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Many skunk works type of products are never really seen by the public eye (such as the Boeing Joint Strike Fighter prototype). The beauty of being a part of a software company that has both desktop and web products is that we have the ability to play with and show off our prototypes to the general public. Enter interactive visualizations within Alteryx!
Part 1: Alteryx: JSON Data Output
Part 2: Alteryx: HTML5 Visualizations
Part 3: Alteryx: Interactive HTML Visualizations
The humble histogram is something many people are first exposed to in grade school. Histograms are a type of bar graph that display the distribution of continuous numerical data. Histograms are sometimes confused with bar charts, which are plots of categorical variables.
Often, we get support requests asking if we can do a quick session on best practices for a particular tool or set of tools. The Spatial Match Tool is certainly no stranger to this request. Even though Alteryx is extremely fast and efficient with spatial processing, there are instances where a slight change in settings could speed up your total module runtime. For example, let us assume that we have 10,000 records that we are trying to perform a spatial match against. Upon looking at our data, we notice that the points were derived from the centroid of another polygon, but the previous polygon field was still in the data stream. Removing the unnecessary spatial field is one of the first steps in optimizing your data stream. Why? If you don't need the data, why are you pushing it all the way through your module? That extra data consumes memory, so removing it from your data stream reduces that consumption, increasing the available resources on your computer. Ultimately, this practice can be used in any aspect of your data, but it is especially relevant with spatial processing. For more tips and tricks regarding your spatial match process, download the included zip package. It contains information that can help you optimize your modules that utilize the Spatial Match tool. Special thanks to Paul Treece for helping out with the module! Until next time! - Chad Follow me on Twitter! @AlteryxChad
Time series forecasting is using a model to predict future values based on previously observed values. In a time series forecast, the prediction is based on history and we are assuming the future will resemble the past. We project current trends using existing data.
I have no doubts that many of you have attempted to geocode a set of records, only to find that your records show up in the middle of the ocean on the other side of the world. I certainly have, and the result is always that I accidentally reversed my non-descriptive latitude and longitude fields just before the Alteryx Create Points Tool. If I had taken a quick look at my data, I would have known that my field with a non-zero hundreds digit (100+) is my longitude field, and setting it properly would have saved me valuable processing time. While the above example is common, there are many others that are equally as common that can be fixed or avoided completely with a bit of additional knowledge. The main question of the article (How accurate does my data need to be?) all depends on your use case. For example, in many point-in-polygon cases you may only need 5 digits of precision rather than 6. Why? 5 decimal places are accurate at 1.1 meters, while 6 decimals are accurate to 0.11 meters. This may seem like a big difference, but when you are trying to search for entire buildings based on a trade area, 5 decimals should do the trick. If you are interested in learning more, please take a look at the StackExchange article How to Measure Accuracy of Latitude and Longitude. This breaks down the difference between accuracy and precision, and goes into detail about each decimal in a latitude and longitude point (with examples!). Big thanks goes out to our very own Paul Treece for his help with the article. Until next time! Chad Follow me on Twitter! @AlteryxChad
RegEx….it can be tough, but extremely useful when looking for ways of extracting information from a string. Regular Expressions are basically a code you can write to match a specific set of characters (it's a pattern matching syntax). It could be something as simple as finding the three-digit number in the string “Eample123Eample” or something more complicated like using hexadecimal to select a certain range of characters. RegEx is something that can be used in Alteryx via the RegEx tool in the Parse toolset and some of the main uses for RegEx are replacing, matching, and parsing specific characters within a string.
There are a handful of ways to search for a particular string within a data field. If you want to perform a query, identifying records with a particular string field within a data field:
Use the Filter tool: the result will be two streams - those records that meet your filter criteria and those that do not.
On the Functions Tab, expand the string tree and select FINDSTRING(String, Target)
Replace the `String` parameter with the field name
Replace the `Target` parameter with the value you are looking to identify
Finish the expression with !=-1 which will separate the true values from the false ones.
Example - If you are trying to identify all of the customers with Joe in a data field [Name] :
The records that meet this criteria will be output from the True anchor ([Name] contains the value "Joe"). All other records will be output from the False anchor.
This function can also be used in the Formula tool; if for example you want to populate a different data column based on the [Name] field, you can use the FINDSTRING in an IF statement. Example: You would like to classify your data in a new field based on the instance of customers with Joe in a data field [Name] :
IF (FINDSTRING([Name], "Joe") != -1) THEN "JOE Customer" ELSE "Other" ENDIF
This will populate a new data field with "JOE Customer" if the field "Name" contains "Joe" otherwise it will populate that field with the value "Other"
In the Formula tool, add a new field by selecting + Add Column, or choose an existing field to update.
Make sure the appropriate Field Type and Size is also specified
On the Functions Tab, expand the Conditional Tree and select IF c THEN t ELSE f ENDIF
On the Functions Tab, expand the String tree and select FINDSTRING(String, Target) to replace c
Replace `String` with the field name
Replace `Target` with the string you are looking to identify
Finish this part of the expression with !=-1 which will separate the true values from the false ones
Replace "t" with the desired value to populate the new field if the condition is met: "JOE Customer"
Replace "f" with the desired value to populate the new field if the condition is not met: "Other"
We’ve developed this handy Getting Started Document to ensure success throughout your lifecycle with our powerful Alteryx technology. The following document outlines the stages and steps taken through the onboarding process. Topics include
Check it out, let us know if you have any questions or comments!
The Tool Container is a tool that allows you to organize your module by placing tools inside of the Tool Container. Because the Tool Container can be opened and closed without impacting the behavior of the tools they contain, you can effectively minimize sections of your module so as to bring greater clarity to the overall picture. From a visual perspective, the Tool Container encourages object-oriented design!
Another feature of the Tool Container is the ability to disable/enable an individual Container.
An example application for this is a module where you need to do some design/testing, but you do not want to write the Output file until everything is working properly. By placing your Output tools inside of a Tool Container, and then disabling the container, you will be assured that no files will be generated. Re-enabling the Output tools is a piece of cake; just uncheck the “Disabled” checkbox. This feature allows you to easily control the generation of Output files or the processing of portions of your module. For users developing complex modules, the Tool Container delivers a powerful time-saving tool for the module design process.
When using a macro placed in a workflow, the Macro Input Tool will be used to pass data from the workflow to the macro. Becoming a master of the macro input will allow you the flexibility to map your fields, have optional inputs, and even optional fields.
In this post, a user asked if you can merge cells in the Table Tool like you can in excel. There are lots of ways you can get creative with the Table Tool, for example you can have your report span across the page in multiple columns as described here or you can add totals to your data to add to your report or create headers and sub-headers as described here.
So, how do you merge cells using the Table Tool itself? You can merge cells by nesting a table inside another table:
Tables can be nested more than one deep, you could start off with city, roll up to region, and then to state:
To clean up the doubled up header rows that are created when nesting tables like that, deselect "show column headings" in the Table Tools' settings:
.... and create the header row in a separate Table Tool. In this example, we are also changing the background color and making the font bigger, and changing the wording to "Region" for the Region column:
Lastly, there should also be subtotals for each region. As with anything in Alteryx, there are different ways of doing this, they could be added as described in this post, or the process used to create the headers can be used. First, the data is summed to create totals, then the totals are put into tables grouped by region:
Lastly, the table snippets are combined in a Layout Tool using the "Vertical Merge Contents to Line Up Table Columns" option:
Also see the attached v2018.1 workflow for the complete example.
Ever needed to learn how to use an Iterative macro? This article walks you through the process with a basic mathematical problem highlighting how the data moves through the macro and loops back to continue processing.
Reading XML in Alteryx is a little different than other input types and the reason for this is simple - XML is not a tabular data format and the data structure can vary from one file to the next. To effectively read and parse XML data in Alteryx, you need to use a combination of tools, namely the Input tool and the XML Parse tools .
Tired of pulling conversion rates manually? Me too!
Let’s build ourselves a currency converter in Alteryx Designer version 10.1 using the http://fixer.io/ exchange rates. Fixer.io is a free JSON API for current foreign exchange rates published by the European Central Bank. The rates are updated daily around 3PM CET.
The first thing we will do is gather the API documentation from the Fixer.io website. This will give us the URL we need to make our REST API call. If you are unfamiliar with making REST calls please take a look at Tasha’s