Groups are useful for both correcting data errors (e.g., combining CA, Calif., and California into one data point) as well as answering “what if” type questions (e.g., “What if we combined the East and West regions? Toucan create a group from a field in the Data pane, or by selecting data in the view and then clicking the group icon.
In the view, select one or more data points and then, on the tooltip that appears, click the group icon. Note : You can also select the group icon on the toolbar at the top of the workspace.
In the Data pane, right-click a field and select Create > Group. The selected members are combined into a single group.
Tip : Toucan search for members using the Find option near the bottom-right of the dialog box. In the Edit Group dialog box, select Include 'Other'.
Toucan make some changes directly in the view, and others through the Edit Group dialog box. In the Edit Group dialog box, select one or more members, and then click Ungroup.
Driving visual analytics over such a number of dimension values may not be feasible as it may not convey any useful insights. However, a combination of different dimensions values based on some similarities between them can allow us to create groups that we can use for analysis.
Twenty major Indian cities represent different sales units across the country. So, we’ll apply the concept of “Groups” to achieve the target.
Moving to the sheet tab, we can find the dimensions DU Code, Quarter, and Sales Unit, and the measure Sales present in the requisite sections. We converted the above table into a vertical bar graph, which conveys the insights in a better way.
Moreover, trying to interpret regional performance through sales units is quite difficult. So, based on the geographical location of the sales units, we’ll classify them into five groups namely East, West, North, South, Central.
When we incorporate this region dimension into the analysis, we’ll be able to have a panoramic view of the sales performance across the entire network. In this case, the dimension is Sales Unit, so, in its drop-down menu, in “Create”, select Group, and click on it.
A “Create Group dialogue box appears, as shown below. In this dialogue box, we can select desired dimension values to create a particular group.
To create the first group, we selected Ahmedabad, Mumbai, Nashik, Pune, and Surat by pressing Ctrl, as shown in the below screenshot. Following the above procedure, we created a second group which is “South” containing Bengaluru, Hyderabad, Kozhikode, and Via as shown below.
The third group we created consists of Bhopal, Delhi, Jaipur, and Varanasi. Last but one, we created the “East” region following the very same process, which is composed of Bhubaneswar, Guwahati, Kolkata, and Patna.
Lastly, we create the “Central” region which contains Nagpur and Raipur. We successfully managed to create groups representing different regions containing different sales units.
However, as can be seen in the above screenshot, we forgot to include Rajkot in a particular group. To include it, right-click on Rajkot, and then click on “Add to” as shown below.
Now, as shown in the screenshot after the below one, from the list of Regional groups, select “West” and click on OK. Following the above procedure just familiarized us with another approach to add values to a group.
Note, the dimension Region will not affect the dataset but only facilitate visual analysis at the view level. So, by combining different dimension values, we can create an effective visual analysis based on the need, without affecting the original data.
The following screenshot gives us a closer look into the newly created dimension Region containing various groups as values. The following bar chart gives us a direct insight into regional sales performance.
In certain situations, requisite data might be present in the dataset but not in the way we wanted it to be. Tableau facilitates the user to apply this approach through the functionality of Groups.
From the above screenshot, toucan see that Tableau intelligence has drawn the bar chart. Let us add the labels to each bar so that we can see the total amount of sales happened for each color.
For this, drag and drop the Sales from Measures region to label Field present in Marks Shelf. Please select the members you want to include in your group by holding the CTRL or SHIFT button, and right-click on them will open the context menu.
Now toucan see our newly created Group in Tableau design area In our previous example, we showed you the steps involved in creating simple groups.
Now toucan see our newly created group in Dimensions pane. In our previous example, we showed you the steps involved in creating groups using marks.
In this example, we explain how to create tableau groups from marks that represent Multiple dimensions. For this demonstration, we are using the Scatter Plot that we created in our previous article.
Please select the members you want to include in the group by holding the Control or SHIFT button. Here, each mark represents both English Country Name, and the Postal Code (Multiple dimensions).
From the below screenshot, see that we are selecting some random low performed states. It will show you the newly created and added Group in Tableau report.
I recreated your exact dataset and pasted it into Tableau so you could see a couple of examples. Here's how toucan see the number of customers who purchased an individual item, plus the number of customers who purchased both items.
Because this is a Window function, we'll need to specify how it's calculated across our dimensions. To do this click the down arrow on the right-hand side of the pill and select Edit Table Calculation...
Robcrock robcrock44922 silver badges99 bronze badges You might want to use Tableau ’s set feature to approach problems like this.
For example, right-click on the field in the data pane (i.e. left sidebar) and choose the “Create Set” command. Define the set using the condition MAX( = “A”).
Similarly, create a set of customers who bought item B. Toucan then select both sets in the data pane, and create a combined set to be the intersection, that is, customers who bought both an item A and an item B. A key to keep in mind for the condition formulas used here is that the condition is an aggregate formula, operating on a block of data records for a customer ID to determine whether the customer ID is in the set.