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How to Outsmart Your Peers on Three Essential Tableau Concepts

Date : 2019-10-29

The road to the mastery of Tableau begins with three essential concepts. We will consider each of the following in turn:

Dimensions and measures
Row-level, aggregate level, and table level
Continuous and discrete

Essential concept 1 - dimensions and measures

Tableau categorizes every field from an underlying data source as either a dimension or a measure. A dimension is qualitative or, to use another word, categorical. A measure is quantitative or aggregable. A measure is usually a number but maybe an aggregated, non-numeric field, such as MAX(Order Date). A dimension is usually a text, Boolean, or date field, but may also be a number, such as Order_ID. Dimensions provide meaning to numbers by slicing those numbers into separate parts/categories. Measures without dimensions are mostly meaningless.

Let\'s consider an example to better understand this concept.

Exercise - dimensions, and measures

In the workbook associated with this chapter, navigate to the worksheet entitled Dimensions and Measures.
Drag Sales to the Rows shelf.
Place Order Date and Category on the Columns shelf:

The result of step 2 is mostly meaningless. The measure Sales is about $2.3 million, but without the context supplied by slicing the measure with one or more dimensions, there is no way to understand what it means. Step 2 brings meaning. Placing Order Date and Category on the Columns shelf provides context, which imparts meaning to the visualization. Get in touch for more update at Tableau Training

Essential concept 2 - Row Level, Aggregate Level, and Table Level

There are three levels of calculations in Tableau: Row, Aggregate, and Table. To understand how these three levels function, it is important to be familiar with the Tableau process flow:

Let\'s follow the flow of the preceding figure to understand where the three levels of calculations take place. We will do so via an example considering the fields Profit and Sales. Assuming SQL, consider the following calculation types, calculated fields, and queries. Note that the SQL is simplified for the sake of the example:

Calculation

type

Row-level
Aggregate level
Table level

The calculated field in Tableau

Profit/Sales
Sum(Profit)/Sum(Sales)
WINDOW_AVG(Sum([Number of Records])/Sum(Quantity))

Query passed to Data Source

SELECT SUM(Profit / Sales)

FROM Orders$

SELECT SUM(Profit),

SUM(Sales) FROM Orders$

SELECT SUM(Profit),

SUM(Sales) FROM Orders$

For the row-level calculation, the computation is completed by the data-source engine. Tableau merely displays the results. For the aggregate level calculation, Tableau does a little bit more than display the results; it also divides the two numbers that are returned by the data-source engine. For the table-level calculation, Tableau may perform additional computations on the returned results. Let\'s explore further via an exercise using similar calculated fields.

Exercise - Row Level, Aggregate Level, and Table Level

In the workbook associated with this chapter, navigate to the worksheet entitled Row_Agg_Tbl.
Navigate to Analysis | Create Calculated Field to create the following calculated fields. Note that each must be created separately; it is not possible in this context to create a single calculated field containing all three calculations:

Name

Lev - Row

Lev - Agg

Lev - Tab

Calculation

[Number of Records]/[Quantity]

SUM([Number of Records])/SUM(Quantity)

WINDOW_AVG([Lev - Agg])

In the Data pane, right-click on the three calculated fields you just created and navigate to Default Properties | Number format.
In the resulting dialog box, select Percentage and click OK.
Place Order Date on the Columns shelf.
Place Measure Names on the Rows shelf and Measure Values on the Text shelf.
Exclude all values except for Lev - Row, Lev - Agg, and Lev - Tab:

Exercise commentary

Lev - Agg is an aggregate-level calculation. The aggregated results of [Number of Records] and [Quantity] are returned by the data-source engine. Tableau divides those results and displays values for each year.

Lev - Row is a row-level calculation. The computation is completed by the data-source engine. [Number of Records] is divided by [Quantity] for each row of the underlying data. The results are then summed across all rows. Of course, in this case, the row-level calculation does not provide useful results; however, since a new Tableau author may mistakenly create a row-level calculation when an aggregate-level calculation is what is needed, the example is included here.

Lev - Tab is a table-level calculation. Some of the computation is completed by the data-source engine; that is, the aggregation. Tableau completes additional computation on the results returned from the data-source engine. Specifically, the results of Lev - Agg are summed and then divided by the number of members in the dimension. For the preceding example, this is (26.29% + 26.34% + 26.30% + 26.55%)/4. Once again, the results, in this case, are not particularly helpful, but they do demonstrate knowledge the budding Tableau author should possess. Learn more info at Tableau Online Training

Essential concept 3 - continuous and discrete

Continuous and discrete are not concepts that are unique to Tableau. Indeed, both can be observed in many areas. Consider the following example:

This is an image of two rivers - River-Left and River-Right. Water is flowing in River-Left. River-Right is composed of ice cubes. Could you theoretically sort the ice cubes in River-Right? Yes! Is there any way to sort the water in River-Left? In other words, could you take buckets of water from the bottom of the river, cart those buckets upstream and pour the water back into River-Left and thereby say, I have sorted the water in the river? No. The H2O in River-Left is in continuous form; that is, water. The H2O in River-Right is in discrete form; that is, ice.

Having considered an example of continuous and discrete in natural phenomena, let\'s turn our attention back to Tableau. Continuous and discrete in Tableau can be more clearly understood via the following seven considerations:

Continuous is green. The discrete is blue. Select any field in the Data pane or place any field on a shelf and you will note that it is either green or blue. Also, the icons associated with fields are either green or blue.
Continuous is always numeric. Discrete may be a string.
Continuous and discrete are not synonymous with dimension and measure. It is common for new Tableau authors to confuse continuous with measure and discrete with dimension. They are not synonymous. A measure may be either discrete or continuous. Also, a dimension, if it is a number, maybe discrete or continuous. To prove the point, right-click on any numeric or date field in Tableau and note that you can convert it:
Discrete is sortable. Continuous is not sortable. Sortable/not sortable behavior is most easily observed with dates, as shown in the following screenshot:
Continuous colors are gradients. Discrete colors are distinct. The following example shows Profit as continuous and then as discrete. Note the difference in how colors are rendered. The left portion of the following screenshot demonstrates that continuous results in gradients and the right portion demonstrates that discrete results in distinct colors:
Continuous pills can be placed to the right of discrete pills, but not to the left. In the following screenshot, note that the Tableau author can place Region to the right of Year when Year is discrete. Further note that the Tableau author is unable to place Region to the right of Year when Year is continuous:
Continuous create axes. Discrete create headers. Note that in the left portion of the following screenshot, Year(Order Date) is continuous and the Year of Order Date axis is selected. Since the Year of Order Date is an axis, the entire x-plane is selected. In the right portion of the screenshot, Year(Order Date) is discrete and 2014 is selected. Since 2014 is a header, only it is selected and not the entire x-plane:

Angular Frameworks versus libraries

Date : 2019-12-17

You have two choices to aid your development efforts—either choose a framework like Rails or AngularJS or choose smaller libraries. The Clojure community (in general) dislikes frameworks, so there wasn\'t a full-fledged web framework such as Rails in the Clojure landscape for long. Let\'s look at the pros and cons of choosing one over the other:

Frameworks

Pros

Less R&D is needed: A framework will solve a lot of problems, and you won\'t need as many libraries to get the work done.
Better code quality: Since the framework has a prescribed way of doing things, you can follow the prescribed best practices and your code will attain a much better quality (than if you were to do things yourself).
Uniformity in code: Different codebases written using the same framework are easier to understand. This is because they all will be following the same structure, patterns, and so on.

Cons

Bigger learning curve: Depending on what a framework does, it can be big or small and the learning curve will be proportionately large or small.
Code quality: Since you are using many different libraries, you might have to come up with ways to organize code. Consequently, the code quality might suffer (this is less of a problem for experienced developers).
Less flexible: Any task for which there is a prescribed way of doing things is easy to implement. However, it may prove to be difficult to implement tasks outside the purview of the framework. For more to learn Angular Certification
Difficult to fix framework bugs: It might be difficult to fix bugs in the framework itself.

Libraries