From the dataset: https://www.kaggle.com/datasets/rajkumarpandey02/world-data-of-gender-inequality-index

Before beginning the exploratory data analysis, develop 3-4 data questions. Part I - Exploring

1. Understand the structure and content of the dataset by reviewing any available descriptions or data dictionaries.
2. Get the dataset ready for analysis by cleaning it. This includes tasks like renaming columns, handling missing values, correcting data types, and other necessary steps to ensure the data is ready for exploration.
3. Gain insights to calculate descriptive statistics into the dataset's key variables.
4. Highlight interesting patterns and trends in the data by generating visualizations

Part II - Expanding

1. Enhance the dataset by generating new variables that provide additional insights or capture specific aspects of the data.
2. Generating alternative views or perspectives for analysis by grouping, summarizing, or transforming the data
3. To construct informative visualizations identify and extract the most compelling data results.

Part III - Communicating

1. Present the findings from the data analysis, including key observations and potential areas for further exploration.
2. Get the dataset ready for analysis by cleaning it. Make sure everything is done in R for data management so it can be reproduced and is transparent.

• A slide deck comprising slides to present the data analysis findings using descriptive statistics and visualizations.
• Include concise written information in the Notes section of each slide to explain the key points of the visualizations.