USING MULTIPLE MEASURES IN TABLEAU

Working with Multiple Measures in Tableau

Using Multiple Measures:-

• There are lots of different ways to compare multiple measures in a single view. 
• For example,you can create individual axes for each measure or you can blend the two measures to share an axis and finally, you can add dual axes where there are two independent axes layered in the same pane.
• In any of these cases you can customize the marks for each axis to use multiple mark types and add different levels of detail. Views that have customized marks are called combination charts.

          We can use Multiple measures in 3 ways.

           1.Individual Axes

           2.Blended Axes

           3.Dual Axes

Changing the Repository Location

Changing the Repository Location

::Changing the Repository Location:-::

You can specify a new location for the Tableau repository if you are not using the default

location in your Documents folder. For instance, if you are required to have your data on a

network server instead of on your local machine, you can point Tableau at the remote

repository.

1. Select File > Repository Location.

2. Select a new folder that will act as the new repository location in the Select a Repository

dialog box.

3. Restart Tableau so that it uses the new repository.

Note:-

Changing the repository location does not move the files contained in the original repository.

Instead, Tableau creates a new repository where you can store your files.

Organizing Sheets

Organizing Sheets

::Organizing Sheets:-::
There are three ways to navigate and view the sheets in a workbook: 
1)Tabs at the bottom of the workbook
2)Filmstrip
3)Sheet sorter. 

1)Sheet Tabs:-

• Each sheet, whether worksheet or dashboard, is represented as a tab along the bottom of the workbook. 
• Dashboards are indicated with a Dashboard icon next to the sheet name. Simply select the tab for the sheet you want to open.

• In the bottom right corner of the application window, there are several controls that you can use to advance through each sheet or quickly jump to the first or last sheet in the workbook. 
• These controls are only available when there are too many sheet tabs to show across the bottom of the application window. You can also navigate between sheets using the Window menu or move through the multiple worksheets by pressing Ctrl + F6 on your keyboard

• To make it easier to identify and group sheets, you can assign each sheet tab a color. Rightclick the tab and select Color. You can select from seven different colors. Selecting None resets the color back to the default.

2)FILMSTRIP:-

• Similar to the sheet tabs, the filmstrip displays along the bottom of the workbook. However,instead of sheet names, the filmstrip shows a thumbnail image of each sheet. 
• The filmstrip is useful when you are using Tableau to present your analysis and works well when you are working in Presentation mode

3.Sheet Sorter:-

• The sheet sorter shows all sheets in a workbook as thumbnail images on a single page and is similar to the slide sorter in Microsoft Power Point. 
• The sheet sorter is useful when you have a large number of sheets in a workbook. 
• Open the sheet sorter by clicking the sheet sorter tab in the upper right corner of the workbook.

WORKSPACE COMPONENTS

WORKSPACE COMPONENTS

::Data Window:-::

• Data fields appear on the left side of the workspace in the Data window. 
• You can hide and show the Data window by clicking the minimize button in the upper right corner of the Data window. 
• The Data window collapses to the bottom of the workbook. Click the minimize button again to show the Data window.

Tableau Data Extract API

::Tableau Data Extract API::

Use the Tableau Data Extract API to connect to data that is not a supported data source. With
the Tableau Data Extract API, you create a program that accesses and processes your data.
You then use that program to create a Tableau Data Extract (TDE) file.
The Data Extract API is available for developers on Windows platforms. Go to http://www-
.tableausoftware.com/data-extract-api, and choose the appropriate version for your platform
and programming language:

•    Data Extract API – Python – 32-bit
•    Data Extract API – Python – 64-bit
•    Data Extract API - C/C++/Java – 32-bit
•    Data Extract API - C/C++/Java - 64bit

::Notes for Developers::

The Data Extract API includes a sample program, makeorder, coded in each supported language
to demonstrate a typical usage scenario: creating an extract containing product orders.
The application creates the extract order.tde with several columns of different types. The general
flow of the sample programs is:
1. Open an Extract object to create a new file.
2. Define the extract’s schema using a TableDefinition.
3. Add the Extract table.
4. Insert rows.
5. Close all objects.

It is important to free memory by closing all objects, and it is particularly critical to ensure
Extract objects are cleaned up properly, particularly in non-native execution environments. See
the note sections below for language-specific details.

String columns in a Data Extract can be 8- or 16-bit and can be sorted according to many available
collations. By default, strings are sorted according to their binary representation, though
this can be changed on a per-table or per-column basis.

::Python Notes::

Objects in the Data Extract API are automatically closed by _del_ when necessary. While garbage collection handles the vast majority of concerns related to releasing resources, it is important to note that the virtual machine provides no guarantee that any particular object will ever be freed. While most objects are merely memory, Extract objects represent physical files
created when close is invoked. Therefore, it is not safe to rely on garbage collection to close
Extract objects. We recommend using with statements to ensure Extract instances are
cleaned up. Alternatively, you can explicitly call close

::Java Notes::

Data Extract API objects are automatically closed by finalize() as necessary. The Java
Virtual Machine does not guarantee that any particular object is ever garbage collected. While
most objects are merely memory that can be safely reclaimed by the operating system at JVM
shutdown, Extract objects represent physical files that are created when close() is invoked.
Therefore, it is important to invoke Java Notes Data Extract API objects are automatically closed by finalize()as necessary. The Java Virtual Machine does not guarantee that any particular object is ever garbage collected. While most objects are merely memory that can be safely reclaimed by the operating system at JVM shutdown, Extract objects represent physical files that are created when close() is invoked.Therefore, it is important to invoke Extract.close() for all Extract instances. We recommend using the try-with-resources construct introduced in Java 7. For earlier versions of Java, you must call Java Notes

::C++ Notes::

Data Extract API objects should be managed according to standard memory management
best practices, such as using stack variables or smart pointers. As in other languages, all
objects have a Close() method to free internal resources. Close() is invoked by the
destructor when necessary. However, it is important to note that Extract::Close() may
throw an exception, so it is safer to call it explicitly, rather than allowing an exception to potentially escape the destructor.

::C Notes::

Objects in the Data Extract C API are managed through opaque TAB_HANDLEs. Every
created object must be closed. It is advisable to free objects in the reverse order of creation. explicitly.
.

EXTRACTING DATA

::Extracting Data::

Extracts are saved subsets of a data source that you can use to improve performance, upgradeyour data to allow for more advanced capabilities, and analyze offline. You can create an extract by defining filters and limits that include the data you want in the extract. After you create an extract you can refresh it with data from the original data source. You can either fully refresh the data, replacing all of the extract contents; or you can increment the extract; which only adds rows that are new since the last refresh.

Extracts can:

• l Improve performance. For file based data sources such as Excel or Access, a full extract takes advantage of the Tableau data engine. For large data sources, a filtered extract can limit the load on the server when you only need a subset of data.

• l Add functionality to file based data sources, such as the ability to compute Count Distinct.
• l Provide offline access to your data. If you are traveling and need to access your data offline, you can extract the relevant data to a local data source.

DATA BLENDING

DATA BLENDING:-

• Data blending is when you blend data from multiple data sources on a single worksheet. 
• The data is joined on common dimensions. Data Blending does not create row level joins and is not a way to add new dimensions or rows to your data. Instead, data blending should be used when you have related data in multiple data sources that you want to analyze together in a single view. For example, you may have Sales data collected in an Oracle database and Sales Goal data in an Excel spreadsheet.
• To compare actual sales to target sales, you can blend the data based on common dimensions to get access to the Sales Goal measure.
• To integrate data, you must first define common dimensions between the primary and secondary data sources. For example, when blending Actual and Target sales data, the two data sources may have a Date field in common. The Date field must be specified as a linking field.
•  If the two dimensions don’t have the same name, you can define a custom relationship that creates the correct mapping between fields.
• For each data source that is used on the sheet, a query is sent to the database and the results are processed. Then all the results are left joined on the common dimensions.
•  The join is done on the member aliases of the common dimensions so if the underlying values aren’t an exact match, you can fix it up in Tableau.
• In general, a good test to see whether data can be integrated smoothly is to drag the dimensions from the primary data source into a text table on one sheet. Then on another sheet, drag the same fields from the secondary data source into a text table. 
• If the two tables match up then the data is most likely going to blend correctly