Just recently, I’ve come across a question on the LinkedIn platform, if it’s possible to create the following visualization in Power BI:

就在最近，我是否在LinkedIn平台上遇到了一个问题，是否有可能在Power BI中创建以下可视化文件：

Since one of the common business requests is to perform different comparisons between various time periods, I would say that Power BI has a lot to offer in this regard. I’ve already explained some basic calculations related to Time Intelligence, but there are obviously a significant number of users who are not quite familiar with them.

由于常见的业务请求之一是在不同时间段之间执行不同的比较，因此我想说Power BI在这方面可以提供很多帮助。 我已经解释了一些与时间智能有关的基本计算方法 ，但是显然有很多用户对它们不太熟悉。

First of all, I would like to emphasize a great feature called “Quick Measures”, where you get out-of-the-box solutions for multiple commonly used calculations, such as: Year-to-date total, Quarter-to-date total, Month-to-date total, Year-over-year change, Rolling Average, etc.

首先，我想强调一个称为“快速度量”的出色功能，在该功能中，您将获得针对多种常用计算的开箱即用解决方案，例如：年初至今总计，季度初至今总数，本月至今的总数，同比变化，滚动平均值等。

In order for Quick Measures to work, you need to have a properly defined Date table.

为了使“快速度量”起作用，您需要具有正确定义的“ 日期”表 。

However, we will not use Quick Measures here to achieve our original goal, so let’s switch over to a Power BI Desktop and get into the action! As usual, I will use the Contoso database for demo purposes.

但是，在这里我们不会使用“快速测量”来实现我们的原始目标，因此让我们切换到Power BI Desktop并开始行动吧！ 和往常一样，我将使用Contoso数据库进行演示。

制定基本措施 (Creating base measures)

The first step is to create a base measure to calculate Sales Amount:

第一步是创建一个基本度量来计算销售金额：

Sales Amt = SUM(FactOnlineSales[SalesAmount])

I will straight away create another measure, which will calculate same figures, but shifting one month back:

我将立即创建另一个度量，该度量将计算相同的数字，但将其移回一个月：

Sales Amt PM = CALCULATE([Sales Amt],
                        DATEADD(DimDate[Datekey],-1,MONTH)
                        )

There are multiple different ways to calculate this measure, but I prefer using DATEADD() function since it gives me more flexibility with shifting periods (that’s an official excuse:)…In reality, I’m coming from the SQL world, where DATEADD() is one of the most important functions when working with dates).

有多种不同的方法来计算此度量，但是我更喜欢使用DATEADD()函数，因为它为我提供了更灵活的移位周期(这是官方的借口：)……实际上，我来自SQL世界，其中DATEADD( )是处理日期时最重要的功能之一)。

Now, when I choose dates between November 17th and December 17th, I can see how my numbers correlate between themselves:

现在，当我选择11月17日至12月17日之间的日期时，可以看到我的数字之间的相互关系：

As you may notice, our formulas work well — as intended, we see that Sales Amt PM for December 17th, matches Sales Amt for November 17th. Also, our Line chart nicely visualizes trends for easier comparison, while Card visuals in the upper left corner show Sales Amount for the selected period and difference between two periods which we are comparing.

您可能会注意到，我们的公式运行良好-按预期，我们看到12月17日的Sales Amt PM与11月17日的Sales Amt相匹配。 同样，我们的折线图很好地可视化了趋势，以便于比较，而左上角的卡片视觉效果则显示了所选期间的销售额和我们正在比较的两个期间之间的差异。

For those differences, I’ve created two additional measures:

对于这些差异，我创建了两个附加度量：

Sales Amt Diff PM = [Sales Amt] - [Sales Amt PM]
Sales Amt Diff  PM % = DIVIDE([Sales Amt],[Sales Amt PM],BLANK()) - 1

Lower Card is conditionally formatted based on the values, so it goes red when we are performing worse than in the previous period, while it shows green when the outcome is the opposite:

Lower Card是根据值有条件格式化的，因此当我们的表现比上一时期差时，它会变成红色，而当结果相反时，它会变成绿色：

添加更多成分 (Adding more ingredients)

Now, that’s fine and you saw how we could easily answer the original question. However, I wanted to add some more ingredients here and enable our users to choose between MoM (Month-over-month) and YoY (Year-over-year) comparison.

现在，这很好，您看到了我们如何轻松回答原始问题。 但是，我想在此处添加更多成分，并使我们的用户可以在MoM(按月)和同比(按年)比较之间进行选择。

In our example, if we choose again dates between November 17th and December 17th, instead of showing me values from the previous month (comparing December 17th and November 17th), with YoY comparison I want to compare December 17th 2009 with December 17th 2008!

在我们的示例中，如果我们再次选择11月17日至12月17日之间的日期，而不是显示上个月的值(与12月17日和11月17日相比)，那么，我想将2009年12月17日与2008年12月17日进行比较！

So, let’s create a measure for this. Again, you can use different functions to achieve this, like SAMEPERIODLASTYEAR() function, but I want to keep consistency and therefore I will again use DATEADD():

因此，让我们为此制定一个措施。 同样，您可以使用不同的函数来实现这一点，例如SAMEPERIODLASTYEAR()函数，但是我想保持一致性，因此我将再次使用DATEADD()：

Sales Amt PY = CALCULATE([Sales Amt],
                        DATEADD(DimDate[Datekey],-1,YEAR)
                        )

Same as for MoM calculations, two additional measures are needed to calculate differences for YoY figures:

与MoM计算相同，需要另外两项措施来计算同比数据差异：

Sales Amt Diff PY = [Sales Amt] - [Sales Amt PY]
Sales Amt Diff PY % = DIVIDE([Sales Amt],[Sales Amt PY],BLANK()) - 1

I will then create two bookmarks, so that users can navigate to MoM or YoY, by clicking on respective buttons:

然后，我将创建两个书签，以便用户可以通过单击相应的按钮来导航至MoM或YoY：

By default, they should see MoM comparison, but as soon as they click on YoY button, the report will look slightly different:

默认情况下，他们应该看到MoM比较，但是一旦他们点击“同比”按钮，报告就会看起来有些不同：

You can notice that numbers in the card visuals changed to reflect YoY difference calculation, while Line chart also shows different trends!

您会注意到，卡片视觉效果中的数字已更改为反映同比差异计算，而折线图也显示了不同的趋势！

Before we conclude, here is the final behavior of our report:

在得出结论之前，这是我们报告的最终行为：

结论 (Conclusion)

As we saw, Power BI is quite a powerful tool when it comes to time intelligence calculations. Basically, all kinds of comparisons between different periods can be created — most common ones even without needing to write a single line of DAX!

如我们所见，在时间智能计算方面，Power BI是一个功能强大的工具。 基本上，可以创建不同时期之间的各种比较-即使不需要编写DAX行，也可以进行最常见的比较！

If you need to expand on built-in Quick Measures, there is a whole range of useful Time Intelligence functions. You can check all of them in more depth here.

如果您需要扩展内置的“快速测量”，则有很多有用的时间智能功能。 您可以在此处更深入地检查它们。

Thanks for reading!

谢谢阅读！