Greetings! You've arrived at the official repository for the CAN Fellow Cholera Project. This collaborative effort aims to leverage data analysis for insightful exploration into cholera research. We appreciate your interest and participation in this project.
To begin, we invite you to clone the repository or download it directly to your computer. This can be accomplished by visiting our GitHub page at https://github.com/gbganalyst/Cholera-Project or by downloading the repository archive from this link. Once downloaded, unzip the file on your local machine.
The project's data sets are crucial for our analysis. You can access them through our shared Google Drive folder. Please download the Cholera-Project-Data, unzip it, and place the Vector-Layers and Raster-Layers directories within the Cholera-Project-main directory on your computer.
This project utilizes Python and R as its core programming languages. To ensure compatibility and ease of use, we recommend the following installations:
-
Anaconda: A comprehensive Python distribution, equipped with a vast library of data science packages. Download from Anaconda.
-
R and RStudio: Essential tools for R programming. Download R from CRAN and RStudio from RStudio Download.
The Python component of our project is developed in Jupyter Lab Notebook. Open Python-Project-Script.ipynb within Jupyter Lab and execute the code blocks sequentially. Instructions within the notebook will guide you on transitioning to R for further analysis.
For the R portion, initiate the Cholera Project.Rproj file to open the project in RStudio. Subsequently, load the R-Project-Script.R script file containing our R code for analysis.
We thank you for your interest in contributing to the Cholera Project. Your efforts are invaluable to the success of this initiative. Should you have any questions or require further assistance, please do not hesitate to reach out.