Frequency
Last updated
Last updated
This visualization is used to visualize how often each category occur, highlighting patterns and anomalies in the dataset.
Enter the name of your Visualization widget, which will appear on your dashboard.
Select your preferred column name from the dropdown. The data from this column will populate the xaxis values to create a frequency visualization.
Enter the alias you want to display for the Xaxis on the visualization graph.
Click on the + icon next to YAxis Properties to add properties for calculating values on the Yaxis. You can add more than one property here.
Enter the alias you want to display for the Yaxis on the visualization graph.
You can write an expression here to calculate the value for YAxis.
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 YAxis Properties as the function attribute for calculations.
The following options are available in the Expression Builder. Ci
represents the attributes/properties selected by you
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 Ecommerce application.
For example: Calculate the total sales revenue from all orders in the Ecommerce application. Here A represents the sales_revenue as a property
The sumIf()
function calculates the total sum of a numeric column, but only for rows that meet a specified condition. It works similarly tosum()
, but adds a filtering condition.
Example: Calculate the Total Sales Revenue for Failed transactions in Hyderabad in an Ecommerce 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'
).
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 Ecommerce Application
In this case, we want to calculate the average sales revenue from all transactions. Here, A
represents the sales_revenue as a property.
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.
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 Ecommerce 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.
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.
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 Ecommerce 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.
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.
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 Ecommerce Application
In this case, we want to find the minimum sales revenue from all transactions. Here, A
represents the sales_revenue as a property.
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 Ecommerce Application
In this case, we want to find the maximum sales revenue from all transactions. Here, A
represents the sales_revenue as a property.
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 Ecommerce 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.
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 Ecommerce 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.
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 Ecommerce 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.
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.
You can customize the colors and add units to both the axes.
Following unit options are available
Second=sec
Minutes=mins
Hours=hrs
Days=days
Week=week
Click on View Chart to preview it before adding it to the dashboard.
Function  Attribute Structure  Usage 

Operator  Action  Example Usage 

Function  Description  Usage 

Function  Description  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)
+
Addition
uniq(C1) + uniq(C2)

Subtraction
uniq(C1)  uniq(C2)
/
Division
uniq(C1) / uniq(C2)
*
Multiplication
uniq(C1) * uniq(C2)
abs()
Return the absolute value of a number
abs(C1)
toInt8()
Converts a value to an 8bit integer
toInt8(C1)
toInt16()
Converts a value to an 16bit integer
toInt16(C1)
toInt32()
Converts a value to an 32bit integer
toInt32(C1)
toInt64()
Converts a value to an 64bit integer
toInt64(C1)
toString()
Converts a value to a string
toString(C1)
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)