Doorda Knowledge Base

Getting Started With Doorda Host Using Tableau

 

 

What is Spine?

With over 100 datasets available in DoordaStats, it may require significant time and effort to understand and create value from them. Therefore, in order to speed up the process and help you merge data easily, we have created `r1_stats_spine`, a base dataset which provides a starting point for you.

`r1_stats_spine` consists of basic details like postcode, outputarea, postcode introduction date, latitude, longitude (to help you to map data) and also total people and households.

 

 

Importing Spine from Google Big Query

The first step is to import the `r1_stats_spine` file from Doorda's database on Big Query. 

 

 

The file can be found under the dataset `DoordaStats` within the project `Doorda-Production`.

Visit the following link for an in-depth guide of connecting to Big Query using Tableau: 

https://www.doorda.com/kb/article/connecting-to-doorda-host-big-query-with-tableau.html

 

 

Mix and Match with the Data you Need

For the purpose of this demonstration, we are comparing electricity and gas consumption of a region.

Hence, additional dataset `r1_household_electricity_consumption` and `r1_household_gas_consumption` are added to Tableau.

 

We need to specify how the 3 dataset should be joined together.

In this case, we're using left join as `r1_stats_spine` is our base file, where we use 'postcode' as the key.

 

Below is the joined data where 'Electricity Usage', 'Gas Usage' and 'Postcode' data are present within one dataset.

 

 

Create Calculated Fields for Individual Utility Usages

Let's find out utility usage on an individual level, create calculated fields to divide usages by population.  

Note: 'Electricity Usage' and 'Gas Usage' represent the total domestic consumption (kWh) for a postcode.

 

 

 

Visualization

Results are visualized into map representations, using location data from the `r1_stats_spine`file.

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