Tutorial 7: Setting up data export

In this tutorial, you will learn how to set up a data export. Each export si defined in the export object.

What you'll create

This is the export window, where you can download the export in a CSV file.

Customers export

Let's have a look at our customers dataset one more time. We will identify the properties that will be included in our export. Note that all active filters and the map selection are counted in the exports.

The requirement is to export customers, who have made a transaction bigger than 3000 CZK, to give them a special voucher.

The properties we want to include are: customer_id, addresssex and age_group.

Name

Title

Data type

Name

Title

Data type

customer_id

Customer ID

integer

neighborhood_code

Neighborhood code

string

city

Customer's city

string

address

Customer's address

string

sex

Customer's sex

string

age_group

Customer's age group

string

lat

Address latitude

latitude

lng

Address longitude

longitude

The export object will look like this:

Customers export syntax
1 2 3 4 5 6 7 8 9 10 11 12 13 { "name": "customers_export", "type": "export", "title": "Customers export", "content": { "properties": [ "customers.customer_id", "customers.address", "customers.sex", "customers.age_group" ] } }

Add this export object to the project using addMetadata. Now we just need to add the export reference to the business_overview_view object.

Business overview view excerpt
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ... "mapOptions": { "center": { "lat": 49.203, "lng": 16.606 }, "zoom": 12, "tileLayer": "mapbox" }, "exports": [ { "export": "/rest/projects/$projectId/md/exports?name=customers_export" } ] }, "accessInfo": { ...

Before we perform the export, we have to apply the required filter first.

Applied histogram filter on the transaction_size property

Also, don't forget that the map window should contain all available areas.

The export is available after clicking the Menu button in the top left corner (), and selecting Export.

Select Customers export and hit Export.

The selected columns, as well as metric results will be present in the customers_export.csv file.

Stores export

This is the stores dataset we've added in the previous tutorial.

Identifier

Title

Data type

Identifier

Title

Data type

store_id

Store ID

integer

address

Store address

string

store_type

Store type

string

opening_hours

Opening hours

string

monthly_rent

Monthly rent

integer

contact

Contact email

string

link

Store web link

string

parking

Designated parking lot

string

employees

Number of employees

integer

lat

Store's latitude

latitude

lng

Store's longitude

longitude

We won't put any filters on this export and we'll export these properties: store_id, address, store_type, contact.

It is possible to include properties from other datasets in an export. We will include some properties from datasets of the Administrative units dimension we've added in one of the previous tutorials.

  • cast_obec_dwh.nazev - the city district where the store is located

  • okres_dwh.nazev - name of the county

  • kraj_dwh.nazev - name of the region

Stores export syntax
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 { "name": "stores_export", "type": "export", "title": "Stores export", "content": { "properties": [ "stores.store_id", "stores.address", "stores.store_type", "stores.contact", "cast_obec_dwh.nazev", "okres_dwh.nazev", "kraj_dwh.nazev" ] } }

Add the object using addMetadata and add the export reference to the business_overview_view object.

Business overview view excerpt
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... "mapOptions": { "center": { "lat": 49.203, "lng": 16.606 }, "zoom": 12, "tileLayer": "mapbox" }, "exports": [ { "export": "/rest/projects/$projectId/md/exports?name=customers_export" }, { "export": "/rest/projects/$projectId/md/exports?name=stores_export" } ] }, "accessInfo": { ...

Push the object using pushProject. Then repeat the export process, but select Stores export and hit Export.

You can see that the stores_export.csv contains all six stores, with respective results of all metrics in all city districts.

 That's it! In the next tutorial, we will learn how to use AreaMapper and use a new visualization - grid.