**Preliminary Work - Getting Started**

From the dataset : https://www.kaggle.com/datasets/divanshu22/online-retail-dataset/data

Before commencing your exploratory data analysis, formulate 3-4 data questions to guide your analysis. These questions will provide direction as you delve deeper into the dataset.

**Part I - Exploring**

1. Examine any available written description of the dataset, commonly referred to as the data dictionary.

2. Clean the data by performing various tasks such as renaming columns, handling missing values, correcting data types, removing unnecessary columns or rows, manipulating strings, reorganizing the data, and any other steps necessary to prepare the dataset for analysis.

3. Calculate descriptive statistics for variables of interest.

4. Generate visualizations from the raw data using appropriate graphical representations like bar charts, histograms, line graphs, scatter plots, etc., to identify and highlight interesting aspects of the dataset.

**Part II - Expanding**

1. Create new variables to enhance the capture of relevant data. For example, consider calculating month-to-month changes in sales if the dataset contains sales data over time.

2. Perform operations such as grouping, summarizing, ranking, arranging, or counting to create new data frames that offer different perspectives of the data.

3. Extract the most intriguing data results and develop visualizations that effectively communicate these findings.

**Part III - Communicating**

1. Compile a report summarizing your insights from the data analysis. Identify 3-5 observations or pose follow-up questions for future exploration.

2. Complete all data management tasks in R.

**Submission Guidelines**

Follow the instructions provided to create a slide deck comprising slides. Use descriptive statistics and visualizations to convey information effectively. include written information in the Notes section of each slide that connects to the visualization's key points concisely.