A Journey Through Global Crop and Livestock Production

Introduction

I was introduced to Tableau during my Udacity business analytics course. I really liked it because it takes complex data and turns it into clear, visual stories. It simplifies the process of understanding trends and patterns, making data much more accessible and insightful.

Driven by a passion for continuous learning and a keen interest in diverse market analyses, I started a project that would allow me to refine my Tableau skills. This project focuses on visualizing agricultural trends, a sector that epitomizes the intricate interplay of global markets, technology, and sustainability. It represents not just an exercise in data analysis, but a step forward in my ongoing journey to harness data for insightful storytelling and informed decision-making in any business context.

Data Collection

For this project, I sourced my data from the Food and Agriculture Organization of the United Nations (FAOSTAT). My aim was to obtain comprehensive data spanning all countries, crops, and livestock items over the past two decades. To achieve this, I acquired the crops and livestock products bulk data file, encompassing information from 1961 for every country and region, detailing aspects such as production, harvested area, and yield.

Data Processing

After downloading the bulk data, I initiated a sequence of data-wrangling steps to transform the data into a usable format. At first, I utilized Python to perform the wide-to-long data transformation of the years using the pandas library and the pd.melt function. A wide-to-long transformation involves converting a dataset from a format where each observation is represented by multiple columns (variables) into a format where those variables are stacked into a single column, typically resulting in a more organized and suitable format for analysis. Following this, I used Excel to implement additional filters for production and measurement units, to change names containing diacritics, as well as to eliminate outdated countries and regions from the dataset.

However, as the process evolved, I developed a Python script capable of automating the entire workflow. This script streamlined the process from retrieving the data directly from FAOSTAT to generating the necessary CSV file for analysis within Tableau, allowing for easier replication. Click here to view the full code

Following the data refinement process, I uploaded the Excel sheet to Tableau. Initially, country names were treated as text within the program. However, by adjusting the data type to 'location' Tableau accurately recognized and mapped these countries geographically.

Data Visualization

The core objective of this project was to design an exploratory dashboard that allows users to effortlessly access information about crops and livestock for any specific country of interest. The world map visually aggregates the data. Below the map, I created two bar charts. These charts dynamically update based on the selected country, crop, livestock, or year, showcasing the top-producing countries and the most prominent crops and livestock produced in a specific country. I hope users have as much fun exploring the data as I had creating this project!