https://github.com/pizofreude/da-with-r
Data analysis with R data centric programming language
https://github.com/pizofreude/da-with-r
data-analysis r
Last synced: 9 days ago
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Data analysis with R data centric programming language
- Host: GitHub
- URL: https://github.com/pizofreude/da-with-r
- Owner: pizofreude
- License: apache-2.0
- Created: 2025-06-27T08:57:53.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-07T09:18:43.000Z (about 1 year ago)
- Last Synced: 2025-10-18T19:52:28.613Z (9 months ago)
- Topics: data-analysis, r
- Language: HTML
- Homepage:
- Size: 1.93 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Data Analysis with R Programming
Welcome to the Data Analysis with R Programming repository, with R as a data centric programming language.
## 📁 Repository Structure
```
da-with-r/
├── .gitignore
├── README.md
├── da-with-r.Rproj # RStudio project file
├── renv.lock # renv lockfile for package reproducibility
├── renv/ # renv local library (auto-created; do not edit manually)
├── requirements.txt # (Optional; renv/renv.lock preferred)
├── data/
│ ├── raw/ # Original, immutable data dumps.
│ └── processed/ # Cleaned & processed data ready for analysis.
├── notebooks/
│ └── exploratory/ # RMarkdown or Jupyter notebooks for EDA.
├── scripts/
│ ├── data_cleaning.R # Scripts for cleaning datasets.
│ ├── analysis.R # Scripts for analysis and visualization.
│ └── functions.R # Custom R functions.
├── reports/
│ ├── figures/ # Generated plots and figures.
│ └── final_report.Rmd # Final RMarkdown report.
├── docs/ # Project documentation, instructions, etc.
├── tests/ # Unit tests for R scripts/functions.
```
## 🚀 Getting Started
1. Clone the repository:
```sh
git clone https://github.com/pizofreude/da-with-r.git
cd da-with-r
```
2. Install required R packages:
```r
install.packages(readLines("requirements.txt"))
```
Or, if you use `renv`, run:
```r
renv::restore()
```
3. Download data into `data/raw/` and follow the scripts or notebooks provided.
## 📦 Requirements
See [requirements.txt](./requirements.txt) for a list of required R packages.
## 📝 Usage
- Place your raw data in `data/raw/`
- Run scripts in `scripts/` for cleaning and analysis
- Explore results in `notebooks/exploratory/`
- Find final reports and figures in `reports/`
## 🧪 Testing
Test your functions using scripts in the `tests/` directory.
## 🙌 Credits
- [R for Data Science](https://r4ds.hadley.nz/preface-2e.html)