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 used for? ›

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 R statistical software free? ›

R is a free statistical software package heavily influenced by S. It can be installed on Linux, Windows and MacOS.

What is the difference between R and SPSS? ›

SPSS and SAS are commercial software, while R is free and open source. R is flexible and easy to understand, and there are millions of free resources in the internet. Once you learn how to use R, you will not want to use SPSS or SAS. So I think everyone should start with R.

How much does R cost? ›

R is available as Free Software under the terms of the Free Software Foundation's GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

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? ›

What is the main difference between Python and R? Python is a general-purpose programming language, while R is a statistical programming language. This means that Python is more versatile and can be used for a wider range of tasks, such as web development, data manipulation, and machine learning.

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.

What is the easiest statistical software to learn? ›

Consider software with drag-and-drop functionality and tutorials or online resources to build your confidence as you navigate through different statistical procedures. SPSS, SAS, and R are examples of user-friendly software for beginners.

Is R programming outdated? ›

The truth is, R is far from dead. While it's true that Python has gained significant traction in recent years, R remains a powerful language that offers unique benefits for data scientists. One of the critical advantages of R is its focus on statistics and data visualization.

What can R do that SPSS cannot? ›

R graphics are more advanced then SPSS. R has at least 3 different graphics programs. The consequence is that R can handle very complex statistical analytics. The advantage of SPSS is that it can perform parallel computing, sometimes using IO to harddisk.

Why do statisticians use R instead of Python? ›

On the other hand, R is purely for statistics and data analysis, with graphs that are nicer and more customizable than those in Python. R uses the Grammar of Graphics approach to visualizing data in its #ggPlot2 library and this provides a great deal of intuitive customizability which Python lacks.

What is the SPSS called now? ›

SPSS Statistics has also gone by the name PASW Statistics, which stood for "Predictive Analytics Software".

Can I install R for free? ›

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

Can I learn R on my own? ›

Yes. At Dataquest, we've had many learners start with no coding experience and go on to get jobs as data analysts, data scientists, and data engineers. R is a great language for programming beginners to learn, and you don't need any prior experience with code to pick it up.

Is R better than Excel? ›

Therefore, Excel is ideal for simple data analysis of small datasets. But, do not think that analyzing small data sets with R is more difficult. You can easily analyze small data sets just like in Excel. Furthermore, if you have to deal with large data sets, R is best.

What is the use of R in statistics? ›

The Pearson correlation coefficient or as it denoted by r is a measure of any linear trend between two variables. The value of r ranges between −1 and 1. When r = zero, it means that there is no linear association between the variables.

What is R most commonly used for? ›

R is widely used in data science by statisticians and data miners for data analysis and the development of statistical software. R is one of the most comprehensive statistical programming languages available, capable of handling everything from data manipulation and visualization to statistical analysis.

What is the purpose of using R? ›

Most commonly, the R language is used for data analysis and statistical computing. It's also an effective tool for machine learning algorithms. R is especially relevant for data science professionals due to its data cleaning, importing, and visualization capabilities.

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.

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