# Frequency This visualization is used to visualize how often each category occur, highlighting patterns and anomalies in the dataset.

### Name of Visualization Enter the name of your Visualization widget, which will appear on your dashboard.

### X-Axis Property Select your preferred column name from the dropdown. The data from this column will populate the x-axis values to create a frequency visualization.

### Alias for X-Axis Enter the alias you want to display for the X-axis on the visualization graph.

### Y-Axis Property Click on the + icon next to Y-Axis Properties to add properties for calculating values on the Y-axis. You can add more than one property here.

### Alias for Y-Axis Enter the alias you want to display for the Y-axis on the visualization graph.

### Expression Builder You can write an expression here to calculate the value for Y-Axis. Start typing the function you want to use and select it from the suggestions. Then, place the cursor inside the brackets and type the property name you selected in Y-Axis Properties as the function attribute for calculations.

The following options are available in the Expression Builder. `Ci` represents the attributes/properties selected by you {% tabs %} {% tab title="Arithmetic Functions" %}

Function	Attribute Structure	Usage
sum()	(attribute)	sum(C1)
sumIf()	(attribute, filter)	sumIf(C1, conditions)
avg()	(attribute)	avg(C1)
avgIf()	(attribute, filter)	avgIf(C1, conditions)
count()	(attribute)	count(C1)
countIf()	(attribute, filter)	countIf(C1, conditions)
uniq()	(attribute)	uniq(C1)
uniqIf()	(attribute, filter)	uniqIf(C1, conditions)
min()	(attribute)	min(C1)
max()	(attribute)	max(C1)
least()	(attribute1, attribute2)	least(C1, C2)
greatest()	(attribute1, attribute2)	greatest(C1, C2)
date_diff()	(return type, attribute1, attribute2)	date_diff('second', C1, C2)

{% endtab %} {% tab title="Operators" %}

Operator	Action	Example Usage
+	Addition	uniq(C1) + uniq(C2)
-	Subtraction	uniq(C1) - uniq(C2)
/	Division	uniq(C1) / uniq(C2)
*	Multiplication	uniq(C1) * uniq(C2)

{% endtab %} {% tab title="Numeric Transformations" %}

Function	Description	Usage
abs()	Return the absolute value of a number	abs(C1)
toInt8()	Converts a value to an 8-bit integer	toInt8(C1)
toInt16()	Converts a value to an 16-bit integer	toInt16(C1)
toInt32()	Converts a value to an 32-bit integer	toInt32(C1)
toInt64()	Converts a value to an 64-bit integer	toInt64(C1)
toString()	Converts a value to a string	toString(C1)

{% endtab %} {% tab title="Transformations" %} | Function | Description | Usage | | ------------- | ---------------------------------------------------- | --------------- | | toTimeStamp() | Converts a value to timestamp | toTimeStamp(C1) | | toWeekDay() | Converts a date to the corresponding day of the week | toWeekDay(C1) | | toUTCHour() | Converts a time to its UTC Hour | toUTCHour(C1) | | toMonth() | Converts a date to its corresponding month | toMonth(C1) | | toDate() | Converts a value to a date format | toDate(C1) | | {% endtab %} | | | | {% endtabs %} | | | ## sum() The `sum()` function is used to calculate the total sum of a numeric column. **Example:** Calculate the **total sales revenue** from all orders in the E-commerce application. For example: Calculate the **total sales revenue** from all orders in the E-commerce application. Here A represents the **sales\_revenue** as a property

## sumIf() The `sumIf()` function calculates the total sum of a numeric column, but **only for rows that meet a specified condition**. It works similarly to`sum()`, but adds a filtering condition. **Example:** Calculate the **Total Sales Revenue** for **Failed transactions** in **Hyderabad** in an E-commerce Application In this case, we want to calculate the total **sales revenue** (`A`) for transactions that **failed** (`B = 'FAIL'`) and are located in **Hyderabad** (`C = 'hyderabad'`).

## avg() The `avg()` function is used to calculate the **average value** of a numeric column. **Example:** Calculate the **Average Sales Revenue** from All Orders in an E-commerce Application In this case, we want to calculate the average sales revenue from all transactions. Here, `A` represents the **sales\_revenue** as a property.

## avgIf() The `avgIf()` function is used to calculate the **average value** of a numeric column based on a specified condition. **Example:** Calculate the **Average Sales Revenue** for Failed Transactions in Hyderabad. In this case, we want to calculate the average sales revenue from transactions that failed and are located in Hyderabad. Here, `A` represents **sales\_revenue**, `B` represents **transaction\_status**, and `C` represents **location**.

## count() The `count()` function is used to **count the number of rows** or occurrences of a specific value in a column. **Example:** Calculate the **Total Number of Transactions** in an E-commerce Application In this case, we want to count the total number of transactions processed in the application. Here, `A` represents the **transaction\_id** as a property.

## countIf() The `countIf()` function is used to count the number of rows that meet a specified condition. **Example:** Calculate the **Total Number of Failed Transactions** in **Hyderabad** In this case, we want to count the total number of transactions that have failed and are located in Hyderabad. Here, `A` represents **transaction\_status** and `B` represents **location**.

## uniq() The `uniq()` function is used to calculate the **number of distinct values** in a specified column. **Example:** Calculate the **Total Number of Unique Customers** in the E-commerce Application In this case, we want to count the total number of unique customers who have made purchases. Here, `A` represents the **customer\_id** as a property.

## uniqIf() The `uniqIf()` function is used to count the **number of distinct values** in a column, but **only for rows that meet a specific condition**. **Example:** Calculate the **Number of Unique Customers** Who Had **Failed Transactions** in **Hyderabad** In this case, we want to calculate the total number of **unique customers** who experienced **failed transactions** and are located in **Hyderabad**. Here, `A` represents **customer\_id**, `B` represents **transaction\_status**, and `C` represents **location**.

## min() The `min()` function is used to calculate the **minimum value** in a numeric column. **Example:** Calculate the **Minimum Sales Revenue** from All Orders in an E-commerce Application In this case, we want to find the minimum sales revenue from all transactions. Here, `A` represents the **sales\_revenue** as a property.

## max() The `max()`function is used to calculate the **minimum value** in a numeric column. **Example:** Calculate the **Minimum Sales Revenue** from All Orders in an E-commerce Application In this case, we want to find the maximum sales revenue from all transactions. Here, `A` represents the **sales\_revenue** as a property.

## least() The `least()` function is used to return the **smallest value** from a set of values across multiple columns or expressions. **Example:** Calculate the **Least Value Between Sales Revenue** and **Discount Amount for Each Order** in an E-commerce Application In this case, we want to find the smallest value between the **sales revenue** (`A`) and **discount amount** (`B`) for each order. Here, `A` represents **sales\_revenue** and `B` represents **discount\_amount** as properties.

## greatest() The `greatest()` function is used to return the **largest value** from a set of values across multiple columns or expressions. **Example:** Calculate the **Greatest Value** Between **Sales Revenue** and **Discount Amount** for Each Order in an E-commerce Application In this case, we want to find the largest value between the **sales revenue** (`A`) and **discount amount** (`B`) for each order. Here, `A` represents **sales\_revenue** and `B` represents **discount\_amount** as properties.

## date\_diff() The `date_diff()` function is used to calculate the **difference between two dates** in a specified unit (such as days, months, or years). **Example:** Calculate the **Number of Days** Between **Order Date** and **Delivery Date** in an E-commerce Application In this case, we want to calculate the number of days between the **order date** (`A`) and the **delivery date** (`B`). Here, `A` represents the **order\_date** and `B` represents the **delivery\_date** as properties.

### Custom Filters You can filter the data in the final output on the dashboard according to your needs. Choose whether to include data using the **=** operator or exclude it using the **!=** operator. You can also use the regex operator and provide the regex formula.

### Customize You can customize the colors and add units to both the axes. Following unit options are available {% tabs %} {% tab title="Time" %} Second=sec Minutes=mins Hours=hrs Days=days Week=week {% endtab %} {% tab title="Numbers" %} Thousand = K Lakh = L Million = M Billion = B {% endtab %} {% endtabs %} ### View Chart Click on View Chart to preview it before adding it to the dashboard.