Crandi

Introduction To Jupyter Notebooks In Cocalc Surge Discover R

Leo Migdal
-
introduction to jupyter notebooks in cocalc surge discover r

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. This is the repository for the SURGE Discover R workshop. This contains Jupyter notebook files that are pushed to our cloud service (CoCalc) for use in the workshops, as well as Markdown versions of the notebooks that are used to support a GitHub Pages... Also contains other files assocaited with teh workshop, such as introductory lecture slides. Released under the Creative Commons CC-BY 4.0 License by SURGE, 2021. 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) Almost all tutorials for Jupyter Notebook talk about Python, and spend significant portion of their contents on installation, neither of which is relevant to this class. Therefore I am writing this short guide for QSBR II students on running R with Jupyter notebook on CoCalc. CoCalc runs on the cloud, so that everything has already been set up and ready to go. You don't have to install any software on your own computer and you can access it through a browser anywhere with internet connection, you can even use it with your iPad.

Once you login to the CoCalc website, remember that you should use the buttons on the webpage to navigate within the website (blue frame in the figure below). Note that the controls of the browser itself (shown in the read frame in the figure below) do not work for that. I. Create a new Jupyter notebook in CoCalc First, let us create a new Jupyter notebook, give it a name, and it will be automatically saved on CoCalc. As shown in the figures below, first, click on the new buttons (see red arrow), and in the next screen, enter the name (I entered "example" in this case but it could be any...

If you click on the "File" button, you will see the file "example.ipynb" has already been created. II. Choose R as the "kernel" of a Jupyter notebook Learn how to use reproducible tools (Jupyter + R) to do data analysis Learn how to solve 4 common problems in Data Science, and when you have the means to do so In this course we define data science as:

the processes used to obtain value (i.e., insight) from data through reproducible and auditable processes. Value (i.e., insight) is gained through asking and answering statistical questions. 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. This notebook is here to demonstrate the integration capabilities of R with the Jupyter Project. The R kernel is still young, so miss a lot of feature, but can still display inline graphincs in notebooks. For simple comparison we reproduce here some part of the famous demo(graphics) command of R in the Jupyter notebook, Where the commend have been converted to markdown cells.

Here is some code which illustrates some of the differences between R and S graphics capabilities. Note that colors are generally specified by a character string name (taken from the X11 rgb.txt file) and that line textures are given similarly. The parameter "bg" sets the background parameter for the plot and there is also an "fg" parameter which sets the foreground color. This code just plots equally spaced hues in a pie chart. If you have a cheap SVGA monitor (like me) you will probably find that numerically equispaced does not mean visually equispaced. On my display at home, these colors tend to cluster at the RGB primaries.

On the other hand on the SGI Indy at work the effect is near perfect We will be using an online platform called Cocalc to run and edit notebooks which run Python code. Cocalc is a collaborative computing platform, which means that you can interactively share your code with other users of the platform. After completing this worksheet, you will be able to: ALL students must do Part 1 Step 1 to set up a new Cocalc project for NSCI0007. The remainder was included in last year’s module NSCI0010 so you only need to do this if you didn’t do that course or if you want to refresh your understanding.

Follow the the instruction below to create a Cocalc account and run a notebook file.

People Also Search

You Are Currently Viewing A Jupyter Notebook File On CoCalc.

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-ab...

They Also Aren’t Terribly Useful. Cells Have Two Modes: Edit

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...

Double-clicking A Cell Will Make It Editable. If You Double-click

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 learni...

Try Executing The Cell And See What Happens. This Is

Try executing the cell and see what happens. This is the repository for the SURGE Discover R workshop. This contains Jupyter notebook files that are pushed to our cloud service (CoCalc) for use in the workshops, as well as Markdown versions of the notebooks that are used to support a GitHub Pages... Also contains other files assocaited with teh workshop, such as introductory lecture slides. Releas...

Create Variables And Perform Basic Math Operations Let's Say Each

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 ch...