R Statistical Software (2024)

R Statistical Software (1)

Run R Online

R Statistical Software (2)

Run R code, R in Jupyter notebooks, RMarkdown, or even Knitr/Rnw LaTeX\LaTeXLATE​X in a full, remote online R environment.

CoCalc makes working with R easy

CoCalc handles all the tedious details for you, regardless of whether you want to work on the

command line, run

Jupyter Notebooks, create RMarkdown files, or use

Knitr in LaTeX\LaTeXLATE​X documents.

This page is about ways to use R in the

CoCalc platform.

Zero setup

  • No need for you to download and install R.
  • CoCalc already provides many packages for you.
  • The LaTeX editor is already integrated with R.
  • You no longer have to maintain everything on your own.

Start working by creating or

uploading R files, RMarkdown documents, or

Jupyter notebooks.

Start free today. Upgrade later.

There are many ways to use

R

online via CoCalc.

R Statistical Software (3)

CoCalc offers a

complete rewrite

of the classical

Jupyter notebook interface. It is

tightly integrated into CoCalc

and adds real-time collaboration, TimeTravel history and more.

There is also support in CoCalc for easily using R with the

classical Jupyter notebook and JupyterLab.

R Statistical Software (4)

Privately share your project with

an unlimited number of collaborators. Simultaneous modifications of your document are

synchronized in real time. You see the cursors of others while they edit the document and also see the presence of watching collaborators.

Additionally, any compilation status and output is synchronized between everyone, because everything runs online and is fully managed by CoCalc.

This ensures that everyone involved experiences editing the document in exactly the same way.

R Statistical Software (5)

The fully integrated

CoCalc LaTeX\LaTeXLATE​X editor

covers all your basic needs for working with .tex,

.Rnw and .Rtex files. The document is synchronized with your collaborators in realtime and everyone sees the same compiled PDF. In particular, this LaTeX\LaTeXLATEX editor

  • Manages the entire compilation pipeline for you,
  • Automatically processes .Rnw and .Rtex files using Knitr,
  • Supports forward and inverse search to help you navigating in your document,
  • Captures and shows you where each LaTeX\LaTeXLATEX or R error happened,
  • and you can useTimeTravelto go back in time to see your latest edits and easily recover from a recent mistake.

This means you can move

your entire workflow online to CoCalc:

  1. Upload or fetch your datasets,
  2. Use Jupyter Notebooks to explore the data and test your hypothesis,
  3. Discuss and collaborate with your research team,
  4. Write your research paper in an .Rtex or .Rnw document,
  5. Publish your datasets, your research code, and the PDF of your paper online, all hosted on CoCalc.

R Statistical Software (6)

You can edit RMarkdown files in CoCalc's code editor, as explained here.

The source file is processed according to the YAML-frontmatter configuration and the view of the generated file is automatically updated in an HTML or PDF panel.

Syntax highlighting for markdown and embedded programming code—according to their language—makes it easy to visually understand the source file.

CoCalc's library

features selected example files to get started quickly: e.g. HTML reports, article templates and a beamer presentation.

CoCalc is able to format your R code.

By simply clicking one button,

your R source code is formatted in a clean and consistent way

using the

formatR package.

This reduces cognitive load reading source code, brings everyone in the team on the same page, and reduces misunderstandings.

R code formatting works with

pure .r files

and

Jupyter Notebooks running an R kernel.

R Statistical Software (7)

All your existing R scripts run on the command line right in CoCalc.

Open a Terminal

and you find yourself in a familiar Linux shell with a local filesystem for your data files, access to

Git and

a large number of commands... Feel at home and run your analysis as usual!

Terminals can be used by multiple users at once. This means you can work with your collaborators in the same session at the same time. Everyone sees the same output, and via

side chat next to the terminal, the whole team can coordinate.

Beyond that, you can simultaneously work with several terminal sessions. This gives you the ability to run your code concurrently.

For long-running programs, you can even close your browser and check on the result later.

R Statistical Software (8)

Collaboration is a first class citizen on CoCalc. A

side-by-side chat

next to your R code, LaTeX\LaTeXLATEX files and notebooks makes it easy to discuss content with your colleagues or students. You can also create dedicated chatrooms.

Avatars show who is currently working on a file.

Collaborators who are not online will be notified about new messages the next time they sign in.

Chat also supports markdown formatting and LaTeX\LaTeXLATEX formulas.

R Statistical Software (9)

CoCalc makes sure that the computational environment for R is regularly updated and ready to work with. Our goal is enabling you to get started with your analysis without any overhead.

Look at our list of available packages

in more detail. If something is missing, please tell us about it (

contact [emailprotected]

) so we can install that package globally.

R Statistical Software (10)

CoCalc helps you share your work with the world. It offers its own hosting of shared documents, alongside with any associated data files.

You can configure if your published files should be listed publicly, or rather only be available via a confidential URL.

R Statistical Software (11)

Snapshots are consistent read-only views of all your files in a

CoCalc project. You can restore your files by copying back any that you accidentally deleted or corrupted.

The

TimeTravel feature

is specific to the CoCalc platform. It records all your changes in editable files like R source code, Jupyter notebook and LaTeX\LaTeXLATEXdocuments in fine detail. You can go back and forth in time across thousands of changes to recover your previous edits.

This allows you to easily recover any part of any version of your file by copying and pasting. You can also see exactly what changed from one version to the next.

You can visualize the entire process of creating a Jupyter notebook from the start. This lets you discover how you arrived at a particular solution and see what you (or your student) attempted before the final solution.

Start free today. Upgrade later.

R Statistical Software (2024)

FAQs

What is R statistical software? ›

R is a free, open source statistical programming language. It is useful for data cleaning, analysis, and visualization. It complements workflows that require the use of other software. You can read more about the language and find documentation on the R Project Website.

Is statistical software R free? ›

R is a free statistical software package heavily influenced by S.

Is it better to learn R or SPSS? ›

If you're a beginner or need to perform basic statistical analyses, SPSS may be a better choice. If you need more flexibility, customization, or advanced statistical tools, R may be a better choice.

How much does R cost? ›

R is a free software environment for statistical computing and graphics.

Can you get R for free? ›

R and RStudio are free and open-source, and can be installed on any Windows, Mac, or Linux computer that you have appropriate permissions to install software. To install them, download the software from their respective websites. (If you wish to install RStudio, you must install R first.)

Is R hard to learn? ›

R is considered one of the more difficult programming languages to learn due to how different its syntax is from other languages like Python and its extensive set of commands. It takes most learners without prior coding experience roughly four to six weeks to learn R. Of course, this depends on several factors.

Is R or Python better? ›

They're both very powerful languages, so the answer has a lot to do with what you intend to do. If you're primarily looking to create and visualize statistical models, R will be the better choice. If your project goes beyond statistics, Python will offer you far more possibilities.

Why do people use R? ›

R is a statistical programming tool that's uniquely equipped to handle data, and lots of it. Wrangling mass amounts of information and producing publication-ready graphics and visualizations is easy with R. So are all sorts of data analysis, mining, and modeling tasks.

Which is better R or SAS? ›

SAS is better equipped to manage large amounts of data than R because it processes data faster and smoother and is more secure. R is less efficient because it uses random access memory (RAM) to compute all of its data.

Is R more useful than Excel? ›

It is evident that the source code of R can be used repeatedly and with different data sets in ways that Excel formulas cannot. R clearly shows the code (instructions), data and columns used for an analysis in ways that Excel does not.

Why do statisticians prefer R over Python? ›

Like Python, R has a robust community, but with a specialized focus on analysis. R doesn't offer general-purpose software development like Python, but it handles these specialized data science projects better because that's the only focus. The R ecosystem includes: RStudio (an R-based IDE)

How long does it take to learn R statistics? ›

Brand new programmers may take six weeks to a few months to become comfortable with the R language. Three months is generally enough time for any new programmer to use the language and start applying it in their professional life. By setting a goal with Pluralsight's Skills app, you learn at your own pace.

Can I learn R on my own? ›

A great way to learn proper programming practices is by reading books. An advantage of books is that they often represent an expert voice, the skill of the community, or both. Most good books for learning programming in R will contain code examples that you can use to sharpen your skills.

Is R still widely used? ›

R is extremely common in research, especially statistics and data science.

What language does R use? ›

R software is open-source and free software. It is licensed by the GNU Project and available under the GNU General Public License. It is written primarily in C, Fortran, and R itself.

What is the use of R in statistics? ›

The sample correlation coefficient (r) is a measure of the closeness of association of the points in a scatter plot to a linear regression line based on those points, as in the example above for accumulated saving over time.

What is R and why is it used? ›

R is a programming language and a software environment for statistical computing and graphics. Microsoft R Open is a version of R that was created by the Microsoft Corporation. Both R and Microsoft R Open are free and open-source tools for data science and analytics.

Why do statisticians use R instead of Python? ›

R is built for statistics: Heavy statistical analysis is possible with Python, but you won't get the syntax-specific libraries and functions as you do with R. The language makes it much more intuitive to build and communicate results from these specific types of programs.

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