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Alteryx Designer Desktop Knowledge Base

Definitive answers from Designer Desktop experts.
how to combine multiple header rows into one
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A large component of data blending is applying mathematical or transformational processes to subsets of your data.  Often, this requires isolating the data that complies with a certain criteria that you’ve set. The Conditional functions build expressions that allow you to apply processes to data that satisfy conditions you set.   #
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Alteryx’s spatial functions are not limited to the tools found in the Spatial Palette.  The Formula Tool, for example, has a variety of spatial functions that expand the Designer’s spatial capabilities beyond those in the Spatial Tool Palette.  This article highlights the Formula Tool’s ability to create spatial objects, particularly points and lines, and calculate distance using the built-in expression library.
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getting an error that says "Type mismatch in operator +" in my formula
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How to create null values in a data set. 
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Selecting and blending records from within a group or category
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It can be tricky to convert text to numbers, this article will detail out steps for when the ToNumber() function isn't ideal
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Use Designer to capture the first available value from a set of fields in two ways: if-statements, data manipulation
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Scenario: You monitor the mileage of multiple trucks as they deliver shipments over the course of a week and record additional information regarding each truck in a file (Truck Metrics).  Each truck’s cumulative mileage per day is recorded in a separate file (Truck Mileage).  Your goal is to update the mileage-related fields in Truck Metrics with the values recorded in Truck Mileage.  Note: today is Tuesday so only fields for Monday and Tuesday will be updated in in the Truck Metrics file.         Methods:  Manually Select Fields to Update Whether the data is uniquely identified by a Truck Number (or Record ID) or identically ordered in both files, data from Truck Metrics (the table to be updated) and Truck Mileage (the data used for updating) can be Joined together.  Then, using the Select functionality within the Join tool, I can manually choose the fields from Truck Mileage that I want to use to replace the fields that need to be updated (Figure 1).       Figure 1: Fields from Truck Mileage (Yellow) replace fields from Truck Metrics (Blue).  Note that fields in yellow are selected while fields in blue have been deselected.  Fields that need to be included from Truck Metrics (Red) remain selected.    Fantastic!  A simple, straightforward way to update fields!  But, as any analyst knows, working with data is rarely simple or straightforward.  What if you’re dealing with 20 fields that need to be updated, not just 2?  In that case, manually selecting fields to update is not only tedious but also error-prone.  For these types of situations, I recommend a process that allows for a more dynamic approach.       'Verticalize' the Data to Dynamically Update Fields Transposing, or ‘verticalizing’ data, allows for a more dynamic workflow when you have unknowns in your processing.  In a scenario such as this one, you may have an unknown or changing number of fields that will need be updated in Truck Metrics.  Using this approach, we’ll first Transpose both data sets to configure the Field Name and its associated value in a single row (Figure 2).    Figure 2: The data is transposed from Truck Mileage and Truck Metrics.  The highlighted fields in Truck Metics indicates which fields will be updated, as the same field exists in Truck Mileage.     Then, we’ll Join our datasets based on two fields: Truck Number AND Name.  This ensures that fields in Truck Mileage will match to the correct fields in Truck Metrics, assuming the fields names in both tables are named in the same way.  The only selecting we’ll have to do is to make sure all fields from the Right data source (in this case, Truck Metrics) are deselected (Figure 3).  This allows that, in the situation of matched Truck Numbers and field names, the updated values will be used.   Figure 3: The Joined fields indicate the fields that exist in both Truck Mileage and Truck Metrics.  Fields in yellow (Truck Mileage) are selected to reflect updated values in downstream tools.  Fields in blue (Truck Metrics) are deselected.    Note that any unmatched fields have fallen out of the Right side of the Join:     To add them back into the data stream, simply Union the Center and Right Joins together, setting the tool to “Auto Configure by Name”.  Then, to rearrange the data into its original format, use the Cross Tab tool (Figure 4).  And, voila!  Your data is updated!   Figure 4: The updated fields are highlighted in yellow.  All other fields originally included in Truck Metrics are included the in the final results as well.              
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counting records in a dataset
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Data Cleansing tool doesn't work in 2020.4 when the Field name contains line breaks unix style line endings.
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There are a couple of different whitespace situations you might get yourself into, but this article has you covered in all of them!
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Any tool that performs a Sort will increase processing time. This article will show you how to improve efficiency!
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Have you ever wanted to do a Cross Tab, but needed the results in a particular order?  You've probably discovered that Cross Tab will dutifully change your data to column headings and keep the rest of the data correlated, but that the output columns will be arranged in alphabetical order.  It's as if there were a Select tool with the "Sort on Field Name" Option thrown into your workflow without you putting it there.  \n  
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Very large numbers don't behave the way users expect. Often, it's better practice to handle a big number like that as a String.
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Very rarely Designer can crash with a big red X across a window. One such situation has been observed and resolved.
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In Alteryx, the + (plus) symbol has different functionalities depending on if you are using text (string) or numeric fields.
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A Calgary Database is a proprietary Alteryx format that allows users to query against a file of millions of records quickly without having to read in all the data.
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Characters that are not on a standard English keyboard may need translation into Unicode or a language-specific code page for Designer and database drivers to read them correctly. Characters with incorrect encoding will often appear as boxes or question marks in the Designer Results screen and error messages. Unicode characters take more bytes than English ASCII characters. Changing the column type and increasing the column size may be needed. In Designer, the column size is the number of characters, not the number of bytes.
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