Set Transform

• test predicates that evaluate to Boolean values (value: myAge == '30' yields a true or false value), or
• computational expressions ( value: abs(pow(myCol,3)) ).

The expected type of value expression is determined by the transform type. Each type of expression can contain combinations of the following:

• literal values:  value: 'Hello, world'
• column references:  value: amountOwed * 10

• functions:   value: left(myString, 4)

• combinations:  value: abs(pow(myCol,3))

The types of any generated values are re-inferred by the platform.

• set - defines the values to use in a predefined column. See Set Transform.

  Tip: Use the derive transform to generate a new column containing a defined set of values. See Derive Transform.

• replace - replaces a string literal or pattern appearing in the values of a column with a specific string. See Replace Transform.
• extract - extracts a pattern-based value from a column and stores it in a new column. See Extract Transform.

Source:

The following dataset contains contact information that has been gathered by your marketing platform from actions taken by visitors on your website. You must clean up this data and prepare it for use in an analytics platform.

Transformation:

Title column: For example, you first notice that some data is missing. Your analytics platform recognizes the string value, "#MISSING#" as an indicator of a missing value. So, you click the missing values bar in the Title column. Then, you select the Replace suggestion card. Note that the default replacement is a null value, so you click Edit and update it:

Request column: In the Request column, you notice that the reg entry should be cleaned up. Add the following transformation, which replaces that value:

The above transformation uses a Alteryx® pattern as the expression of the on: parameter. This expression indicates to match from the start of the cell value, the string literal reg, and then the end of the cell value, which matches on complete cell values of reg only.

This transformation works great on the sample, but what happens if the value is Reg with a capital R? That value might not be replaced. To improve the transformation, you can modify the transformation with the following Alteryx pattern in the on parameter, which captures differences in capitalization:

Add the above transformation to your recipe. Then, it occurs to you that all of the values in the Request column should be capitalized in title or proper case:

Now, all values are capitalized as titles.

Phone column: You might have noticed some issues with the values in the Phone column. In the United States, the prefix 555 is only used for gathering information; these are invalid phone numbers.

In the data grid, you select the first instance of 555 in the column. However, it selects all instances of that pattern, including ones that you don't want to modify. In this case, continue your selection by selecting the similar instance of 555 in the other row. In the suggestion cards, you click the Replace Text or Pattern transformation.

Notice, however, that the default Replace Text or Pattern transformation has also highlighted the second 555 pattern in one instance, which could be a problem in other phone numbers not displayed in the sample. You must modify the selection pattern for this transformation. In the on: parameter below, the Alteryx pattern has been modified to match only the instances of 555 that appear in the second segment in the phone number format:

Note the wildcard construct has been added (%*). While it might be possible to add a pattern that matches on the last four characters exactly (%{4}), that matching pattern would not capture the possibility of a phone number having an extension at the end of it. The above expression does.

LeadId column: You might have noticed that the lead identifier column (LeadId) contains some embedded information: a date value and an identifier for the instance within the day. The following steps can be used to break out this information. The first one creates a separate working column with this information, which allows us to preserve the original, unmodified column:

You can now work off of this column to create your new ones. First, you can use the following replace transformation to remove the leading two characters, which are not required for the new columns:

Notice that the date information is now neatly contained in the first characters of the working column. Use the following to extract these values to a new column:

The new LeadIdworking2 column now contains only the date information. Cleaning up this column requires reformatting the data, retyping it as a Datetime type, and then applying the dateformat function to format it to your satisfaction. These steps are left as a separate exercise.

For now, let's just rename the column:

In the first working column, you can now remove the date information using the following:

You can rename this column to indicate it is a daily identifier:

Results:

• AVERAGEIF - Average of a set of values by group that meet a specified condition. See AVERAGEIF Function.
• MINIF - Minimum of a set of values by group that meet a specified condition. See MINIF Function.
• MAXIF - Maximum of a set of values by group that meet a specified condition. See MAXIF Function.
• VARIF - Variance of a set of values by group that meet a specified condition. See VARIF Function.
• STDEVIF - Standard deviation of a set of values by group that meet a specified condition. See STDEVIF Function.

Source:

Here is some example weather data:

Transformation:

The following computes average temperature for rainy days by city:

The following computes maximum wind for sub-zero days by city:

This step calculates the minimum temp when the wind is less than 5 mph by city:

This step computes the variance in temperature for rainy days by city:

The following computes the standard deviation in rainfall for Center Town:

You can use the following transforms to format the generated output. Note the $col placeholder value for the multi-column transforms:

Since the following rely on data that has only one significant digit, you should format them differently:

Results: