Tableau data analysis & data visualization analysis platform

Tableau data analysis & data visualization analysis platform

​ The resource packages and materials involved in this article are all from the Internet and are only used for communication, study and research, and strive to improve your own article. The copyright of various installation packages and materials belongs to the original copyright owner. The copyright dispute has nothing to do with me. The user cannot use it for commercial or illegal purposes after downloading.
Baidu Netdisk:
Link: https://pan.baidu.com/s/1wU49uLr75qZ9E7ZMFFVjrA? pwd=eytq
extraction code: eytq

1. Install Tableau_Desktop2021

• Preparation

• The 2021 version and the 2019 version are shared in the cloud disk. Some machines may have abnormal display of some charts in 2021. You can try to install the 2019 version.

Tableau Desktop 2021 software download:
[Software name]: Tableau Desktop 2021
[Software size]: 462MB
[Software language]: Chinese Simplified
[System environment]: Win7/Win8/Win10/Win11

Baidu Netdisk:
Link: https://pan.baidu.com/s/1wU49uLr75qZ9E7ZMFFVjrA?pwd=eytq
Extraction code: eytq
Tableau Desktop 2021 64-bit.zip or Tableau Desktop Pro v2019.4.1

[Work before installation]:
1. The installation process must be disconnected from the Internet, otherwise the installation may fail.
2. Turn off the anti-virus software, otherwise the installation may fail.

Disable the network or disconnect wifi or unplug the network cable.

If you open anti-virus software, right-click the menu on the taskbar in the lower right corner and select Exit.

• Install

1. After downloading the installation package, unzip the zip file and install: TableauDesktop-64bit-2021-1-0.exe
   • Check I have read and accept the terms of this license agreement
   • Click Customize and set the installation path. All options are checked by default.
   • Click Install, the software is being installed, please wait patiently until the installation is completed.

2. Green crack application software
   • Copy the tabui.dll in the Crack folder to the bin of the installation directory and replace the original tabui.dll file.
   • Open the software. At this time, the software has been successfully cracked and can be used for free.

2. Introduction to Tableau’s main products

• Tableau Desktop

  • Product positioning: Desktop analysis software. After connecting to the data source, you can quickly create interactive views and dashboards by simply dragging and dropping.

• Tableau Server

  • Product positioning: Used to publish and manage reports produced by Tableau Desktop, and can publish and manage data sources

• Tableau Online

  • Product positioning: Built for cloud analysis, the hosted version of Tableau Server requires no hardware deployment and maintenance.

• Tableau Public

  • Product positioning: Visualization results produced by Tableau Desktop can be published and shared publicly.

3. Common concepts of Tableau Desktop workspace

Tableau Desktop workspace is a
working environment for connecting data sources, data preprocessing, making views, designing dashboards, generating stories, publishing and sharing workbooks, including data source workspace, worksheet workspace, dashboard workspace and story workspace district.

• data source

​ If you want to use Tableau to analyze data and display it through visualization, you first need to use Tableau to connect the data.

source. After the connection is successful, you can enter the data source workspace. The data source page usually includes the left data pane area, canvas area, and metadata grid.

Four parts: area and data preview area

• worksheet

After connecting data in Tableau, you can enter the worksheet workspace. The worksheet workspace usually contains menus, toolbars, data

Windows, pages and areas such as filters and mark cards can generate visual charts by dragging and dropping fields onto the rows and columns ribbon. You can also

Perform simple data processing on data

• Dashboard

​ The dashboard workspace organizes worksheets and some pictures, text, and web page type objects in a certain layout. meter

The board workspace usually contains dashboard windows, layout windows, dashboard views, dashboard object windows, etc. In the dashboard workspace we can

To integrate previously processed worksheets into a data dashboard

• story

​ A story is a PPT that connects multiple worksheets and dashboards in a certain logical order. Stories are generally used as a presentation tool. The story
workspace usually contains dashboard and worksheet windows, story view area, story description and navigator settings, etc.

4. Tableau Desktop file management

​ In Tableau Desktop , you can use a variety of different Tableau file types, such as workbooks, packaged workbooks, data extraction

and data sources, etc. to save and share work results and data sources.

• Tableau Workbook/Packaged Workbook (.twb/.twbx)

File - save as

Tableau data sources (.tds / .tdsx)

Data - corresponding data source name - added to the saved data source

• Tableau Data Extraction (.hyper)

Data - corresponding data source name - data extraction

5. Introduction to Tableau data

5.1-Tableau Data Role

After Tableau connects the data, it will display the data on the left side of the workspace, which we call the data window. At the top of the data source window are the dimensions

The degree window and the measurement window below are used to display the imported dimension fields and measurement fields respectively. Dimensions and measures are a number in Tableau

According to the way of dividing roles, discrete and continuous are another way of dividing

• Dimensions and measures

  • Dimensions: The perspective from which data is viewed. It contains quantitative values ​​(such as names, dates, or geographical data). We can use dimensions to
    classify, segment, and reveal details in the data. Dimensions affect the level of detail in a view
  • Measurement: The statistical value of the observed data, including measurable numerical quantitative values, and the measurement can be aggregated. When you drag a measure into a view
    , Tableau will by default apply an aggregation method, such as SUM, to the measure. Measures can be converted into dimensions

• discrete and continuous

  • Discrete variables: can only take on a limited number of values, and their values ​​can be listed one by one. For example, "number of employees", "number of products", etc.
  • Continuous variable: A variable that can take any value in one or more intervals. Its values ​​are continuous and cannot be listed one by one. Such as "temperature
    ", "height", etc.

• blue fields and green fields

  • Blue measures and dimensions are discrete and discrete values ​​are considered finite. Typically discrete fields add titles to the view
  • Green measures and dimensions are continuous, and continuous field values ​​are treated as infinite ranges. Typically, continuous fields add an axis to the view

  Tableau represents data differently in views depending on whether the fields are discrete (blue) or continuous (green
  )

5.2-Tableau field types

• text value
• date value
• Date and time values
• Numeric value
• Boolean value
• Geographical value
• cluster group

5.3-Tableau field type conversion

In Tableau, we can change the data type of a field in the "Data Source" page or the worksheet "Data" pane page

• Click the icon on the data source page

• Worksheet Data pane

5.4-Simple processing of Tableau fields

• data interpreter

  • Data Interpreter can help us quickly detect and bypass headers, comments, footers, empty cells, etc. to effectively identify data

    Concentrated actual fields and values ​​(for example, after importing data and using the data interpreter, if there is no field name, it can effectively assist in identifying field names, gender, date, name, etc.)

• column split

If your data has a string field that contains multiple units of information (for example, a customer's first and last name), split the values
​​in that field into separate fields based on your analysis needs. At this point we can use the "Split" or "Custom" split options in Tableau
to separate values ​​based on delimiters or the pattern of repeating values ​​present in each row of the field.
Generally, it can be split in two ways: the "Data Source" page or the "Data" pane of the worksheet.

• Transpose

​ Analyzing data stored in crosstab format in Tableau is sometimes difficult to implement. When working with Microsoft Excel, text file
, and .pdf data sources, data can be pivoted and converted from crosstab to column format.

For example, suppose there are sales of mobile phones of various major brands in four separate fields. At this time, we can pivot the data so that the mobile phone brand
is in one field and the sales are in another field.

• hide

​ When there are too many fields in the source data, in order to facilitate analysis, we can temporarily hide unnecessary field columns.

For example, among the four brands of mobile phones, we only want to analyze the sales of Apple mobile phones separately. At this time, we can first hide the fields of the other three mobile phones.

• double naming

​ In the source data, sometimes the field names are not standardized or do not conform to our own habits. At this time, we can correct them in Tableau .

field name to rename

6. Data types imported by Tableau

6.1-Local file data

File data supports a variety of types, which are listed here.

• Excel file

  • If the Excel file has only one sheet page, the default is the sheet page data;
  • If the Excel file has only multiple sheet pages, multiple sheet pages will be read by default and displayed in the "Worksheet" area of ​​the data source page
    .

• Text file (CSV/TXT)

  • Tableau reads all text files in the same folder by default

    
    For example, when we connect a text file in the "Jin Yong Martial Arts Novel" folder, Tableau will read all text files (csv/txt) in the folder.

6.2-Remote connection to server database

​ Tableau supports connecting to company data warehouses, including MySQL and Hadoop Hive , which are currently used mainstreamly . We need to download them first.

Download and install the driver, then connect

Uploaded the commonly used MySQL-connector and mariadb-connector to the network disk

Baidu Netdisk:
Link: https://pan.baidu.com/s/1wU49uLr75qZ9E7ZMFFVjrA?pwd=eytq
Extraction code: eytq

MySQL data information on the server:

MariaDB [(none)]> use wow;
Database changed
MariaDB [wow]> show tables;
+---------------+
| Tables_in_wow |
+---------------+
| wow_info      |
+---------------+
1 row in set (0.000 sec)

MariaDB [wow]> select * from wow_info;
+----+------+-------------+
| id | role | pinyin      |
+----+------+-------------+
|  1 | fs   | fashi       |
|  2 | ms   | mushi       |
|  3 | ss   | shushi      |
|  4 | dz   | daozei      |
|  5 | ws   | wuseng      |
|  6 | xd   | xiaode      |
|  7 | sq   | shengqi     |
|  8 | zs   | zhanshi     |
|  9 | dk   | siwangqishi |
| 10 | dh   | emolieshou  |
+----+------+-------------+
10 rows in set (0.000 sec)

Connect using Tableau:

Configuration information

verify the data:

Modify MySQL server-side data:

MariaDB [wow]> select * from wow_info;
+----+------+--------------+-------------+-----------+
| id | role | role_cn      | role_pinyin | zhuangbei |
+----+------+--------------+-------------+-----------+
|  1 | fs   | 法师         | fashi       | 布甲      |
|  2 | ms   | 牧师         | mushi       | 布甲      |
|  3 | ss   | 术士         | shushi      | 布甲      |
|  4 | dz   | 盗贼         | daozei      | 皮甲      |
|  5 | ws   | 武僧         | wuseng      | 皮甲      |
|  6 | xd   | 德鲁伊       | xiaode      | 皮甲      |
|  7 | sq   | 圣骑士       | shengqi     | 板甲      |
|  8 | zs   | 战士         | zhanshi     | 板甲      |
|  9 | dk   | 死亡骑士     | siwangqishi | 板甲      |
| 10 | dh   | 恶魔猎手     | emolieshou  | 皮甲      |
+----+------+--------------+-------------+-----------+

Refresh the data and you can see that the data has been refreshed into Tableau.

7. Tableau implements data fusion

7.1-Data connection (expanded column association)

​ Sometimes in order to get complete results, we need to get results from two or more tables. At this time, you need to use Tableau
's data connection function to combine rows from two or more tables based on the common fields between these tables.
Tableau's data join function is similar to Excel's vlookup function, MySQL's join function, and Pandas' merge
function.

• Connection method
  • Left Join
  • Right Join
  • Inner Join
  • Outer Join

Example demonstration:

We now have an Excel file "Martial Arts Hero List.xlsx". This file has two sheets , "Hero List" and "User Dimension

Information", now we need to add three fields of "ID card", "sect role" and "gender" to each hero on the "Hero List" page. Next

This can be achieved with the help of data connection function

The data sample file is shared on the network disk: Martial Arts Hero List.xlsx

Data - New data source - Local file - Excel - Martial Arts Hero List.xlsx

Drag the new union to the right workspace, and then drag the identified hero list table and user dimension information table to the right workspace.

Add a connection relationship to complete the requirement

7.2-Data merging (accumulation of data entries)

Data merging is the process of merging two or more tables by appending values ​​(rows) from one table to another. It is used to merge data with completely consistent data structures
. Merging does not add new columns, but only appends data from different files together, increasing the number of rows.

The following order data exists in three tables according to region: "Northeast Region", "North China Region", and "East China Region"

Data - New data source - Excel - Orders by region.xlsx

7.3-Data Mixing Relationships

Data blending maintains the independence of the two data sources and can be flexibly modified on each view. Data mixing can be understood as
cross-database/table query at the data level.

For example, in the "Example - Supermarket" table, there are two sheet pages: "Order" and "Return". In the "Order" table, we can query the order sales of each category of products. If we want to increase the number of return orders, we
need Add a return field to the "Returns" table. At this time, you can
edit the mixed relationship between the two tables and create a temporary query view.

8. Tableau data loading method

There are two basic ways to load data in Tableau : one is a live connection and the other is a data extraction

8.1-Real-time data loading

Tableau obtains query results from the data source and does not store the source data itself

• Adapt to the scene
  • High requirements for real-time data
  • High data confidentiality requirements

Generally, when adding a data source, the real-time option is selected by default.

8.2-Data extraction method

Tableau pulls data into Tableau ’s data engine, where it is managed by Tableau

• Applicable scene
  • Source database performance is poor
  • Need to access data offline
  • Reduce pressure on source systems

Method 1: After completing the data connection, extract data from the data source; click Data on the menu bar, select the corresponding data source, and click "Data Extraction"

Method 2: Select the "Extraction" method when creating a new data source; select "Data Extraction" on the data source page

9. Important components of a chart

9.1 - Rows and columns

• How to add rows and columns

  • Drag and drop
  • Double-click the indicator to add
  • If you want to cancel, just drag out the area box.

• Metrics are aggregated by default

  • Sum
  • average value
  • median number
  • count
  • maximum value
  • minimum value
  • standard deviation
  • variance
  • percentile

9.2-View area (chart display area)

A. Field Label: A label added to a discrete field in the Row or Column shelf to describe the members of that field. For example, Category is a discrete
field that contains three members: Furniture, Office Supplies, and Technology.
B. Title: The name provided for the worksheet, dashboard or story. The system will automatically display the title for worksheets and stories.
C. Range/Cell: Represents data at the intersection of fields (dimensions and measures) included in the view. Markers can be represented by lines, bars, shapes, maps, text,
etc.
D. Legend: A legend that describes how the data in the view is encoded. For example, if you use shapes or colors in your view, the legend describes
what each shape or color represents.
E. Axis: Created when a measure (field that contains quantitative numerical information) is added to a view. By default, Tableau
generates a continuous axis for this data.
F. Abscissa field name or label: member name of the field.

9.3-Page card, create canvas clone

Dragging a field onto a page card creates a page player that makes the worksheet more flexible.

For example, when we drag the "Order Date" field to the "Page Card", a "Year (Order Date)"
player will automatically appear on the right side of the view area. Click the play button of the player to dynamically play the view.

9.4-Filter, effectively filter chart information

​ Sometimes you only want Tableau to display a certain part of the data, such as only looking at the sales of each category in 2015. In this case, you can complete the above selection through filters.

9.5-Mark cards to beautify visual effects

• color
  • Show different marker colors based on dimension and measure fields
• size
  • Express sizes in terms of dimension and measure fields
• Label
  • Display one or more field labels in the view
• details
  • Break down the view by field
• tooltip
  • Show information about fields in tooltips on mouseover
• shape
  • Mark different shapes in the view, a figure can only have one marked shape

10.0 Introduction to commonly used charts

10.1-Basic table

• concept

Basic table, also known as text table and crosstab, is a table in a general sense. It is the most intuitive way to express data. It is used in data analysis.

plays an important role that cannot be ignored in the analysis

• Application scenarios

It can replace lengthy text descriptions and facilitate calculation, analysis and comparison.

10.2-Bar graph

• concept

Bar chart, also known as bar chart, column chart, column chart, is one of the most commonly used chart types. It displays dimensions through vertical or horizontal bars.

Distribution of degree fields

• Application scenarios

Best for comparing sizes across categories

10.3-Histogram

• concept

​ A histogram is similar to a bar chart. The main difference is that the horizontal axis of the bar chart is a single category, regardless of the measurement value on the vertical axis.
The length represent the number of each category; while the horizontal axis of the histogram For the grouping of analysis categories (called bucketing in Tableau), the width of the horizontal axis represents the
distance between each group, and the vertical axis represents the number of samples at each level.

• Application scenarios

​ Suitable for grouping statistical analysis of categories. The reason for grouping may be that the categories are continuous, or the categories are discrete but too numerous.

Many, it can be regarded as approximately continuous, and of course it can also be based on certain business needs.

10.4- Line chart

• concept

A statistical chart that uses the rise or fall of a polyline to represent the increase or decrease in statistical quantities.

• Application scenarios

Best for time series data

10.5-Pie Chart/Donut Chart

• concept

Graphics that use the angles of circles and sectors within circles to represent numerical values

• Application scenarios

Best used to show the ratio of the size of each value in a data series to the sum

step1: Create the calculated field "Profit Situation"
step2: Drag "Profit Situation" to the column, drag "Segmentation" to the row, drag "Sales" to the label, and select "Pie Chart" in the intelligent recommendation area step3: Drag "Segmentation" to the
row Drag "Points" from the row to the column
step4: Select "Total Percent" in the mark card "Total (Sales)" quick table calculation, and select "Table Down" for the calculation basis
step5: Combine "Profit Situation" and "Total (Sales) )" to the "Label" option in the mark card, and then
select "Total - Show row sums" from the "Analysis" drop-down menu in the menu bar

10.6-Scatter plot

• concept

Use two sets of data to form multiple coordinate points, examine the distribution of the coordinate points, determine whether there is some correlation between the two variables or summarize the coordinate points

The distribution pattern of

• Application scenarios

Used to effectively analyze the relationship between different variables, showing whether one variable can accurately predict another variable, or whether the changes in two variables are

Are they independent of each other?

10.7-Bubble Chart

• concept

Each bubble represents a value of the dimension field, and the size or color of each bubble represents the size of the measurement value.

• Application scenarios

Visually appealing and able to demonstrate the size of the data in a very intuitive way

10.8-Dendogram

• concept

Also called a treemap, a set of nested rectangles is used to display data and is a method of highlighting unusual data points or important data.

• Application scenarios

Suitable for displaying hierarchical and proportional relationships between data

step1: Right-click the region field and create "region set"
step2: Create the calculated field "asymmetric province"

IF [region set] THEN [province/autonomous region]
ELSE [region]
END

step3: Right-click the asymmetric province field and create a "province set"

step4: Create the calculated field "Asymmetric City"

IF [province set] THEN [city]
ELSE “”
END

step5: Generate dendrogram

10.9-Map

• concept

Achieve geographical location display of data at different detailed levels such as national, provincial/autonomous region, prefectural and municipal levels, etc.

• Application scenarios

Suitable for visually displaying the distribution of data from geographical latitudes

step1: Convert the right-click type of the province/autonomous region field from text to geography type
step2: Drag the "province" field into the column ribbon, select "Map" to automatically generate, drag "Customer ID" into the "label" of the mark card, and then add the degree
Select "Count" for the measurement method
step3: Select "Shape" in the mark card
step4: Select more shapes in the "Shape" of the mark card, humanoid shape, select a humanoid pattern
step5: Drag "Count (Customer ID)" into the mark card , the measurement is replaced by count, and the color and size are added respectively.

11.0 Integration Worksheet

11.1-Dashboard

A dashboard is a collection of views that allow us to compare various data at the same time. For example, if we have a set of data that we review every day, such as revenue data, performance goal achievement data, user data, etc., we can create a dashboard that displays all views at once (picture below) and integrate these data into one on a dashboard instead of navigating to a separate sheet

• Dashboard functional interface

Dashboard editing and beautiful design is a long learning process. You need to gradually study and understand each function before you can make the dashboard more high-end and refined.

11.2-Story

A story is a series of virtual items that work together to convey a message. Stories can be created to tell the story of the data, provide context, demonstrate how decisions relate to outcomes, or simply create a compelling case. At the same time, stories are a collection of worksheets arranged in sequence. Each individual sheet in the story is called a "story point." (The story is similar to PPT)

Up to this point, it is equivalent to having simple basic knowledge and operation of Tableau.

12.0 Tableau Hierarchy

Hierarchical structure is a top-down organizational form between dimensions. Tableau includes a hierarchical structure for certain fields by default, such as
date, date/time, and geographic roles. Taking the date dimension as an example, the date field itself contains the hierarchical structure of "year-quarter-month-day"

For example, in the "Example - Supermarket" order data, if you want to quickly check the sales of different regions, provinces, and cities in China
, you can quickly drill down and roll up by creating a regional hierarchical structure.

In addition to Tableau's default built-in hierarchical structure, Tableau allows users to customize hierarchical structures for dimension fields. After the hierarchical
structure is created, it will be displayed in the dimension window. Hierarchies play an important role in regrouping dimensions, and drill-down and roll-up are
the most effective ways to navigate hierarchies.

12.1-Creating a hierarchy

Method 1: Direct drag and drop

• CTRL to select the three dimension fields of "Region", "Province/Autonomous Region" and "City" at the same time and drag them into "Country"
• In the pop-up Create Hierarchy Structure dialog box, name it, for example: Hierarchy-Region
• In the left dimension window, a new dimension field "Hierarchy - Region" appears

Method 2: Right-click menu creation

• Select the three dimension fields of "Country", "Region", "Province/Autonomous Region" and "City" at the same time, right-click the mouse, and "Hierarchical Structure - Create Hierarchical Structure" will pop up
  .
• In the pop-up Create Hierarchy dialog box, name it
• In the left dimension window, a new dimension field "Hierarchy - Region" appears

12.2-Application scenarios of hierarchical structure

​ In Tableau, there are two ways to drill down and scroll up. One is to click the "+" sign in front of the field in the rows and columns ribbon, and the other is to right-click on the view title and select drill down
.

For example, if we want to check the sales of different regions, provinces, and cities in China, we can use the drill-down and roll-up functions of the hierarchical structure
.
Drill down in the rows and columns functional area.
Click the "+" button in front of "China" to automatically drill down to the "Region" dimension.
Click the "+" button in front of "Region" to automatically drill down to the "Province/Autonomous Region" dimension.
Repeat... …

13.0 Tableau Group Concepts

A group is a combination of discrete values ​​of dimension members or measures. Through grouping, you can recombine dimension members and measure values.

Classified by scope

13.1-Create groups

​ In Tableau , there are two methods for grouping. One is to create a group based on a certain dimension in the data window, and the other is to create a group by directly selecting dimension members in the view.

For example, in the "Example - Supermarket" order data, the purchasing user "segmentation" information is divided into "company", "small business" and "individual". We want to group this: enterprise class and individual category, to analyze the sales contributed by these two categories of customers, you can use the grouping function to achieve

Method 1: Create with the right mouse button

• Right-click in the "Segmentation" field and select "Create - Group" to pop up the "Create - Group" dialog box
• In the "Create - Group" dialog box, press CTRL to select "Company" and "Small Business" at the same time, and then click Group to pop up the "Edit - Group" dialog box
• In the "Edit - Group" dialog box, name the group, here named "Enterprise Class"

Method 2: Create directly in the view area

• Select "Company" and "Small Business" at the same time, right-click and select "Group"
• Right-click on "Companies and Small Businesses", select "Edit Alias", and rename in the "Edit Alias" dialog box

13.2-Usage scenarios of grouping

The "Segmentation (Group)" field of the created group contains grouped members (enterprise class) and ungrouped (consumer) members. Tableau displays ungrouped members and grouped members at the same time by default.

Click the drop-down menu of "Segmentation (Group)" in the column ribbon and select the "Include Others" option, so that "Segmentation (Group)" is divided into "Enterprise Class" and "Others", that is, undefined groups Members of are grouped as "Other" by default.

14.0 Tableau Set Concepts

14.1-Definition and concept of set

Sets are custom fields that define subsets of data based on certain conditions, and can be understood as partial members of dimensions. Sets can be used for calculations and participate in the editing of calculated fields.

• Classification of sets

Sets can be divided into two major categories according to whether they can change dynamically with data: constant sets and calculated sets.

Classification	changes with data	Number of dimensions allowed	How to create
constant set	No, static set	single or multiple dimensions	Create objects by selecting them directly in the view
calculation set	Yes, dynaset	single dimension	Right click on the data window to create a dimension

• The role of set

Sets are mainly used for filtering. By selecting some members of dimensions as data subsets, different objects can be selected. Therefore, it is possible

It can be summarized as the following two functions:

1. Comparative analysis of members inside and outside the group: By selecting "Show within/outside", you can directly perform aggregate comparative analysis on members inside and outside the group.

2. Comparative analysis of members in a set: When focusing on the members of a set, you can select "Show members in the set". At this time, the set functions as a filter and only displays members located in the set.

• Create set

14.2-Creating constant sets

• Add column row indicators to sort cities by field

• Click the mouse to select the top 10 cities by sales, right-click the selected area, and click the "Create Set" option on the pop-up tool tip.

• In the pop-up "Create Set" dialog box, enter the name "Top 10 Cities by Sales" and click "OK"

14.3-Creating calculation sets

• Right-click "City" in the dimension window and select "Create Set"
• In the pop-up "Create Set" dialog box, enter the name "Top 10 Profit Cities"
• Click the "Top" tab to set it up, select "By Field": "Top" - "10", By "Profit" - "Sum" and click "OK"

14.4-Creating a merged set

​ If we want to filter out the cities with "high sales and high profits", that is, the cities with the top 10 sales and the top 10 profits, we can create a merged set for these two sets.

• At the same time, select the two sets to be merged, "Top 10 Cities by Sales" and "Top 10 Cities by Profit", and right-click the menu and select "Create Merged Set
  "
• In the "Create Set" dialog box, enter the name of the newly created merged set: "High Sales and High Profit Cities", then select the merge
  method "Shared Members of Two Sets", that is, intersection, Finally click "OK" to create the merged set

14.5-Set usage scenarios

If we want to analyze the distribution of "high sales and high profits" cities in different regions, we can use comparative analysis of members inside and outside the group.

Method, in which the members of the set are cities with "high sales and high profits". By adding a quick table calculation, the number of cities in the set can be quickly calculated

% of the region

15.0 Tableau Parameter Settings

​ Parameters are user-defined dynamic values ​​that can be used for interaction. They are the most common and convenient method to achieve control and interaction. They are widely used in fields that can be dynamically interacted (calculated sets, custom calculated fields, etc.), Filters and reference lines (including reference intervals, etc.), we can easily interact with the worksheet view by controlling parameters.

15.1-Creating parameters

• Create directly in the data window

• Created when using a calculation set

15.2-Usage scenarios of parameters

​ To analyze the sales proportion of the "Top N Cities", you can right-click the parameter "Top N Cities Sales Parameters" in the data window and select "Show Parameter Control". At this time, the parameter control will be displayed in the view area. upper right corner. By adjusting the value of the parameter, the proportion of sales contributed by cities with different rankings can be dynamically observed.

16.0 Tableau Calculated Fields

Calculated fields are fields defined by using functions and operators to construct formulas based on data source fields (including dimensions, measures, parameters, etc.). Like other fields, calculated fields can be dragged and dropped into various ribbons to build views, and can also be used to create new calculated fields. The return value of a calculated field can also be differentiated into numeric type, string type, etc.
The calculation field creation interface includes an input window and a function window. In the input window, you can enter calculation formulas, including operators, calculated fields, and functions. Among them, operators support all standard operators such as addition (+), subtraction (-), multiplication (*), and division (/). Character, numeric, date/time, set, parameter and other fields can be used as calculated fields.
The function window is a list of calculation functions that comes with Tableau, including numbers, strings, dates, type conversions, logic, aggregation,
table calculations, etc. Double-click the function to appear in the "Input Window", and it can also be automatically completed in the "Input Window".

16.1-Creating calculated fields

​ Similar to the creation of parameters, there are two ways to create calculated fields: create calculated fields directly in the data window; create when using calculated sets, calculated fields, reference lines and others

• Create measure fields

• Create a dimension calculation

• Create dimension fields with parameters

1. Create parameters

2. Create a calculated field

16.2-Application scenarios of calculated fields

​ In the "Example - Supermarket" order data, we not only focus on the sales contributed by different products, but also on their profit levels and profitability. Therefore, next we need to analyze the sales profit margin of each type of product and the profitability and profitability of the product. In the case of a loss-making order, you need to create an auxiliary measurement field "sales profit margin" and a dimension field "whether it is profitable" to determine the profitability of each order.

17.0 Tableau Guides

​ A reference line is to add a line on the axis to mark the position of a constant or calculated value. The calculated value can be generated based on the specified field or parameter. Commonly used values ​​include the average value, minimum value, maximum value of the axis, etc. Guides can be set based on tables, ranges, or cells.

17.1-Creating reference lines

• In the abscissa area, as shown below in the blue background abscissa range, right-click and select "Add Reference Line" in the pop-up menu. The "Edit Reference Line, Reference Interval" dialog box can be found in the pop-up menu.

• In the "Edit Reference Line, Reference Interval" dialog box, select "Line", select "Each District" for the range, select "Sum (Sales) - Average" for the value, and click OK

17.2-Reference interval

​ Reference interval refers to adding an interval on the axis to mark a certain range. After marking the view, the interval between two constants or calculated values ​​on the axis

The area of ​​​​is shown as shaded

Create a reference interval

• Right-click on the abscissa, select "Add Reference Line" in the pop-up menu, and the "Edit Reference Line, Reference Interval" dialog box will pop up.

• In the "Edit Reference Line, Reference Interval" dialog box, select "Interval", select "Each District" for the range, select "Sum (Sales) - Minimum Value" for the beginning of the interval, and select "Sum (Sales) - Maximum Value" for the end of the interval. ”, click OK

18.0 Tableau Common Functions

Tableau supports many functions for Tableau calculations. Mainly include the following types

• Numeric functions
• String functions
• date function
• type conversion function
• logical function
• aggregate function
• user function
• table calculation function
• space function

18.1- Summary

For each mark in the view, the summary table calculation aggregates the values ​​merged across partitions. It can do this by totaling the values, averaging the values, or replacing all values ​​with the lowest or highest actual value

18.2-Differences

For each mark in the view, the difference table calculation calculates the difference between the current value and another value in the table. For difference calculations, there are always two values ​​to consider: the current value and the value against which the difference should be calculated. Most of the time we just calculate the difference between the current value and the previous value. But in some special cases, there will be a difference. According to the different values ​​​​based on the calculation, it can be divided into the following types:

18.3-Percent difference

For each mark in the view, the percent difference table calculation calculates the percent difference between the current value and another value in the table

In the order data of "Example - Supermarket", we have used the "difference" calculation in the quick table calculation to know the year-on-year increase in sales each year. Next, we want to know more about the year-on-year increase, that is, the year-on-year sales increase, so This can be achieved with the help of the "Percent Difference" calculation in Quick Table Calculation. Double-click the field and you can see that the common formula for the quick table calculation is: (ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN( SUM([Sales])), -1))

18.4 - Total percentage

For each mark in the view, the Percent of Total table calculation calculates the value as a percentage of all values ​​in the current partition

In the "Example - Supermarket" order data, if we want to know the proportion of sales for each product in each of the four years from 2015 to 2018, we can use the total percentage calculation in the quick table calculation. Double-click the field and you can see that the formula for quick table calculation is: SUM([Sales]) / TOTAL(SUM([Sales]))

18.5-Sort

Ascending order ranks values ​​from lowest to highest. Descending order ranks values ​​from highest to lowest.

In the "Example - Supermarket" order data, we already know the annual sales data of each product. Next, if we want to rank the annual sales data, we can use the "sort" calculation in the quick table calculation. . Double-click the field and you can see that the formula for quick table calculation is: RANK(SUM([Sales]))

18.6-Percentile

For each mark in the view, the "Percentile" table calculation calculates the percentile rank of each value in the partition.

In the "Example - Supermarket" order data, we already know the annual sales data of each product. Next, if we want to percentile rank the annual sales data, we can use the "percentile" in the quick table calculation. Quantile" calculation is implemented.

Double-click the field and you can see that the formula for quick table calculation is: RANK_PERCENTILE(SUM([Sales]))

18.7-Mobile Computing

For each tag in the view, the Moving Calculation table calculation performs an aggregation (total, average, minimum, or maximum) of a specified number of values ​​before and/or after the current value to determine the tag value in the view

In the "Example - Supermarket" order data, the annual sales data of the products are constantly changing over time. We can use mobile computing to analyze the sales trend of each product over a period of time. We can use the quick table calculation to "Moving average" calculation implementation.

Double-click the field and you can see that the formula for quick table calculation is: WINDOW_AVG(SUM([Sales]), -2, 0)

18.8 - Compound growth rate

For each marker in the view, the Compound Growth Rate table calculation calculates a compound growth rate for the current value. The formula for calculating the growth rate is: (current year data/basic data)^(1/number of years) - 1

In the "Example - Supermarket" order data, using the sales data in 2015 as the baseline data, we can analyze the compound growth rate of each subsequent year, which can be achieved by using the "compound growth rate" calculation in the quick table calculation.

Double-click the field and you can see that the formula for quick table calculation is: POWER(ZN(SUM([Sales]))/LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/( INDEX()-1))) - 1

18.9 - Year-on-year growth

For each mark in the view, the Year-over-Year Growth table calculation calculates a year-over-year growth for the current year's value.

In the order data of "Example - Supermarket", for each year's sales data, we want to display the year-on-year growth data of each product. This can be achieved by using the "year-on-year growth" calculation in the quick table calculation.

Double-click the field and you can see that the formula for quick table calculation is: (ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN( SUM([Sales])), -1))

18.10-YTD total (Year To Date)

For each mark in the view, the "YTD Total" table calculation accumulates the current year's value, from January 1 of this year to today.

In the "Example - Supermarket" order data, for each product, we want to display the cumulative sales data as of that day. This can be achieved by using the "YTD total" calculation in the quick table calculation.

Double-click the field and you can see that the formula for quick table calculation is: RUNNING_SUM(SUM([Sales]))

18.11-YTD Growth (Year To Date)

For each marker in the view, the YTD Growth table calculation performs a year-over-year calculation on the cumulative value for the current year (January 1 of the current year to today)

In the "Example - Supermarket" order data, for each product, we want to display the year-on-year situation of the cumulative sales data as of that day. This can be achieved by using the "YTD growth" calculation in the quick table calculation.

18.12-Quick table calculation basis

• table crossing

  • Operates across the length of the table and restarts after each partition

• table down

  • Works down the length of the table, starting again after each partition

• table across, then down

  • Operate across the length of the table first, then down the length of the table

• table down, then across

  • First operate down the length of the table, then across the length of the table

• area down

  • First operate down the length of the table, then across the length of the table
  • Operate down the entire region

• area across and then down

  • Operate across the entire zone first, then down the zone

• area down, then across

  • Operate down the entire region first, then across the region

18.13-Special functions

Level of detail expressions (also known as LOD expressions) allow us to calculate values ​​at the data source level and visualization level. Furthermore, LOD expressions give us greater control over the level of granularity to be calculated. They can be executed at a higher level of granularity (Inclusive), a lower level of granularity (Exclusive), or at a completely independent level (Fixed).
There are three functions for level-of-detail expressions, namely INCLUDE, EXCLUDE, and FIXED. Each function can achieve aggregation with different levels of detail. Among them, the INCLUDE function can be used to create calculated fields with a level of detail higher than the visual display content. The EXCLUDE function can be used to create calculated fields with a level of detail lower than the visual display content. The application of the FIXED function is not limited by the level of visual detail and can be used to create specified fields. A calculated field with a level of detail whose calculation results can be more or less detailed than the visual display content.

• INCLUDE function

  • 
    INCLUDE level-of-detail expressions can be evaluated at a finer level of detail, which creates calculated fields that are more detailed than the visual representation.

• EXCLUDE function

  • 
    The EXCLUDE level-of-detail expression declares dimensions to be omitted from the view's level of detail, which creates a calculated field that is less detailed than the visual display.

• FIXED function

  • FIXED Level-of-detail expression calculates value using specified dimensions without referencing dimensions in the view

19.0 Advanced Visualization

19.1-Pareto Chart

​ A Pareto chart is a graph that calculates the proportion of categories according to certain categories based on data, arranges them in a rectangle from high to low, and displays the cumulative sum of the proportions. It is mainly used to analyze the main factors leading to the results. The Pareto chart is in line with the Pareto Principle (also known as the "20/80 Principle", that is, 80% of the results are caused by 20% of the causes). It embodies two important pieces of information through graphics: "the vital few" and “insignificant majority.”

19.2-Box-and-whisker plot

​ Box and whisker plots, also called box and whisker plots, are a commonly used statistical graphic used to display the location, degree of dispersion, outliers, etc. of data. The box plot mainly includes 6 statistics: lower limit, first quartile, median, third quartile, upper limit and outliers. By drawing a box-and-whisker plot and observing the position of the data among similar groups, you can know which ones perform well and which ones perform poorly. By comparing the full interquartile range and the length of the line segments, you can see which groups are scattered and which groups are more concentrated.

19.3-Gantt Chart

A Gantt chart, also known as a Gantt chart, is a graphic representation of the activity sequence and duration of any specific project through an activity list and time scale. The horizontal axis of the Gantt chart represents time, the vertical axis represents activities (projects), and the lines represent the duration of the activity or project during the entire period, so it can be used to compare the duration of different activities (projects) related to dates. Gantt charts are also commonly used to show dependencies between different tasks and are commonly used in project management.

19.4-Waterfall chart

​ Waterfall chart is a common graph in data visualization analysis. It uses a combination of absolute value and relative value, and is suitable for expressing the quantitative relationship between several specific values. It has a good display function for a series of cumulative positive/negative values. It can not only assist in understanding the size of the data, but also visually display the increase or decrease in the data, reflecting the impact of the data in different periods or by different factors. result.

19.5-Radar chart

​ Radar chart is a professional chart specially used for comparative analysis of multi-index systems. It is mainly used to display the operating status of an enterprise - the evaluation of profitability, productivity, liquidity, safety and growth. Its main features are simplicity, convenience, accuracy and intuitiveness, and it can project multi-dimensional data onto the same plane to realize the visualization of multi-dimensional data.

19.6-Dynamic graphics

Dynamic charts, as the name suggests, dynamically change according to different option settings. Allow readers to dynamically and interactively view complex data information from different dimensions. Various dynamic rankings that have been very popular in the short video industry in recent years use the principle of dynamic graphs.

Example:

The GDP values ​​and rankings of countries around the world change every year. Some countries have fallen behind, while others have struggled to get up. If we make a dynamic chart of the GDP ranking changes of various countries in the world in recent decades, everyone can very intuitively find the development of China's GDP, just like the final sprint of a 10,000-meter long distance race, which is very shocking.

Link: https://pan.baidu.com/s/1wU49uLr75qZ9E7ZMFFVjrA?pwd=eytqExtraction
code: eytq

GDP data of countries around the world.xlsx

Implementation steps:

Step1: After importing the source data, right-click the Rank field in the worksheet and convert "Rank" into a dimension field.
Step2: Drag "Rank" into the ribbon, drag "GDP" into the column ribbon, and drag "Year" into the "page card" ”, drag “Place” into “Mark Card – Label”

Step3: Create a dimension calculation field "Chinese Color

IF [place]='China' THEN 1
ELSE 0
END

Step4: Drag "Chinese Color" into the "Mark Card - Color" and edit the color in the editing area on the right

Step5: Filter the playback speed in the "Page Card" on the right and click to automatically play