Tableau Write For Us – There are certain concepts fundamental to understanding the preparation of data and how to structure it for analysis. Data can be generated, collected, and stored in various formats, but when it comes to analyzing it, not all data formats are created equal.
Data preparation is getting adequately configured data into a single table or multiple related tables so it can be analyzed with Tableau. This includes both the structure, rows and columns, and aspects of data cleanliness, such as correct data types and values.
How Structure Affects Analysis
The structure of the data may not be somewhat you can control. The rest of this topic assumes that you have access to the basic information and the tools you need to shape it, such as Tableau Prep Builder. However, there may be situations where you can’t pivot tables or aggregate the data as you want. Often it is still possible to do the analysis, but you may need to change your calculations or approach to the data. For an example of how to perform the same examination with different data structures, see Tableau Prep Day-to-Day Scenarios: Second Date Analysis in Tableau Desktop(Link opens in a novel gap). But if you can enhance the data structure, the analysis will likely be and easier.
Tableau Desktop is the whole thing best with data in benches formatted like a spreadsheet. The data is is stored in rows and columns, with the column headers in the first row. So what should be a row or a pillar?
What is a row?
A noise, or record, can be anything, from information about a transaction at a retail store to time measurements at a specific location or statistics about a social media post.
Knowing what a record (row) represents in the data is important. This is the granularity of the data.
What is a field or a column?
A column of data in a table is entered into Tableau Desktop as a field in the data panel, but they are interchangeable terms. (To describe certain visualisations, we saved the term column in Tableau Desktop for use on the Columns and Rows shelves.) A data field should contain items that can be grouped in a broader relationship. The elements are called standards or members (only discrete dimensions have members).
The domain of the field determines the values allowed in a area (see note below). For sample, a column for “grocery store departments” might cover the member’s “deli,” “bakery,” “produce,” etc., but would not include “bread” or “salami” since those are items, not sections. . Stated additional way, the domain of the section field is limited to possible grocery store departments.
Also, a well-structured data set would have a pillar for “Sales” and another for “Profit” and not a single column for “Money” because income is a separate concept from sales.
- The domain of the sales field would have a value greater than or equal to 0 since sales cannot be negative.
- The benefit field domain, however, could have all values since benefits can be harmful.
Each column in the data table included in Tableau Desktop as a field appears in the Data panel. Fields in Tableau Desktop can be either a measurement or a measure (separated by lines in the Data panel ) and discrete or continuous (depending on their colour: blue fields are discrete and green arenas are endless).
- The dimensions qualitative, meaning they cannot be measured but described. Dimensions are usually belongings like city or nation, eye colour, category, team name, etc. The dimensions are generally discreet.
- Measurements are quantitative, which means that they can measure and recorded with numbers. Measurements can be things like sales, height, clicks, etc. Measurements are usually continuous. In Tableau Desktop, the measures added automatically; the default aggregation SUM (sum).
- A Discretevalue one individually separated or distinct. In Tableau Desktop, discrete values are included in the view as a label and create panels. Toyota is different from Mazda.
- Continuous means to form a continuous and uninterrupted whole. In Tableau Desktop, continuous values included in the view as an axis. Between 7 and 8 is the same distance between 8 and 9, while 7.5 would fall halfway between 7 and 8.
- The dimensions are usually discrete, and the measurements are generally continuous. But it doesn’t continuously have to be that way. Dates can be discrete or continuous.
- Dates dimensions are automatically included in the view as discrete values (also known as date shares, such as “August”, which reflects the month of August without considering other information, such as the year). A trend line applicable to a timeline with different dates will split into multiple trend lines, one per panel.
- We can choose to use continuous dates if preferred (date truncation, such as “August 2024”, which is different from “August 2025”). A trend line applied to a timeline with incessant dates will have a single trend line for the whole date axis.
At Tableau Prep, no distinction is made in dimensions or measurements. However, it’s essential to understand the concepts behind discrete vs continuous for things like detailed presentation and summary presentation of data in the profile panel.
- Detail – The detail view shows each domain component as a discrete label and has a visual scroll bar to provide an overview of all the data.
- Summary – The summary view displays the values grouped on a continuous axis as a histogram.
Bins and histograms
A field such as age or salary considered continuous. There is a relationship between the age of 34 then 35, and 34 is as far after 35 as 35 is from 36. However, once we get past ten or so, we usually stop saying things like “9 and a half” or “7 and ¾”. We are already reducing our age-to-size increments of one year. Similarly, age groups often used instead of actual ages. Somebody who is 12,850 days old is older than someone who is 12,790, but we draw a line and say that they are both 35 years old. Children’s movie ticket prices may be for boys and girls 12 and under, or a survey may ask you to choose your age collection, such as 20-24, 25-30, etc.
Histograms used to imagine the delivery of numerical data using bins. A histogram similar a bar chart, but instead of being discrete categories per bar, the rectangles that make up the histogram span a group of a continuous axis, such as the range of several flowers (0-4, 5-9, 10-14, etc.)—the tallness of the rectangles determined by the frequency or count of those values. Here, the Y axis the count of plants in each group. Seven plants have 0-4 flowers, two plants have 5-9 flowers, and 43 plants have 20-24 flowers.
In Tableau Prep, the summary view is a histogram of binned values. The detailed picture shows the frequency for each value and has a visual scroll bar next to it that shows the overall distribution of the data.
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