RStudio is great but needs some improvements
April 19, 2022

RStudio is great but needs some improvements

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with RStudio

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
  • Better visualization output
  • Quick implementation of statistical models
  • Improved analytical capacity
RStudio stacks up pretty well against its competition. For me, it is really up to personal preference and what you are used to when deciding between the competitions. I like that Python packages have the most external resources, so it's easier to troubleshoot. But RStudio does have some advantages when it comes to optimized outputs for statistics.

Do you think Posit delivers good value for the price?

Yes

Are you happy with Posit's feature set?

Yes

Did Posit live up to sales and marketing promises?

Yes

Did implementation of Posit go as expected?

Yes

Would you buy Posit again?

Yes

Atlassian JIRA Align (formerly AgileCraft), Asana, GitHub
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.

Posit Feature Ratings

Connect to Multiple Data Sources
7
Extend Existing Data Sources
7
Automatic Data Format Detection
7
Visualization
8
Interactive Data Analysis
8
Interactive Data Cleaning and Enrichment
6
Data Transformations
8
Multiple Model Development Languages and Tools
8
Single platform for multiple model development
8
Self-Service Model Delivery
8
Flexible Model Publishing Options
7
Security, Governance, and Cost Controls
6