sei-africa/rseiRAP

# Technical Assessment This repository contains the technical assessment for the **Research Associate – Programmer** recruitment process. The objective is to develop an R package for reading, processing, analyzing, and visualizing seasonal rainfall data. ## Installation ### 1. Download the Test Data Create a directory to store the test data, then clone the `dataseiRAP` repository. ```bash # Replace this path with a suitable location on your computer mkdir -p /home/enacts/SEI_TEST_data cd /home/enacts/SEI_TEST_data git clone github.com ``` ### 2. Fork the Repository Create a fork of the `rseiRAP` repository under your GitHub account. ``` github.com ``` ### 3. Clone Your Fork Clone your fork of the repository to your local computer. ```bash # Using SSH git clone git@github.com: /rseiRAP.git # Or using HTTPS git clone github.com /rseiRAP.git ``` --- # Assessment Complete the following tasks. ### 1. Implement the Functions Implement the following functions in the `R/` directory. - **`read_daily_rainfall`** Read daily rainfall data from a CSV file. A sample dataset (`daily_rainfall.csv`) is available in the **dataseiRAP** repository. - **`rolling_3months_seasonal_total`** Compute rolling three-month seasonal rainfall totals from daily rainfall data. - **`probability_exceeding`** Compute the empirical cumulative distribution function (CDF) and a smoothed CDF using Gaussian kernel density estimation. - **`plot_probability_exceeding`** Plot the probability of exceeding using both the empirical and smoothed CDFs. ### 2. Document the Functions Document each function using **`roxygen2`**. ### 3. Export the Functions Export all public functions in the package. ### 4. Verify the Package Before committing your changes, ensure that the package: - builds successfully; - passes all checks; - installs without errors; and - produces the expected output. ### 5. Commit and Push Your Chan …