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Alteryx Knowledge Base

Definitive answers from Designer experts.
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With the release of 2018.3 comes the long-awaited and highly anticipated Python Tool! This article is a general introduction to using the tool.
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When your Python libraries don't work the way they should in Python tool, restoring the tool to it's original state could be the solution. This article walks through how to restore Python libraries and the virtual environment associated with the Python tool.
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What happens when one installs the YXI file of a Python-based tool in Alteryx Designer.
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The Run Command tool is a great way to take your workflow to the next level of efficiency. It allows you to interact with the command line directly, just as you would if you were to access it manually and type in a command. Which is great because sometimes we have a lot of important things to do in the command line.
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Steps for Harvesting Data in Google Big Query!
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One of the greatest strengths of modern web APIs is their flexible, developer-friendly nature, which provides numerous options for both the provider and the user. However, this flexibility can make it more intimidating for business users to deal with the various data formats that these APIs provide. The purpose of this article is to familiarize you with the main data formats used by the vast majority of web APIs, and provide the basic knowledge that will allow you to confidently process the data they return into a typical tabular format.
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For most tools that already have “dynamic” in the name, it would be redundant to call them one of the most dynamic tools in the Designer. That’s not the case for Dynamic Input. With basic configuration, the Dynamic Input Tool  allows you to specify a template (this can be a file or database table) and input any number of tables that match that template format (shape/schema) by reading in a list of other sources or modifying SQL queries. This is especially useful for periodic data sets, but the use of the tool goes far beyond its basic configuration. To aid in your data blending, we’ve gone ahead and cataloged a handful of uses that make the Dynamic Input Tool so versatile:
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Have you ever tried writing to multiple tabs within the same workflow and have received an error? This article is for you!
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Use RegEx and build a Macro to Validate and sort IP Address.
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As currently designed, the  Amazon S3 Download tool  only allows one file, or object, to be read in at a time. This article explains how to create a workflow and  batch macro  that will read in the list of objects in a bucket and allow you to filter for the file(s) you want using wildcards!
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API connections give access to many web-based applications, database systems, or programs by exposing objects or actions to a developer in an abstracted format that can easily be integrated into another program.
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To assist you in your R adventures in Alteryx, we've developed a R Tool Cheat Sheet which you can download to have as your very own. This article reviews and explains the functions included in the Alteryx - R cheat sheet. 
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A product idea recently introduced the ability to compare workflows using the command line from Windows. I thought it would be good to turn this idea into a more GUI friendly Alteryx Analytic App!
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Following the launch of our premier  Developers Space Community  for all things surrounding Alteryx platform extensibility we are happy to share our b rand new design for our Developers Help Page!
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Have you ever wanted to restrict the fields that flow through your workflow based on their data type? What about only allowing fields that begin with the same word or are in a specific position? The  Dynamic Select  tool is just what you’re looking for!
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Binary Large OBject (BLOB) data types are often used to store images, audio, and other multimedia files/objects in a single, standardized, format for simplified database management - making them a frequent filetype in the Alteryx Designer. Fortunately, with the  Blob Convert Tool , along with the  Blob Input  and  Blob Output   Developer Tools , working with BLOB objects is no more difficult than the file types they represent!
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Image Face Recognition Using Microsoft Cognitive Services API!
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Full details on the addins can be found on my   blog; the addins can be download from   GitHub. Hopefully it will make creating some formulas easier!
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The Field Info Tool  is another one of the gems hidden in the Developer Tool Category  – however don’t be intimidated, this is a tool for all of us! The purpose of the Field Info Tool is to give you the information about the fields in your data in a way that you can use down-stream as part of your workflow. There are no settings to configure, so just drop it on your canvas and you’re good to go!
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SPSS Output   Overview   When working with SPSS, values can have both a Text label and a numeric representation of the categories (equivalent of string factors in R). Columns can also have an encoded name ex. Q_1 and a longer descriptive name that maps Q_1 to the original question that was asked (closest thing in R is the data frame attribute on the column).   Alteryx reads .sav files and loads either the numeric representation or the textual representation of values based on the user’s selection. It also reads the variable labels into the Alteryx Field Description.  When writing .sav output, Alteryx will write either the text or the numeric values (depending on what was used in the workflow) as well as the SPSS variable labels which were displayed in the description field. However sometimes to maintain the integrity of the whole SPSS file, clients will want the value labels, value levels, and variable labels to appear in the output file. For these cases, using the Alteryx tools and a few lines of R code (while leveraging the thousands of R packages on CRAN) wrapped in a macro gives us the needed functionality. Attached is a macro which will write the data, variable & value labels back into SPSS.     Macro Process In this section, we will explain the R code logic that is specific to this macro. You can get an introduction to writing custom R code in Alteryx here.      Before we can do anything, we will need to pass the data to the tools inside the macro (more details on macros here). The raw numeric data should be connected to the D input of the macro. In addition, and because the data frames created in R don’t contain the Field Description data, we need to pass Field Description values to the M input (M for Metadata) of the macro. We’re using the Field Info Tool to extract the description into the rows and send it to the macro. With that done we can now look inside the macro.   Inside the Macro       Inside the macro, we are using the R Tool to contain the main functionality. We connect it to the Interface tools (Macro Inputs, File Browse, Action Tool) to get the data, metadata, and output file path from the user. Finally, we’re using the API tool to pass a message back to the user in the Alteryx Execution Messages.   The R Tool contains the code to properly format the input data and write it out to the .sav file. The majority of the work is already done for us in the ‘sjmisc' package from CRAN (R users know that most of the time they can find a package that does what they want). This package, among other features, implements reading and writing .sav files with both variable and value labels. We will first check if the package is not already installed and we’ll attempt to install it.   This workflow installs the additional sjmisc package. To avoid package version and dependency issues, it is possible to use Microsoft R Client as the base R. More details here.   if(!require(sjmisc)){ install.packages("sjmisc") require(sjmisc) }   If you would like to install the package separately you can use the R install packages App from the Alteryx Gallery.   filePath <- "c:\\temp\\outputRAlteryx.sav" inputData <- read.Alteryx("#1", mode="data.frame") ColumnLabels <- as.vector(read.Alteryx("#2", mode="data.frame")$Description)   Within the R code we also define a static ‘filepath ‘ pointing to where the output data should be written. The Action Tool that we previously mentioned will update this filepath to the one chosen by the user while at the same time correctly escaping the backslashes to work in R.   inputData <- read.Alteryx("#1", mode="data.frame") ColumnLabels <- as.vector(read.Alteryx("#2", mode="data.frame")$Description)   We then read the data that we pass to the macro from input ‘#1’ into an R data frame. In this case we are depending on R’s default behavior of transforming text to factors to get the Value encodings for all columns ex Male(1), Female(2). In addition, we read the metadata from input ‘#2’ into R. The sjmisc function, set_label, that applies the variable names to the data frame expects the variable names to be passed as an object of type vector. To make it work, we have to convert the Description column of the data frame we’re reading in into a vector with the as.vector() base R function. For more details about ‘sjmisc’, you can find the documentation here.   labeledData <- sjmisc::set_label(inputData,ColumnLabels) sjmisc::write_spss(labeledData,filePath)   Finally we label inputData with the labels we just created and we store the result in the labeledData dataframe and then write it to the user’s filepath using the sjmisc’s write_spss function.   MessageOut <- paste("file written to: ",filePath) names(MessageOut) <- "Output File Path" write.Alteryx(MessageOut, 1)   We also pass the filepath as a message to the R Tool output to be displayed to the user.       Edit: It was brought to our attention that the macro has an issue writing out text columns that are longer than 120 characters. Unfortunately this is a defect in the underlying R package. As a workaround for now, the macro was modified to trim all text fields to 120 characters. Please keep this in mind when writing out data.   Mandatory Note: This macro and sample code were developed by the authors as a proof of concept to show what's possible. This is not a production-ready macro and is not supported by Alteryx. Do ask questions on this thread - BUT use at your own risk!   WriteSPSSWithLabels_sjlabelled.yxzp has been updated from using the R package sjmisc because the set_label command has been deprecated from sjmisc and is now in sjlabelled.     Best,  Jordan Barker & Fadi Bassil Solutions Consultants
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