![]() I’ll then find the column called F1 and click on it while holding the Control key to deselect it. This selects the first column header, the last column header, and everything in between. Jackson has so many movie titles to pivot, I’ll click the first title, navigate to the last title and hold the Shift key while clicking on it. When choosing multiple fields, you can use Control + Click to select additional fields one at a time or Shift + Click to select multiple fields at once. In my case, I’ll choose every single field except for F1 which contains the Critics Score and Audience Score. To do so, choose every column header that you want pivoted to rows, and drag them into the area that says “Pivoted Fields”. In this case, every column other than the first (the unnamed field for Critics Score and Audience Score) are columns that need to be pivoted. The first pivot we’ll do is a traditional pivot where we convert the columns to rows. Next, click the plus sign next to the input and choose Add Pivot. Jackson Excel file, and dragging the Rotten Tomatoes table from the Connections pane to the Flow pane. Our three fields should be in the column headers with one record per row containing the movie title along with their two scores.įirst, I will connect to the data source by opening Tableau Prep, clicking the green Connect to Data button, finding my Samuel L. Jackson has appeared in – ouch! Let’s clean this up using Tableau Prep so we have one dimension called Movie Title, a measure for Critics Score, and a measure for Audience Score. ![]() That’s one measure for each of the 108 movies Samuel L. With the data in this format, Tableau will interpret Column A as a dimension called F1 (because it’s not named), and the remaining columns as measures. Here’s how some of the underlying data looks for the highest grossing actor of all time, Samuel L. There was just one problem: the data source contained the measures in rows going down the first column, and all of the movie titles were going left to right in the column headers. ![]() The visualization required a critical data source from Rotten Tomatoes that was used to create the the scatter plot, Critics Score callout, and Audience Score callout. Tableau Prep was used to shape, combine, and clean 50 different data sources into one consolidated data connection! This flow was needed to prepare the data source to create my recent BLOCKBUSTER visualization. Using Tableau Prep to Shape, Combine, and Clean Dataīy the end of this post, you will be able to recreate this flow in Tableau Prep that pivots some columns to rows, then some rows to columns: Premier Visual Analytics eLearning & Resources with Playfair+ This post uses the Rotten Tomatoes data source used to create my BLOCKBUSTER visualization to show you how to pivot columns to rows (pivot), rows to columns (unpivot), or both, in Tableau Prep 2019.1 or later. As long as you are a Tableau Desktop user with a Tableau Creator license, you can install Tableau Prep and do this yourself! You have been able to pivot columns to rows for a long time in both Tableau Desktop and Tableau Prep, but now it is even easier to quickly restructure an existing Excel report so you can start exploring it in Tableau right away. That’s why I was so excited to see that a new feature in Tableau Prep version 2019.1 is the ability to pivot rows to columns – or unpivot the fields. When used as a data connection, all these aspects of the data layout can be problematic for how Tableau interprets the data source and prevents you from easily exploring the data as Tableau was designed to do. These traditional reports often have dates in the column headers, measures in the rows instead of the columns, and subtotals, among other potential pitfalls. One of the first topics I cover during my live Tableau training events is what I view as the single biggest barrier to Tableau adoption: connecting to a data source that was structured to be human-friendly in Excel.
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