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

Is R enough for data analysis? ›

Ultimately, the best language for you will depend on your specific needs. If you need a general-purpose programming language that can be used for a wide range of tasks, then Python is a good choice. If you need a statistical programming language that is well-suited for data analysis, then R is a good choice.

Is R statistical software easy to use? ›

R makes handling data from various sources easy, from import to analysis. Plus the R system itself and the CRAN library offer plenty of data visualization functions and tools, which makes it easy for professionals to present their research and findings in an impactful and easy-to-read format.

Is R easier than SPSS? ›

Learning Curve and Support SPSS has a relatively low learning curve and offers user-friendly tutorials and documentation. It also has a support team and community forum for troubleshooting and answering questions. R, on the other hand, has a steeper learning curve and requires some programming skills.

Is statistics with R difficult? ›

For those who have a background in other programming languages or have worked previously in the data sciences, it may be easier to learn R than those who are novices to coding or this field. However, some Data Scientists struggle using R due to its numerous GUIs, extensive commands, and inconsistent function names.

Is R still relevant in 2024? ›

Perform statistical analysis in R with functions and packages. Performing statistical analysis in R is a valuable skill for aspiring data analysts to learn in 2024. R provides a wide range of functions and packages that make it easier to prepare data and perform complex analyses.

Is Python or R better for statistics? ›

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.

Is it better to learn R or Stata? ›

R is a programming language that allows you to go beyond what Stata can achieve. If you have a basic understanding of coding or are familiar with the coding environment. Stata, on the other hand, should be preferred over R if you have little or no coding experience.

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.

Is R useful anymore? ›

Is R Used Anymore? R still remains a popular language in data science, though Python has overtaken it as a favorite in the field.

Is R harder than Excel? ›

Most people already learned the basics of Microsoft Excel in school. Once the data has been imported into an Excel sheet, using a point-and-click technique we can easily create basic graphs and charts. R, on the other hand, is a programming language with a steeper learning curve.

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.

Is Python tougher than R? ›

R is easier to learn when you start out, but gets more difficult when using advanced functionalities. Python is a beginner-friendly language with English-like syntax. RStudio. Its interface is organized so that the user can view graphs, data tables, R code, and output all at the same time.

Why is R hard to learn? ›

When working with R, you will need to recall more commands, making it much harder to learn and use than other languages. Another factor contributing to the difficulty of learning R is that this language has inconsistent analysis ways when more than one variable is present.

Can I pass statistics if I'm bad at math? ›

While taking a statistics class terrifies some students, one need not be a mathematical or statistical genius to pass this class. Read on to discover tips and strategies that will help you pass statistics.

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 or Excel better for data analysis? ›

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 disadvantage of using R as a data analytics tool? ›

R's data must be stored in physical memory. However, the increasing use of cloud-based memory may eventually make this drawback moot. It doesn't have consistent documentation/package quality. Docs and packages can be patchy and inconsistent, or incomplete.

Which is better for data analysis R or SQL? ›

R is a powerful tool for performing complex statistical analyses and creating visualizations, while SQL is better suited for working with large datasets and performing more basic operations like filtering, sorting, and aggregating data. Ultimately, they're both tools that are incredibly useful for data analysis.

What can Python do that R can't? ›

Increases efficiency: Python's codes offer excellent control and integrations with other programming languages. This makes it so programmers won't have to rewrite code in some circ*mstances. Faster: Python renders data much faster than R because it runs using a simple syntax (which also makes it easy to read).

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