Slicing by Aggregate | VizPainter. Update: These options are good if you are using Tableau 8.3 or earlier. But if you’re using 9.0 or later, here is a much better option: Level of Detail Calculations. Show me a distribution of customers by the number of items they bought.How many students took how many classes? How many people responded to our advertisements? How many advertisements did it take?
All of these questions require a special kind of slicing. Normally, we group or slice data by dimensional values. Bins are special dimensions based on measures, but they don’t aggregate the measures first. Have any other options? O Extract, Where Art Thou? This was yesterday’s contribution to a Tableau forums discussion on data extracts, I thought it deserved a separate post that I could keep updated.
There are some subtle behaviors and idiosyncracies in working with data connections, Tableau data extracts, and Tableau Server that aren’t fully fleshed out in the documentation, here’s my attempt! I start out with a review of the common file types and .twb vs. .twbx, and then get into some details on different types of connections and what happens based on different orders of operations, and toss in a gratuitous Buffy reference.
File Types Here are the major types of Tableau files that we work with in Desktop: .twb – A Tableau Workbook. When we open up a .twb file in Tableau Desktop, Tableau reads the connection information and connects to the data source(s). Data Extracts When we’re extracting data, there are a few different scenarios depending on whether the Tableau workbook is a .twb or .twbx. Saving .twb vs. .twbx Some Wrinkles Conclusion. Tableau Data Blending, Sparse Data, Multiple Levels of Granularity, and Improvements in Version 8. Tableau’s data blending feature is great for mashing up data sets from a whole variety of data sources. Want do download local weather data from Weather Underground to see how precipitation affects your coffee sales in Seattle?
Sure! However, blending can be a little tricky to set up to get the appropriate level of detail in the view, especially when you need to blend at one level of granularity and aggregate at another. In this post, I’ll walk you through a technique for doing this in v7, and how version 8 makes this process easier, using an example drawn from my own work that adds a level of complexity because the data is sparse. This makes a great case study for how to integrate different features of Tableau to create the desired view.
As part of improving patient safety, we track all patient falls in our healthcare system, and the number of patient days – the total of the number of days of inpatient stays at the hospital. Sparse Data – As I’m writing this, it’s March 7th. Like this: 9 data blending tips from #data14de. Data blending is the ability to bring data from multiple data sources into one Tableau view, without the need for any special coding. Do you do data blending? Or wish you knew more about it? Here are 9 tips from one of our Tableau sessions delivered at the Tableau Conference in Munich this week. Bethany Lyons is one of our product consultants in EMEA.
She's an expert on blending, table calculations and delivering high-energy conference sessions (she will be repeating these sessions in London, so register to get a chance to see her in action). Here are some of her tips. For more resources, follow the links at the end of this post. 1. 2. 3. 4. 5. 6. 7. 8. 9.
Bethany went into much more detail than this list! The slides and recording of Bethany's talk are available to conference attendees. Understanding Data Types and Roles. Data types and roles are fundamental components that contribute to how Tableau categorizes your data. They also play a part in how visualizations behave. For example, the data type and role can determine the following: Which icon appears next to each field in the Data window. Which fields are categorized as dimensions and which are measures. Whether a field that you drag to a view creates an axis with a scale or an axis that shows discrete category headings.
When you understand data types and roles, you can use them to communicate complex data relationships more meaningfully. Identifying and Changing Data Types Each field in any data source has an associated data type. In the following image, the Customer Name field as it appears in Tableau shows an Abc icon, indicating that its data type is string. When you connect to a data source, Tableau identifies the data types for each field. Data Roles: Dimensions and Measures In addition to a data type, each field has an associated role. Understanding Functional Differences Between OLAP and Relational Data Source Connections. Tableau can connect to many relational data sources, and two OLAP/cube data sources: Oracle Essbase and Microsoft Analysis Services. There are functional differences when connecting to OLAP versus relational due to the underlying language differences between MDX and SQL, the respective query syntax for OLAP and relational data sources.
As a result, some features are not available when connecting to OLAP data sources, but are available when connecting to relational data sources, and vice versa. This article lists the major differences or missing functions as well as an alternative solution for the specified functionality. The attached workbook contains a connection to the Microsoft Analysis Services AdventureWorks cube. This is a sample cube that ships with Microsoft Analysis Services. To use this workbook, you simply need to change the Server from scdemo-dbs to the name of your MSAS server running AdventureWorks. Alternate Search Terms: Information Data Sources.