Posit

Score10 out of 10

235 Reviews and Ratings

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What is Posit?

Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.

Categories & Use Cases

Media

Screenshot of Posit runs on most desktops or on a server and accessed over the web

Screenshot of Posit supports authoring HTML, PDF, Word Documents, and slide shows

Screenshot of Posit supports interactive graphics with Shiny and ggvis

Screenshot of Shiny combines the computational power of R with the interactivity of the modern web

Screenshot of Remote Interactive Sessions: Start R and Python processes from Posit Workbench within various systems such as Kubernetes and SLURM with Launcher.

Screenshot of Jupyter: Author and edit Python code with Jupyter using the same Posit Workbench infrastructure.

Screenshot of Posit Connect enables users to deploy Interactive Python Applications (including Dash, Bokeh and Streamlit), in the same place Shiny apps are shared.

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Screenshot of Posit runs on most desktops or on a server and accessed over the web

Extend Existing Data Sources
Use R or Python to create custom connectors for any APIs or databases
Category average: 8.9
Automatic Data Format Detection
Automatic detection of data formats and schemas
Category average: 9.2
Interactive Data Analysis
Ability to analyze data interactively using Python or R Notebooks
Category average: 8.8

Security, Governance, and Cost Controls
Built-in controls to mitigate compliance and audit risk with user activity tracking
Category average: 8.6
Connect to Multiple Data Sources
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
Category average: 8.8
Visualization
The product’s support and tooling for analysis and visualization of data.
Category average: 8.3

Verified User

Analyst in Research & Development (10,001+ employees employees)

Use Cases and Deployment Scope

I use the open source software available through Posit, specifically Quarto and some of the main packages they manage (i.e, dplyr) for research analysis at work. Specifically, I work for a large government agency and do analyses on large source (i.e., tens of thousands) data of employees.

Pros

User-friendly data analysis
Sharing workflows across multiple people on a team
Manage and clean large datasets
Provide "print-outs" (e.g., LaTex) to share with stakeholders not as versed in analysis

Cons

Greater clarity on error codes for software packages that Posit manages
Faster LaTex export

Return on Investment

Allows us to manage large datasets
Allows us to deliver findings to our stakeholders faster
Allows us to replicate analyses quickly via the easy sharing and modification of code

Usability

Alternatives Considered

IBM SPSS Statistics

Other Software Used

Microsoft 365, Microsoft Teams

Samrit Pramanik View profile

Data Scientist at Genpact (10,001+ employees employees)

Use Cases and Deployment Scope

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

Return on Investment

Open source hence cost effective
Clean UI/UX for coding
Help page is really a great resource
support of multiple programming languages like python, C++ etc

Alternatives Considered

Jupyter Notebook and Minitab Workspace

Other Software Used

Jupyter Notebook, Microsoft Excel, Microsoft Windows

Chris Beeley View profile

Senior Analyst in Research & Development at Nottinghamshire Healthcare NHS Foundation Trust (10,001+ employees employees)

Use Cases and Deployment Scope

My team uses RStudio products, but we distribute reports and dashboards to 100+ users. The business problem it addresses is how to get the data science work that we're doing in R and Python (for example, text mining), as well as more day-to-day reporting based on some of the data structures that we have written in R/SQL.

Pros

The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.

Cons

I'm only a data scientist, I'm not DevOps, but I did find RStudio Connect hard to install. I have also tried and failed to get proxy authentication with Apache working, I'm sure it's me being thick but I have never gotten there with it.
Another thing I don't like is some of the abstractions in RStudio Connect. There isn't really a file system at all, and data refresh is done with a scheduled RMarkdown report, which is okay but it's a bit round the houses and it's very different from the code on my local machine.
I don't love the pricing model of RStudio Connect where you pay just as much for publishers as you do for consumers. I wish we could have, say, 5 publishers and more consumers on our current licence.

Return on Investment

RStudio has enabled us to launch data science projects in the cloud at scale, which has brought in at least 10x more money than we spent on the licences.
RStudio products are nice to use for my staff and I think are good to retain good people.
RStudio allows us to put analytic products in the hands of managers rapidly, which is vitally important for us.

Alternatives Considered

Qlik Analytics Platform, Microsoft Power BI and Tableau Desktop

Other Software Used

FileZilla, MySQL, Slack

Andrew Choens View profile

Data Science and Engineering Leader in Information Technology at Acuitas Health, LLC (11-50 employees employees)

Use Cases and Deployment Scope

We license the RStudio Connect (RSC) product from RStudio. We also use, for free, the open source packages and development environment offered by RStudio. Without going into specifics, our Connect license is about 1/6th the cost of our QlikView license, which we will discontinue once we are done porting legacy dashboards off of it. A direct comparison between Qlik and RSC is unfair. Products such as QlikView and PowerBI are BI tools which licensed users use to build dashboards. I refer to RSC as a content management platform for data science. We use it to: Validate our data and alert us to problems. Email reports to clients in PDF and Excel. Upload data to FTP servers and to send HL7 messages (via a HL7 engine). Hosts our internal API. Host machine learning models. Host custom-built dashboards. And, staff love developing against it.I could calculate an ROI for everything, except staff satisfaction. But the value add is there and it is valuable.

Pros

Deliver data/insights to customers
Multi-language (R, Python)
Empower staff

Cons

The name causes people to incorrectly think it is an R-focused product. It isn't.

Return on Investment

We will save money replacing legacy dashboard tools.

Alternatives Considered

QlikView

Other Software Used

QlikView

Kunal Sonalkar View profile

Data Research Analyst in Information Technology at Southwest Florida Water Management District (5001-10,000 employees employees)

Pros

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.

Cons

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.

Return on Investment

Positive impact is when you automate excel reports using Shiny applications, it ends up saving a lot of time and money.
It's easy to catch on so with a little training and sound math background you can start coding right away.
Its compatibility with other platforms like SQL databases, Salesforce, Tableau , etc is amazing and makes it worth the investment. It doesn't have any negatives as such.

Alternatives Considered

Tableau Desktop

Other Software Used

Tableau Desktop, DB2, Oracle Advanced Analytics, IBM Analytics Engine

Posit

What is Posit?

Categories & Use Cases

Media

Key Features

Top Performing Features

Extend Existing Data Sources

Automatic Data Format Detection

Interactive Data Analysis

Areas for Improvement

Security, Governance, and Cost Controls

Connect to Multiple Data Sources

Visualization

Reviews

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

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