Quick-start
Use the menu bars above:
- OpenIntro Labs: Labs from OpenIntro Statistics
- Other Labs: Labs from other sources
- Datasets: Codebooks for datasets used in labs
- Exercises: contains a blank workbook for running R
From these menus you can run R directly in your web browser. It is running in your browser, not on a remote server, so you can run it offline. You can also download the code to run in your own R environment.
About WASM and webR
These labs use WASM (WebAssembly) and webR to provide a user-friendly alternative for running R directly in your browser. This approach eliminates the need for local installation or a Cloud service, and allows for interactive coding and visualization within the browser environment.
Running R in your web browser can sometimes lead to issues such as
- Your work is not saved: Data is stored in your browser’s local storage, which is separated from your local filesystem. This means that data and code are not persistent across sessions, and you will lose your work if you close the browser. You can print the page to save your work, or copy the code to a local text file.
- Performance Limitations: Browser-based execution may be slower compared to local execution. Even if you have a powerful computer, your browser likely only supports up to 4GB of memory for WebAssembly applications.
- Browser Compatibility: Ensure you are using a modern browser that supports WebAssembly, such as a current version of Chrome, Firefox, Safari, or Edge.
About OpenIntro Labs
OpenIntro labs (see OpenIntro labs tab above) were developed by OpenIntro Statistics and adapted for use on the web by Levi Waldron at CUNY SPH. OpenIntro Labs promote the understanding and application of statistics through applied data analysis. Labs are titled based on topic area, which correspond to particular chapters in all three versions of OpenIntro Statistics, a free and open-source textbook. The textbook as well as original versions of the labs can be found at https://www.openintro.org/book/ims/.
About R and how to use these labs
What are R and CRAN?
R is a powerful programming language and environment designed for statistical computing and graphics. It is widely used among statisticians and data scientists for data analysis and visualization. CRAN (Comprehensive R Archive Network) is a repository that hosts R packages, which are collections of R functions, data, and compiled code that extend the capabilities of R. CRAN hosts more than 20,000 active packages, or libraries, each adding extra functionality to R.
How is R Different from SAS and SPSS?
R is an open-source language, which means it is free to use and has a large community contributing to its development. Unlike SAS and SPSS, which are commercial software with licensing fees, R offers a vast array of free add-on libraries for various statistical techniques. R valued in academic and research environments for its extensibility and active community support.
RStudio: A Powerful IDE for R
RStudio is a popular integrated development environment (IDE) for running R. It provides a feature-rich but complex interface for data analysis and software development in R. While RStudio is powerful, it has a learning curve, especially for those new to programming or R. You can install R and RStudio for free on your own computer here.
Cloud Services for Running R
There are several cloud services that offer free tiers for running R, making it accessible without the need for local installation. These offer persistent storage and more resources than running R in your browser.
- posit.cloud is cloud-based service by Posit (formerly RStudio) that allows you to run R and RStudio using your browser. The free tier has limited resources but is sufficient for running the labs in this course. Note: whereas https://cuny-epibios.github.io/PUBH614/ runs code in your browser and it is actually running on your computer, posit.cloud and other cloud services run code on a remote server.
- Google Colab is another cloud-based service that uses Jupyter notebooks, an alternative system for running code interactively. Jupyter notebooks are widely used in data science and machine learning, and are less feature-rich and simpler to use than RStudio. Google Colab provides more generous resources in the free tier cheaper paid tiers than posit.cloud, and makes use of your Google Drive for storage.
Reporting Issues
If you encounter any issues, notice any errors or problems, or have suggestions for improvement, please let Levi Waldron and other potential contributors know by opening an issue.
Building this site
Datasets for these labs are stored here and source code here.
Test individual labs from the command-line:
Rebuild entire site from R:
pkgdown::build_site()Modify website layout in _pkgdown.yml, modify custom css in pkgdown/custom.css.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.