
# Summary

Extract-transform-loaded (ETL): GA data (1.4 million records) ➜ BigQuery ➜ Google Cloud Storage ➜ Tableau Server Connection.
	
BigQuery was used to calculate desirable metrics (Page views, time on page, bounces, sessions) for use as KPIs in Tableau. Created filters and parameters for segmentation and comparison analysis.

# What is this data?
The sample dataset contains obfuscated Google Analytics 360 data from the [Google Merchandise Store](https://www.googlemerchandisestore.com/shop.axd/Home?utm_source=Partners&utm_medium=affiliate&utm_campaign=Data%20Share%20Promo). It consists of 900k records (4.2 million unnested), 338 variables, and includes the following kinds of information:

-   Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc.
-   Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc.
-   Transactional data: information about the transactions that occur on the Google Merchandise Store website.

You can query the data in GCP BigQuerry by date in the following way:  
`FROM 'bigquery-public-data.google_analytics_sample.ga_sessions_20170101'`

The schema can be found [here](https://support.google.com/analytics/answer/3437719?hl=en)

# SQL Breakdown
Collect 8 descriptors and 5 Measures for import

Descriptors:
- hit_year
- hit_month
- pagePath
- source
- channel_grouping
- device_category
- country
- city 

Measures:
- pageviews
- unique_pageviews
- total_time_on_page_combined
- total_bounces
- total_sessions

## Constant Categories
The descriptor variables will be selected for all measure queries.
```SQL
SUBSTR(date, 1, 4) AS hit_year, 
SUBSTR(date, 5, 2) AS hit_month, 
hits.page.pagepath AS pagePath, 
trafficsource.source AS source, 
channelgrouping as channel_grouping, 
device.deviceCategory as device_category, 
geonetwork.country as country, 
geonetwork.city as city
```

# Tableau Calculations
Parameters:
- Comparison Selector: M/M (current month vs previous month), Y/Y (current month vs month from previous year), YTD (current YTD vs previous YTD)
- Date Selector: Select the Month/Year of interest

Categorical Descriptors:
- Geo Location
- Device Category
- Traffic Source
- Channel Grouping

Metrics:
- Sessions
- Pageviews
- Unique Pageviews
- Time on Page
- Bounces
- Average Unique Pageviews per Session
- Average Time on Page per Session
- Percentage of Sessions that Bounce

Sessions:
- Total Session Comparison: SUM(current session range) - SUM(previous session range) / SUM(previous session range)
- Session YTD (Year to Date): Return [Total Sessions] for selected Month and prior, within same Year.
- Session CM (Current Month): Return [Total Sessions] for selected Month and Year
- 

CM: Current Month
PM: Previous Month
PY: Previous Year
YTD: Year-to-Date
PYTD: Previous Year-to-Date
LTD: Life-to-Date (for grey charts)

For Percent of Total (Avg Unique Pageviews CM for Channel Grouping), the calculation must be modified from TOTAL() to WINDOW_SUM(). This is because the TOTAL() function does not work properly on measures that are already an aggregate function. [ref](https://kb.tableau.com/articles/issue/percent-of-total-adds-up-to-more-than-100)



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# Description

Statistics and research data was sourced from [Our World in Data](https://ourworldindata.org/covid-deaths) to visualize how covid-19 has spread over time and between countries. The data was explored and cleaned with Microsoft SQL and visualized in Tableau.

# Finished Product

<div id='image'>
	![Dashboard Screenshot](/images/covid-data-vis/dashboard_screenshot.png)
</div>

The interactive dashboard is hosted on Tableau Public and can be found [here](https://public.tableau.com/app/profile/kyle.rozic/viz/CovidVisualization_16319417831480/Dashboard1?publish=yes)

Kyle Rozic

Tableau

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Covid-19 Data Dashboard