Posit Reviews and Ratings

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.

Likelihood to Recommend

I'm not sure if there's software that is better-suited than Posit for doing data analysis in an organization, so long as you have folks who are well-versed in data analysis and statistics (i.e., not basic SPSS users). The fact that most of their software is open source, and there's so many free online resources for its use, you can't beat it.

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.

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.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Posit is well-suited to just about any scenario you can throw at it. Basic data manipulation, statistical analysis, visualizations, machine learning, simulation - there is a function or library out there for everything. It's also lightning fast, which is a blessing when computing large calculations. Less well-suited to Posit is its learning curve - syntax is relatively unique and picking up a new package usually means learning new behaviours.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

RStudio is well suited for professionals who need to interpret and analyze large sets of data. It can have a step-learning curve though so maybe less functional and not appropriate for an inexperienced user. Additionally, if users are trying to collaborate on a set of data, additional programs or software may be needed in addition to RStudio in order to collaborate.

Use Cases and Deployment Scope

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.

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.

Use Cases and Deployment Scope

We use RStudio to build data science and machine learning pipelines for AI models. The pipeline that we create on R studio help in end-to-end data processing, cleaning, RDA, model training, and prediction. The scripts that we write on RStudio are also used for automation and creating machine learning tools using R shiny as well.

Likelihood to Recommend

RStudio is well suited for data processing and visualization. The tool provided a very interactive and user-friendly environment to understand each step in the data processing. However, RStudio lacks in adoption among the data science community as python is not available and most of the machine learning libraries are custom built for python.

Use Cases and Deployment Scope

I have used the R language since around 2010 and before (along with S-Plus). RStudio as soon as it was available, also around 2010. Example use cases: 1. bionanoengineering - descriptive statistics (describing biological motility or nano surfaces), in parallel with image analysis in ImageJ and MATLAB; 2. bioinformatics - producing descriptive statistics for the motility of Neurospora crassa (filamentous fungus) to prove that how one use statistics matters and how it impacts business decisions; 3. pharma - benefit-risk analysis and data visualizations along with Spotfire 4. healthcare - clinical programming along with Stata and Python (one suggestion: it would be nice to have R interface in Stata and improved R interface in Spotfire); 5 - in product development for creating data monitoring & evaluation apps in RShiny. RStudio has been with me since the very beginning of my professional career. I could easily write up a Ph.D. on the use cases of R in life sciences, pharma, healthcare, and computer science. I would highly recommend RStudio for those who need to deliver fast tailored, customized applications, attractive visualizations or need to use Bayesian statistics, for example, to validate pharmacovigilance scores.

Likelihood to Recommend

well suited: creating and delivering apps for multi-disciplinary teams, for example, http://drugis.org/index or https://shiny.rstudio.com/gallery/covid19-tracker.html less appropriate: Kaggle competitions, multi-community collaborations, Google collab...scenarios when the whole communities decide to work on a specific problem in Python and R is left behind, e.g. in 2015 my colleague delivered better results with Bayesian statistics simply cause he decided to go for Python to visualize joint distributions (priors and posteriors) ...even if I had way more knowledge on the algorithmic side, I was simply slower because I chose R; what I have learned over the years is that when it comes to the stakeholders, a good visualization (==communicating the results and effectively advertising) is everything as without it there is no funding and without funding no science, no R&D

Use Cases and Deployment Scope

It is used by the Data Science team within the department. It helps the team with the reporting functions, tackles business problems from an analytical perspective, and builds up quick interactive tools.

Likelihood to Recommend

Any analytical problems that start with data could be tackled with R in an agile and flexible manner, and since RStudio does such a good job of embedding R to the product itself, it is great for industries/companies with such problems.

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.

Likelihood to Recommend

Well Suited: Doing data science. Not Well Suited: It isn't a rapid application tool/environment for CRUD applications.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Based on my experience, I would like to recommend RStudio to anyone that needs to run small to medium-sized statistical analysis quickly and cost-effectively. Many packages are written pretty friendly for producing readable output for regressions results. However, it is less suited to large-scale big data projects that require large processing power.