Heat maps are a visualization where marks on a chart are represented as colors. As the marks “heat up” due their higher values or density of records, a more intense color is displayed. These colors can be displayed in a matrix / crosstab, which creates a highlight table, but can also be displayed on a geographical map or even a customized image – such as a webpage used to show where users are clicking.
That being said, heat maps are defined somewhat differently in Tableau, and this post shares how to create a Tableau heat map. If you are interested in creating a traditional heat map using a custom image, see the post, How to Make Small Multiple Stadium Maps in Tableau.
Heatmaps vs Highlight Tables
To first get more specific about how Tableau defines heat map, let’s take a look at the requirements to draw a heat map under Tableau’s Show Me options.
“For heat maps try 1 or more dimensions and 1 or 2 measures”
This is very close to the requirements for drawing a highlight table with Show Me:
“For highlight tables try 1 or more dimensions and 1 measure”
The key distinction between the two chart types is that with a heat map, you are able to encode the marks by one additional measure. With a highlight table, you’re only option is to color the marks by one measure. Since you can only color marks by one thing at a time, your encoding is limited to exactly one measure. With a heat map in Tableau, you can color the marks by one measure, but also size the marks by a second measure. Depending on your analysis, this additional encoding can add value to your visualization.
Creating a Heat Map
Let’s say we’ve been tasked with evaluating the product sub-category sales in the Sample – Superstore data set by Month of Order Date to see if we can identify any seasonal trends in the data. The element of time (Month of Order Date) may give you the instinct to go with a line graph for this analysis, which would look like this:
As you can see, this graph is a bit of a mess. The 17 lines are causing a lot of overlap and several of the sub-categories at the bottom are on a much smaller scale than the rest, making it challenging to gain insights. In this case, a heat map may be a better option.
To create a heat map in Tableau, start by laying out the rows and columns which will serve as the grid for the visualization. We would like the months in this analysis to be listed along the top of the view. Since the months will create columns, we know that we should put the Month of Order Date dimension on the Columns Shelf. Conversely, we would like each sub-category to have its own row, so we will place that dimension on the Rows Shelf.
By default, the mark type is set to Text. We prefer Tableau heat maps to be created with circles, so we will change the mark type to circle to lay the foundation for the view. The Shape or Square mark types are also good choices.
Now that we have a mark at each intersection of Sub-Category and Month of Order Date, we can encode them by two measures; one which will determine the size of the marks and one which will determine the color intensity of the marks. This encoding is produced by placing the measures we want to encode the marks by onto the Size Marks Card and Color Marks Card, respectively. For my first analysis, we will size and color the circles by the same measure: Sales.
This visualization uses the exact same fields as the line graph above, but it is now much easier to compare sub-categories within a specific month (reading the chart vertically) or compare the seasonality across each sub-category (reading the chart horizontally). The “double-encoding”, where the size and color are both based on the same field, are meant to help the insights ‘pop’. However, you have the option to use one measure for the size, and a different measure for the color. For example, here is what the heat map looks like if we size the circles by the Quantity measure instead of sales.
With this analysis, large and light circles would mean that a sub-category sold a relatively large quantity but made a relatively low amount of revenue: low sales per item. Conversely, small and dark circles would mean the sub-category sold a relatively small quantity, but generated a relatively high revenue: high sales per item.
Beware that this type of mixed encoding can be confusing for end users unless you explicitly state what the size and color represents. Despite some of their limitations, Tableau heat maps provide a viable alternative to a line graph or highlight table if you need to compare dimension members with varying scales across multiple measures.
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Build a Highlight Table or Heat Map
Use highlight tables to compare categorical data using color.
In Tableau, you create a highlight table by placing one or more dimensions on the Columns shelf and one or more dimensions on the Rows shelf. You then select Square as the mark type and place a measure of interest on the Color shelf.
You can enhance this basic highlight table by setting the size and shape of the table cells to create a heat map.
To create a highlight table to explore how profit varies across regions, product sub-categories, and customer segments, follow these steps:
Connect to the Sample - Superstore data source.
Drag the Segment dimension to Columns.
Tableau creates headers with labels derived from the dimension member names.
Drag the Region and Sub-Category dimensions to Rows, dropping Sub-Category to the right of Region.
Now you have a nested table of categorical data (that is, the Sub-Category dimension is nested within the Region dimension).
Drag the Profit measure to Color on the Marks card.
Tableau aggregates the measure as a sum. The color legend reflects the continuous data range.
In this view, you can see data for only the Central region. Scroll down to see data for other regions.
In the Central region, copiers are shown to be the most profitable sub-category, and binders and appliances the least profitable.
Click Color on the Marks card to display configuration options. In the Border drop-down list, select a medium gray color for cell borders, as in the following image:
Now it's easier to see the individual cells in the view:
The default color palette is Orange-Blue Diverging. A Red-Green Diverging palette might be more appropriate for profit. To change the color palette and to make the colors more distinct, do the following:
Hover over the SUM(Profit) color legend, then click the drop-down arrow that appears and select Edit Colors.
In the Edit Colors dialog box, in the Palette field, select Red-Green Diverging from the drop-down list.
Select the Use Full Color Range check box and click Apply and then click OK.
When you select this option, Tableau assigns the starting number a full intensity and the ending number a full intensity. If the range is from to , the color representing negative numbers changes in shade much more quickly than the color representing positive numbers.
When you do not select Use Full Color Range, Tableau assigns the color intensity as if the range was from to , so that the change in shade is the same on both sides of zero. The effect is to make the color contrasts in your view much more distinct.
For more information about color options, see Color Palettes and Effects.
Modify the size to create a heat map
Drag the Sales measure to Size on the Marks card to control the size of the boxes by the Sales measure. You can compare absolute sales numbers (by size of the boxes) and profit (by color).
Initially, the marks look like this:
To enlarge the marks, click Size on the Marks card to display a size slider:
Drag the slider to the right until the boxes in the view are the optimal size. Now your view is complete:
Check your work! Watch steps below:
Note: In Tableau and later, the Data pane no longer shows Dimensions and Measures as labels. Fields are listed by table or folder.
Hi again - thinking more on this overnight, custom bins might be the answer to getting a better distribution of data values to colour gradations. The following post was very helpful to my thinking - Create custom bins . I first added the following calculation
[sorry I don't know how to embed code snippets!]
title: YoY Change (Bins)
code: IF ([YoY Jump Last 2yrs] < 1) then '<1x'
ELSEIF (([YoY Jump Last 2yrs] <= ) and [YoY Jump Last 2yrs] >= 1) then ' - x'
ELSEIF (([YoY Jump Last 2yrs] <= ) and [YoY Jump Last 2yrs] > ) then ' - x'
ELSEIF (([YoY Jump Last 2yrs] <= ) and [YoY Jump Last 2yrs] > ) then ' - x'
ELSEIF (([YoY Jump Last 2yrs] <= 2) and [YoY Jump Last 2yrs] > ) then ' - 2x'
I then applied the new created dimension "YoY Change (Bins)" to colour attribute and I get this! (which now gives me better distribution of values across the colour bins)
Because the colour bins are custom defined, I can group/bucket large data values together (i.e. those >2). Custom bins make it easier to judge differences amongst lower values - by specifying the bins of most interest.
File attached - hopefully, it may help others
Ken Flerlage - thank you so much for being part of this, your suggestions helped me understand the problem better (I wouldn't have gotten here without that). Thank you! (as I'm new to these forums, please let me know the correct forum etiquette to acknowledge your contributions )
Tableau Tip: Create a beautiful heat map in under 30 seconds
This tip is a follow up to my post about asking How common is your birthday?. In this post, I created a heat map and Matt Stiles asked me if I could write a tutorial showing how I did it so quickly in Tableau.
The steps are for creating the viz only. Im assuming you already connected to the data.
Step 1 Hold the CTRL key and click the Day, Month and Rank fields (they should all be highlighted after you choose them)
Step 2 Open the Show Me window on the toolbar and click on the Highlight Table option
You should see the view below with Days in the columns and Months in the rows.
Step 3 Click on the swap icon to place Day on the row shelf and the Month on the column shelf
Step 4 Right click on the Month column label and choose Hide Field Labels for Columns
Step 5 Drag the Rank measure off of the Label shelf
You should now this view.
Step 6 Double click the color shelf to show the Edit Colors window. Choose Orange from the Pallet list and the Reversed option, then click OK.
You need to choose the reverse option if you want the highest ranking days to be the darkest. You final view should look like this.
Thats it! Six steps, less than 30 seconds, and you have a beautiful heat map.
Give it a shot.
- Get the data here
- See my final viz (including interactivity) here
Heat column by tableau map
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The elf's eyes widened with indignation at my actions, but he did not resist, but, on the contrary, reciprocated my kiss, which made my eyes widen.TABLEAU HEAT MAP CALENDAR
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