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Alteryx Knowledge Base

Definitive answers from Designer experts.
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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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Tips and tricks on how to output multiple sheets to an Excel file with the Output tool or with Reporting tools. 
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How to save your predictive model.
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Every so often we get questions about a .tde (Tableau Data Extract) file that is being output from Alteryx that has a file size of 30k when the original data is much larger. When the file is opened in Tableau this error sometimes comes up: An error occurred while communicating with data Source ‘yourfilename.tde’
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Any time you want to get a good point across, it’s best to show your data. Show your data off in style in your reports or presentations by adding formatting to otherwise bland data with the   Table Tool! Found in the   Reporting Tool Category, the Table Tool will make it easy flair to your raw data, and give it the pop it needs to really sink in.
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Ever have to output tables of differeing schemas to the same Excel workbook? Ever need to output to different tabs? This article covers your bases with the cunning use of Reporting tools! Also included are links to other helpful "outputting to Excel" Knowledgebase Articles.
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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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If you have a file that you want to output to separate Excel files you can first create the desired file path with the  Formula   tool  and then utilize the  Output   tool  to change the entire path.
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One of the great features of the  output tool  is the option to take the file or table name (or part of it) from a field. It allows you to append a suffix, prepend a prefix, change the entire file name, or the entire file path. It also gives you the option whether to keep the field on output.
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By combining Alteryx and Microsoft Power BI, organizations can streamline and accelerate the process of preparing and analyzing data. This provides a faster way to deliver an end-to-end experience for data access, preparation, analysis, visualization and consumption — delivering deeper business insight faster with a more complete set of data.
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Alteryx provides GUI tools that offer similar functionality to many SQL commands. Although minimal SQL scripting may be necessary in order to properly configure tools, the amount required to complete analysis is significantly reduced.
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A relative path is in relation to the location of the App on the users system. Check out our examples of how and when to use them!
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Suppose that your spreadsheet has multiple sheets with the same structure and you would like to read several sheets into your module at once. In this case, the preferred alternative is to use the Dynamic Input tool. 
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How do I output to an Excel template file? It is possible to output your data to an existing Excel document that already has modified formats and column names. For example, the below Excel file has existing data in the first 4 rows. If you wanted to add addresses to this file while keeping the first 4 rows, the first step would be to highlight the area you want to write to. If you don’t know the exact length/width of your data, I would recommend going large: Once you have your desired area highlighted, right-click and choose the Define Name… option: A popup box will appear, enter in a name of your choosing and click OK: You also need to make sure that the sheet you are saving to doesn’t contain any spaces in the sheet name. Once verified, save the template and close out: Below is an example of the sample data that will be added to the above template: In Alteryx, use a Input tool to point to the data you would like to use to update the template file: In the Output, you will want to choose the template file, which will cause the below message to appear, choose yes to overwrite: When saving to Excel, the below window will popup, enter the name you used for the range you highlighted in the template file: After clicking OK, the Output configuration area will populate. Change the Output Options to Delete Data & Append: You can now run the module. Once the module is finished, you can open the updated template file, you should see your previously formatted rows/columns plus the new data you wanted to append: If you set a format to the range you named (color, text style, bold, etc), Excel will keep it so that the data you are writing to the file will appear with the specified format.
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In a  previous article , we've shown you how you can upload to FTP using the  Download  tool. With the release of Alteryx 10.5, the Download tool now supports uploading to  SFTP . With this addition, we'll take the opportunity to show you some more examples of uploading data to SFTP/FTP and show you how seamless it can be.
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You may have a use case whereby you have a large dataset and you want to output it to separate excel files. However, in each of these excel files you would like to apply a template format.
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Connecting to Google Analytics is becoming more and more popular. There are a few things you need in order to use the Google Analytics macro; a Google Account (e.g., Gmail) and authorized access to an existing Google Analytics account. This article will help you get the rest of the way.
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Is there a workaround for not being able to use the Folder Browse Tool in the Gallery? Though it may not be as clean as being able to use the Folder Browse Tool, the simple workaround for this is to use the Text Box Interface Tool instead. This will allow the user to copy a directory path from Windows Explorer and paste it into the Text Box. In the workflow, all you need to do is connect the Text Box Tool to an Output Data Tool and have the Action Tool update the path portion of the Output Data Tool. You can even enter in a default path in the Default Text section of the Text Box if there is a path that is most commonly used.
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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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Believe it or not, data can be beautiful. Take your black and white data points and add some color to them in visuals with the suite of tools found in the Reporting Category https://help.alteryx.com/current/index.htm#Getting_Started/AllTools.htm#Report_Presentation_Tools ! If you’re looking to create reports, presentations, images, or simply output data with a bang, you can use the Render Tool https://help.alteryx.com/current/PortfolioComposerRender.htm paired with other Reporting Tools to create HTML files (*.html), Composer files (*.pcxml), PDF documents (*.pdf), RTF documents (*.rtf), Word documents (*.docx), Excel documents (*.xlsx), MHTML files (*.mht), Power Point presentations (*.pptx), PNG images (*.html), and even Zip files (*.zip) – packed with formatting and visual aesthetic that’ll make any data-geek’s mouth water.
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