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Research Guides

Tableau for Data Visualization

Introduction of using Tableau for data visualization

1. Importing Data. You can import data from excel files, text files or data base. And in the following guide, we would used the data set "Sample-Superstore".

2. Once we select the data set, we will go to this UI. And similar to excel, we can select sheets on the bottom left tabs

3. In the yellow area, we can observe the data set. And in the red area, different sheets of data can be dragged to the blue area for analysis.

4. In this guide, we only analyze the data sheet "orders"

5. Let's go back "sheet 1". The most basic thing to know about Tableau, is that every data are categorize into "Dimension" and "Measure"

6. "Measure" are always numbers, they quantify the extent one thing is. On the other hand, "dimension" are usually names, categorizing the "measure" in different kinds. As you can see, we can drag any dimension or measure into the red area for analysis

7. And in the "Show Me" Panel, you can choose 27 chart types in Tableau for data visualization

8. You can see when we choose the "Map Chart", our data is expressed in a map.

9. Let's go back to the bar chart with "year" on the column and "discount" on the row

10. The "Color" function here can illustrate new dimension without adding complexity to the original chart. To do so, drag data on the left to the "Color" mark

11. However, the "Color" can applied only to one data. To add more data in your chart, one can drag the data to either columns or rows, which inevitably add complexity to the chart. Here we dragged the "manufacture" data to the column

12. By clicking on the "+" or "-" sign on each data, we can expand or shrink the data for more detail or better Interpretation.

13. In the "Analysis" Menu, we can performance different data analysis. Here, we choose the "filter", which enable us filter out the desired data quickly and instinctly

14. By right clicking any chart, we can also adjust the chart, we can add trend line or do a forecasting based on the data. Here we gave an annotation to explain the data

15. Similar to the "Color", we can use "Size", which is different shape to represent another dimension without adding complexity. Here, we use different shape to represent different year of order date

16. And the same as "Color" and "Size", we can using labeling to extent the data dimension but keeping the graph simple. Here, we labeled the name of different cities

17. Also, we can switch the column and row, or sort the chart simply by clicking the corresponding button. Here, we sort from biggest to smallest based on the name of the product

18. After all the data analysis, we can create a "story" by clicking the button circled. This is exactly the same making a presentation of your analysis

19. Then, we illustrate our analysis though chart and words. Notice that this is a interface, anyone can also adjust the output based on your customization option

20. Finally, we can save our "Story" for further use on devices like PC, tablet or phone, expressing your great idea!