This chapter has been written with the intent of empowering you with design knowledge and Tableau-centric techniques for creating effective dashboards.

In this chapter, we will cover the following topics:

• Visualization design theory
• Formatting rules
• Color rules
• Visualization type rules
• Compromises
• Keeping visualizations simple
• Dashboard design
• Dashboard best practices for user experience

Now that we have discussed the topics of this chapter, let's dive into design theory, which will provide you with rules that can be applied to every single dashboard you are going to build.

Visualization design theory

Any discussion about designing dashboards should begin with information about constructing well-designed content. The quality of the dashboard's layout, and the utilization of technical tips and tricks, do not matter if the content is subpar[ˌsʌbˈpɑːr]低于标准的. In other words, we should first consider the worksheets displayed on dashboards and ensure that those worksheets are well-designed. Therefore, our discussion will begin with a consideration of visualization design principles. Regarding these principles, it's tempting to declare a set of rules, such as the following:

• To plot change over time, use a line graph.
• To show breakdowns of the whole, use a treemap.
• To compare discrete elements, use a bar chart.
• To visualize correlations, use a scatter plot.

But of course, even a cursory review of this list brings to mind many variations and alternatives! Thus, in the next section, Formatting rules, we will consider various rules, while keeping in mind that once you're comfortable with the basics, rules can be broken to serve a purpose.

Formatting rules

The following formatting rules encompass fonts, lines, and bands. Fonts are, of course, an obvious formatting consideration. Lines and bands, however, may not be something you typically think of when formatting—especially when considering formatting from the perspective of Microsoft Word. But if we broaden formatting considerations to think of Adobe Illustrator, InDesign, and other graphic design tools, lines and bands should certainly be considered. This illustrates that data visualization is closely related to graphic design and that formatting considers much more than just textual layout.

Keep the font choice simple

Typically, using one or two fonts on a dashboard is advisable. More fonts can create a confusing environment and interfere with readability.

Fonts chosen for titles should be thick and solid, while body fonts should be easy to read. In Tableau, choosing appropriate fonts is simple because of the new Tableau font family. Select Format | Font to display the Format Font window to see and choose these new fonts.

Assuming your dashboard is primarily intended for the screen, sans-serif fonts are best. On the rare occasion that a dashboard is primarily intended for print, you may consider serif fonts, particularly if the print resolution is high.

Use lines in order of visibility

We tend to order line visibility in the following order: trend line, chart line, reference line, drop line, and grid line. For example, trend-line visibility should be greater than fever-line visibility. Visibility is usually enhanced by increasing line thickness, but may be enhanced via color saturation or by choosing a dotted or dashed line over a solid line. Let's look at each of the line types, in order of decreasing visibility:

• Trend lines: The trend line, if present, is usually the most visible line on the graph. Trend lines are displayed via the Analytics pane and can be adjusted via Format | Lines.
• Chart lines: The chart line (for example, the line used on a time-series chart) should not be so heavy as to obscure twists and turns in the data. Although a chart line may be displayed as dotted or dashed by utilizing the Pages shelf, this is usually not advisable because it may obscure visibility. The thickness of a chart line can be adjusted by clicking on the Size shelf in the Marks remove bold area.
• Reference lines: Usually less prevalent than fever or trend lines and can be formatted via Format | Reference lines.
• Drop lines: Not frequently used. To deploy drop lines, right-click in a blank portion of your view and select Drop lines | Show drop lines. Next, click a point in the view to display a drop line. To format drop lines, select Format | Drop Lines. Drop lines are relevant only if at least one axis is utilized in the visualization.
• Zero lines: These are sometimes referred to as base lines, and only display if zero or negative values are included in the view, or positive numerical values are relatively close to zero. Format zero lines via Format | Lines.
• Grid lines: These should be the most muted lines on the view and may be dispensed with altogether. Format grid lines via Format | Lines.

Use bands in groups of three to five

Visualizations composed of a tall table of text or horizontal bars should segment dimension members in groups of 3 to 5.

Please follow along with the steps to experience an efficient use of bands in Tableau:

1. Navigate to https://public.tableau.com/app/profile/marleen.meier to locate and download the workbook associated with this chapter.
2. Navigate to the Banding worksheet.
3. Select the Superstore data source and place Product Name on the Rows shelf.
4. Double-click on Discount, Profit, Quantity, and Sales. Note that after completing this step, Tableau defaulted to banding every other row每隔一行加彩条. This default formatting is fine for a short table but is quite busy for a tall table.
5. Navigate to Format | Shading and set Band Size under Row Banding so that 3 to 5 lines of text are encompassed by each band每个带包含3到5行文本. Be sure to set an appropriate color for both Pane and Header:

The Band in Groups of Three to Five rule is influenced by Dona W. Wong, who, in her book The Wall Street Journal Guide to Information Graphics, recommends separating long tables or bar charts with thin rules to separate the bars in groups of 3 to 5 to help readers read across.

Color rules

It seems slightly ironic to discuss color rules in a book that will be printed in black and white (of course, that may not be true if you are reading this book on an electronic device). Nevertheless, even in a monochromatic setting, a discussion of color is relevant. For example, exclusive use of black text communicates something different than using variations of gray. The following survey of color rules should be helpful for ensuring that you use colors effectively in a variety of settings.

Keep colors simple and limited

Stick to the basic hues and provide only a few (perhaps 3 to 5) hue variations只提供少数（也许3到5种）色调变化. In his book, The Functional Art: An Introduction to Information Graphics and Visualization, Alberto Cairo provides insight as to why this is important:

"The limited capacity of our visual working memory helps explain why it's not advisable to use more than four or five colors or pictograms[ˈpɪktəɡræms]象形图,图形符号 to identify different phenomena on maps and charts."

Respect the psychological implications of colors

There is a color vocabulary in every region of the world so pervasive[pərˈveɪsɪv]普遍深入的 that it's second nature. Red is often used for love or danger; in some Asian countries, it can also mean fortune[ˈfɔːrtʃən]财富 and is common for wedding dresses. Green can mean luck, whereas in Mexico, green represents independence. Green traffic signals tell us to go, red to stop. Similarly, colors on dashboards are often used with purpose. Reds and oranges are usually associated with negative performance, while blues and greens are associated with positive performance. Using colors counter-intuitively can cause confusion以违反直觉的方式使用颜色会导致混淆, so be aware of color usage and of your audience and their interpretation.

Be colorblind-friendly

Colorblindness is usually manifested as an inability to distinguish red and green, or blue and yellow. Red/green and blue/yellow are on opposite sides of the color wheel.

Consequently, the challenges these color combinations present for colorblind individuals can be easily recreated with image editing software such as Photoshop. If you are not colorblind, convert an image with these color combinations into grayscale and observe. The challenge presented to the 8.0% of men and 0.5% of women who are colorblind becomes immediately obvious! Or, use the following website to upload an image of your dashboard and experience how your user might see the colors used: https://www.color-blindness.com/coblis-color-blindness-simulator/.

Use pure colors sparingly谨慎

The resulting colors from the following exercise should be a very vibrant[ˈvaɪbrənt]鲜艳的 red, green, and blue. Depending on the monitor, you may even find it difficult to stare directly at the colors. These are known as pure colors and should be used sparingly, perhaps only to highlight particularly important items.

Please follow these steps:

1. Open the workbook associated with this chapter and navigate to the Pure Colors worksheet.
2. Select the Superstore data source and place Category on both the Rows shelf and the Color shelf.
3. Set Fit to Entire View.

5. In the Edit Colors dialog box, select one after the other member; that is, Furniture, Office Supplies, and Technology, and select blue, green, and red, respectively:
Create Custom Color Palettes :
C:\Users\liqin\OneDrive\Documents\My Tableau Repository

Preferences.tps
https://htmlcolorcodes.com/color-names/

<?xml version='1.0'?><workbook><preferences><color-palette name="my color" type="regular" > <color name="White">#FFFFFF</color><color name="Very Light Gray">#DCDCDC</color>   <color name="Gray">#808080</color><color name="Brown">#A52A2A</color><color name="Black">#000000</color><color name="Red">#FF0000</color><color name="Light Orange">#FFA07A</color><color name="IndianRed">#CD5C5C</color><color name="Green">#00FF00</color><color name="Very Light Yellow">#FAFAD2</color><color name="Beige">#FFDAB9</color><color name="Yellow">#FFD700</color><color name="Blue">#0000FF</color><color name="Very Light Blue">#E6E6FA</color><color name="Light Purple">#D8BFD8</color><color name="Violet">#EE82EE</color><color name="Purple">#800080</color><color name="Pink">#FF69B4</color><color name="Orange">#FFA500</color><color name="Tomato">#FF6347</color></color-palette></preferences>
</workbook>

See for yourself how you experience looking at those pure colors and select three other, less vibrant, colors to compare. Too vibrant colors can be very heavy on your eye and make it hard to focus. Colors that are too similar, however, make it hard to distinguish. Finding a good balance is key.

Choose color variations over symbol variation选择颜色变化胜过选择符号变化

Deciphering[dɪˈsaɪfərɪŋ]辨认,解读 different symbols takes more mental energy for the end user than distinguishing color. Therefore, color variation should be used over symbol variation. This rule can actually be observed in Tableau defaults. Create a scatter plot and place a dimension with many members on the Color shelf and the Shape shelf, respectively. Note that, by default, the view will display 20 unique colors but only 10 unique shapes. Older versions of Tableau (such as Tableau 9.0) display warnings that include text such as "the recommended maximum for this shelf is 10."

See the following diagram for an example of symbol variation:and

To sum up, keep your color palette simple and be aware of your regional, cultural meanings of color. Also, take into account that people in your audience might be color-blind or have a color deficiency[dɪˈfɪʃnsi]色弱; tools can help you choose colors wisely and Tableau also offers a colorblind palette. Lastly, use pure colors sparingly, since too many highlight colors will make it hard for the audience to focus on the most important items. Also don't use too many shapes; instead, use color to distinguish between values.

Visualization type rules

Since there's a chapter dedicated to visualization types, Chapter 8, Beyond the Basic Chart Types, and since much of this book explores various visualizations, we won't take the time here to delve into a lengthy list of visualization type rules.

If you want to get additional information on this topic, I highly recommend checking out the Visual Vocabulary that Andy Kriebel created, and which was inspired by the Financial Times (https://ft-interactive.github.io/visual-vocabulary/), available on Tableau Public: https://public.tableau.com/app/profile/andy.kriebel/viz/VisualVocabulary/VisualVocabulary.

Deviation

Emphasise variations (+/-) from a fixed reference point. Typically the reference point is zero but it can also be a target or a long-term average. Can also be used to show sentiment (positive/neutral/negative)

bar-diverging条形发散

A simple standard bar chart that can handle both negative and positive magnitude valuesReds and oranges are usually associated with negative performance,
while blues and greens are associated with positive performance.

Diverging Stacked Bar

Perfect for presenting survey results which involve sentiment (eg disagree, neutral, agreed
Similar to the stacked vertical bar chart, stacked horizontal bar charts can be used to show the totals across different categories but also give a sense of the subcomponent pieces. They can be structured to show either absolute values or sum to 100%.

I find this latter approach can work well for visualizing portions of a whole on a scale from negative to positive, because you get a consistent baseline on both the far left and the far right, allowing for easy comparison of the left‐most pieces as well as the right‐most pieces. For example, this approach can work well for visualizing survey data collected along a Likert scale (a scale commonly used in surveys that typically ranges from Strongly Disagree to Strongly Agree), as shown in Figure 2.19.
A good way of showing the size and proportion of data at the same time – as long as the data are not too complicated.

ORhttps://blog.csdn.net/Linli522362242/article/details/124786459

spine-chart

Splits a single value into 2 contrasting components (eg Male/Female)

Surplus/Deficit Filled Line盈亏填充线

The shaded area of these charts allows a balance to be shown; either against a baseline or between two serie

Correlation

Show the relationship between two or more variables. Be mindful that, unless you tell them otherwise, many readers will assume the relationships you show them to be causal因果关系 (i.e., one causes the other).

scatterplot

The standard way to show the relationship between two variables, each of which has its own axis

Line + Column

A good way of showing the relationship between an amount (columns) and a rate (line). Columns work well for showing change over time - but usually best with only one series of data at a time.

Connected Scatterplot

Usually used to show how the relationship between 2 variables has changed over time

Bubble

Like a scatterplot, but adds additional detail by sizing the circles according to a third variable.
I like to make a modification of this picture, that is, Sales on the x-axis, Profits on the y-axis, Discount on the size shelf, subcategory in detail (do not add to the color shelf, because there are many subcategories. if there are few categories, you can consider it) and the order date

Consider the following scatterplot, which looks for a relationship between the average minutes to service and the average number of days spent in the hospital, broken down by department type() and doctor():

The dimensions of Department Type and Doctor on the Marks card define the view level of detail. Color has been used to make it easy to see the department type where each doctor practices. Each mark in the view represents the average minutes to service and average days in the hospital for patients seen by a doctor in a department type. The Size of each circle indicates the total number of patients seen by that doctor.

There does not appear to be much correlation between minutes to service and days in the hospital per doctor. However, the scatterplot is useful for seeing some grouping patterns for doctors within certain departments and also illustrates that Intensive Care (the marks in the upper right) are potentially outliers.

XY Heatmap

A good way of showing the patterns between 2 categories of data, less good at showing fine differences in amounts不太擅长显示数量上的细微差异.

and
https://blog.csdn.net/Linli522362242/article/details/121896073

==>
CAD/USD, AUD/USD(0.96), and NZD/USD(0.92) seem to be most correlated, with CHF/USD(0.82), GBP/USD(0.76) and JPY/USD(0.48) being least correlated to CAD/USD.

Ranking

Use where an item’s position in an ordered list is more important than its absolute or relative value. Don’t be afraid to highlight the points of interest.

Ordered Bar

Standard bar charts display the ranks of values much more easily when sorted into order.https://blog.csdn.net/Linli522362242/article/details/123116143
This bar chart makes it easy to compare the number of patient visits between various departments in the hospital.
As a dimension, Department slices the data according to each distinct value such as ER急诊室, ICU, or Cardiology心脏病学. It creates a header for these values because it is discrete (blue).
As a measure, Number of Patient Visits gives the sum of patient visits for each department. Because it is a continuous (green) field, it defines an axis, and bars are rendered to visualize the value.

Notice that the bar chart is sorted by the department having

the highest sum of patient visits at the top
and the lowest at the bottom.
Sorting a bar chart often adds a lot of value to the analysis because it makes it easier to make comparisons and see rank order. For example, it is easy to see that the Microbiology微生物科 department has had more patient visits than the Nutrition department. If the chart wasn't sorted, this may not have been as obvious
Highlighting categories of interest
Let's say one of your primary responsibilities at the hospital is to monitor the number of patient visits for the ICU and Neonatal[ˌniːoʊˈneɪtl]新生的 departments. You don't necessarily care about the details of other departments, but you do want to keep track of how your two departments compare with others. You might design something like this:
Now, as the data is refreshed over time, you will be able to immediately see how the two departments of interest to you compared to other departments

Ordered Column

Standard bar charts display the ranks of values much more easily when sorted into order.

Ordered Proportional Symbol有序比例符号

Use when there are big variations between values and/or seeing fine differences between data is not so important.当值之间存在很大差异和/或看到数据之间的细微差异不是那么重要时使用。

dot-plot-strip (Dot Strip Plot)

Dots placed in order on a strip are a space-efficient method of laying out ranks across multiple categories在条带上按顺序排列的点是一种在多个类别中排列等级的节省空间的方法.

https://blog.csdn.net/Linli522362242/article/details/123116143
Consider the following view, which shows how each doctor compares to other doctors within the same type of department in terms of the average number of minutes it takes to start treating a patient:
Certain doctors do better or worse on average(average lines and values) than others in terms of the time it takes to start treating a patient.

It is also interesting to note that certain types of departments take more or less time on average. This makes sense as each type of department has different constraints and operating procedures. There are also certain departments where time is more critical than others.

Being able to evaluate doctors within their type of department makes comparisons far more meaningful.

slope

Perfect for showing how ranks have changed over time or vary between categories

Slopegraphs can be useful when you have two time periods or points of comparison and want to quickly show relative increases and decreases or differences across various categories between the two data points.

The best way to explain the value of and use case for slopegraphs is through a specific example. Imagine that you are analyzing and communicating data from a recent employee feedback survey. To show the relative change in survey categories from 2014 to 2015, the slopegraph might look something like Figure 2.10.
A Slope Chart shows a change of values from one period or status to another. For example, here is a Slope Chart demonstrating the change in sales rank for each state in the South region from 2016 to 2017:https://blog.csdn.net/Linli522362242/article/details/123970001
Data highlighters give the user the ability to highlight marks in a view by selecting values from the drop-down list or by typing (any match on any part of a value will highlight the mark; so, for example, typing Carolina would highlight North Carolina and South Carolina in the preceding view). Data highlighters can be shown for any field you use as discrete (blue) in the view and will function across multiple views in a dashboard as long as that same field is used in those views.

Slope Charts can use absolute values (for example, the actual values of Sales ) or relative values (for example, the rank of Sales , as shown in this example). If you were to show more than 2 years to observe the change in rankings over multiple periods of time, the resulting visualization might be called a Bump Chart凹凸图, like this:

Lollipop Chart

Lollipops draw more attention to the data value than standard bar/column and can also show rank and value effectively.

A Lollipop[ˈlɑːlipɑːp] Chart is very similar to a bar chart, but typically uses thinner lines ending in a circle. This allows for some stylistic interest as well as a place to show values or other labels. Here, for example, is a Lollipop Chart for Sales of Categories in the Technology department:https://blog.csdn.net/Linli522362242/article/details/123970001

Distribution

Histogram

The standard way to show a statistical distribution - keep the gaps between columns small to highlight the ‘shape’ of the data保持列之间的间隙较小以突出数据的“形状”.
A histogram looks similar to a bar chart, but the bars show a count of occurrences of a value. For example, standardized test auditors looking for evidence of grade tampering[ˈtæmpərɪŋ]干预,贿赂 might construct a histogram of student test scores. Typically, a distribution might look like this:https://blog.csdn.net/Linli522362242/article/details/123116143

The test scores are shown on the x axis and the height of each bar shows the number of students that made that particular score. A typical distribution should have a fairly recognizable bell curve, with some students doing poorly, some doing extremely well, and most falling toward somewhere in the middle.

What if auditors saw something like this?

Something is clearly wrong. Perhaps graders have bumped up提高 students who were just shy of passing差点及格 to barely passing. It's also possible this may indicate bias in subjective grading主观评分 instead of blatant[ˈbleɪt(ə)nt]公然的 tampering. We shouldn't jump to conclusions不应该草率下结论, but the pattern is not normal and requires investigation. Histograms are very useful in catching anomalies like this.
https://blog.csdn.net/Linli522362242/article/details/122955700

#a check whether the data['returns'] values are indeed log-normally distributed.

#如果峰度大于三，峰的形状比较尖，比正态分布峰要陡峭。反之亦然

#在相同的标准差下，峰度系数越大，分布就有更多的极端值，那么其余值必然要更加集中在众数周围，其分布必然就更加陡峭

Box and whisker plots

Summarise multiple distributions by showing the median (centre) and range of the data
Box and whisker plots (sometimes just called box plots) add additional statistical context to distributions. To understand a box and whisker plot, consider the following diagram:
Here, the box plot has been added to a circle graph. The box is divided by the median, meaning that half of the values are above and half are below. The box also indicates the lower(IQ1) and upper(IQ3) quartiles, which each contain a quarter of the values. The span of the box makes up what is known as the Interquartile Range (IQR). The whiskers extend to 1.5 times the IQR value (or the maximum extent of the data). Any marks beyond the whiskers are outliers.https://blog.csdn.net/Linli522362242/article/details/123116143

The box plots help us to see and compare the medians, ranges of data, concentration of values, and any outliers. You may edit box plots by clicking or right-clicking the box or whisker and selecting Edit. This will reveal multiple options, including how whiskers should be drawn, whether only outliers should be displayed, and other formatting possibilities. https://blog.csdn.net/Linli522362242/article/details/91037961

Violin Plot

Similar to a box plot but more effective with complex distributions (data that cannot be summarised with simple average).

A violin plot combines the kernel density estimate of our data with the box plot. Both box plot and violin plot display the median and interquartile range, but a violin plot goes one step further by showing the full estimated probability distribution that is fit to the data. Therefore, we can tell whether there are peaks是否存在峰值 within the data and also compare their relative amplitude[ˈæmplɪtuːd]幅度.

中间的黑色粗条(the thick black bar in the centre represents the interquartile range)表示四分位数范围，从其延伸的幼细黑线代表 95% 置信区间（The thin black line extended from it represents the upper (max) and lower (min) adjacent values in the data(1.5* IQR = (Q3-Q1)*1.5 ). Sometimes the graph marker is clipped from the end of this line），而白点则为中位数，记住有时候在中位数附近还有一个点表示mean平均值. A violin plot depicts distributions of numeric data for one or more groups using density curves. The width of each curve corresponds with the approximate frequency of data points in each region. Densities are frequently accompanied by an overlaid chart type密度经常伴随着一个重叠的图表类型,
Violin plots are used when you want to observe the distribution of numeric data, and are especially useful when you want to make a comparison of distributions between multiple groups. The peaks(,Higher probability), valleys, and tails( or , lower probability)of each group’s density curve can be compared to see where groups are similar or different.https://chartio.com/learn/charts/violin-plot-complete-guide/#what-is-a-violin-plot

Suppose you want to find out whether the stable or constrictive population type has a higher median BigMac index value假设您想了解稳定人口类型或收缩人口类型是否具有更高的 BigMac指数中值median.

Population Pyramid

A standard way for showing the age and sex breakdown of a population distribution; effectively, back to back histograms.
https://blog.csdn.net/Linli522362242/article/details/93617948
constrictive populations have a lower proportion of young people, so the pyramid base appears to be constricted

So what is a population pyramid? As its name suggests, it is a pyramid-shaped plot that shows the age distribution of a population. It can be roughly classified into three classes, namely constrictive, stationary, and expansive for populations that are undergoing negative, stable, and rapid growth respectively. For instance, constrictive populations have a lower proportion of young people, so the pyramid base appears to be constricted. Stable populations have a more or less similar number of young and middle-aged groups. Expansive populations, on the other hand, have a large proportion of youngsters, thus resulting in pyramids with enlarged bases.
Stable populations have a more or less similar number of young and middle-aged groups

Dot Strip Plot

see previous dot-plot-strip

Dot Plot

A simple way of showing the change or range (min/max) of data across multiple categories.
A Dumbbell[ˈdʌmbel]哑铃 Chart is a variation of the circle plot that

compares two values for each slice of data,
emphasizing the distance between the two values.
Here for example, is a chart showing the Difference in Profit between East and West regions for each Category of products:https://blog.csdn.net/Linli522362242/article/details/123970001

Barcode Plot

Like dot strip plots, good for displaying all the data in a table, they work best when highlighting individual values.

Cumulative Curve

A good way of showing how unequal a distribution is: y axis is always cumulative frequency, x axis is always a measure.

Change over Time

Give emphasis to changing trends. These can be short (intra-day) movements or extended series traversing decades or centuries: Choosing the correct time period is important to provide suitable context for the reader.

Line

The standard way to show a changing time series. If data are irregular, consider markers to represent data points.
For example, using the month date part for columns and the year date part for color gives a time series that makes year-over-year analysis quite easy. The year date part has been copied to the label so that the lines can be labeled:

In some cases, the line in your line graph may represent a summary statistic, like the average, or the point estimate of a forecast. If you also want to give a sense of the range (or confidence level, depending on the situation), you can do that directly on the graph by also visualizing this range. For example, the graph in Figure 2.9 shows the minimum, average, and maximum wait times at passport control护照检查 for an airport over a 13‐month period.

Column

Columns work well for showing change over time - but usually best with only one series of data at a time.https://blog.csdn.net/Linli522362242/article/details/122932763
The standard way to compare the size of things. Must always start at 0 on the axis.

The plain vanilla bar chart is the vertical bar chart, or column chart. Like line graphs, vertical bar charts can be single series, two series, or multiple series. Note that as you add more series of data, it becomes more difficult to focus on one at a time and pull out insight, so use multiple series bar charts with caution. Be aware also that there is visual grouping that happens as a result of the spacing in bar charts having more than one data series. This makes the relative order of the categorization important. Consider what you want your audience to be able to compare, and structure your categorization hierarchy
to make that as easy as possible.

Line + Column

see previously

Step Lines and Jump Lines

Step Lines are often useful when you want to show discrete change over time, as opposed to a standard line chart where angled lines标准折线图 might imply a gradual change. For example, the number of generators running at any given time is a discrete whole number and you may wish to emphasize this fact with a Step Lines chart.

Linear is the default: angled lines that emphasize movement between values. Jump Lines are useful when you want to show values that indicate a certain state that may exist for a given period of time before jumping to another state. For example, you might wish to show

how many products are on the showroom floor over time,
or how many patient beds are filled day-to-day.

Spark Lines

Spark Lines refers to a visualization that uses multiple small line graphs that are designed to be read and compared quickly. The goal of Spark Lines is to give a visualization that can be understood at a glance. You aren't trying to communicate exact values, but rather give the audience the ability to quickly understand trends, movements, and patterns.

Stock Price

Usually focused on day-to-day activity, these charts show opening/closing and hi/low points of each day.
In the financial world, the candlestick plot is almost the default choice for describing price movements of stocks, currencies, and commodities over a time period. Each candlestick consists of the body, describing the opening and closing prices, and extended wicks[wɪk]灯芯，蜡烛心 illustrating the highest and lowest prices of a particular trading day. If the closing price is higher than the opening price, the candlestick is often colored black. Conversely, the candlestick is colored red if the closing price is lower. The trader can then infer the opening and closing prices based on the combination of color and the boundary of the candlestick body.https://blog.csdn.net/Linli522362242/article/details/121172551

Slope

see previous

Area Chart

Use with care – these are good at showing changes to total, but seeing change in components can be very difficult.

a visualization of patient visits over time, segmented by hospital branch
Each band represents a different hospital branch location. In many ways, the view is aesthetically[esˈθetɪkli]审美地 pleasing and it does highlight some patterns in the data. However, it suffers from some of the same weaknesses as the stacked bar chart. Only the bottom band (South) can be read in terms of the values on the axis.

The other bands are stacked on top and it becomes very difficult to compare. For example, it is obvious that there is a spike in February of each year. But is it at each branch? Or is one of the lower bands pushing the higher bands up? Which band has the most significant spike?

Table(down) (Addressing) : Computes down the length of the table and restarts calculation after every partition(Month of Date of Admit is partioned).

This view uses a quick table calculation, similar to the stacked bars example. It is no longer possible to see the spikes, as in the first chart. However, it is much easier to see that there was a dramatic increase in the percentage of patients seen by the East branch (the middle band) around February 2018, and that the branch continued to see a significant amount of patients through the end of the year.

Fan Chart(Forecasting)

Use to show the uncertainty in future projections - usually this grows the further forward to projection.

As we've seen, trend models make predictions. Given a good model, you expect additional data to follow the trend. When the trend is over time, you can get some idea of where future values may fall. However, predicting future values often requires a different type of model. Factors such as seasonality can make a difference not predicted by a trend alone季节性因素等因素可能会产生影响，而仅靠趋势无法预测. Starting with version 8.0, Tableau includes built-in forecasting models that can be used to predict and visualize future values.

Here, for example, is the view of the population growth over time of Afghanistan and Australia with forecasts shown:https://blog.csdn.net/Linli522362242/article/details/123606731 Note that, when you show the forecast, Tableau adds a forecast icon to the SUM(Population) field on Rows to indicate that the measure is being forecast. Additionally, Tableau adds a new special Forecast Indicator field to Color so that forecast values are differentiated from actual values in the view.

Connected Scatterplot

A good way of showing changing data for two variables whenever there is a relatively clear pattern of progression.

Calendar Heatmap

A great way of showing temporal patterns (daily, weekly, monthly) – at the expense of showing precision in quantity.
CalendarViewDate

DATE( STR( MONTH( DATETRUNC('week',[Order Date]) ))+'/1/'+STR( YEAR( DATETRUNC('week',[Order Date]) )))

MMMM/1/YYYY
Sunday is the first day of the week in the US

CalendarViewDate

DATE( STR( MONTH( [Order Date] ))+'/1/'+STR( YEAR( [Order Date] )))

CalendarViewDate

DATE( STR( MONTH( [Order Date] ))+'/1/'+STR( YEAR( DATETRUNC('week',[Order Date]) )))

Filter out the row that is not 2014 (here is 2013)

DATETRUNC(date_part, date, [start_of_week])
If the data source does not initialize the start_of_week day, then Tableau consults the system's locale settingsTableau 会参考系统的区域设置.The day which is considered the first day of the week varies from region to region. For example, Sunday is the first day of the week in the US, while Monday is the first day in the EU.
Profit Ratio2

SUM([Profit])/SUM([Sales])

Priestley Timeline 普里斯特利时间表

Great when date and duration are key elements of the story in the data.

Circle Timeline

Good for showing discrete values of varying size across multiple categories (e.g., sales by quarter).

Seismogram[ˈsaɪzməˌɡræm]震波图,地震图

Another alternative to the circle timeline for showing series where there are big variations in the data.

Part-to-Whole

Show how a single entity can be broken down into its component elements. If the reader’s interest is solely in the size of the components, consider a magnitude-type chart instead.

Stacked Column

A simple way of showing part-to-whole relationships but can be difficult to read with more than a few components. (see previous Diverging Stacked Bar:)

Proportional Stacked Bar

A good way of showing the size and proportion of data at the same time – as long as the data are not too complicated.

For example, at the hospital, we might want to know what the patient population looks like within each type of department. Perhaps each patient was assigned a risk profile on admission也许每位患者在入院时都被分配了风险概况. We can visualize the number of pentient visits broken down by risk profile as a stacked bar, like the left chart, and in the right chart, we can see the percent of patient's visit with the different Patient's Risk Profile within a given department. :https://blog.csdn.net/Linli522362242/article/details/123116143

Using both the absolute values and percentages in a single view can reveal significant aspects and details that might be obscured with only one of the charts.

Pie Chart

A common way of showing part-to-whole data – but be aware that it’s difficult to accurately compare the size of the segments.https://blog.csdn.net/Linli522362242/article/details/123116143 Observe that the preceding pie chart uses the sum of revenue to define the angle of each slice; the higher the sum, the wider the slice. The Hospital Branch dimension is slicing the measure and defining slices of the pie. This view also demonstrates the ability to place multiple fields on the Label shelf. The second SUM(Revenue) field is the percent of total table calculation you saw previously. This allows you to see the absolute values of revenue, as well as the percent of the whole.

Pie charts can work well with a few slices. In most cases, more than two or three become very difficult to see and understand. Also, as a good practice, sort the slices by sorting the dimension that defines the slices. In the preceding example, the Hospital Branch dimension was sorted by the SUM of revenue descending. This was done by using the drop-down menu option. This causes slices to be ordered from largest to smallest and allows anyone reading the chart the ability to easily see which slices are larger, even when the size and angles are nearly identical.

Donut Chart

Similar to a pie chart – but the centre can be a good way of making space to include more information about the data (e.g., total).https://blog.csdn.net/Linli522362242/article/details/124786459

First, the grid layout in combination with the Log Sales calculated field creates a map immediately evident as of the United States, while ensuring that the sizing of the various pie charts changes only moderately from greatest to least. Thus, each slice of each pie is reasonably visible; for example, the district of Columbia sales are as easily visible as California sales.

Second, the end user can clearly see the top-selling category for each state via the color of the inner circle (that is, the hole of the donut). This was accomplished with the LOD calculations. Thanks to the LOD, we were able to differentiate the best-selling category from the other two. Since all three categories live in the same column, you need to use an LOD calculation. You can refer to https://blog.csdn.net/Linli522362242/article/details/124730062, Level of Detail Calculations, for more details on LOD calculations. The end result is an information-dense visualization that uses pie charts in a practical, intuitive manner.
States.csv

Column   Number of Records   Row State Abbr  State
10  1   10  HI  Hawaii
40  1   10  TX  Texas
90  1   10  FL  Florida
40  1   20  OK  Oklahoma
50  1   20  LA  Louisiana
60  1   20  MS  Mississippi
70  1   20  AL  Alabama
80  1   20  GA  Georgia
20  1   30  AZ  Arizona
30  1   30  NM  New Mexico
40  1   30  KS  Kansas
52  1   30  AR  Arkansas
62  1   30  TN  Tennessee
72  1   30  NC  North Carolina
80  1   30  SC  South Carolina
90  1   30  DC  District of Columbia
10  1   40  CA  California
18  1   40  UT  Utah
28  1   40  CO  Colorado
38  1   40  NE  Nebraska
50  1   40  MO  Missouri
60  1   40  KY  Kentucky
70  1   40  WV  West Virginia
80  1   40  VA  Virginia
90  1   40  MD  Maryland
100 1   40  DE  Delaware
10  1   50  OR  Oregon
20  1   50  NV  Nevada
30  1   50  WY  Wyoming
40  1   50  SD  South Dakota
51  1   50  IA  Iowa
61  1   50  IN  Indiana
71  1   50  OH  Ohio
80  1   50  PA  Pennsylvania
90  1   50  NJ  New Jersey
100 1   50  CT  Connecticut
110 1   50  RI  Rhode Island
10  1   60  WA  Washington
18  1   60  ID  Idaho
28  1   60  MT  Montana
38  1   60  ND  North Dakota
52  1   60  MI  Michigan
45  1   60  MN  Minnesota
62  1   60  IL  Illinois
90  1   60  NY  New York
100 1   60  MA  Massachusetts
60  1   70  WI  Wisconsin
100 1   70  VT  Vermont
110 1   70  NH  New Hampshire
10  1   80  AK  Alaska
110 1   80  ME  Maine

Treemap(Marimekko Charts)

Use for hierarchical part-to-whole relationships; can be difficult to read when there are many small segments.

A Marimekko Chart (sometimes also called a Mekko Chart) is similar to stacked bar chart, but additionally use varying widths of the bars to communicate additional information about the data. Here, for example, is a Marimekko Chart showing the breakdown of sales by region and department.https://blog.csdn.net/Linli522362242/article/details/123970001 Creating Marimekko Charts in Tableau leverages the ability to fix the width of bars according to the axis' units.
The width of the bars communicates the total sales for the Region
while the height of each segment gives you the percentage of sales for the Department within the Region

Sunburst(森伯斯特)

Another way of visualisaing hierarchical part-to-whole relationships. Use sparingly (if at all) for obvious reasons

Arc

Another way of visualisaing hierarchical part-to-whole relationships. Use sparingly (if at all) for obvious reasons.

Gridplot

Good for showing % information, they work best when used on whole numbers and work well in multiple layout form.

Venn

Generally only used for schematic representation.

Waterfall(瀑布图)

Can be useful for showing part-to-whole relationships where some of the components are negative.

The trick with the waterfall chart is to create a calculated field that is the negative of the Measure you are charting. This will allow you to create columns that stretch from each current horizontal line down to the previous level.

A Waterfall Chart is useful when you want to show how parts successively build up to a whole. In the following screenshot, for example, is a Waterfall Chart showing how profit builds up to a grand total across Departments and Categories of products. Sometimes profit is negative, so at that point the Waterfall Chart takes a dip, while positive values build up toward the total:

Gantt Charts can be incredibly useful for understanding any series of events with a duration[djuˈreɪʃn]持续时间, especially if those events have some kind of relationship. Visually, they are very useful for determining whether certain events overlap, have dependency, or take longer or shorter than other events.

The following Gantt Chart shows a series of processes that run when an application is started. Some of these processes run in parallel, and some are clearly dependent on others. The Gantt Chart makes these dependencies clear:

To create a Gantt Chart in Tableau, you can select the Gantt mark type on the marks card dropdown. This places a Gantt bar mark starting at the value that was defined by the field(Start Time) defining the axis. The length of the Gantt bar is then defined by the field on the Size card(Sum(Event length)), with positive values stretching to the right and negative values to the left.

Magnitude

Show size comparisons. These can be relative (just being able to see larger/bigger) or absolute (need to see fine differences). Usually these show a ‘counted’ number (for example, barrels, dollars or people) rather than a calculated rate or per cent.

Column : see previously

The standard way to compare the size of things. Must always start at 0 on the axis.

Bar

The standard way to compare the size of things. Must always start at 0 on the axis. Good when the data are not time series and labels have long category names.

Bar-in-bar chart

Another possibility for showing relationships between two values for each category is a bar-in-bar chart. Like the bullet chart, the bar-in-bar chart can show progress toward a goal, but it can also be used to compare any two values. For example, you might compare revenue to a target, or you might compare the revenue for the current year to the previous year:

Bullet chart – comparing to a goal, target, or threshold

A bullet graph (sometimes also called a bullet chart) is a great way to visually compare a measure with a goal, target, or threshold.

The bar indicates the measure value,
while the line indicates the target.
Tableau also defaults to shading to indicate 60% and 80% of the distance to the goal or threshold.
The line and the shading are reference lines that can be adjusted (we'll explore how in detail in future chapters):

https://blog.csdn.net/Linli522362242/article/details/124786459

As you survey our results thus far, you will notice that there are some important aspects to this visualization. For example, the reference lines and the colored bars clearly delineate[dɪˈlɪnieɪt]标明,标示 when a quota was met and missed. Furthermore, the percentages communicate how close the actual profit was to the budgeted profit for each market.

https://blog.csdn.net/Linli522362242/article/details/123116143

visualize the relationship between actual and target(each department has unique capabilities and requirements, so they have defined different goals for each department) minutes to service

Pareto charts帕累托图

https://blog.csdn.net/Linli522362242/article/details/124786459

Paired Column

As per standard column, but allows for multiple series. Can become tricky to read with more than 2 series.

Paired Bar

As per standard bar, but allows for multiple series. Can become tricky to read with more than 2 series.

if you want to further understand sales amounts for departments across various regions?https://blog.csdn.net/Linli522362242/article/details/122932763

By placing Region before Department , you are able to easily compare the sales of each department within a given region.
For example: the Technology department has the most sales in every region, except in the East, where Furniture had higher sales. Office Supplies never has the highest sales in any region.

Proportional Stacked Bar : see previously

Proportional Symbol

Use when there are big variations between values and/or seeing fine differences between data is not so important. https://blog.csdn.net/Linli522362242/article/details/122932763

Dot Density

Used to show the location of individual events/locations – make sure to annotate any patterns the reader should see.

This density map displays a high concentration of orders from the East Coast. Sometimes, you'll see patterns that merely reflect population density—in which case, your analysis may not be particularly meaningful. In this case, the concentration on the East Coast compared to the lack of density on the west coast is intriguing[ɪnˈtriːɡɪŋ]非常有趣的.

Isotype (pictogram图标, Unit chart/symbol charts)

Excellent solution in some instances – use only with whole numbers (do not slice off an arm to represent a decimal).

A unit chart can be used to show individual items, often using shapes or symbols to represent each individual. These charts can elicit[ɪˈlɪsɪt]引出,使(潜在的东西)显现 a powerful emotional response, because the representations of the data are less abstract and more easily identified as something real. For example, here is a chart showing how many customers had late shipments for each Region:https://blog.csdn.net/Linli522362242/article/details/123970001

The preceding unit chart might elicit more of a response from regional managers than a standard bar chart when they are gently[ˈdʒentli]温柔地 reminded that poor customer service impacts real people. Granted, the shapes are still abstract, but more closely represent an actual person. You could also consider labeling the mark with the customer name or using other techniques to further engage your audience.

Once you know the meaning of a shape, you no longer have to reference a legend.

Lollipop Chart : see previously

Radar Chart

A space-efficient way of showing value of multiple variables– but make sure they are organised in a way that makes sense to reader.

Parallel Coordinates

An alternative to radar charts – again, the arrangement of the variables is important. Usually benefits from highlighting values.

Spatial

Used only when precise locations or geographical patterns in data are more important to the reader than anything else.

Basic Choropleth (rate/ratio)基本等值线（比率/比率）

The standard approach for putting data on a map – should always be rates rather than totals and use a sensible base geography.https://blog.csdn.net/Linli522362242/article/details/123970001

Heat Map

Grid-based data values mapped with an intensity colour scale. As choropleth map – but not snapped to an admin/political unit. Heatmaps can show you a spatial concentration and are perfect for very dense datasets. In the preceding figure, you can see that we lost the surroundings of the city, the polygon structure; however, we can still see the densely populated areas人口稠密的地区 and now every city has the same size, making it easier to compare smaller with bigger geographic locations. And we were able to prove that a spatial file does not need to be used with polygons.

Flow Map

For showing unambiguous不含糊的,明确的 movement across a map.
https://blog.csdn.net/Linli522362242/article/details/124919818

Now, let's look at what we have created. We can see that

goods are being transported from every star to a circle city.
Both cities are connected by a yellow-to-red range colored line.
- More yellow-toned means that the delivery was fast,
- more red-toned means that the delivery took longer.
The destination country is colored in a [ˈtɜːrkwɔɪz]蓝绿色turquoise-to-dark blue color range. The darker the color, the more weight has been transported there.
We can easily spot that Italy received the heaviest transport, coming from Istanbul and being delivered within 6 days.
The slowest delivery was from Warsaw to Budapest, which took 26 days.

#####https://public.tableau.com/app/profile/andy.kriebel/viz/VisualVocabulary/VisualVocabulary

However, it does seem appropriate to review at least a couple of rules. Here, we will consider keeping shapes simple and effectively using pie charts.

Keep shapes simple(Isotype/pictogram图标/ Unit chart/symbol charts)

Too many shape details impede[ɪmˈpiːd]妨碍,阻碍 comprehension. This is because shape details draw the user's focus away from the data. Consider the following exercise using two different shopping cart images.

Let's look at the following exercise steps:

1. Open the workbook associated with this chapter and navigate to the Simple Shopping Cart worksheet. Note that the visualization is a scatter plot that shows the top-10-selling sub-categories in terms of total sales and profits.
2. Navigate to the Shapes directory located in the My Tableau repository. On my computer, the path is C:\Users\liqin\OneDrive\Documents\My Tableau Repository\Shapes
3. Within the Shapes directory, create a folder named My Shapes.
4. Reference the link included in the comment section of the worksheet to download assets associated with this chapter. In the downloaded material, find the images entitled Shopping_Cart and Shopping_Cart_3D, and then copy those images into the My Shapes directory.
5. In Tableau, access the Simple Shopping Cart worksheet.
6. Click on the Shape shelf and then select More Shapes.
7. Within the Edit Shape dialog box, click the Reload Shapes button:
8. Select the My Shapes palette and set the shape to Simple Shopping Cart.
9. After closing the dialog box, click on the Size shelf and adjust as desired. Also, adjust other aspects of the visualization as desired
10. Navigate to the 3D Shopping Cart worksheet and then repeat Steps 8-11 above. Instead of using Simple Shopping Cart, use 3D Shopping Cart. See the following screenshot for a comparison of the simple and 3D shopping carts:

Compare the two visualizations. Which version of the shopping cart is more attractive? It's likely that the cart with the 3D look was your choice(But it will also distract you). So, why not choose the more attractive image? Making visualizations attractive is only a secondary concern. The primary goal is to display the data as clearly and efficiently as possible. A simple shape is grasped more quickly and intuitively than a complex shape. Besides, the cuteness of the 3D image will quickly wear off.

Use pie charts sparingly谨慎

Edward Tufte makes an acrid comment against the use of pie charts in his book The Visual Display of Quantitative Information, saying that a table is always better than a pie chart, because we humans fail to interpret the visual dimension of pie charts.

The present sentiment in data visualization circles is largely sympathetic to Tufte's criticism. There may, however, be some exceptions; that is, some circumstances where a pie chart is optimal. Consider the following visualization:
a quad layout, Figure 11.6

Which of the four visualizations best demonstrates that A accounts for 25% of the whole(A 占整体的 25%)? Clearly, it's the pie chart! Therefore, perhaps it is fairer to refer to pie charts as limited and to use them sparingly, as opposed to considering them inherently undesirable.

Compromises

In this section, we will transition from more or less strict rules to compromises[ˈkɑːmprəmaɪz]妥协. Often, building visualizations is a balancing act. It's not uncommon to encounter contradictory direction from books, blogs, consultants, and within organizations. One person may insist on utilizing every pixel of space while another advocates simplicity and whitespace. One counsels a guided approach, while another recommends building wide-open dashboards that allow end users to discover their own path. Avant-garde[ˌævɒŋ ˈɡɑːd]前卫的 types may crave esoteric[ˌesəˈterɪk]只有内行才懂的,深奥 visualizations, while those of a more conservative[kənˈsɜːrvətɪv]保守的 bent prefer to stay with convention. Let's explore a few of the more common competing requests and then suggest compromises.

Making the dashboard simple and robust

Recently, a colleague[ˈkɑːliːɡ] showed me a complex dashboard he had just completed. Although he was pleased that he had managed to get it working well, he felt the need to apologize by saying, I know it's dense and complex, but it's what the client wanted. Occam's Razor encourages the simplest possible solution for any problem. For my colleague's dashboard, the simplest solution was rather complex. This is OK! Complexity in Tableau dashboarding need not be shunned[ʃʌnd]避免. But a clear understanding of some basic guidelines can help the author intelligently determine how to compromise between demands for simplicity and demands for robustness如何在简单性要求和健壮性要求之间进行折衷:

• More frequent data updates necessitate[nəˈsesɪteɪt]需要,迫使 simpler design:
Some Tableau dashboards may be near real-time. Third-party technology may be utilized to force a browser that displays a dashboard via Tableau Server to refresh every few minutes, to ensure the latest data is displayed. In such cases, the design should be quite simple. The end user must be able to see, at a glance, all pertinent[ˈpɜːtɪnənt]相关的 data and should not use that dashboard for extensive analysis最终用户必须能够一目了然地看到所有相关数据，并且不应使用该仪表板进行广泛的分析. Conversely, a dashboard that is refreshed monthly can support high complexity and thus may be used for deep exploration每月刷新的仪表板可以支持高复杂性，因此可用于深度探索.
• Greater end user expertise supports greater dashboard complexity更高的最终用户专业知识支持更高的仪表板复杂性:
Know thy users. If they want easy, at-a-glance visualizations, keep the dashboards simple. If they like deep dives, design accordingly.
• Smaller audiences require more precise design较小的受众需要更精确的设计:
If only a few people monitor a given dashboard, it may require a highly customized approach. In such cases, specifications may be detailed, which are complex and difficult to execute and maintain. This is because the small user base has expectations that may not be natively easy to produce in Tableau.
• Screen resolution and visualization complexity are proportional屏幕分辨率和可视化复杂度成正比: Users with low-resolution devices will need to interact fairly simply with a dashboard. Thus, the design of such a dashboard will likely be correspondingly uncomplicated. Conversely, high-resolution devices support greater complexity.
• Greater distance from the screen requires larger dashboard elements: If the dashboard is designed for conference room viewing, the elements on the dashboard may need to be fairly large to meet the viewing needs of those far from the screen. Thus, the dashboard will likely be relatively simple. Conversely, a dashboard to be viewed primarily on end users' desktops can be more complex.

Although these points are all about simple versus complex, do not equate simple with easy. A simple and elegantly designed dashboard can be more difficult to create than a complex dashboard.

As Steve Jobs said, simplicity can be harder than complexity; however, simplicity can move mountains.

Presenting dense information

Normally, a line graph should have a max of four or five lines. However, there are times when you may wish to display many lines. A compromise can be achieved by presenting many lines and empowering the end user to highlight as desired. The following line graph displays the percentage of internet usage by country from 2000-2012. The user can select a country and compare it to the rest.

We can see this example in the following screenshot:
When using line charts versus many line-charts, it should not be important how a line performs compared to a specific other line, but rather how one line behaves in a lake of other lines.

Telling a story

In his excellent book, The Functional Art: An Introduction to Information Graphics and Visualization, Alberto Cairo includes a section where he interviews prominent data visualization and information graphics professionals. Two of these interviews are remarkable for their opposing views. Jim Grimwade mentions that visualization designers should not try to make editors out of users; expecting them to make up a story on their own is not the approach he likes. On the contrary, Moritz Stefaner is fascinated by being able to explore key insights on big datasets on his own.

Fortunately, the compromise position can be found in the Jim Grimwade interview, where he states that the New York Times lets you explore complex datasets, but that beforehand, they give the reader some context.

Although the scenarios considered here are likely quite different from the Tableau work you are involved in, the underlying principles remain the same. You can choose to tell a story or build a platform that allows the discovery of numerous stories. Your choice will differ, depending on the given dataset and audience. If you choose to create a platform for story discovery, be sure to take the New York Times approach suggested by Grimwade. Provide hints, pointers, and good documentation to lead your end user to successfully interact with the story you wish to tell or to successfully discover their own story.

Maximizing documentation on a dashboard

In the Telling a story section, we considered the suggestion of providing hints, pointers, and good documentation, but there's an issue. These things take up space. Dashboard space is precious. Often, Tableau authors are asked to squeeze more and more stuff on a dashboard and are hence looking for ways to conserve space节省空间. Here are some suggestions for maximizing documentation on a dashboard while minimally impacting screen real estate:

Craft titles for clear communication为清晰的沟通制作标题: Titles are expected. Not just a title for a dashboard and worksheets on the dashboard, but also titles for legends, filters, and other objects. These titles can be used for effective and efficient documentation. For instance, a filter should not just read Market. Instead, it should say something such as Select a Market. Notice the imperative statement. The user is being told to do something and this is a helpful hint. Adding a couple of words to a title will usually not impact dashboard space.
Use subtitles to relay instructions使用字幕传递指令: A subtitle will take up some extra space, but it does not have to be much. A small, italicized font immediately underneath a title is an obvious place a user will look for guidance. Consider an example: Red represents loss. This short sentence could be used as a subtitle that may eliminate the need for a legend and thus actually save space.
Use intuitive icons使用直观的图标: Consider a use case of navigating from one dashboard to another. Of course, you could associate an action with some hyperlinked text that states Screentext style to navigate to another dashboard. But this seems unnecessary when an action can be associated with a small, innocuous[ɪˈnɑːkjuəs] 无害的,无伤大雅的 arrow, such as is natively used in PowerPoint, to communicate the same thing.
Store more extensive documentation in a tooltip associated with a help icon在与帮助图标相关的工具提示中存储更广泛的文档: A small question mark in the upper-right corner of an application is common. Currently, I'm composing this chapter in Word 2013, which has such a question mark. This clearly communicates where to go if additional help is required. As shown in the following exercise, it's easy to create a similar feature on a Tableau dashboard.

Follow these steps to create an informative tooltip to help users:

1. Open the workbook associated with this chapter and navigate to the Help worksheet(Custom Shapes Size).

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla quam lacus, egestas at ultricies molestie, consequat at lacus. Curabitur et neque vitae risus pharetra pulvinar. Proin mattis orci interdum massa maximus, ut pulvinar mauris aliquam. Donec tincidunt diam eros, ut condimentum neque imperdiet ac. Vestibulum et orci mauris. Proin vehicula dui tortor, vitae consectetur purus dignissim vel. Donec blandit aliquam nibh, id ornare massa laoreet ut. Donec condimentum finibus massa a pulvinar. Donec neque sapien, bibendum eget orci sit amet, vulputate porttitor quam. Aliquam malesuada est eu nunc accumsan, et condimentum enim interdum. Ut tempus justo consectetur mi sollicitudin tempor. In vitae consectetur lacus. Curabitur congue libero eget euismod eleifend. Sed facilisis massa vitae ipsum laoreet, sed auctor urna lobortis. Fusce id arcu nec tortor elementum faucibus.

Resize icons==>==>==>==>Stretch the icon size==>

2. Hover over the light bulb on the worksheet and note the text that appears:
Note that the Lorem ipsum... text shown in the preceding screenshot is commonly used by web designers, who borrowed it from typesetters, who have been using this Latin text as a placeholder for centuries. Visit https://loremipsum.io/ to learn more.

The text in this worksheet was deployed via Worksheet | Tooltip. This worksheet could be thoughtfully placed on a dashboard (for example, in the upper-right corner) to give very detailed documentation that minimally impacts space.

Keeping visualizations simple

Some people tire of seeing the same chart types over and over. This leads to requests such as, can we spice[spaɪs]香料,调味品,给…增添趣味 up the dashboard a bit? Normally, such sentiments should be resisted. As stated at the beginning of this chapter, introducing variety for its own sake is counterproductive为了多样性而引入多样性会适得其反. Nevertheless, there are times when a less common visualization type may be a better choice than a more popular type. When are those times?

Use less common chart types in the following scenarios:

• The chart is used to catch the end user's attention.
• The chart type presents the data more effectively

Sometimes, a less common chart type can be effectively used to catch the end user's attention for some particular goal, such as humor, making a salient[ˈseɪliənt]突出的,显著的 point, or making the visualization more memorable. One such example can be found on the Tableau 404 error page. Navigate to http://www.tableau.com/asdf and observe Sasquatch in a packed bubble chart. Note that this page changes from time to time, so you may see Elvis, aliens, or some other visualization.

An example of the second point is using a treemap over a pie chart. Both are non-Cartesian chart types (visualizations with no fields on the Rows or Columns shelves) used to show parts of a whole. Pie charts are the more common of the two, but treemaps usually present the data better. There are at least three reasons for this:

• A treemap can represent more data points.
• The rectangular nature of a treemap fits monitor space more efficiently.
• Pie slice sizes are more difficult to distinguish than sizes of treemap segments.

Sometimes, using a less common visualization type may elicit[ɪˈlɪsɪt] 引起 complaints: I like pie charts. Give me a pie chart! In such cases, a compromise may be possible. Later in this chapter, we will consider sheet swapping. As you will learn, sheet swapping can allow the end user to determine which visualization type to view. In the end, if a compromise is not possible and the person responsible for your paycheck desires a less-than-ideal chart type... well, I recommend you doing so!

Dashboard design

Now that we have completed our discussion of visualization theory, let's turn our attention to dashboard design. We'll begin by asking the question, what is a dashboard? This is rather difficult to answer; however, its usage in everyday conversation in many organizations would suggest that people have a definite idea as to what a dashboard is. Furthermore, search engine results provide no shortage of definitions. But those definitions can differ significantly and even be contradictory[ˌkɑːntrəˈdɪktəri]相互矛盾的.

Why is it so difficult to define dashboard? In part, it is because data visualization as a whole, and dashboarding specifically, is an emerging field that combines many other disciplines. These disciplines include statistical analysis, graphic and web design, computer science, and even journalism. An emerging field with so many components is a moving target, and as such is difficult to define.

For our purposes, we will begin with Stephen Few's definition as it first appeared in an issue of Intelligent Enterprise in 2004. He states that a dashboard is a visual display of vital statistics we need to reach, and how all these details are present on a sole screen, so that this information can be observed at first sight. Then, we'll extend and adapt that definition for Tableau dashboards.

Although this definition is good, Tableau takes a broader approach. For instance, a Tableau dashboard may be contained on a single screen but can be designed (and quite effectively so) to require scrolling. More importantly, Tableau dashboards are typically interactive, which opens up a world of exploration, analysis, and design options. Therefore, let's attempt a Tableau-centric dashboard definition:

A Tableau dashboard is a display that contains one or more data visualizations designed to enable a user to quickly view metrics. This display may provide interactive elements, such as filtering, highlighting, and drill-down capabilities that enable further exploration and analysis.

Dashboard layout

The layout of a dashboard is important for the same reason that the layout of a magazine foldout杂志折页 or a web page is important. Placing the right information in the right place helps the viewer quickly and efficiently gather information and draw conclusions. In order to appreciate this fact, consider the last time you had to hunt through a poorly constructed web page to find important information. Your time could have been better used actually applying that important information!

The golden ratio layout

You have probably heard of the Fibonacci sequence or the golden ratio. Since it may have been a few years since you attended a math class, a brief reminder may prove helpful.

The Fibonacci sequence is a series of numbers where every number is the sum of the previous two numbers; for example, 1, 1, 2, 3, 5, 8, 13, 21.

A Golden Rectangle is achieved when the ratio of the longest side to the shortest side of a rectangle is approximately 1.618. This ratio is known as the Golden Ratio. Mathematically, the Golden Ratio is represented as follows:

You can see the connection between the Fibonacci sequence and the Golden Ratio when dividing each number of the Fibonacci sequence by the previous number; for example, take the following sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34

This leads to the following:

Now, let's consider a dashboard layout using the Golden Ratio. The layout shown here is constructed of rectangles so that each is 1.618 times longer or taller than the next. The spiral (known as the golden spiral) is displayed to demonstrate the order of the rectangles: The Fibonacci sequence/Golden Rectangle/Golden Ratio appears endlessly in nature and throughout history. Many seashells and flowers adhere to the Fibonacci sequence. The Great Pyramid of Giza appears to have been constructed with the Golden Ratio in mind. Phidias likely used the Golden Ratio to design his statues for the Athenian Parthenon. Indeed, the Parthenon itself was designed with Golden Rectangle proportions.

So, does the golden ratio, as pictured in the preceding diagram, represent the ideal dashboard layout? Perhaps it's truer to say that this image represents one acceptable dashboard layout. The ideal is not so much found in the abstract as it's found in the application. Dashboard layouts may sometimes approximate the golden ratio but, as we will see, other dashboard layouts may be better for different scenarios.

The dashboard pictured here (which is also available in the Tableau workbook that accompanies this chapter) utilizes the Golden Rectangle:Notice that this example does not attempt to follow the Golden Ratio through to smaller and smaller rectangles. There are practical limits.

The Golden Rectangle layout is particularly good for guiding the viewer from coarser to finer granularity. In this example, the left portion of the dashboard displays the coarsest[ˈkɔːrsɪst]非精制的,粗糙的 granularity, 17 subcategories. The map is next. Finally, the scatter plot displays the finest granularity. Creating actions that follow this path would make a natural interaction for the end user. For example, an end user might first click on Tables and then click on the state of Pennsylvania in order to observe outliers in the scatter plot.

The quad layout

The quad layout divides a dashboard into four more or less equal quadrants. It's easy to implement. On a blank dashboard, simply double-click on four worksheets in the Dashboard pane. The result is a quad layout, though some small adjustments may need to be made to account for legends, filters, and parameters. To observe a quad layout, refer to the diagram in Figure 11.6.

The small multiple layout

A small multiple layout displays many views on a single dashboard. Like the quad layout, a small multiple layout can be implemented simply by double-clicking on each desired worksheet in the Dashboard pane. Small multiples are useful when viewing information that utilizes the same visualization type repeatedly. Also, a consistent theme is helpful; for example, the following screenshot demonstrates a theme of profit performance per state in the USA. Attempting to create a small multiple with multiple visualization types and multiple themes will likely be messy and difficult to interact with: Some layouts are essentially variations of a theme. For instance, a layout that displays three views where one view on the dashboard covers double the real estate of each of the other views may essentially be a variation of the quad layout. Other layouts defy easy categorization and are unique to a given dashboard. Regardless, this will hopefully provide some food for thought as you design dashboards in the future.

The next section was already announced when we discussed compromises; sheet swapping is a great feature for that. If your stakeholder asks you to build something you know might not be as good or not following visualization best practice, you can always add a sheet swap, making your stakeholders happy and deliver an alternative for other users. We will walk through a sheet swapping exercise in the next section.

Utilizing sheet swapping(collapse)

Sheet selection, often referred to as sheet swapping, allows the Tableau author to hide and display visualizations, as well as to move worksheets on and off the dashboard. These techniques have been used in creative ways with some very impressive results. For example, Tableau Zen Master Joshua Milligan has built various games, including Tic-Tac-Toe and Blackjack, using sheet selection. For our purposes, we will stick to using sheet selection to assist with creating dashboards that adhere to the design principles we've discussed.

In the Use pie charts sparingly section, we discussed pie charts and treemaps, and we noted that a treemap is a better visualization. However, people are often more comfortable with pie charts. As a compromise, in the first exercise, we will review an example that allows the end user to choose whether to see a pie chart or a treemap.

Please follow these steps:

1. Open the workbook associated with this chapter and navigate to the Population Pie worksheet.
2. Select the World Indicators data source and note the calculated field, called Blank. The code is composed of single quotes with a space in-between.
3. Place Blank on the Columns shelf.

==>
and exclude Canada and United States
All Using This Data Source

Custom Shapes SizeOR
==>
4. In the view, select all pie slices via Ctrl + A or by dragging a marquee[mɑːrˈkiː]选取框 around the entire pie. Non-Cartesian visualization types (for example, visualizations with no fields on the Rows or Columns shelves) require this step for sheet swapping to work. Otherwise, when placing the worksheet in a vertical container on a dashboard, the worksheet will not properly stretch to fill the width of the container.
5. Right-click on any pie slice and choose Annotate | Mark.
6. In the resulting dialog box, delete all the text except <Country> :==>==>
7. Position and format the annotations as desired. Note that additional adjustments may be required once the pie chart has been deployed on a dashboard.
8. Create a parameter named Select Chart Type with the following settings:
9. Create a calculated field entitled Sheet Swap containing the following code:
```
[Select Chart Type]
```
10. Place Sheet Swap on the Filters shelf and select Pie Chart in the resulting dialog box.
11. Display the parameter by right-clicking on Select Chart Type and choosing Show Parameter Control.
12. Navigate to the Population Tree worksheet.
==>go back to the Population Pie worksheetSelected Worksheets...

go back to the Population Tree worksheet
13. Place Blank on the Columns shelf.
==>
14. Place Sheet Swap on the Filters shelf and select Treemap in the resulting dialog box.

If Treemap does not display as a choice, make sure that the Select Chart Type parameter is toggled to Treemap and click OK:
==>
15. Navigate to the Population Map worksheet.go back to the Population Tree worksheet
Double Country then use color shelf==>Ctrl + A, then Right-click on any map block and choose Annotate | Mark
16. Navigate to the Population Line worksheet
go back to the Population Map worksheet
YoY Pop Growth Change and Null==>Hide Indicator
16. Create a new dashboard entitled Latin American Population.
17. In the Dashboard pane, double-click on Vertical to place a vertical container on the dashboard.
18. In the Dashboard pane, double-click on Population Tree and Population Pie in order to add them to the vertical container.
19. Right-click on the titles Population Tree and Population Pie, then select Hide Title for both.
20. Place Population Map and Population Line on our dashboard, then position them as desired:
21. Shift-drag the Select Chart Type parameter over our Treemap in order to float the control; position it as desired and Hide the title.
22. Delete all legends and quick filters. Format, document, and position all dashboard elements as desired. The user can now determine whether to view a Treemap or a Pie Chart.Click dropdown below to switch between treemap and pie chart.
Click dropdown below to switch between treemap and pie chart.

You can use the sheet swap feature in case some of your users prefer one visualization over the other.
Next, we will create a filter menu that can be hidden on the dashboard. In the Maximizing documentation on a dashboard section, the point was made that dashboard space is precious. Therefore, in the second exercise, Creating a collapsible menu, we will review an example that will use sheet selection to allow the end user to show or hide filters, and thus make more efficient use of screen real estate.

Creating a collapsible menu创建可折叠菜单

Please follow these steps to learn how to add a collapsible[kəˈlæpsəbl]可折叠的 filter menu to your dashboards:

1. Duplicate the dashboard Latin American Population we created in the preceding exercise and rename it Collapsible Menu. If you did not complete the preceding exercise, utilize the dashboard included with the solution workbook provided with this chapter.
2. In the bottom portion of the Dashboard pane, select Floating:
3. Navigate to the Menu Icon worksheet
#108001

Custom Shapes Size==>

Drag the Menu Icon worksheet from the dashboard pane onto the dashboard.
4. Right-click on the title and select Hide Title.
5. Set the fit of the Menu Icon worksheet to Entire View:
6. Size and position the Menu Icon worksheet so that it fits in the upper-right corner of the dashboard:
7. In the bottom portion of the Dashboard pane, select Tiled.
9. Navigate to the Menu worksheet
then go back to Population Map worksheet

and take a moment to explore the settings of each dimension on the Filters shelf. Note that the region filter (Latin American Countries) is set to Context and that Country Copy is displayed as a quick filter(Only Relevant Values)==>.
Also, note that the Country (copy) quick filter is set to Show All Values in Context. It is necessary to display the Country (copy) filter as opposed to the Country filter because Country displays the condition (that is, AVG([Population Total]) > 10,000,000 and exclude Canada and United States, and Apply to Worksheets | All Using This Data Source) in the quick filter:
8. Place the Menu worksheet anywhere on the dashboard. The filters for Years and Country should display on the right-hand side of the dashboard:
10. Return to the Collapsible Menu dashboard. Right-click on the Menu title and select Hide Title.
11. Make the Menu worksheet floating by clicking on the arrow next to the worksheet and selecting Floating:
12. Then, size and position with the following dimensions:
Note that the goal of this step is to make the Menu worksheet as small as possible. It is necessary for the Menu worksheet to remain on the dashboard so that the accompanying Year and Country filters can be used.
13. On the Dashboard pane, drag a Horizontal container onto the view in approximately the middle of the dashboard.
14. Within the Layout pane, select Floating then set the width of the Horizontal container to 400:
15. In the lower-right section of the dashboard, right-click the container that houses the Country and Years filters. In the resulting pop-up menu, choose Edit Width and set the value to 200:
16. Drag the filter container selected in the previous step and place it in the container that is floating in the center of the dashboard:
17. From the Dashboard pane, drag a vertical container and place it inside the left portion of the floating horizontal container:
18. Right-click on the newly created vertical container and select Fix Width. The width of the vertical container should now be 200 pixels(the width of the Horizontal container : 400, the width of the container that houses the Country and Years filters : 200, 400-200=200)
19. Navigate to the Expander worksheet==>
Place the Expander worksheet in the newly created vertical container by pressing Shift and dragging.
20. The Expander worksheet contains a small, transparent image that will be used to expand and collapse the menu we are creating. However, an image does not have to be used; for example, text could be used instead. A transparent image is sometimes used in web design to stretch tables; we will make use of this method from web design here.
21. Set the fit for the Expander worksheet to Entire View.Entire View
22. Right-click on the Expander worksheet title and select Hide Title. The results should look similar to the following screenshot:

then go back to Expander worksheet
net go back to Collapsible Menu dashboard==>
23. Within the Layout pane, select the horizontal container that contains the Country and Years filters and the Expander worksheet by right-clicking it and selecting Select Container: Horizontal, or by selecting the horizontal container from Item hierarchy under Layout.
You can change the naming convention in the Item hierarchy to create different Dashboard Zone Names and find container and/or worksheets faster.
24. Enter the following dimensions:
25. Select Dashboard menu | Actions and create the following filter action, called Collapse/Expand:
==>
26. In Item hierarchy, right-click on the vertical container that is housing the Expander worksheet and deselect Fix Width.
This step is necessary so that the Collapse/Expand action will work properly. It was necessary to fix the container's width previously to properly align the contents of the
container.
27. On the dashboard, click on Menu Icon. The Expander worksheet should appear and disappear, causing the filters to move on and off the dashboard. When you're finished with this step, make sure that the Expander worksheet is visible/the filters are located off the dashboard.
The Expander worksheet should disappear
The Expander worksheet should appear,the Expander worksheet is visible/the filters are located off the dashboard.
28. From the Dashboard pane, press Shift and drag 2 blank containers onto the dashboard. Position one directly above the Expander worksheet and one directly below. Size the blank containers so that the Expander worksheet is as small as possible, as indicated by the red arrow:
29. Be sure to continually test the Menu icon action as you adjust the size so as to not inadvertently[ˌɪnədˈvɜːrtntli]无意地,不经意地 break the collapsing functionality of the container.
30. Click the drop-down menus associated with both the Country and Years filters, then choose Apply to Worksheets | Selected Worksheets. In the resulting dialog boxes, choose Population Pie, Population Tree, Population Map, and Population Line.
31. Adjust the formatting and positioning as desired. The Country and Years filters can be hidden or shown by selecting the Menu button:
hidden the titles of Popolation Map, Population Lines

Menu button setting:

then Hiding the Keep Only and Exclude Buttons from Tooltips :
Select Worksheet > Tooltip.
Clear the Include command buttons check box and click OK.

==>

In this section, we learned that by using containers and a filter, Tableau is able to automatically resize the selected worksheet within the container. This feature can be used to make it seem as if a worksheet can be swapped. However, Tableau basically just minimizes one and maximizes the other, even though this is not visible to the user's eye. Brilliant, isn't it?

After completing this exercise, you may think, OK: the filters do not technically display on the dashboard, but they are still visible to the side. While this is true in Tableau Desktop, if you are able to upload the workbook to an instance of Tableau Server, you will note that when clicking on the menu icon, the filters appear and disappear and do not display to the side. In the solution workbook provided with this chapter, the Hider worksheet has been floated off the right-hand side of the dashboard to completely hide the filters from view. Review the Hider approach if your design needs require completely hiding material that is beyond the confines of the dashboard.

use a Hider worksheet to hide the filters

hide the text

Duplicate the dashboard Collapsible Menu, Collapsible Menu (Hider sheet)

==>drag and move the Hider sheet to top-right, then size it

==>

then fit to Entile View
and Hide the title of Hider worksheet

rename it to Collapsible Menu (Hider sheet)

use a Show/Hide button to hide/show the filters

Duplicate the dashboard Collapsible Menu ==> Collapsible Menu (Show/Hide button)

right click Vertical Container that contains the Country and Years filters and select Add Show/Hide button

rename it to Collapsible Menu (Show/Hide button)

The previous Collapsible Menu dashboard is a very nice add-on and contributes to the simplicity of your dashboard. The users can focus on the data and use the filter only if needed.

Dashboard best practices for user experience

Visualization best practices are not limited to visual features; actions, filters, and organizing data can be as important. A few of those features that Tableau has to offer shall be mentioned in this section. Each of them will help you to either improve the user experience or keep track of what you have built—since sometimes, you can get lost when working with many different worksheets.

In total, we will be discussing four features, two of each category mentioned previously. Actions (six in total) and export buttons contribute to a better user experience, and "Used In" as well as item hierarchy are part of a better dashboard building experience. Let's start!

Actions

Implicitly, we have discussed this a few times before, but let's have an explicit look at what we can achieve with regards to visualization and dashboard design by using Actions. Actions come into play if you want your user to be redirected by clicking or hovering on data in a viz. This can be helpful if you have a lot of data to show and don't want to put it all on the same dashboard. Neither should you, because it is distracting and if it is hierarchical data, actions are the perfect way to implement a drill-down technique.

The following sections will explain all the different actions you can use in Tableau for the purpose of adding interactivity to support a clean and effective dashboard design.

Filter actions

In this example, we will use a filter action to show data initially hidden. By doing so, we will make sure to present smaller chunks of data at a time and the user can drill down to any point of interest. Follow these steps:

1. Please open the worksheet Quantity by State:
You can see in the preceding screenshot that each state represents the quantity of a given product sold. But wouldn't it be nice to see more details by clicking on a given state? In order to do so, we must set up actions.
2. First, we need a second worksheet we want to use in our action. Please open the worksheet Profit by State by Category:
You can see in the preceding screenshot that every state, every category, and the corresponding profit are shown.
3. Now, go back to the worksheet Quantity by State to enable an action that will prompt the user to go to the Profit by State by Category worksheet, and then filter it on the state they clicked on.
4. On the Quantity by State worksheet, select Worksheet menu | Actions; the following window will appear. Click on the Add Action button and select Filter...:
5. In the next window, add a filter action called Map to table (quantity to profit) and copy the settings, as shown in the screenshot. Then, click OK:
6. If you now click on a state on the worksheet Quantity by State (Source Sheet), Tableau will look for that same State on Target Sheet and filter accordingly. The following is an example after clicking on Texas:

The filter action discussed in this section can be used to declutter[diːˈklʌtər]清理 your dashboard. Following design principles often means less is more, and if you have lots of datapoints to share, a drill down or filter action can help you achieve this goal. Present high-level data first, then present a more detailed data layer behind a filter action. You can also add a sentence for your users below the title, like "Click on a state to see more details."

Highlight actions

Other options are, for example, a highlight action. This is more useful for worksheets on the same page because it will simply highlight applicable fields. You can show all data on the same dashboard, but the highlight action makes it easier for your users to find related data points. Follow these steps:

1. Therefore, please open the dashboard Qty & Profit, which combines the two worksheets we used previously:
2. Click on Dashboard | Actions... and this time, copy the following Highlight Action parameters:
3. The preceding parameters mean that, on the Qty & Profit dashboard, our source worksheet will be Quantity by State and by hovering over it, the corresponding data on the target sheet will be highlighted. In this case, the only common data is State, so the highlighter will only take State into account. The result looks like this:

By hovering over California on the map, the same state will be highlighted in the right-hand table. As you can see, implementing a highlight filter can help your users find related data points faster. In contrast to the navigation action, this filter should be used for worksheets on the same page and as visual help to find a value.

URL actions

The next action is the URL action. This one allows you to open a URL when clicking on a data point in the dashboard. Follow these steps:

1. Go to the Quantity by State worksheet and open Worksheet | Actions.
2. Delete every action that is visible.
3. Now, select Add Action and select Go to URL.... Copy all the parameters, as shown, and click on the Insert arrow next to the Wikipedia URL. Fields from your data source will appear, which enables you to add a field to the URL that will automatically return a value based on the dashboard. Select State: https://en.wikipedia.org/wiki/<State>
4. Click on a state on your map on the Quantity by State worksheet and see how Wikipedia will open the respective web page.

URL actions are very useful when you want to show your users additional information available on websites. You can also use latitude and longitude values to open Google Maps(https://www.google.com/maps/place/), link the URL action to company internal websites with regulations, or maybe a ticketing system.

Navigation actions

The next action used to be commonly used in combination with a button to give the user a more interactive experience. Instead of clicking on tabs to go from one to another page, you will add buttons with an action to go to a specific sheet:

1. Open a new worksheet and call it Button.
2. create a calculated field called String with the code
```
"Go back to [...]"
```
This will serve as our button shortly.
3. Place the calculated field String by dragging and dropping Details as well as Label on it.
4. Select Shape from the Marks card dropdown and select any shape (and color) of your choice.
5. Hide the title and disable Tooltip.
6. Click on Label and select Bottom-Centre Alignment. The result will look as follows:
7. Go back to the Qty & Profit dashboard, select Floating, and drag the Button worksheet onto the dashboard; for example, into the bottom-left corner:
8. Click on Dashboard menu | Actions... and copy the following parameter:
9. Click OK and click on the Button worksheet on the Qty & Profit dashboard.

It will now take you back to the Latin American Population dashboard! Nowadays, this action is less used since the release of a specific navigation object, but it is useful to know about it for the sake of completeness.

Parameter actions

The next one in line is Parameter Action. This one is used to change a parameter by clicking on a data point in the dashboard rather than using the parameter control. Follow these steps:

1. Place the Categories worksheet on the Qty & Profit dashboard; for example, in the top-right corner:
2. Click on Dashboard | Actions... | Change Parameter... and copy the following parameters:
3.Go the worksheet Profit by State by Category and create a calculated field called Cat Param with the following code:
```
IF [Parameter Action] = [Category]THEN [Category]
ELSE""
END
```
4. Place Cat Param on the Filter shelf on the worksheet Profit by State by Category and select the empty value and enable Exclude, as shown here, before selecting OK:
5. If you now hover over any Category at the top, the following table will adjust accordingly:

Set actions

The last action that we will discuss is the set action. A set splits your data in two, In or Out. If you were to create a set called animals based on the following list: [cat, tree, dog, leopard, house, car], you would mark cat, dog, and leopard[ˈlepərd]豹 as In and tree, house, and car as Out. You can then use the set as a new field for your dashboard. Follow these steps:

1. Go to the Qty & Profit dashboard and select the western states California, Oregon, and Washington. Note that the pop-up window shows two rings in the top-right part. Using this icon is one way to create a set; go ahead and click on it:
2. The following window will open; click OK:
West Coast
In the data pane of the worksheet Quantity by State, a new field has been created—a set called West Coast.
3. See how it works by changing the Profit by State by Category worksheet as follows, removing State from Rows, and adding West Coast instead (you can remove Cat Param from the filter shelf or leave it; both will work):
The profit in the Out band takes into account all states except for California, Oregon, and Washington, whereas the In band shows numbers relative to those three mentioned states.
4. Go back to the Qty & Profit dashboard and select Dashboard | Actions... | Change Set Values... to copy the following parameter into a Set Action:
5. If you now click on any one or multiple states, all selected states will be the In group of your set. Therefore, the profit numbers will show the profit of your selection against the rest, like so:
6. You can also rename the In and Out values by clicking and selecting Edit Alias… :

Those were the 6 Tableau actions;

with the Filter and Highlight actions, you can make it easier for your users to find more detailed information by drilling down.
The Go to Sheet and Go to URL actions( the Navigation and URL actions) can be seen as interactive buttons that will redirect your user to additional information, either on another sheet or on the web.
And lastly, the Change Parameter and Change Set actions(the Parameter and Set actions) can be used to adjust a predefined value or multiple values, which will allow your users to interact with and analyze the data from different angles.

Export Dashboard Button

Next to actions, another item that's part of the user experience is a built-in download button. Adding such a button to your dashboard will allow your users to share an image or a screenshot, if they like, with others, use it for documentation, or for presentation purposes. Adding a button is super easy, just follow the steps:

1. Open the dashboard Golden Rectangle, select Floating in the bottom-left corner, and drag Download to the top-right corner:
2. Double-Click on the newly added button; a Edit Button popup will appear:
3. Click Presentation Mode button , then click Download PDF button, a Print to PDF popup will appear
4. Click OK, after which you can open the dashboard as a PDF from your desktop.

That's it! In only one simple step, you can make many users that like to have a printout very happy. A Tableau workbook as well as Tableau dashboards can become very overwhelming, depending on how many worksheets are being used to create them. Luckily, Tableau helps us with organizational features like Item hierarchy and Used In.

Item hierarchy

As we briefly mentioned in the Creating a collapsible menu section, if you open the Latin American Population dashboard, you will notice Item hierarchy at the bottom-left of the Layout pane. By clicking on the arrow next to the word Tiled, you will be able to see all the different part of the dashboards and the worksheets used in each container. You can also spot containers in containers and the structure of your layout:

Try to keep a logical order within your item hierarchy. This will make it easier for you to make changes to your dashboard later on. Which logic you choose is up to you; one I use often is high-level to more detailed, or most important to least important, or from spacious worksheets to smaller ones. A nice article on keeping your dashboard clean can be found here: https://www.thedataschool.co.uk/joe-beaven/layout-containers-how-to-get-your-item-hierarchy-under-control

Used In

The Used In feature shows you on which dashboards a certain worksheet is used. Just right-click on a worksheet tab and select Used In:

This feature is especially useful if you want to make changes to a worksheet. Before execution, you can check which dashboards will be affected, and the risk of breaking an existing dashboard will decrease tremendously[trəˈmendəsli]极大地,巨大地.

Summary

We began this chapter by considering visualization design theory. We looked at formatting rules, color rules, and rules about which visualization types to use and which we need to avoid. We also explored how to compromise when contradictory design goals are expressed by end users. Then, we discussed dashboard design principles. We covered three popular layouts: the Golden Rectangle, quad, and small multiple.

Afterward, we looked at how to use sheet selection techniques as an ally in good design. Specifically, we explored how to allow the end user to choose which visualization type to view, and how to hide and display filters(sheet swapping(collapse)) so as to make the best use of screen real estate. Finally, we discussed 6 actions and download buttons for a better user experience, as well as item hierarchies and the Used In feature, which is very handy for organizing your dashboard. This, in turn, will help to improve the layout design.

In the next chapter, we will focus on use cases. Going from a dataset to a product will be the theme, and we will practice doing this with a World Index dataset and a couple of geospatial ones. The knowledge you gained from the previous chapters will be very useful!

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