Posit Description
One source of truth for R packages and Python packages RStudio is the preferred professional data science solution for every group. A Python and R integrated development environment with syntax-highlighting editor, console, and code execution. It also includes tools for workspace management, history, plotting, and plotting. You can publish and distribute data products throughout your organization. One-button deployment of Shiny applications and R Markdown reports, Jupyter Notebooks, etc. To increase reproducibility and reduce the time spent installing and troubleshooting R packages, you can control, organize, and manage your use of them. RStudio is committed to sustainable investment in open-source and free software for data science. RStudio has been certified as a B Corporation. This means that our open-source mission has been codified in our charter. Our professional software products are enterprise-ready and provide a modular platform that allows teams to adopt open-source data sciences at scale.
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Statistics and graphs Date: Mar 23 2022
Summary: I've been studying statistics for the last year and RStudio is the easy part. You get a lot of value by visualising stuff and it's easy. It automatically calculates correlations, associations, confidence intervals, prediction intervals, p/t/f values... Even if you just have a straight list of numbers you can get a boxplot, standard deviation, test normality...
Positive: Powerful statistical tools.
Good quality customisable plots, all different types using all different data and models.
Easy to import from csv and there's plenty of datasets around if you go looking.
Easy to create and export graphs.
Being able to quickly and easily visualise (and share) data is more useful than I could have imagined. All the "academic" data online is suddenly easy to use.
I haven't even scratched the surface of community created packages, but they're easy to install, not always so easy to use.Negative: You can get unhelpful syntax error messages.
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The basics work well but sometimes just changing something (eg colours) in specialised plots feels like a hard programming puzzle.
It's mostly straight-forward like "plot(data)", but the commands are written in a programming language and get tricky depending on your needs.
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