TrustRadius Insights for Posit are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
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.
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.
Pros
Efficient coding
Clean IDE
Help page and large community
Cons
Data view support for all kinds of data formats
More organized help page
Installation packages of older version as well as latest one
Likelihood to Recommend
I will highly recommend Posit to anyone who works in Advanced analytics because of its high computing power and seamless delivery of model output of various analytical case studies and problems.
Based on my experience, I use Posit software aka RStudio Pro and Posit Workbench for almost everything in our company as well as clients network. From data preparation to statistical and predictive model building, I use RStudio Pro exclusively. In addition to this, data visualization and data manipulation are also done by Posit software. I have used multiple R packages for various kind of data analysis from logistic regression, classification to LASSO and elasso (Network Analysis).
Only one scenario I would like to say that it is less appropriate is to view the data of formats other than data frame. I really wish to see this issue will be solved in the next major updates of Posit.
Overall Posit is really a good software and platform for any kind of data analysis and visualizations. Thanks.
We use RStudio as an analysis tool to perform complex data analysis problems and scenarios. We build different statistical models to understand business data and perform forecasts. It has good visualizations and is a very flexible tool. As Business Analyst it is a good tool to understand big data in the organization.
Pros
Visualization tool
Statistical Analysis
Forecasting
Cons
More flexibility to import tamplates for the visuals
More documentation about the formulas
More coding automation
Likelihood to Recommend
RStudio is appropriate to perform complex analysis and data modeling exercises while is not that useful where the analysis is simple due to complexity where Excel will better suit. Also, if your organization is not used to it, probably, is better to use other software. Any kind of statistical analysis like regressions or decision trees would be a very good option to model with R Studio.
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.
Pros
Great statistical packages
Good code visualization (formatting/color coding options)
Decent integration with other languages
Cons
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
Likelihood to Recommend
RStudio is well suited, particularly to providing an environment for the statistical analysis of datasets and leveraging various data analysis packages using R. Its user interface is highly customizable and provides all the information users need to script, run, and generate various GUIs and dashboards. Overall it's well suited to R and perhaps less well suited (although it does allow) for other languages such as Python. Overall it's well suited for analysis needs but probably less suited for other development needs, especially if they require the heavy use of other languages.
VU
Verified User
Engineer in Research & Development (10,001+ employees)
We use RStudio for analytics, data science, reporting, and statistical modeling for business clients in all enterprise functional groups. The system is extensible to a wide array of use cases, including quality, machine reliability, finance, supply chain, marketing, and business intelligence. RStudio connects to our Azure and on-premise data assets.
Pros
Data visualization
Big data
Statistical modeling
R
Python
Web sites
Cons
Databricks
Azure DevOps
Likelihood to Recommend
RStudio is the premier statistical workbench and development environment for professionals. It is well suited for serious data science and statistical analysis on local compute hardware and in the cloud. RStudio is not a graphical toy.
We use RStudio Team for data science related tasks with research and development. We have a smaller user base, but our experience with the tool is fantastic, and we are actively recruiting new internal users. The primary challenge it addresses is empowering researcher to use apply their subject matter expertise to data analysis in a transparent, reproducible way.
Pros
RStudio is a great partner that listens to our needs with a creative mind focused on improving our work, not just on increasing sales. They were extremely accommodating of the stresses COVID-19 placed on our work and our budgets.
RStudio provides fantastic access to technical experts to address your needs. This close working relationship was key to implementing a capability researchers are excited to use.
Cons
Language interoperability is a focus of theirs but I look forward to additional improvements. Specifically, I'd like to see more tooling to allow python users to use R code.
A community plugin ecosystem (like VS Code) could be a compelling feature. I'd love to see what the community might come up with.
Likelihood to Recommend
RStudio is well suited to teams that use code-based data analysis as a need, rather than a want. The community around R and RStudio is extremely welcoming, perfect for those new to coding or data anlaysis beyond Excel. It may be less suited to teams focused on things like app development.
The data scientists in our company have been using RStudio on a daily basis for years. We have seen this software kept improving for the past few years. RStudio Pro is an excellent tool for data analysis in R, and the RStudio Connect is super useful to host and present the analysis report to our collaborators in our company.
Pros
RStudio Pro is an excellent tool for data analysis in R.
RStudio Connect is super useful to host and present the analysis report to our collaborators in our company.
It's very straigtforward to mange the users.
Cons
The efficiency and stability of the shiny apps on RStudio connect could be improved.
Likelihood to Recommend
RStudio Pro is the best tool for data analysis in R, the R versions could be switched easily, which is very helpful
VU
Verified User
Team Lead in Research & Development (11-50 employees)
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.
Pros
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.
Cons
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.
Likelihood to Recommend
RStudio provides a host of FOSS and Commercial offerings, so it has well suited offerings for almost every level of use. Their FOSS IDE and 'tidyverse' packages are well suited for individual analysts. The server offerings are easy to spin up for small departments with a high need for consistent environments to enable collaboration, their tools like 'renv' and 'packrat' further assist with collaboration by making it easier to spin up consistent environments. Their publication environments of Shiny Server, Shiny Server Pro, shinyapps.io, and RStudio Connect have a host of pros and cons. Shiny Server, while free, doesn't provide a real identity management / kerberos style security, so it would only be appropriate for non-sensitive solutions. Shiny Server Pro is the commerical offering that can be configured to provide real identity management out of the box. It's licensing model is based on concurrent users which makes it well suited for a highly transitive department-ish sized solution. RStudio Connect is a far more elegant product than Shiny Server Pro, but prices based on named users greatly limiting the scope of impact it can have.
We use RStudio Connect for deliverying data science product (dashboard and documents) across all companies and areas of the group. So far have been addressing several business problems concerning HR analytics, sales optimization, stock optimization, database automatic consolidation, utility expenditure forecast. Many other projects are ongoing exploiting the APIs provided by the platform
Pros
Easy to use. Not only for power user but also for people who need a reliable platform to deliver contents.
Very versatile. There are many tools that can serve the scope of communicating results.
Constant updates and newsletter keeps you on the track.
Cons
Management of some deeper aspects of the platform is not a so straight-forward, especially when it comes to deal to customization (connections, packages management...).
Administration console may be a bit richer, making available of some operations that you may be interested on doing by user interface and not by shell.
Deploying apps is still a bit problematic for some particular (rare!) packages, make it easier to install packages not from the CRAN.
Likelihood to Recommend
Talking about RStudio Connect, we felt very comfortable using it from the first moment. With a very low effort you can kick project, distribute results across the organization through catchy apps. This brings a lot of value (considering the license cost and comparing it with the analogous software for data science). So, scenarios in which you have to be fast, agile but still not dirty. On the other hand, when it comes to structuring a more complex architecture in which RStudio Connect is only a part of it, it becomes more complicated. Of course we must say that we have received a lot of support in doing that!
RStudio is used by multiple departments in our organisation mainly in the R&D area, quantitative genetics, breeding and bioinformatics.
Pros
RStudio staff is very knowledgeable and supportive.
The product documentation compared to other products we use is very good.
Product roadmap is interesting and suits our future needs.
Cons
For me the RStudio Launcher documentation (slurm/kubernetes) is not as clear as the rest. I had to put serious effort and a lot of trial and error to get all parts working.
Admin web interface should provide clusterwide information - not per server.
Developers are struggling to find a good way of working with tools like plumber & postman (web api) that start a locale service within RStudio server.
Similar while switching from local IDE to RStudio Server Pro some developers ran into issues using oauth authentication flows.
Likelihood to Recommend
Most of the time I would recommend RStudio server:
Integration with slurm, ability to run jobs that could not be run on a local workstation/laptop.
Not have to troubleshoot local installations (dependency issues), sort out once on a central installation.
Integration with external authentication.
HA setup.
Less appropriate:
Less suited for developers who are used to have full freedom to do whatever they want on their workstation.
It is used by our Advanced Analytics department. We use it both as an IDE for individuals as well as using RStudio Connect and RStudio Server Pro. RStudio Connect allows us to easily deploy shiny apps, rmarkdown documents and jupyter notebooks. RStudio Server Pro allows us to easily use RStudio in a server environment. Essentially RStudio lets data scientists do their work and share their work without needing an entire team of data engineers to support them.
Pros
Brilliant IDE for coding.
Easy publishing of apps and documents.
Ease of use for data engineering team.
Cons
It's consistently growing Python support, but there is still some room to grow here to make it a truly bilingual platform for data science. That said, it does server our Python users fairly well, even in its current form.
Likelihood to Recommend
RStudio Connect is pretty easily the best simple publishing solution I've worked with for sharing data science apps.
