Week 9 Part 2 Flashcards
Relationships: Example
Business Scenario
* Own a Spice Shop, a small brick and mortar store that sells spices, drinkware, and utensils.
* Analyze transaction data to answer business questions
* The owners of the Spice Shop want our help to analyzing the data to answer these questions:
* What were the best and worst selling products?
* How many transactions did each of our customers make?
Relationships in Tableau Exercise
You are trying to find which of your customers has the most purchases (transactions) for a new store you opened called Wholesale Carbonated Beverages. You also want to know the quantity sold for each of your products. You have two Excel spreadsheets as your data sources. One contains sheets or tables for Products and Customers, and the other contains Transactions. You need to connect to these two data sources, create relationships between the various tables, and build two visualizations to answer these questions:
1. Which customer had the most purchases?
2. What was the best-selling product?
Types of joing
inner join
full joing
left join
right join
Joins: Tableau Example
You have information about orders and returns for an office supply store in a data source called Orders and Returns.xlsx. This data source contains two tables: Orders and Returns. You’ll join them and then build two views.
1. In the first part of the scenario, you want to see only orders by product category which have been returned.
2. In the second part of the scenario, you want to see how the number of orders returned compares to the number of orders not returned by product category.
Dashboard
Dashboard
* Create some views to show sales by segment, region, and profit.
* You’d like to be able to monitor and share all three views at once, and answer the following questions:
1. Which category of the product has the lowest profit in different regions?
2. Which countries and segments are generating negative profit?
* You also want to share this dashboard with your manager and colleagues so that they can explore the data in the dashboard and find their own insights. To improve its usability and exploratory capabilities, add interactivity to the dashboard by:
1. Using the pie chart as a filter to drill down on data.
2. Making the Market filter global so that the filtering applies to all views.
3. Using the Segment color legend to highlight important data.
- When you complete the activity, your dashboard should look like the following images on clicking the pie chart (1) or the Segment color legend (2).
Types of actions
____ help guide analysis by using the data from one view to filter data in another a filter action sends information from a selcted marke to another sheet showing related formation bhenind the science fileter action send data calues fromt ehrelevant source field as filters to the target sheet
____ allow you to call attention to marks of interest by retaining color for specifc marks and dimming all others you can highlight marks in the view using a variety of method for exame you can manually selct the makrs you want to hgighl, use the legend to select related marks, use the higher to search for makrs in the context or create and advanced highlight actioon
___ simplify navifation to other worksheeets dashboards or stories
___ create hyperlinks to external resources such as a web page, email link or a fiile
___ let your audience change a parameter value through direction interaction with a viz, such as clicking or selcting a mark
Filter actions help guide analysis by using the data from one view to filter data in another. Typically, a filter action sends information from a selected mark to another sheet showing related information. Behind the scenes, filter actions send data values from the relevant source fields as filters to the target sheet.
* Highlight actions allow you to call attention to marks of interest by retaining color for specific marks and dimming all others. You can highlight marks in the view using a variety of methods. For example, you can manually select the marks you want to highlight, use the legend to select related marks, use the highlighter to search for marks in context, or create an advanced highlight action.
* Go to Sheet actions simplify navigation to other worksheets, dashboards, or stories.
* URL actions create hyperlinks to external resources, such as a web page, email link, or a file.
* Parameter actions let your audience change a parameter value through direct interaction with a viz, such as clicking or selecting a mark.
Methods for running an action
__ rest the mouse pointer over a mark in the view to run the action. the hover option works well for highlight actions.
_ click a mark in. the view to run the action. this option works well for most types of actions however the select methodsis considered less favorable for URL action because its not a good practice to redirect user to another ite in a single click
___ click a selected mark in the view and then select an option on the tooltip, the menut iption is a best practice to direct users to external URLs
Hover: Rest the mouse pointer over a mark in the view to run the action. The hover option works well for highlight actions.
* Select: Click a mark in the view to run the action. This option works well for most types of actions. However, the select method is considered less favorable for URL actions because it’s not a good practice to redirect users to another site in a single click.
* Menu: Click a selected mark in the view and then select an option on the tooltip. The menu option is a best practice to direct users to external URLs.
Tableau I
Tableau I
* Connecting to Data
* Customize Data Source
* GroupbyFolder
* Edit Attributes * Rename
* Aliases
* Editafield’sdefaultproperties
* Creating Groups in Your Data
* Creating Hierarchies in Your Data
* Understanding Filtering in Tableau
Creating Groups in Your Data
___ lets you combine serveral members of a single dimension into a singe data point or category type, creating a new dimension filed that didnt orignalyy exist in your data
A group lets you combine several members of a single dimension into a single data point or category type, creating a new dimension field that didn’t originally exist in your data.
Ad hoc (in the moment) analysis:
* Isolating a problem: An airline company has a spike in lost baggage escalations. Grouping the destinations that are most affected allows the company to discover what they have in common, such as shared stopover location that is using outdated baggage scanners.
* Strategizing an exhibit: A museum of natural history wants to set up a new exhibit. They need to decide if they should focus on exhibits that are catered to children or to adults. They group sales of previous exhibits by audience to determine which brought the most visitors in the past.
Reuse:
* A single solution to answer multiple needs: A university is analyzing average test scores by categories of majors. English and History are combined into a group called Liberal Arts Majors, while Biology and Physics are grouped as Science Majors, and so on. After the groups have been created, they are able to be used in several other ways. In this example, students can compare hours of electives between different majors.
Creating Groups in Your Data
“What if” Scenarios:
* Where to invest: A retail company needs to determine what will bring the most profit for future investments. Grouping a long list of products into categories such as technology, healthcare, clothing, furniture products etc. allows the company to make informed decisions based upon overall profit.
* Developing a learning plan: A corporation wants to set up an employee improvement program. Grouping offerings together into categories help employees build a personalized plan from each category, creating a well rounded individualized plan.
Correcting Data Errors:
* Avoid data duplication (typos): Grouping CA, Calif., and California into one data point helps to avoid duplication in the data,
* Name changes: A hair care product has been renamed, but it is made up of the same formulation. Grouping these together creates the ability to see sales over time, before and after the name change.
Creating Hierarchies in Your Data
A hierachy is an arrangement of data filed in a hierarchial format with an ___ and ___ strucutre
When a hiereacly exsit sin your data you ca see how field realte to one another in ___ view the strucure of it , and access the data in he sheflf and within the visulalization
view the examples
n Tableau, a hierarchy is an arrangement of data fields in a hierarchical format with an “above” and “below” structure.
* When a hierarchy exists in your data, you can see how fields relate to one another in tiers, view the structure of it, and access the data in the shelf and within the visualization.
* A hierarchy preserves the ordering, creates drilling capabilities in the visualization, and it can be used over and over.
* Examples:
* Geographical:Continent,NationRegion,Division,StateCounty,City,District * DateHierarchies:Year,Quarter,Month,Day,Time
Understanding Filtering in Tableau
Filtering data makes it easier to focus on relevant information in a large dataset or table of data. Filtering does not remove or modify data; it just changes the data that appears in your view.
Types of Filters:
Extract filters
* When you connect to data, you can choose to extract it, saving a snapshot of how it looks in your workbook and ultimately reducing the number of times Tableau queries the data source.
* As part of this process, you can filter out data that isn’t relevant to your analysis.
* For example, you are querying a data source that has data for the last 20 years, but you only want last 5 years of data. Extract filters would be ideal in this case.
Understanding Filtering in Tableau
Types of Filters:
Data source filters
Data source filters reduce the amount of data being fed into Tableau and restrict what data the viewer sees.
* With certain access rights, the viewer can view all of the underlying data in a data source.
* If sensitive data hasn’t been excluded from the data source, use data source filters to filter it out.
* For systems that rely heavily on partitions or indexing, data source filters may yield tremendous control over the performance of queries issued by Tableau.
Understanding Filtering in Tableau
Types of Filters:
Context filters
All filters in Tableau are computed independently.
* If you want one filter to be applied before other filters, make it a context filter so it will be processed first.
* For example, if you have two-dimension filters, one on Region and another top 10 on products, both filters will apply to the entire data set.
* If you want to see the top 10 products in each region, set the Region filter as the context filter.
Understanding Filtering in Tableau
Types of Filters:
Dimension filters
Dimensions contain discrete categorical data, so filtering this type of field generally involves selecting the values to include or exclude.
* For example, use a dimension filter to show products that have low sales in specific regions
Understanding Filtering in Tableau
Types of Filters:
Measure filters
Date filters
Table Calculation filters
Types of Filters:
Measure filters
* Measures contain quantitative data, so filtering this type of field generally involves selecting a range of values that you want to include. For example, show only products that have sales greater than 10,000.
Date filters
* Date filters allow you to filter data by a range of dates, relative dates, or exact dates. For example, show products that have been ordered starting January 2017.
Table Calculation filters
* These filters are applied after the view has been produced. So if you want to filter the view without filtering the underlying data, Table Calculations filters are the way to go.
Fildering order of operations
extract filters-data source filters-context filters(sets, conditionall filters, top N, fixed LOD)-dimension filters(include/exclude LOD, data blending)-measure filers(forecasts, table calcs, clusters, totals)-rable calcfileters (trend lines, refernce lines)
Bar-in-Bar Charts & Bullet Graphs to Compare Measura
To reiterate the differences between the bar-in-bar chart and the bullet graph: in addition to the vertical blue line representing Quota (rather
than Actual and Quota being nestled together, as in the bar-in-bar), there are also reference bands, in progressively lighter shades of blue—the same color as the blue Quota line—that represent stages of progress towards the Quota goal.
