 2021
The R-Studio suite is a well thought out solution to development, control and publishing r apps and services.
RStudio is a great way to allow teams to develop r-shiny apps without needing to go and install lots of software on each developer's individual machine. It helps people to get ideas together much faster that you can traditionally. And by pairing it with other products in the suite you can then deploy them to non-devs too for quick feedback.
- Centralised admin
- Ability to manage allocations of CPU / RAM per user
- SSO
- Set up can be complex
- Automated updates via the admin screens
September 06, 2021
RStudio for quick prediction prototyping
Very few of us are getting into predictions using Machine Learning and Data Science. We use Rstudio to program our algorithms. There are only a handful of people in the whole organization who use Rstudio right now. We use it in pockets, and do the proof of concepts with Machine Learning using R.
- We use it for a quick visual representation of data
- We do exploratory data analysis to understand data
- We do predictions using RStudio
- When we have to run 100 iterations using more than 10000 records, RStudio gets stuck or takes a very very long time to respond
- Generating a pdf report from an RMD file is very difficult from RStudio.
- Generating a pdf report in RStudio cloud is straightforward and inbuilt.
September 05, 2021
RStudio - Dire wolf of "Game of Data Science"
Most of the DNA teams within our organization are using RStudio right from data pulls to visualization & reporting. UI/UX for shiny applications has been phenomenal and has been utilized in broader initiatives that enabled huge dollar savings. 'RMarkdown' has eased report generation to a great extent and 'odbc' drivers have made connecting to databases an easy task.
- Abundant development on statistical and data science libraries.
- Interaction with other programming languages and BI tools.
- Customized application building and reporting framework through shiny and markdown.
- Simple IDE with competent and robust functionalities.
- Strong and active community.
- Highly approachable core members and teams @Rstudio.
- Integration with Google Cloud Platform.
- Flexibility of choosing a remote R-interpreter (as is present in IntelliJ/PyCharm).
- Memory issues and slowdowns when it comes to working with large datasets.
- Orchestration of production workflows with Airflow.
- Production pipelines for RStudio Connect content.
September 04, 2021
RStudio is the Swiss Army knife of R Solutions
RStudio provides a number of products and services, from their best-in-class IDE for R to their collaboration and publication platform, RStudio Connect. Our Data Scientists leverage RStudio Server on a daily basis to do analysis, develop dashboards and Shiny applications. They deploy these to either our Shiny Server Pro environment or, more commonly, our RStudio Connect environment. Others at the company use the RStudio IDE to do analysis on their local machines. R, as a statistical programming language, is mostly commonly used by our data scientists who support the whole organization, often in a paired environment. By using RStudio Server we can ensure consistent environments for deployment of assets and ease of managing security. There are pockets of other scientists, marketing and logistics analysts who use R to amplify their work and they use the desktop IDE because they have no need for collaboration.
- Excellent integration of both R and Python IDEs in one.
- Simple publishing of dashboards and applications from RStudio IDE to RStudio Connect.
- Integration of package management with projects to support collaboration.
- Excellent contributors to the R Open Source community, really invested in its health.
- Support integration with Enterprise AD environments for security.
- Python integration is newer and still can be rough, especially with when using virtual environments.
- RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
- Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.
We use RStudio for development within bioinformatics group. The product (R Shiny) from our group is used across the whole organization, it is used for data integration, both clinical and pre-clinical biomarker data and other types of data integration.
- Best IDE for R programming.
- Good ecosystem for R Markdown and R Shiny.
- RStudio Connect is very useful for publishing and user authentication.
- It could have its own consulting team to support company to build R related products instead of partnering.
- It could also offer tailored paid training for small and large companies.
September 02, 2021
Well Suited for Data Visualization and Modeling
We use RStudio as an GUI interface for R, which we use to visualize and model data. For modeling data, we use lots of machine learning techniques like Regression, and R provides an excellent package to implement various flavors of regression like lasso and ridge regression. For data visualization we also use Shiny apps.
- Debugging
- Front-end interface to R
- Provide shortcuts to some R commands
- RStudio connect experience was not very smooth
- Web service configuration for the RStudio server is not very intuitive
- Some Visual DataGrid GUI would be beneficial
July 14, 2021
RStudio for data maturity
It is used to deliver data focused clinical research covering a wide range of clinical areas. It is also being used to develop automated documents and analytics using r markdown documents. This is covering areas of interest within both the university and also our clinical collaborators including hospital, primary care and other healthcare stakeholders.
- User friendly.
- Good customer support.
- Integration of more than R.
- Good documentation.
- Automatically advising of updates.
- The pricing model for RStudio Connect makes it an unfeasible prospect for large public sector organisations.
- The sales yes can sometimes feel quite pushy.
As an analytics organization embedded within the marketing team, we support roughly 90-100 marketing professionals. While we have an extensive BI and analytics stack, RStudio Connect really helps us with serving insights to these stakeholders at the right moment. We utilize parameterized reporting to cut down time it takes to report, schedule processes that previously relied on cumbersome crontab processes, and host bespoke, highly customized dashboards that allow non-technical users to interact with data better than they would in a BI environment.
- The CSMs are fantastic - they solve our problems with the platform rather than being salespeople in disguise.
- Ease of use - The entire environment is very easy to understand and use, both for developers and non-technical end users.
- Scheduling - We replaced our entire crontab based scheduling system, saving us time through efficiency.
- Git-backed deployment - if you're working with version control already, RStudio Connect works flawlessly with it.
- Python support is still new, though we haven't run into large issues.
- To get Python support, we had to sign up for the mid-pricing tier that comes with 100 users, while we really only needed 10-15 developer seats, so I think some more flexibility in the pricing model would be nice. That said, overall the product seems very(!) reasonably priced.
June 04, 2021
RStudio Cloud recommendation for teaching
Score 10 out of 10
Vetted Review
Verified User
Even though we go back to in-person teaching next academic year, we have taken the decision to continue using RStudio Cloud because of its other benefits, like avoiding the wasted first day of debugging student installations and letting me ID students that are putting in low numbers of hours outside of class, which I can use to intervene early with students who might be having trouble. Also, it's nicer for everybody that I and the demonstrators can just sign into a student's RStudio instance to debug code instead of having to do the shoulder-to-shoulder dance when in class. Finally, RSC gives every student a powerful instance of R, including students who only have a Chromebook, which lets me assign large datasets and RAM+CPU-intensive exercises.
May 24, 2021
Best all-in-one IDE for R
RStudio is used by several working groups within a larger project for the University of Vermont. It is used mainly for statistical analyses, manipulating spatial data, spatial analyses, and other programming/statistical tasks. I use my personal version of Rstudio as well as Rstudio server for analyses for this project. Rstudio is one of the best IDEs I have come across for R. I can keep track of variables within my workspace, view the files in my working directory, run the code and inspect output, and look at plots on different panels of the Rstudio interface. This helps keep my work organized and efficient. Rstudio has helped increase the overall productivity of the working group in which I work. Also, Rstudio interfaces with GitHub, which has been used for collaborative coding efforts.
- Rstudio is very customizable. You can easily change font colors, sizes, and screen layout. I am particular about how I like my IDE setup, so this is a big plus for me.
- Rstudio allows you to look at datasets in your workspace with the click of a button. I do a lot of data manipulation, so I am constantly having to look at datasets after operations to make sure they look correct. The view option in Rstudio makes checking datasets very fast.
- Finally, I love the way Rstudio manages plotting. Your plots can be viewed in one of the panels. Those plots can easily be copy/pasted or exported into a variety of file types. You can also magnify the plots and scroll between plots to look at previous plots.
- Sometimes Rstudio crashes when you work with big datasets.
- I've had some issues installing packages, which is very annoying. Sometimes I can install packages on my PC but not on my Mac, and vice versa.
- Rstudio is not exactly a lightweight IDE, so it is not ideal for computationally intensive tasks.
March 11, 2021
RStudio just works out of the box
Our organization has devoted considerable resources to implement RStudio across the firm. We have dedicated servers, a team of specialists on staff to manage them, and many individuals who use the product. We use RStudio to analyze data in many forms--AMI data, utility participation data, survey response management, etc.
- Code completion is a life saver when I vaguely know the function or variable and the popup fills in the correct choice
- Help on function definitions right in the tool; no need to search the web
- Repositioning panels on the fly allows me to minimize parts that matter and get more room for my analysis in the console or in the R file
- Built in Git helps me remember to keep the repo current
- Code formatting sometimes rearranges code that was formatted the way I wanted
- There is a bit of a learning curve setting up projects, and it makes folders even if I already had them; perhaps modal on what each choice would do might help
- We do not save environment when closing, perhaps include a one time for all checkbox
January 05, 2021
light user
It's being used as preferred tool by some data analysts/scientists. I use it for data transformation and simple stats analysis (e.g. a/b testing, linear regression, exploratory analysis, etc.).
- Data manipulation, easy and handy.
- Exploratory analysis, nice plots.
- Simple stats analysis, t test.
- Output format, it could be better so we can easily add output to a doc/ppt for sharing.
- Error message, it could be more informative.
- Data processing, it gets slow when data is big (e.g. millions of rows).
December 28, 2020
RStudio free IDE data analysis to AI
It’s used for data analysis, data visualization and data visualization app and machine learning.
It is used across whole organization.
It is used across whole organization.
The type of Uses from analyzing the injuries, and violence data. The death data from different sources and visualization. Also, analyzing disease data like HIV, STD and other Infectious diseases and visualization analysis!
Developing Machine Learning Analysis on predicting/ identify cases from the free text fields. Performing Natural Language Processing and other AI analysis!
Developing Machine Learning Analysis on predicting/ identify cases from the free text fields. Performing Natural Language Processing and other AI analysis!
- The tool provides vie of Code, log, data and library , plots on same places!
- Lots of predestined code , projects and samples available to ready to use with little modifications!
- Lots of Machine Learning algorithms, Artificial Intelligence related samples, and projects available.
- RShiny is great tool to develop a web applications and data visualization.
- Free of cost tool, easy to install and manage libraries. Does not take too much expertise to install or maintain RStudio tool!
- The dropdown menus or tool in order to test or select some of basic Machine Learning algorithms for analysis. Which requires No to very little coding.
- Some ready to use tool or menus in order to create different types of charts, GIS maps or other data visualization like MS Excel!