Cocalc Introduction To R Ipynb

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 After completing this lab you will be able to: Here we have a dataset that includes one row for each movie, with several columns for each movie characteristic:

length_min - Length of the movie (minutes) cost_millions - Movie's production cost (millions in USD) foreign - Is the movie foreign (1) or domestic (0)? ... stands for 'Collaboritive Calculation in the Cloud'. Their platform allows you to:

You can set up an account and do all this for free without installing any software on your own computer, other than a web browser. (Acknowledgment: This web page is a revision of one originally authored by Paul Meyer-Reimer.) A project is like a folder for your work. But you have to create one before you can do anything else. After you Create an account on CoCalc, or after you sign in, you'll land on the Projects page. Your new notebook will open.

Let's get oriented... 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) ... stands for 'Collaboritive Calculation in the Cloud'.

Built with open source tools, it's a platform that has been shepherded by the mathematician William Stein, first as SageMath, since about 2007. The CoCalc platform supports Jupyter notebooks. When you see familiar terms spelled with py instead of pi, it's because developers using the python computing language have been involved! A notebook (files end in .ipynb) mixes computer math calculations, graphics, data visualizations and documentation including publication-quality math typesetting. Within a single Jupyter notebook you can include all these things: [(*) These are the ones you'll be using in Calculus I & III. ]

You can set up an account and do all this for free without installing any software on your own computer, other than a web browser. (Right now, the GC Math Department is picking up the tab for improved performance for your class projects. Watch this Video introduction (10 minutes) to CoCalc (below). It was prepared for a programming (Python) class. We'll be doing slightly different things. Learn how to use reproducible tools (Jupyter + R) to do data analysis

Learn how to solve 3 common problems in Data Science Predict a class/category for a new observation/measurement (e.g., cancerous or benign tumour) Find previously unknown/unlabelled subgroups in your data (e.g., products commonly bought together on Amazon) Predict a value for a new observation/measurement (e.g., 10 km race time for 30-35 year old males with a BMI > 25). 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 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. intermediate level Python starting from a basic programming background basic knowledge in at least one object oriented programming language must know: variables, types, functions, basic I/O handling

good to know: objects, classes, attributes, function arguments Official Github repository: https://github.com/tuw-python/tuw-python-2022WS

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