# Using Pivoting for Multi-Dimensional Analysis

Pivoting allows you to transform raw data into a structured format, making it easier to analyze multiple dimensions in a single chart. By using pivot columns, you can compare values across categories while maintaining a clear and organized data structure.

### **1. Understanding Pivoting**

Pivoting enables you to convert rows into columns, allowing for multi-dimensional analysis. Instead of viewing data in a linear format, you can categorize and compare different segments efficiently.

* Without Pivoting: The dataset remains in its original format, displaying values in a linear structure.
* With Pivoting: A categorical field is used to split data into multiple columns, making trends and comparisons more visible.

***

### **2. Selecting Pivot Columns**

To apply pivoting, follow these steps:

1. **Go to the Chart Components Section**
   * This section dynamically loads based on the selected chart type.
   * For pivot-supported charts, the pivot column option will be visible.
2. **Choose a Column to Pivot On**
   * Click on the **Pivot Columns** section.
   * Select a categorical column (e.g., `Market`, `Region`, `Product Category`).
   * The selected column’s values will become headers in the data table.
3. **Apply Aggregation to Y-Axis Values**
   * Since pivoting transforms rows into columns, the Y-axis metrics must be aggregated.
   * Common aggregation methods include:
     * **SUM** - Total values across each category.
     * **AVG** - Mean value of each category.
     * **COUNT** - Number of occurrences.
     * **DISTINCT COUNT** - Unique occurrences in each category.
     * **MIN/MAX** - Minimum or maximum value for each category.

<figure><img src="/files/5mykBYB9Ad2mODA1HDob" alt=""><figcaption></figcaption></figure>

***

### **3. Data Before and After Pivoting**

Here’s how the dataset transforms when a pivot is applied:

#### **Before Pivoting (Raw Data Table)**

| ORDERDATE   | SALES     | MARKET       |
| ----------- | --------- | ------------ |
| Jan 1, 2012 | 28,570.44 | Asia Pacific |
| Jan 1, 2012 | 11,740.89 | Africa       |
| Jan 1, 2012 | 21,833.02 | LATAM        |
| Jan 1, 2012 | 22,477.44 | Europe       |
| Jan 1, 2012 | 14,276.74 | USCA         |

#### **After Pivoting (Transformed Data Table)**

| ORDERDATE   | ASIA PACIFIC | AFRICA    | LATAM     | EUROPE    | USCA      |
| ----------- | ------------ | --------- | --------- | --------- | --------- |
| Jan 1, 2012 | 28,570.44    | 11,740.89 | 21,833.02 | 22,477.44 | 14,276.74 |
| Feb 1, 2012 | 41,283.40    | 8,941.92  | 10,678.33 | 33,957.75 | 8,856.61  |
| Mar 1, 2012 | 36,461.61    | 3,869.13  | 17,203.32 | 24,369.90 | 53,842.56 |

As shown above:

* The `Market` column is pivoted into individual columns.
* Each column now represents a category (e.g., `Asia Pacific`, `Africa`, etc.).
* The sales values are aggregated for each market by order date.

***

### **4. Applying and Updating Pivot Settings**

After selecting the pivot column:

1. Click **Apply** to refresh the dataset.
2. The system will regenerate the data structure and update the visualization.
3. If changes are needed, you can:
   * Modify the pivot column selection.
   * Change the aggregation type for Y-axis metrics.
   * Adjust filters to refine the data further.

***

### **5. Best Practices for Pivoting**

* Choose pivot columns wisely: Use categorical fields that make sense for analysis.
* Ensure Y-axis metrics are aggregated appropriately.
* Use filters to manage large datasets efficiently.
* Always hit **Apply** after making changes to update the visualization.

***

By using pivoting effectively, you can analyze data from multiple dimensions in a structured way, making comparisons clearer and insights more actionable.


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