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 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
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.
VU
Verified User
Analyst in Research & Development (10,001+ employees)
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.
Pros
Data analysis
Data sharing
Graphs
Cons
User interface
Cleaner file storage in desktop
Collaboration
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.
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.
Pros
Data processing
Data visualization
Machine learning
Tool development (Rshiny)
Cons
User interface
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.
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.
Pros
Data cleaning
Statistical packages
Machine learning algorithms
Cons
Installation process is a bit confusing
Steep learning curve for non technical person
Better UI
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.
VU
Verified User
Analyst in Research & Development (1-10 employees)
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.
Console errors are difficult to understand and not informative
Steep learning curve, especially for those unfamiliar with R and programming
Likelihood to Recommend
RStudio is the best option I've seen for data cleaning, prep, and transformation. Other tools, such as Tableau or Excel, are not easily transferrable to other formats or are manual and take too much time. RStudio is less appropriate for small datasets and academic courses that won't dedicate as much time to learning the fundamentals of R.
VU
Verified User
Employee in Research & Development (201-500 employees)
We use RStudio for statistics-related endeavors on our research projects. We use it frequently for accessing and analyzing our data in descriptive and predictive type analyses. It helps us address issues such as underperformance in schools and other education settings, or even issues of inequity and exclusion of vulnerable populations.
Pros
Descriptive analyses.
Predictive analyses.
Accessing data.
Replicating syntax.
Cons
The interface.
A more beginner-friendly walkthrough.
Have also had issues with program versions impacting syntax execution.
Likelihood to Recommend
RStudio is perfect for statisticians who want to run descriptive and predictive analyses but do not want to spend big money to acquire a license from a competing statistics software. It is less suited for scenarios in which a company will reimburse for a license, in which I would recommend IBM's SPSS over Rstudio.
It's used by my small team. We do econometric analysis using R.
Pros
Notebooks, where you can run chunks and see the output
I can view data frames
Integration with git
Cons
Queries to external data warehouses (e.g., using RJDBC::dbGetQuery) are blocking things to the extent that Rstudio freezes and I need to force quit it to stop the query
I want to have tools to manage the variables by size
Sometimes I want to clean the memory and it would be nice if RStudio suggested an easy way to rank variables by size in the environment
Likelihood to Recommend
It works great for me. I heard that R ecosystem may be a bit behind Python ecosystem for machine learning but I personally don't feel restricted.
VU
Verified User
Employee in Research & Development (10,001+ employees)
I am the primary statistician on my team and I use RStudio almost exclusively to perform the product efficacy analyses. I use RStudio to automate many of our data cleanup processes and also run dynamic analyses to answer our research questions.
Pros
Automate processes
Statistical Analyses
Portable Code
A very good IDE for R programming
Cons
Can be intimidating to non-programmers
I wish I could copy data to the clipboard easier
I never have a big enough screen to see all of the data I want to see
Likelihood to Recommend
I really like that RStudio has the ability to run code line by line. That is crucial in my work as I am constantly modifying and testing little things that would not be practical/desired to run the whole code.
VU
Verified User
Analyst in Product Management (1001-5000 employees)
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.
Pros
It's super quick.
It has inbuilt functions for most of the analytics procedures.
It has great visualizations.
Cons
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.
Likelihood to Recommend
It's is best suited for data cleaning and analytics. It is an awesome tool if you want to apply some statistics operations. It can handle large amounts of data It is not the best tool if you want to start with coding in general as concepts are a little tough.
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.
Pros
Quantitative analyses.
Descriptive analyses.
Graphs.
Cons
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.
Likelihood to Recommend
I would use RStudio if you need a cheap way to effectively analyze data using social network analyses. Linear regressions are also fairly easy to run in RStudio, but if you have the money I'd recommend going another direction for your statistics needs.
