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Brucellosis Prevalence Analysis 🐄🐐🐑

Ilcome to the Brucellosis Prevalence Analysis repository! This analysis provides valuable insights into the prevalence of Brucellosis in different livestock species, including Bovine 🐄, Caprine 🐐, and Ovine 🐑, in various districts 🏘️. I utilize a set of data analysis tools and packages to explore the distribution of this disease, uncover variations among species and districts, and unveil correlations their prevalence.

Author 📝

Author: Africano Byamugisha

Contact: 📧 [email protected]

LinkedIn: 🔗 @africanobyamugisha

Methodology 📊📈

Here's a peek into the methodology and the tools I've harnessed for this analysis:

  1. Data Preparation 📋:

    • I start by loading and tidying the data using the tidyverse, dplyr, readr, and janitor packages to ensure it's in a pristine format for analysis.

  2. Data Visualization 📊📉:

    • I employ the versatile highcharter package to craft an array of captivating data visualizations. These visual aids help grasp the prevalence of Brucellosis in different livestock species and districts.

  3. Statistical Wizardry 🧙‍♂️:

    • The statistical analysis relies on the trusty Hmisc package. It aids in conducting correlation tests, unveiling the intricate relationships betIen species' prevalence rates.

  4. Interactive Magic 🪄✨:

    • To keep things dynamic and engaging, I've made up interactive charts and tables using packages such as kableExtra, DT, and highcharter.

  5. Report Crafting 📄🖋️:

    • The rmarkdown package is The parchment and quill, helping create rich reports that seamlessly blend code, stunning visualizations, and eloquent narratives to tell the story of Brucellosis prevalence.

  6. Tabular Elegance 🧐📊:

    • I've used the gtsummary package, with DT and JavaScrip to create a custom function to craft beautifully formatted tables, making it a breeze to present detailed data summaries.

Overview 📝

The analysis offers a deep dive into the prevalence of Brucellosis in different livestock species, with a special focus on Bovine 🐄, Caprine 🐐, and Ovine 🐑 populations. I explore prevalence by district, identifying areas with higher or loIr rates 🏘️. The correlation analysis reveals hidden relationships betIen species' prevalence rates. Interactive visuals and beautifully formatted tables enhance the presentation of The analysis results.