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#CORRECT WEIRD LINE AROUND TABLES IN EXCEL 2016 FOR MAC UPDATE#
#CORRECT WEIRD LINE AROUND TABLES IN EXCEL 2016 FOR MAC MANUAL#

Excel Pivot Table as a Data Sourceįor Excel sources besides connecting to worksheets with raw data we can connect to worksheets that are built as a pivot table. There are a number of cases where a Tableau data blend is sufficient, we’ll be demonstrating one in the next week. Unfortunately we can’t publish the extract to Tableau Server or Online and join to that published data source (yet), otherwise that would be an easy workaround.

#CORRECT WEIRD LINE AROUND TABLES IN EXCEL 2016 FOR MAC UPDATE#

You’ll need one workbook or workflow to update the extract and then use the extract in a second workbook. Where this gets complicated is handling data updates. This may be ok for some use cases, there are others where this will break views that depend on that extracted data.įinally, if you want to join on this aggregated extract you’ll need to join directly to the. Secondly if new columns are later added to the data they are automatically added to the extract.

One possibility is using Tableau’s extract API.

#CORRECT WEIRD LINE AROUND TABLES IN EXCEL 2016 FOR MAC MANUAL#

There are a few things to keep in mind when using aggregated extracts: First of all there’s the need to refresh them to keep up with the data so if you have Tableau Server you’ll need to set up an appropriate schedule, if not then you’ll need to set up your own manual or automated workflow that gets the results you need. Voila, you now have an aggregated extract source that you can use in Tableau data blends and/or join to! Notes on Aggregated Extracts Tableau will ask where to save the extract. Click the Hide All Unused Fields button.Click on the Aggregate data for visible dimensions checkbox.Right-click on the source and choose Extract Data… The Extract Data window opens.Create a single worksheet with the field(s) you want to use as dimension pills, I usually just put them on Rows as discrete (blue) pills:.The resulting extract then just has a record for each combination of field(s) that we want to use. Aggregated Extractsįor this method we’re going to connect to the data source and build an extract only we’ll be telling Tableau to aggregate the data to the desired level of detail (the field(s) we want to use) before Tableau builds the extract. The one caveat to getting data back from your ask is that you’ll need to go through some validation to make sure the list matches up with the “real” data, sometimes the amount of validation and cleansing isn’t worth the effort and one of these other approaches is better. However if you’re in a data-starved environment the kind of relationships you can make by asking for data can lead to more trust and ultimately more access to the data you want (and need). I might phrase the request like “I want to make sure I’m using the latest list of departments, can you give me that list?” I’ve found that most people want the data they produce to be used and used well and if I’m coming back to them asking for something so I can do more with “their” data they are happy to accommodate me. If the data you are working with is coming from someone else then go ahead and ask them if they have a list of unique values. This might seem obvious, but sometimes we’re stressed out and under deadlines and don’t realize we might be able to get help. In this post I’ll go through each of these options. This post goes through a three different methods to get these lists in Tableau: But not everyone has those skills or resources, and in the case of users who just have Excel and/or text files we need to get creative.

then obtaining or generating these kinds of lists is pretty straightforward. If you are starting out with a well-structured data warehouse with dimension tables, can write SQL, Python, or R, build custom views on the data source, use data preparation tools like Alteryx or Easymorph or Trifacta, etc.

The last multi-select highlighter method from Multiple Ways to Multi-Select and Highlight in Tableau can use a self-union.

Situations where we’d want to do a union or cross product of the data to do something like a market basket analysis but the union or cross product would be prohibitively large, so instead we only union or cross product desired dimension(s) and then join in the original data as necessary.

Creating scaffold data sources to pad out data and ensure there are no sparse combinations of values.Cross data source filters with higher performance when the list of filter values can be small compared to the volume of data.Using a filter action value as a parameter in the target source (look for posts from myself and Rody Zakovich on this in the next week).