– Third in a series for the 2019 Data VizArt Student Challenge –
Calling all Data Wizards! Here’s a handy Tableau Tip that’ll help you in creating some epic Data visualizations. Instead of having to copy and paste data in one big file which can be tedious, and time consuming, different data files or sheets can be joined together with ease, using the power of Tableau. Joining datasets combines common fields in different tables together resulting in a larger new table.
Let’s start out with an overview on the different types of joins that can occur. Inner, Left, Right and Full Outer are all different types of joins that can be used to combine data in Tableau. Each join type depends on the information of the connected data files and the desired outcome. The different functions of each join type are outlined in the table below.
First, you’ll have to connect to your data source. Tableau accepts a wide range of data file types from Comma Separate Values (CSV) to Text Files (TXT) to Shape (SHP) files just to name a few.
It’s a good practice to do a quick sanity check of the data loaded into Tableau to make sure the data is correct and structured properly.
Joining Datasets – The Procedure
Once you’ve connected to your data, drag selected sheets into the highlighted area. If you’re joining files from the same data source, such as multiple sheets from the same Excel Workbook, drag all your desired sheets over to the right.
In the example below, both Business General and Num of Businesses by NAICS Code sheets from the rel8edto_Businesses Workbook are joined on a Full Outer join. Both data sources have common fields on the FSA column; multiple join clauses can be used if desired. Note that changing the data type of the join will cause the join to break.
In this example, the FSA Mapping sheet from the MDM_FSA Mapping workbook was dragged into the highlighted area and is joined on a left join with the Business General sheet from the rel8edto_Businesses workbook. Like the previous example, both have common fields on the FSA column. Data from Business General is shown by the blue highlight above the column headers and data from FSA Mapping is shown by the orange highlight.
It’s as simple as that! If you have any questions or want more information, feel free to contact us at email@example.com. And remember, the submission deadline for the 2019 Data VizArt Student Challenge is January 25th. Participants can count on more how-to’s coming this week.
Tags: 2019, challenge, CIBC, data, Data VizArt, Deloitte, DVA, join, manifold data mining, merge, student, Tableau
Categorised in: Business Data Analysis, Data Scientist, Data Visualization, Tableau
This post was written by Connor James