The code in this repository is an implementation of the methodology developed by Samadi et al. (2017) that combines Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) methods to incorporate the time dependencies of time-series data. This implementation was developed as a project for the curriculum unit "Estatística Multivariada" given by Adelaide Freitas at the University of Aveiro.

The implementation uses the NCAR Research Data Archive dataset (ds578.1), which contains monthly mean surface temperature (degrees C) and monthly accumulated precipitation (millimetres) from 160 land stations in China from 1951 to 2000. Only the temperature data was used for this project and it's located in the files ch160sta.txt and ch160temp.txt.

The code was featured on a poster via a QR code for the One Day Meeting CIDMA conference.

Installation

1. Download the repository: You can download this repository to your local machine either by cloning it or by downloading it as a zip file.

   • To clone the repository, use the following command in your terminal:

   git clone https://github.com/Adrilihan/PCA-CCA.git

   • To download the repository as a zip file, click on the Code button on the repository page and then click Download ZIP. Extract the zip file to your desired location.

2. Open the R project: Navigate to the directory where you downloaded the repository and open the PCA-CCA.Rproj file. This will start the R environment with the correct working directory.

3. Install the required packages: This project uses the renv package for dependency management. If you don't have renv installed, you can install it using the following command in the R console:

   install.packages("renv")

   Then, to install the project dependencies, use the following command:

   renv::restore()

4. Run the R Markdown file: Finally, you can run the PCA.Rmd file to execute the code and generate the report.

Please note that this project was developed in R, so you need to have R installed on your machine. If you don't have R installed, you can download it from The Comprehensive R Archive Network (CRAN).

Usage

Currently, this repository contains an R Markdown file, PCA.Rmd, which provides a detailed walkthrough of the steps taken to format the data and apply the PCA-CCA methodology. Users can refer to this file to understand the process and use the code provided.

In the future, we aim to develop a function that can perform the analysis given formatted data. Please stay tuned for updates.

License

This project is licensed under the MIT License. See the LICENSE file for details.

References

Data: https://rda.ucar.edu/datasets/ds578.1/

Paper: https://link.springer.com/article/10.1007/s00180-016-0667-1

PCA wiki: https://en.wikipedia.org/wiki/Principal_component_analysis

CCA wiki: https://en.wikipedia.org/wiki/Canonical_correlation

R plotly package documentation: https://plotly.com/r/

CCA R implementation: https://cmdlinetips.com/2020/12/canonical-correlation-analysis-in-r/