Recipes

1. Bar (0:05)

Bar charts are used to measure and compare categorical information.

Tableau can plot multiple measures against both a single hierarchical category or, multiple grouped categories or, as you will see further down, with the addition of information, you can also plot stacked and / or side-by-side charts too.

2. Side-by-side (0:07)

Cluster analysis:

Similar to bar charts, the side-by-side chart lets you compare multiple measures of dimensions or dimension members at the lowest selected grain, clustered in groups or cohorts suitable to your story or perspective.

3. Line (0:17)

Time series chart: track one or more measures over a time grain

4. Mixed Mode (0:15)

Often seen as a mixture of line and bar charts, the added benefit here can provide more insightful tracking analytics such as tracking actual to forecast and then, use bars to illustrate the % difference between the two points, which is especially useful if the gap is not immediately obvious.

Like line charts, mixed mode line-to-bar charts are plotted as continuous charts, but can only support a single dimension; it is not easy for example to create a side-by-side mixed-mode chart as Tableau will treat a multi-dimension chart as a discrete chart converting your lines into individual marks; I demonstrate how to achieve this in video 37.

5. Pie (0:10)

Discrete part-to-whole chart: Allows comparisons of a single measure and a single dimension.

Pie charts really should only be placed on summary or overview sheets and need to be limited to less than 10 (ideally less than 5) dimensions so where more dimensions are likely to be available, consider switching to use a Top 10.

Pie-charts can be awkward to see the comparison due to the width of the slice reducing as the slice reaches the centre; alternate suggestions would be:

• stacked bar chars
• doughnut charts
• treemap's

6. Doughnut (0:39)

The same as a pie but with the centre portion removed.

Doughnut charts  do not have the centre portion thus, they are actually easier to understand than pie-charts as they do not use slices, the central section can be used for text or, if you are feeling particularly confident, a pie-chart perhaps for grouping the outer portion.

Unfortunately, doughnut charts are not native to Tableau and need to be built with a bit of trickery.

7. Stacked Bar (0:07)

Combination chart: Break-down the bar-chart values to the lower-grain components to understand contributions

10. 100% Stacked Area (0:28)

Exactly the same as the combination of the 100% Stacked Bar and, the area charts

 12. Lollipop (0:29)

Lollipop charts, nothing really more than a dot plot, but with the addition of a thinly-defined bar linking the dot to the y-axis to help your users identify the attribute to which the dot relates.

Can be thought of as a three-dimensional chart, looking at the flow of the dots demonstrates overall velocity, the bars, as a value and performance indicator, and the overall attribute as a indication of performance. 

13. Heatmap (0:09)

The classic method of bringing highlighting colour to a chart, helping you spot even the most subtle change in a sea of numbers.

Uses both size and colour so can be multi-dimensional.

15. Scatter Plot (0:21)

The de-aggregate chart that plots the individual records on their own merit. Useful for measuring overall velocity, with the added benefit of a reference line, Tableau is able to provide your statistical reference information including the all-important p-value.

Extremely useful in identifying outliers and trend-setters.

16. Waterfall (0:31)

The exploded stacked bar-chart of the later 1990's, making a massive comeback since mid 2013. With colour, you are able to identify different perspectives.

As a cumulative chart, this helps the user understand each attributes contribution to the bottom-line (grand total), with negative values being given a level forum for their effect on the overall figure, as the next attribute shall begin its plot from the final position of the previous value.

Priceless in the efforts to demonstrate the absolute effect of a set of levers. 

18. Tile (General Purpose) (0:31)

Tiles can be useful as bordered windows, colour can be applied from a different dimension to the information reported allowing for multi-dimensional reporting: the information in the tile relative to a given perspective, the colour can reflect that perspective for a single dimensional analysis or, can be made of that across the whole dashboard, to provide that all-important cohort analysis.

19. Treemap (0:10)

Functionally the same as the heatmap only, the attributes are drawn together providing another level of meaning to how the data correlates to its neighbour, in a part-to-whole analysis guided and driven by both size and colour.

20. Word Cloud (0:27)

The word-cloud. Used less in business dashboards, and more for info-charts and marketing boards.

Dimension members as their actual identity, sized according to their measurable. often used as a more elaborate for of filter on interactive dashboards, or when trying to call-out a specific product or member, this chart should be used sparingly owing to the amount of space required to allow the chart to reach maximum impact.

22. Bubble (0:19)

Unlike word-clouds, this type of chart is rarely used, yet this can often provide a deeper clarity than the word-cloud.

Very similar to a word-cloud, perfect circles are drawn surrounding each member, the circumference of which is determined by the measure in use.

Use as an alternative to the pie or doughnut chart

24. Shape (General) (0:35)

Mark each data-point with a shape, very useful where many data-points are to be plotted such that seeing subtle-differences could be easily missed

25. Pareto (1:41)

The 80-20 rule chart. Not always 80-20 but still, this chart helps understand where the greatest impact may be had, whether trying to identify who your most valuable customers are, or most effective employees, or where your greatest problems are.

Forget Top-n lists: Top-n is literally that, your Top-n items, but what happens if your Top-n is too wide or not wide enough?

This chart while provide you with that first-level identifier of just how wide you need to cast your net.

26. Period Over Period (1:19)

Use the methods employed in this video to create a period over period eg This Year vs Last Year chart.

28. Bump (0:30)

Ranking: Classic comparison charts. Dimension rank of period

30. Bar in Bar (0:49)

Similar to a stacked bar chart - indeed, this really is a stacked bar chart exploded so one measure is superimposed over the second.

Really only used for comparing two measures such as this year vs last year

31. Slope Chart (0:53)

Similar to a dumbbell chart, this provides a point-to-point analysis between two points - lit. the slope which can be quite powerful when combined with other marks: unlike a normal chart where we try to reduce the amount of marks, it is the mark breakdown that helps to provide context in the comparison

33. Funnel (1:05)

The basic funnel typically used to illustrate the path taken by users and where they are likely to drop off.

The video demonstrates how to build a solid-looking funnel by switching to an area chart in the last couple of seconds, however, maintaining as a bar chart really helps you to understand where the greatest shrinkage happens

35. Expansive Drill (Brushing) (2:30)

Mostly used with bar charts, this isn't a new chart, but rather a means of drilling the selected item to a lower grain whilst keeping the rest of the chart at the higher grain.

The purpose is to allow users to see the breakdown of an attribute without losing focus on the remaining chart for example, a time bar chart at quarter-grain, enabling users to drill to the moths of Q2 (April, May, June), whilst keeping the rest of the chart at quarter:

Q1, April, May, June, Q3, Q4

36. Small Multiples (Trellis) (2:46)

Functionally the same as a line chart, this chart allows us to define a pane per attribute, which is useful in reducing chart clutter, especially when multiple measures need to be considered such as last period, current period and forecast.

Keeping the package together on the same axis, we can be sure that all axis are at the same scale to make comparison easy between each attribute.

37. Lines with Side-by-Side Bars (6:05 // 2:20)

Analyse and compare measures from two or more cohorts such as sales performance vs forecast, for say East region vs West region; should be simple right? One of the most simplest charts that even Excel can build yet in Tableau, this is nigh impossible without a custom set.

38. Waffle (4:02)

Another part-to-whole chart, although this relatively smart chart will squash-up into really small 200 x 200 spaces making it ideal for when space is a premium, and, you can format the shape and colours and, the direction to read the chart: the one featured here reads bottom up.

Uses a custom scaffold as this chart tends to break when relying solely on the data and, being one of the highly customised charts, you shall need to make a chart per attribute

41. Ratings (3:18)

For those moments when you want to visualise the outputs of those "on a scale of 1-10 how well did we do" questions. Tableau cannot natively build a star grid, but with the use of a 1-row/1-column update to the data, we can have a grid built in a jiffy, without harming the rest of the calcs throughout the workbook.

Just remember to HIDE rather than exclude the anchoring data