Google Data Studio Help – Sturdy Getting Started Guide [Part 2]

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Hello everybody and welcome to the second part of Getting Started With Google Data Studio.

In the first part of the series we talked about KPIs, data sources, and templates, so now it’s time to continue the Google Data Studio help with some more advanced features of the tool.

By the end of this article you will walk away with a solid understanding of:

  • Date ranges
  • Dimensions vs. metrics
  • Filters vs. filter controls
  • Calculated fields and types of calculated fields
  • Blending data

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Ready? Let’s get started then!

But before that, let’s make sure we’re on the same page with dimensions and metrics.

Dimensions vs. Metrics in Google Data Studio

We consider it’s very important to know how to handle dimensions and metrics in your reports. These are crucial data fields used to create charts in GDS.

If you’re using Google Analytics, you may already be familiar with dimensions and metrics. For the sake of clarifications, let’s see what these are.

According to the Google Data Studio Help Center, we call dimension “a set of unaggregated values by which you can group your data”, and metric: “a specific aggregation that you can apply to a set of values.”

Simply put, dimensions describe or categorize your data, and metrics measure the dimensions.

  • Examples of dimensions include: Campaign name, Country, Product ID, Gender, Browser, and anything you might use to group information in a chart.
  • Examples of metrics: Sessions, Bounce rate, new users, or any aggregated numbers in charts.

Dimensions will appear in your data source as green fields, whereas the metrics will appear as blue fields.

In GDS, the metrics are calculated either as:

  • Overview totals (a summary statistic for an entire column, as in the Scorecard chart). E.g.: average session duration for all users. Or:
  • In association with one or more dimensions. E.g.: average session duration paired with the User Type dimension, so you’ll be able to see returning vs. new users.

Visualization tricks to interact with dimensions in a better way

1. Use drill-down to make your reports even more interactive

Within a chart you can reveal additional levels of detail using dimensions drill-down, like:

  • Geography: Country > Region > City
  • Date: Year > Month > Day
  • Product: Department > Category > SKU
  • Google Analytics events: Event Category > Event Action > Event Label.

To add a drill-down capability to your chart, first you have to define a dimension hierarchy, from the most general to the most specific.

To turn it on, go to the right of your chart. You’ll see it in the DATA properties panel, under Dimension.

Why is drilling down so useful?

Well, the main reason is that you can see the data from a single chart, rather than building two or more charts in your report. Also, you can easily get more insights on different levels of detail from your data.

2. Use breakdown dimensions to group data

The Breakdown Dimension is another visualization technique that displays the metric data broken down according to a selected dimension. Basically, it allows you to sub-categorize data into smaller chunks. Any other additional dimension you put into the chart to group the data into finer levels of details is called breakdown dimension.

Let’s see an example:

In this case, the primary dimension is City and the breakdown dimension is Month. The metrics in the table are aggregated first by city, then by month.

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How Google Data Studio helps you be in control with date ranges

Once you build a dashboard in Google Data Studio, you can interact with your data in any way you want. For instance, you can use date ranges. 

What are date ranges? They’re dimensions you configure in GDS to load data for certain periods of time. Basically, depending on what you want to see, you can set your default date range to: last 7 days, 14 days, 30 days, or you can customize it as you want with the advanced date combinations.

Here’s how to set the date range dimension:

  • Select the chart
  • Select the DATA tab
  • Click the Date Range Dimension from the Data Source section
  • Use the dimension picker to select a valid date dimension.

If you want to set the Date Range Dimension at the report or page level, use File > Report settings menu or the Page > Current page settings menu.

A big plus on working in GDS is that you can set up a comparison date range. That means you can compare the last 30 days performance to the previous 30 days, or any other time frames you want. 

Comparison date ranges are available for time series, tables, area charts, or scorecards. To set them up:

  • Simply scroll down to the Default Date Range section in the DATA tab of the properties panel
  • Choose the comparison period under Comparison date range
  • Hit APPLY.

You can also add a date range control, which is a customizable calendar filter that enables your viewers to change the time frame of a report.

Filters vs filter controls - how do they work?

Chart interaction filters

Chart interaction filters in Data Studio are another great way to enhance your reports. How do they work? Simply click on a dimension in a chart and it will filter / highlight all the other charts on that page for that particular dimension you chose.

Here’s an example from the Google Data Studio Help Center. Click on any medium in the table below and it will display the respective sessions in the scorecard, in the time series, and in the geo pie chart as well:

Here are the steps to enable chart interactions:

  • Select a chart
  • Scroll to the bottom of the DATA properties panel
  • Select Apply filter from the Interactions section
  • Repeat all the above for each chart where you want to use the filter.

If you want to learn more about filters in Data Studio, have a look here.

Filter controls

Filter controls allow users to bring more flexibility to subsets of data and focus on the data that is important to them.

To give you an example, with a filter control based on the Country dimension, all the reports on that page would show data for the selected countries traffic specifically.

You can use filter control to filter any charts, other filter control, or groups. Just be aware of the 10,000 values maximum display for a list style filter.

Calculated fields and types of calculated fields

Calculated fields are a great feature that helps you create new metrics and dimensions from your data, directly in Google Data Studio.

A few examples of calculated metrics you can play with are: revenue per user, transactions per user, average order, calculated profit, and much more.

With calculated fields you can:

  • Do basic math like addition, subtraction, division, or multiplication.
  • Manipulate your data with functions like SUM(Quantity), PERCENTILE(Users per day, 50) etc.
  • Use branching logic – CASE statements, which is a “if/then/else” style logic.

For instance:

CASE

WHEN Country IN (“Namibia”,”Ghana”,”Ethiopia”) THEN “Africa”

WHEN Country IN (“Saint Lucia”,”Jamaica”,”Saint Vincent and the Grenadines”) THEN “Caribbean Islands”

ELSE “Other”

END

In this situation, the formula puts the specified countries in certain regions if they match your conditions, and groups the ones that are not specified into a general category: “Other”.

Here’s the full function list that can be used inside of calculated field formulas, and here you can see a brief scenario that can help you learn how to use calculated fields.

Data source vs. chart-specific calculated fields

Depending on where you create them, there are two types of calculated fields:

  • Calculated fields in data sources: you can use them in charts, controls, and other calculated fields, and they are available in any report that uses that data source.
  • Chart-specific calculated fields: they can be added directly to a chart in your report. Similar to calculated fields in a data source, they can do math, use functions, and return results based CASE statements.

However, the benefits of using Chart-specific calculated fields over data source calculated fields are:

  • No need to access the data source – add fields more quickly and easily.
  • Ability to create chart-specific calculated fields based on blended data (see what blended data is below.)
  • Data source calculated fields can be included in chart-specific calculated fields.

Blending data with Google Data Studio

Blended data is another amazing capability of this platform. 

How does joining information from multiple sources sound to you? To us, it sounds like joining forces to convey a better view of the data. Google Data Studio helps you blend data and create charts based on multiple data sources (up to 4 data sources). 

As Google puts it, you can even “blend two different Google Analytics data sources to measure the performance of your app and website in a single visualization”.

Here’s a video demonstration on blending data:

Similar to SQL, in order to join the data, you must use a “join key”. This is the data point they have in common. If they don’t share anything in common, then they aren’t blendable. 

Unfortunately, at the moment, Data Studio only supports left outer joins. This article on the Dcycle blog explains what that means:

Contrary to an outer join which uses columns from both the left and right data sources, a left join uses only those columns which are in the left data source, and if they also happen to be exist in the right data source, will use them also, but will completely ignore all data which is in the right data source only.

Filters on Blended Data in Google Data Studio

Dcycle Blog

However, you may find some workarounds to do table joins other than a left join. For example, you could use Google’s BigQuery as a bridge between your data sources and GDS or make your data blending directly in a Google Sheet.

That’s a wrap 🌯 for today!

We hope that this piece on Google Data Studio helps you get a firmer grasp on how to get started. Maybe you’ve even spotted some opportunities to fine-tune your dashboards for you and your team. We’ll continue to come up with useful info to grow your appetite for GDS.

We would be happy to assist you with building a Data Studio dashboard. Or feel free to drop us a line in the comments section below with anything you want to know about dashboards.

Until next time, keep your data fresh! 😉

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