R Step By Step With Jupyter Cocalc Manual Documentation

Suppose you have an R script that you would like to debug, or step through line by line collaboratively with others. You can do this using a Jupyter notebook and CoCalc’s built-in collaboration. Start by using the jupytext command to convert a script to blocks of code in cells in a notebook. If your R script is rscript.R, then do the following: In the Jupyter notebook, select the “R (system-wide)” kernel. You will see the script separated into multiple cells in the notebook.

You can then step through the script by running one cell in the notebook at a time. These pages explain how to accomplish a certain task or run a specific example on CoCalc. © Copyright 2025, Sagemath, Inc., CC BY-4.0 licensed. Please contact us for Questions and Feedback, Documentation Repository, Privacy Policy and ToS. Start CoCalc – start free, upgrade later! As the official page of the Jupyter project states:

A notebook is a shareable document that combines computer code, plain language descriptions, data, rich visualizations like 3D models, charts, graphs and figures, and interactive controls. A notebook, along with an editor (like JupyterLab), provides a fast interactive environment for prototyping and explaining code, exploring and visualizing data, and sharing ideas with others. Nowadays a Jupyter Notebook is a de facto standard document format, typically stored in .ipynb files, which records interactive sessions with a kernel. It is made up of cells, which can either store one or more lines of code or formatted text. When you run a code cell, the active kernel session evaluates the piece of code in it and the resulting output is shown below. The combination of communicating back and forth with a kernel and adding descriptive text makes this form of document very attractive.

CoCalc has its own implementation of the user interface to work with Jupyter Notebooks, which supports our unique features such as real-time collaboration, AI Assistant, and TimeTravel. However, the underlying format of .ipynb files is exactly the same. You can bring your existing notebooks to CoCalc and resume working, or you can download your work from CoCalc to a local computer or another cloud platform, there is no vendor-lock-in! Most of the time you don’t have to think much about the Jupyter kernel which you are using, because your notebook already has the right kernel selected or your default kernel is picked automatically... But when you want or need to, you can choose the programming language and environment by selecting a kernel explicitly. See Jupyter Kernel Selection

You are currently viewing a Jupyter notebook file on CoCalc. Jupyter notebooks are composed of cells. Cells can be of three types: code, Markdown, or raw. This cell you’re reading is Markdown, a simple language for formatting rich text. The cell below is a code cell, where you can write and run Python commands. Raw cells are “raw” text — they aren’t fancy-formatted Markdown, and they aren’t run-able as code.

They also aren’t terribly useful. Cells have two modes: edit and command. Edit mode is indicated by a green border around the cell, while command mode has a blue border. In edit mode, you can type into the cell and edit it. In command mode, you can run the cell, or manipulate it in certain ways (e.g., deleting a whole cell, or moving it). You execute (run) cells by selecting them (by clicking once on the cell) and then pressing the Shift & Enter keys on your keyboard simultaneously.

Double-clicking a cell will make it editable. If you double-click on this (or any other Markdown) cell, the text will change to a fixed-width font and you’ll see the Markdown formatting tags (like # for headings). Try it! Then hit Shift & Enter to execute the cell (which applies and renders the Markdown formatting). Below is a code cell with some very simple Python code. You haven’t started learning Python yet, but as you can see, at its simplest Python can act like a calculator.

Try executing the cell and see what happens. After completing this lab you will be able to: Create variables and perform basic math operations Let's say each of your friends tells you their favorite movies. You do some research on the movies and put it all into a table. Now you can begin exploring the dataset, and asking questions about the movies.

For example, you can check if movies from some certain genres tend to get better ratings. You can check how the production cost for movies changes across years, and much more. The table gathered includes one row for each movie, with several columns for each movie characteristic: length_min - Length of the movie (minutes) This page describes how to install an R package in a CoCalc project. CoCalc already includes hundreds of R packages, but you can install even more or update them inside your project.

Your project must have the “Internet access” upgrade in order to download software from a remote repository (i.e. CRAN) to your project. Installing an R package will require you to add a license or add upgrades so that your project has internet access. Otherwise you have to download the tarball of the R package to your own machine, and upload it to your project. Then, you can tell R to install the package directly from there as a local file. In a nutshell, a CoCalc project is a Linux environment inside a Docker container.

This means, almost everything you can do as a “normal” Linux user also works. In particular, you can install packages locally, such that R instances can pick them up. Hello, and welcome to CoCalc, an online platform for learning, teaching, writing, research, development - anything where you need to calculate or collaborate! CoCalc’s goal is to provide the best real-time collaborative environment for Jupyter Notebooks, LaTeX documents, and SageMath, scalable from individual use to large groups and classes. We empower you to focus on the task at hand rather than technical details. Work in CoCalc is organized in Projects where you and your collaborators can

code in Jupyter Notebooks as well as in R, C, Julia, and other languages; edit LaTeX, Markdown, and many other documents;

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