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Alteryx Designer Desktop Knowledge Base

Definitive answers from Designer Desktop experts.
Predictive Grouping is an approach that allows users to assess and create the appropriate number of clusters (groups) for their data to be assigned based on their similarity to each other in the same cluster and dissimilar to other data assigned to other clusters. K-Centroids represent a class of algorithms for doing what is known as partitioning cluster analysis. These methods work by taking the records in a database and dividing (partitioning) them into the best K groups based on some criteria. The purpose of creating clusters is to assist you in the business decision-making process as it relates to the clustered data.
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Occasionally, users might have issues when it comes to installing the predictive tools on their machines. The first common error (show below) can occur when first trying to install predictive tools after finishing a 64-bit Alteryx Designer installation.
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If there's a piece of functionality that you're looking for that's lacking in Alteryx but is available in R and you have modest R coding abilities, you can extend Alteryx by creating your own R-based Alteryx tool.
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This post is part of the "Guide to Creating Your Own R-Based Macro" series.
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This post is part of the "Guide to Creating Your Own R-Based Macro" series.
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This post is part of the "Guide to Creating Your Own R-Based Macro" series.
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With the Python Tool, Alteryx can manipulate your data using everyone’s favorite programming language - Python! Included with the tool are a few of pre-built libraries that extend past even the native Python download.
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R-based tools available to download from Gallery.
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How to save your predictive model.
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Alteryx Designer comes with tools (based on both R and Python) to create and use predictive models without needing to write any code. But what if you've got custom models written in R or Python outside of Designer that you want to use in Designer, or vice versa?
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When using the Predictive tools, you may get an error stating that the Rcpp package cannot be loaded, or that the namespace load failed.
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K-Centroids Cluster Analysis: No valid fields were selected
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The TS Factory tools can build time series models and forecasts for multiple sets of historical data groups, without the need to separate them.  This is their relationship to the batch macro.
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Overview: I wrote this as a short example into how one might use Alteryx to write a further Alteryx module to do complicated or repetitive tasks dynamically that would be difficult to do through the front end. This module will automatically produce another Alteryx module that will do frequency statistics for a file. This should save the manual time (for files with lots of columns) adding a summarize for each column. It also saves transposing the file (which for large files is very slow to run). Instructions: Open Producer.yxmd Change the input to that module to whichever file you like (or use Testing.yxmd which is provided) Run it – this will create the Result.yxmd module Open Result.yxmd – and change the input in the module to be the same file you used in step 2 Change the output if necessary (it defaults to an Alteryx database) Run Results.yxmd At the moment it does deal with &’s and single quotes in files, but won’t do anything clever like do stats on substrings for long fields.   I hope this inspires people to use this technique and build on the module I’ve built.  
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We were recently approached by a concerned client with "Help! I have a model object in a .yxdb but my computer crashed and I need to document the predictor variables!" This naturally led to a discussion on how we can pull these variables back for the client, and what kind of scenarios would lead to this. The first scenario is the most obvious (the case of the client). The model object was created using Alteryx and was stored in a .yxdb.  During another process, my computer crashed and I lost all of my data! Luckily, I still had the model object in a shared location, but I need to document the variables and the model object looks like this: Unfortunately, this does not give us any information about the data or more importantly, the predictor variables. Luckily, a simple script can break down this model object and fill you in on all of the details.   Within Alteryx, attach an R Tool to your data stream (I am using the Forest Model Object that is created from an Alteryx Sample):   Next, copy and paste the following script into your R Tool code builder: model.data <- read.Alteryx("#1") the.obj <- unserializeObject(as.character(model.data$Object[1])) print(the.obj$call) This script states to take the data coming in from Input #1 and label it "model.data".  Next, unserialize (break down) the data in the field Object (specified by "model.data%Object[1]").  Finally, print the results in the Alteryx Output window.  The final results for this particular object are then printed out, as shown. As you can see, the output clearly states that my predictor variables are Chk_Bal, Duration, Credit_Hist, Purpose, etc.  The end result is quick, clean, and can really help get you out of a jam if you lose your data.
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 Alteryx has a full set of integrated predictive tools but even with developers working at full speed, it is hard to keep up with the R community. Sometimes users want to install and utilize their favorite R packages. This post demonstrates how to use and install additional R packages.
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My code runs in R, but not in the R Tool?
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Regression analysis is widely used for prediction and forecasting. Alteryx customers use these statistical tools to understand risk, fraud, customer retention and pricing, among many other business needs.
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A number of the Predictive tools have Interactive Visualizations.  A Predictive tool will have this capability if you see an output anchor with the letter ‘I’ (for "Interactive").\n  
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  With the release of 10.6 came awesome new features, and an upgrade in the underlying R version (from 3.1.3 code named "Smooth Sidewalk," to 3.2.3 code named "Wooden Christmas-Tree").  Using the incompatible R version will cause errors in your R macros.     Simply make sure that your Predictive Tools download is the version compatible with your Alteryx Designer version:     Users on 10.5 should continue to use the R3.1.3 version.   When using Alteryx and Microsoft Revolution R Enterprise, a separate predictive tools install is required (in green). For details, see the Alteryx and Revolution Analytics Integration Guide.   And remember to use the non-Admin Predictive version with non-Admin versions of Alteryx Designer.   To install Predictive Tools for Alteryx 10.0, go to Previous Releases. For Alteryx 9.5, within Designer, go to Help > Install Predictive Tools.    Happy Alteryx-ing!
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