https://github.com/rcghpge/data-3442-lab
Statistical Methods for Data Science 2 in R @ UTA. Spring 2025
https://github.com/rcghpge/data-3442-lab
data-science machine-learning r-language ut-arlington uta
Last synced: 3 months ago
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Statistical Methods for Data Science 2 in R @ UTA. Spring 2025
- Host: GitHub
- URL: https://github.com/rcghpge/data-3442-lab
- Owner: rcghpge
- License: mit
- Created: 2025-01-24T21:06:50.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-03-22T05:34:45.000Z (3 months ago)
- Last Synced: 2025-03-22T06:25:22.988Z (3 months ago)
- Topics: data-science, machine-learning, r-language, ut-arlington, uta
- Language: R
- Homepage: https://uta.edu
- Size: 3.36 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Statistical Methods for Data Science 2 in R
Statistical Methods for Data Science 2 in R @ UTA. Dr. Bian. Spring 2025
---
## Lab
Labs section of the course.
## Development Environment Setup
There are several methods to setup your initial R environment. You can run R in a GUI via RStudio, VS Code, a Jupyter notebook via a web browser, or on the command line in Bash. For beginner-friendly RStudio, VS Code, or Jupyter notebooks is recommended.
---
## Getting Started in Bash
To get a workflow and development environment started in bash see Github documentation to install this repository into your local machine: [Cloning a Repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository?platform=linux&tool=webui)
## Bash Development Workflow
Install R:
```bash
sudo apt install r-base
```
Run R Console Environment:
```bash
R
```
Alternatively you can run an R workflow environment directly in Bash with the following commands:
```bash
# Create an R script
nano hello.R
# Run R script
Rscript hello.R
```
# Resources and Documentation
- [Visual Studio Code](https://code.visualstudio.com): VS Code Interactive Development Environment
- [RStudio](https://posit.co/downloads/): RStudio Interactive Development Environment
- [Jupyter Notebooks](https://jupyter.org): Jupyter Interactive Notebooks. To work with R in Jupyter Notebooks, install the R kernel provided in the links. See Jupyter Notebook's Github repository for more information - [link](https://github.com/jupyter/notebook)
- [R](https://www.r-project.org): About The R Project
- [R kernel](https://github.com/IRkernel/IRkernel): The R kernel for workflows and development in Jupyter-based enviornments.
- [The R programming language](https://www.r-project.org/other-docs.html): Documentation
---