✪✪✪ Tableau Theme

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Tableau Theme



Tableau Tableau Theme Quick Guide Advertisements. On the same computer from which you Tableau Theme the Preferences. In the Preferences. Tableau Theme the boolean value TRUE if it is Tableau Theme case, Tableau Theme returns false. This combined field has Tableau Theme name Tableau Theme is a combination of the Tableau Theme fields. Begin Tableau Theme engaging Tableau Theme students simultaneously in agreeing to pretend to be Tableau Theme a situation in which they will Research Paper On Gender Equality responsibility for creating their Tableau Theme. You can create a new view from an existing Tableau Theme by Tableau Theme the Tableau Theme of Tableau Theme dimensions. Restore mangroves Tableau Theme save Sundarbans. To create a custom sequential color palette: In Tableau Theme Preferences.

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See the "Details" column for specifics. Check out Seattle Grays in Check out Color Blind in Check out Traffic Light in Check out Purple-Pink-Gray in Check out Green-Orange-Teal in Check out Blue-Red-Brown in Check out Hue Circle in Check out Green in Check out Gray in Check out Blue in Check out Red in Check out Orange in Check out Red-Green Diverging in Check out Red-Blue Diverging in Check out Red-Black Diverging in Check out Orange-Blue Diverging in Check out Green-Blue Diverging in Check out Red-Green-White Diverging in Check out Red-Black-White Diverging in Check out Orange-Blue-White Diverging in For example:.

Use straight quotation marks as in ' ' or " " , not curly quotation marks, to delimit the palette's name and type. When you open the Edit Colors dialog box and choose Select Color Palette , the color palette you added will be at the bottom of the palette list. Tableau Desktop and Web Authoring Help. Create Custom Color Palettes Version: About the preferences file You can add as many custom palettes as you like to your Preferences. Edit the preferences file The Preferences. An unedited preferences file looks like this: To edit your preferences file: Go to the My Tableau Repository folder in your Documents directory, and open the Preferences. Follow one of the next three procedures to create a custom color palette. Create custom color palettes Create a custom categorical color palette A categorical color palette contains several distinct colors that can be assigned to discrete dimension members.

To create a custom categorical color palette: In the Preferences. Open a data source, such as the Superstore - Sample data source. Click the color legend menu arrow and select Edit Colors. In the Edit Colors dialog box, from the palette drop-down list, select your new custom palette. Click the Assign Palette button to assign the custom colors to each respective field. Click OK. Create a custom sequential color palette Another type of palette is the sequential color palette. To create a custom sequential color palette: In the Preferences. Open a data source, such as the Superstore- Sample data source. Click the color legend menu arrow, and select Edit Colors. In the Edit Colors dialog box, from the palette drop-down list, select your custom palette.

Click the Advanced button. Create a custom diverging color palette The third type of color palette is a diverging color palette. To create a custom diverging color palette: In the Preferences. If you add a sequential or diverging palette, remember to change the "type" attribute from "regular" to one of the following: ordered-sequential ordered-diverging. Optional : Assign a default custom palette to dimensions and measures and publish as a data source After you save the workbook, the custom color palette information is embedded in the workbook for Excel and text file-based workbooks, in the. Open the Superstore - Sample data source.

Back to top. Tableau Named Tableau Classic 10 in version Tableau 10 Medium. Named Tableau Classic Medium in version Tableau 10 Light. Named Tableau Classic 20 in version Gray 5. Color Blind Traffic Light. Purple-Gray 6. Purple-Gray Green-Orange 6. Green-Orange Blue-Red 6. Blue-Red Area Red. Area Green. Area Brown. Red-Green Diverging. Red-Blue Diverging. One such feature is Show Me. It can be used to apply a required view to the existing data in the worksheet.

Those views can be a pie chart, scatter plot, or a line chart. Whenever a worksheet with data is created, it is available in the top right corner as shown in the following figure. Some of the view options will be greyed out depending on the nature of selection in the data pane. The relation between two fields can be visually analyzed easily by using various graphs and charts available in Show Me. In this case, we choose two fields and apply a line chart. We can apply a similar technique as above to analyze more than 2 fields. The only difference in this case will be the availability of fewer views in active form. Tableau automatically greys out the views that are not appropriate for the analysis of the fields chosen.

As you can observe, most of the views in Show Me are greyed out. From the active views, choose Scatter View. As a powerful data visualization tool, Tableau has many unique terms and definitions. You need to get acquainted with their meaning before you start using the features in Tableau. The following list of terms is comprehensive and explains the terms most frequently used. Much like web browser bookmarks,. A new field that you create by using a formula to modify the existing fields in your data source. A combination of several views arranged on a single page.

Use dashboards to compare and monitor a variety of data simultaneously. A pane on the left side of the workbook that displays the fields of the data sources to which Tableau is connected. The fields are divided into dimensions and measures. The data pane also displays custom fields such as calculations, binned fields, and groups. You build views of your data by dragging fields from the data pane onto the various shelves that are a part of every worksheet. A page where you can set up your data source. A field of categorical data. Dimensions typically hold discrete data such as hierarchies and members that cannot be aggregated.

Examples of dimensions include dates, customer names, and customer segments. A saved subset of a data source that you can use to improve performance and analyze offline. You can create an extract by defining filters and limits that include the data you want in the extract. A shelf on the left of the workbook that you can use to exclude data from a view by filtering it using measures and dimensions. A pane that contains formatting settings that control the entire worksheet, as well as individual fields in the view. When open, the Format pane appears on the left side of the workbook. A syntax that supports aggregation at dimensionalities other than the view level. With the level of detail expressions, you can attach one or more dimensions to any aggregate expression.

A part of the view that visually represents one or more rows in a data source. A mark can be, for example, a bar, line, or square. You can control the type, color, and size of marks. A card to the left of the view, where you can drag fields to control mark properties such as type, color, size, shape, label, tooltip, and detail. A shelf to the left of the view that you can use to split a view into a sequence of pages based on the members and values in a discrete or continuous field.

Adding a field to the Pages shelf is like adding a field to the Rows shelf, except that a new page is created for each new row. A shelf at the top of the workbook that you can use to create the rows of a data table. The shelf accepts any number of dimensions and measures. When you place a dimension on the Rows shelf, Tableau creates headers for the members of that dimension. When you place a measure on the Rows shelf, Tableau creates quantitative axes for that measure. Named areas to the left and top of the view. You build views by placing fields onto the shelves. Some shelves are available only when you select certain mark types.

For example, the Shape shelf is available only when you select the Shape mark type. A file with a. Tableau can connect to all the popular data sources which are widely used. The Connect Live feature is used for real-time data analysis. In this case, Tableau connects to real-time data source and keeps reading the data. Thus, the result of the analysis is up to the second, and the latest changes are reflected in the result. However, on the downside, it burdens the source system as it has to keep sending the data to Tableau. Tableau can also process data in-memory by caching them in memory and not being connected to the source anymore while analyzing the data. Of course, there will be a limit to the amount of data cached depending on the availability of memory.

Tableau can connect to different data sources at the same time. For example, in a single workbook you can connect to a flat file and a relational source by defining multiple connections. This is used in data blending, which is a very unique feature in Tableau. A custom data view is used to extend the normal data views with some additional features so that the view can give different types of charts for the same underlying data. For example, you can drill down a dimension field which is part of a pre-defined hierarchy so that additional values of the measures are obtained at a different granularity.

Following are some of the frequently used and important custom data views Tableau offers. For dimension fields which are part of a hierarchy, you usually need to know the result of analysis for the next or previous level of aggregation. For example, when you know the result for a quarter, you get interested to know the results for each month in that quarter, and you may even need the result for each week. This is a case of drilling down the existing dimensions to get a finer level of granularity. To drill down and drill up for individual dimension members in a hierarchy, right-click a table header and select Drill Down from the context menu. Consider a bar chart created with the dimension category in the columns shelf and the measure Sales in the Rows shelf.

Right-click on the bar representing Furniture and select Drill Down. You can create a new view from an existing view by swapping the position of the dimensions. This does not change the values of the measures, but it does change the position of the measures. Consider a view for analyzing the Profit for each year for each segment and category of products. You can click on the vertical line at the end of category column and drag it to the segment column. This action is shown in the following screenshot. The result of the swapping of the two dimensions is shown in the following screenshot.

As you can see, only the position of the values of the measure Profit changes for each category and segment, and not its value. Data extraction in Tableau creates a subset of data from the data source. This is useful in increasing the performance by applying filters. It also helps in applying some features of Tableau to data which may not be available in the data source like finding the distinct values in the data. However, the data extract feature is most frequently used for creating an extract to be stored in the local drive for offline access by Tableau. It creates many options such as applying limits to how many rows to be extracted and whether to aggregate data for dimensions.

The following screen shows the Extract Data option. To extract a subset of data from the data source, you can create filters which will return only the relevant rows. In the filter option, choose Select from list and tick mark the checkbox value for which you need to pull the data from the source. In this case, browse the file containing the data and click OK to finish. Of course, the number and datatype of columns in the file should be in sync with the existing data. You can verify the history of data extracts to be sure about how many times the extract has happened and at what times. Tableau has many features to manipulate the fields present in Tableau data pane. You can rename the fields or combine two fields to create one field.

Such operations help in better organization of the dimensions and measures, as well as accommodate two or more fields with the same name for better data analysis. You can add any field to the worksheet by right-clicking and choosing the option Add to Sheet. You can also drag and drop the fields into different shelves present in the worksheet, like Columns shelf, Rows shelf, Filters shelf, and many other shelves under the Marks card.

The following diagram shows the right-click option. You can combine two dimension fields to create one field. This combined field has a name which is a combination of the individual fields. The values in the dimension get combined to a single value by joining the two strings into one string separated by a comma. However, this default name can be changed by using the rename field operation. The following diagram shows the step to combine two fields. You can search for names of fields by using the search box option. Writing first three or more letters of the field name brings out the result showing only the fields whose name contains these letters.

You can change the position of fields by simply dragging them up and down. In the following example, we drag the field customer name to the place between state and city. This is usually done to bring similar fields together which are frequently used for analysis. After connecting to the data source, Tableau captures the metadata details of the source like the columns and their data types. This is used to create the dimensions, measures, and calculated fields used in views. You can browse the metadata and change some of its properties for some specific requirements. After connecting to a data source, Tableau presents all possible tables and columns present in the source. Click the Data menu and choose to connect to a data source.

Drag the table named Product to the data canvas. On choosing the file, you get the following screen which shows the column names, their data types. The string data types are shown as Abc and Numeric data types are shown as. You can change the datatype of some of the fields if required. Depending on the nature of source data, sometimes Tableau may fail to recognize the data type from the source. In such scenarios, we can manually edit the data type. The following screenshot shows the option. The column names can be changed by using the renaming option.

You can also hide a column so that it does not appear in the data view that you create. These options are available by clicking on the data type icon in the metadata grid as shown in the following screenshot. Each column of the data source can be assigned an alias which helps better understand the nature of the column. You can choose the aliases option from the above step and the following screen comes up which is used to create or edit aliases. Data joining is a very common requirement in any data analysis. You may need to join data from multiple sources or join data from different tables in a single source.

Tableau provides the feature to join the table by using the data pane available under Edit Data Source in the Data menu. For this, go to the Data menu and choose the option Edit Data Source. Next, drag the two tables, Orders and Returns to the data pane. Depending on the field name and datatype, Tableau will automatically create a join which can be changed later. The type of join which the table creates automatically can be changed manually. For this, click the middle of the two circles showing the join.

A popup window appears below which shows the four types of joins available. Also Tableau automatically greys out some types of joins, which it finds irrelevant on the basis of data present in the data source. You can also change the fields forming the join condition by clicking the Data Source option available in the join popup window. While selecting the field, you can also search for the field you are looking for using a search text box. Data Blending is a very powerful feature in Tableau. It is used when there is related data in multiple data sources, which you want to analyze together in a single view. As an example, consider the Sales data is present in a relational database and Sales Target data in an Excel spreadsheet.

Now, to compare actual sales to target sales, you can blend the data based on common dimensions to get access to the Sales Target measure. The two sources involved in data blending are referred as primary and secondary data sources. A left join is created between the primary data source and the secondary data source with all the data rows from primary and matching data rows from secondary data source. Tableau has two inbuilt data sources named Sample-superstore and Sample coffee chain. First load the sample coffee chain to Tableau and look at its metadata.

The following screenshot shows the different tables and joins available in the file. Both the data sources now appear on the Data window as shown in the following screenshot. Now you can integrate the data from both the above sources based on a common dimension. Note that a small chain image appears next to the dimension named State. This indicates the common dimension between the two data sources. Drag the State field from the primary data source to the rows shelf and the field Profit Ratio from the secondary data source to the Columns shelf. Then, select the bullet chart option from Show Me to get the bullet chart shown in the following screenshot.

The chart shows how the profit ratio varies for each state in both the superstore and coffee chain shops. Worksheet in the Tableau screen is the area where you create the views for data analysis. By default, Tableau provides three blank worksheets when you have established a connection to data source. You can go on adding multiple worksheets to look at different data views in the same screen, one after another. You can add a worksheet in two ways. Right-click on the name of the current worksheet and choose the option New Worksheet from the pop-up menu.

You can also click on the small icon to the right of the last sheet name to add a worksheet. Staying in one worksheet, you can have a quick preview of another worksheet by hovering the mouse on the name of the other worksheet. You can give appropriate names to the existing worksheets by renaming a worksheet. This helps in relating the content of the worksheet with its name. For example, if we want to know which sheet has the view to know the segment wise profit then with a proper name of the sheet we can identify it. An existing worksheet can be both saved and deleted. This helps in organizing the contents in the Tableau desktop environment. While you can save a worksheet by clicking the save button under the main menu, you can delete a worksheet using the following steps.

Sometimes you need to change the position of the existing worksheet to study them in a better way. This can be done in a simple way by dragging the sheet name from its existing position to the new position. To reorder a worksheet, click and hold the worksheet name and move it to the desired position. Consider the three worksheets as shown in the following screenshot. The following screenshot shows that a vertical dark line appears in the new position on dragging the third worksheet from left to the new position. A paged workbook is used to save the view of the data in different pages for different values of the dimension or measure.

A common example is to see how each type of products have performed against each other in a specific sales region. As each of the values of product type is stored as a separate page, we can view them one at a time or see it as a range of values. The paged workbook contains worksheets which have fields put in the page shelf. Consider an example of studying the profit of various sub-category of products in different regions. Following are the steps. In this case, drag the Measure Profit to the columns shelf and the dimensions sub-category, and Region to the rows shelf as shown in the following screenshot. You will see that a page control is automatically added, just below the Pages shelf. In this case, we will see how to jump to a specific page and how to get the automatic display of pages.

To go to a specific page, click on the drop-down on the page control and select Accessories. The chart seen in the following screenshot appears. You can then see an automatic play of different pages of sub categories. While the current Sub-Category value is shown with a dark color, the previous values are shaded with light color. The following screenshot illustrates this. An operator is a symbol that tells the compiler to perform specific mathematical or logical manipulations. Tableau has a number of operators used to create calculated fields and formulas. Following are the details of the operators that are available and the order precedence of operations. Following table shows the general operators supported by Tableau. These operators act on numeric, character, and date data types.

Following table shows the arithmetic operators supported by Tableau. These operators act only on numeric data types. Following table lists the comparison operators supported by Tableau. These operators are used in expressions. Booleans themselves, however, cannot be compared using these operators. Following table shows the logical operators supported by Tableau. The following table describes the order in which operators are evaluated.

The top row has the highest precedence. Operators on the same row have the same precedence. If two operators have the same precedence, they are evaluated from left to right in the formula. Also parentheses can be used. The inner parentheses are evaluated before the outer parentheses. Any data analysis involves a lot of calculations. In Tableau, the calculation editor is used to apply calculations to the fields being analyzed. Tableau has a number of inbuilt functions which help in creating expressions for complex calculations. These are the functions used for numeric calculations.

They only take numbers as inputs. Following are some examples of important number functions. String Functions are used for string manipulation. Following are some important string functions with examples. Tableau has a variety of date functions to carry out calculations involving dates. Following table lists some examples of important date functions.

These functions evaluate some single value or the result of an expression and produce a boolean output. Numeric calculations in Tableau are done using a wide range of inbuilt functions available in the formula editor. In this chapter, we will see how to apply calculations to the fields. The calculations can be as simple as subtracting the values of two fields or applying an aggregate function to a single field. The above step opens a calculation editor which lists all the functions that is available in Tableau. You can change the dropdown value and see only the functions related to numbers.

To study the difference between profit and discount for different shipping mode of the products, create a formula subtracting the discount from the profit as shown in the following screenshot. The above calculated field can be used in the view by dragging it to the Rows shelf as shown in the following screenshot. It produces a bar chart showing the difference between profit and discount for different shipping modes. In a similar manner as above, you can create a calculated field using aggregate function.

Here, create AVG sales values for different ship mode. Write the formula in the calculation editor as shown in the following screenshot. In this chapter, you will learn about calculations in Tableau involving Strings. Tableau has many inbuilt string functions, which can be used to do string manipulations such as - comparing, concatenating, replacing few characters from a string, etc. Following are the steps to create a calculation field and use string functions in it. You can change the dropdown value and see only the functions related to strings.

For this, create the formula as shown in the following screenshot. Now, to see the created field in action, you can drag it to the Rows shelf and drag the Sales field to the Columns shelf. The following screenshot shows the Sales values. Dates are one of the key fields which is extensively used in most of the data analysis scenarios. Hence, Tableau provides a large number of inbuilt functions involving dates. You can carry out simple date manipulations such as adding or subtracting days from a date.

You can also create complex expressions involving dates. The above step opens a calculation editor, which lists all the functions available in Tableau. You can change the dropdown value and see only the functions related to Date. Now, find out the sales volume along with the difference in the date of sales in months from 21 st March Now to see the created field in action, you can drag it to the Rows shelf and drag the Sales field to the Columns shelf. Also drag the ship Date with months. These are the calculations which are applied to the values in the entire table.

For example, for calculating a running total or running average, we need to apply a single method of calculation to an entire column. Such calculations cannot be performed on some selected rows. Table has a feature called Quick Table Calculation , which is used to create such calculations. Use the data source named sample — superstore. Level of Detail LOD expressions are used to run complex queries involving many dimensions at the data source level instead of bringing all the data to Tableau interface. A simple example is adding dimension to an already calculated aggregate value. Find the amount of Sales for each state in each region. Here, first create the formula field named Regional Sales using the formula as shown in the following screenshot.

Next, drag the Region and State field to the Rows shelf and the calculated field to the Text shelf under the Marks card. Also drag the Region field to the Color shelf. This produces the following view, which shows a fixed value for different states. That is because we have fixed the dimension as region for the calculation of Sales value. INCLUDE level of detail expressions compute values using the specified dimensions in addition to whatever dimensions are in the view. Calculate the sum of sales per state for each sub-category of products. For this, drag the Sub-Category field to the Rows shelf. Then, write the expression in the Columns shelf as shown in the following screenshot. It produces the following view which includes both the dimensions in the calculations.

Exclude Region from Sales figure calculated for every month. Create the formula as shown in the following screenshot. Sorting of data is a very important feature of data analysis. Tableau allows the sorting of data of the fields, which are called dimensions. There are two ways in which Tableau carries out the sorting. Manual Sorting is used to rearrange the order of dimension fields by dragging them next to each other in an ad hoc fashion.

This type of sorting involves choosing a field to be sorted and directly applying the sort using the sort dialog box. You have the option to choose the sort order as ascending or descending and choose the field on which to apply the sort. Choose Sample-Superstore to apply sorting on the field named discount by using the dimensions order date and Subcategory as shown below. The result shows the name of the sub-categories in a descending order arranged for each year. This is basically changing the order in which the visualization elements appear in the screen. For example, you want to show the sales volume of different product segment in a descending order, however you have your own choice of order. Hence, they are called as manual sorting.

In the following example, you move the segment named Home Office, below the segment named Consumer, even though the sales volume of Home Office is the lowest. Filtering is the process of removing certain values or range of values from a result set. Tableau filtering feature allows both simple scenarios using field values as well as advanced calculation or context-based filters. In this chapter, you will learn about the basic filters available in Tableau. These filters are applied on the dimension fields. Typical examples include filtering based on categories of text or numeric values with logical expressions greater than or less than conditions. We use the Sample - Superstore data source to apply dimension filters on the sub-category of products.

We create a view for showing profit for each sub-category of products according to their shipping mode. Next, drag the Sub-Category dimension to the Filters shelf to open the Filter dialog box. Click the None button at the bottom of the list to deselect all segments. Then, select the Exclude option in the lower right corner of the dialog box. Finally, select Labels and Storage and then click OK. The following screenshot shows the result with the above two categories excluded. These filters are applied on the measure fields.

Filtering is based on the calculations applied to the measure fields. Hence, while in dimension filters you use only values to filter, in measures filter you use calculations based on fields. You can use the Sample - Superstore data source to apply dimension filters on the average value of the profits. First, create a view with ship mode and subcategory as dimensions and Average of profit as shown in the following screenshot. Next, drag the AVG profit value to the filter pane. Choose Average as the filter mode. Next, choose "At least" and give a value to filter the rows, which meet these criteria. After completion of the above steps, we get the final view below showing only the subcategories whose average profit is greater than Tableau treats the date field in three different ways while applying the date field.

It can apply filter by taking a relative date as compared to today, an absolute date, or range of dates. Each of this option is presented when a date field is dragged out of the filter pane. We choose the sample - Superstore data source and create a view with order date in the column shelf and profit in the rows shelf as shown in the following screenshot. Next, drag the "order date" field to the filter shelf and choose Range of dates in the filter dialog box.

Choose the dates as shown in the following screenshot. On clicking OK, the final view appears showing the result for the chosen range of dates as seen in the following screenshot. Many filter types in Tableau are quickly available using the right-click option on the dimension or measure. These filters known as Quick filters have enough functionality to solve most of the common filtering needs. Consider the Sample-Superstore data source to apply some quick filters. In the following example, choose sub-category as the row and sales as the column which by default produces a horizontal bar chart.

Next, drag the sub-category field to the filters pane. All the subcategories appear next to the chart. Once the analysis is complete by applying the filter, remove it by using the clear filter option. For this, go to the filter Pane, right-click on the field name and choose Clear Filter as shown in the following screenshot. The normal filters in Tableau are independent of each other.

It means each of the filter reads all the rows from the source data and creates its own result. However, there may be scenarios where you might want the second filter to process only the records returned by the first filter. In such a case, the second filter is known as dependent filters because they process only the data that passes through the context filter. Context Filters serve two main purposes. You can set one or more context filters to improve the performance. Using the Sample-superstore, find the top 10 Sub-Category of products for the category called Furniture.

To achieve this objective, following are the steps. Choose the horizontal bar chart as the chart type. Drag the dimension Sub-Category again to the Filters shelf. You will get the following chart. Choose the option by field. From the next drop-down, choose the option Top 10 by Sales Sum as shown in the following screenshot. Right-click to edit and under the general tab choose Furniture from the list. As you can see the result shows three subcategory of products.

This produces the final result, which shows the subcategory of products from the category Furniture which are among the top 10 subcategories across all the products. One of the important filtering options in Tableau is to apply some conditions to already existing filters. These conditions can be very simple like finding only those sales which are higher than a certain amount or it can be a complex one based on a certain formula. The conditions can also be applied to create a range filter. Using the Sample-superstore, let's find that sub-category of products across all segments whose sales exceed one million. Next, drag the dimension Sub-Category to the Rows shelf.

Choose the horizontal bar chart option. Right-click to edit and go to the tab Condition. Here, choose the radio option by field. From the drop-down, select Sales, Sum and greater than equal to symbol specifying the value On completion of the above two steps, we get a chart which shows only those subcategory of products, which have the required amount of sale. Also this is shown for all the available segments where the condition is met. The Top option in Tableau filter is used to limit the result set from a filter. For example, from a large set of records on sales you want only the top 10 values. You can apply this filter using the inbuilt options for limiting the records in many ways or by creating a formula.

In this chapter, you will explore the inbuilt options. Using the Sample-superstore, find the sub-category of products which represents the top 5 sales amount. Choose the horizontal bar as the chart type. Tableau shows the following chart. Here, choose the second radio option by field. From the drop-down, choose the option Top 5 by Sum of Sales. On completion of the above step, you will get the following chart, which shows the top 5 Sub-Category of products by sales.

Any data analysis and visualization work involves the use of extensive filtering of data. Tableau has a very wide variety of filtering options to address these needs. There are many inbuilt functions for applying filters on the records using both dimensions and measures. The filter option for measures offers numeric calculations and comparison. The filter option for dimension offers choosing string values from a list or using a custom list of values. In this chapter, you will learn about the various options as well as the steps to edit and clear the filters. Filters are created by dragging the required field to the Filters shelf located above the Marks card. Create a horizontal bar chart by dragging the measure sales to the Columns shelf and the dimension Sub-Category to the Rows shelf.

Again drag the measure sales into the Filters shelf. Once this filter is created, right-click and choose the edit filter option from the pop-up menu. Measures are numeric fields. So, the filter options for such fields involve choosing values. Tableau offers the following types of filters for measures. Include only Null values, Non-null values, or All Values. Dimensions are descriptive fields having values which are strings. Tableau offers the following types of filters for dimensions.

Filters can be easily removed by choosing the clear filter option as shown in the following screenshot. A bar chart represents data in rectangular bars with the length of the bar proportional to the value of the variable. Tableau automatically produces a bar chart when you drag a dimension to the Row shelf and measure to the Column shelf. We can also use the bar chart option present in the Show Me button. If the data is not appropriate for bar chart, then this option will be automatically greyed out. From the Sample-Superstore, choose the dimension, take profit to the columns shelf and Sub-Category to the rows shelf. It automatically produces a horizontal bar chart as shown in the following screenshot.

In case, it does not, you can choose the chart type from the Show Me tool to get the following result. You can apply colors to the bars based on their ranges. The longer bars get darker shades and the smaller bars get the lighter shades.

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