How to Make a Time Series Plot in Looker

Tracking your business performance over time is fundamental to understanding what’s working. A time series plot, which shows your data points in chronological order, is one of the most effective ways to visualize these trends. This article will walk you through exactly how to create and customize a time series plot in Looker to see the story your data is telling.

What is a Time Series Plot (and Why Should You Care)?

At its core, a time series plot is simply a chart that displays data over a consistent time interval. It usually takes the form of a line chart, with time on the horizontal (X) axis and the metric you're tracking on the vertical (Y) axis. Think of it as a historical record of a specific business metric.

Why is this so valuable? Because it helps you answer critical questions that are impossible to see in a single number:

Website Traffic: Did our traffic grow after last month's marketing campaign? Is there a dip in traffic on the weekends?
Sales Revenue: Are we seeing steady month-over-month growth? Does our revenue spike during certain holiday seasons?
New User Sign-ups: How did our recent product launch impact the rate of new sign-ups? Is our user base growth accelerating or slowing down?
Customer Support Tickets: Is the number of daily support tickets increasing as our user base grows?

By plotting these metrics over time, you can spot trends, identify seasonal patterns, and measure the impact of your actions. It’s the difference between knowing you had 10,000 visitors last month and seeing that 8,000 of them all came in the final week after you launched a new blog post.

Before You Build: Prepping Your Data in Looker

Before you can build your chart, you need two key ingredients from your data. In Looker terminology, these are a dimension and a measure.

A Time Dimension: This is your X-axis. It’s a field in your data that represents time. Looker works best with date and time fields that are properly formatted. Examples include created_at for users, order_date for sales, or session_start_time for website visits. Good data models in Looker often break these down into specific "dimension groups" like Date, Week, Month, and Year, which makes plotting much easier.
A Measure: This is your Y-axis value. It's the number you actually want to track over time. It's almost always an aggregation, like a count, sum, or average. Examples include a count of users, the sum of revenue, or the average order value.

If you're not sure you have these, open an Explore in Looker (your data starting point). Dimensions are typically colored blue, and measures are colored orange. Look in the left-hand field picker for a date-related dimension and a numeric measure you’d like to analyze. For instance, in a dataset of website users, you might look for Users Created Date (dimension) and Count (measure).

Step-by-Step Guide to Creating Your Time Series Plot

Let's build a classic time series plot: tracking the number of new users signing up each day. The process is straightforward and lays the foundation for more advanced charts.

1. Start in an Explore

Every analysis in Looker starts from an Explore. An Explore is a curated starting point for a query, designed to make it easy to find the fields you need. Navigate to the Explore section from the main Looker menu and choose the one that contains your user data (it might be called "Users," "Customers," or something similar).

2. Select Your Time Dimension and Measure

In the left-hand field picker panel, find your time dimension. In our example, we'll find a Created Date dimension group and click on the Date timeframe. This tells Looker we want to group our results by each individual day.

Next, find your measure. For this example, we’ll click on Count under the Users view. This tells Looker we want to count the number of users for each of the days we selected.

You should now see these two fields - Users Created Date and Users Count - listed in your Data pane.

3. Add a Filter (Important!)

If you ran the query now, you might get a chart showing data for all time, which can be messy and slow to load. You almost always want to filter your time dimension. Find your Users Created Date field in the Filters section at the top. Click on it, and set a reasonable timeframe, like "is in the past 90 days."

4. Run the Query

Click the Run button in the top right corner. Looker will query your database and return a data table with two columns: a date and the count of users created on that date.

5. Choose Your Visualization

Above the data table, you'll see a Visualization pane. Looker might default to a specific chart type, but for a time series, a line chart is usually best. Click on the Line chart icon.

And that’s it! You should now see a line chart displaying the daily trend of new user sign-ups over the past 90 days. You’ve officially created your first time series plot.

Pro Tips for More Insightful Plots

A basic line chart is good, but a few tweaks can make it great. Click the Edit button in the top right of the Visualization pane to open the settings menu and try these out.

Tip 1: Compare Categories with Pivots

What if you want to compare sign-ups from different sources (e.g., Google, Facebook, Direct) over time? This is where pivoting comes in handy.

Go back to your field picker and add a new dimension, like Traffic Source. Then, instead of clicking to add it as a column, hover over it and click the Pivot icon. Re-run your query. Looker will now create separate lines on your chart for each traffic source, so you can easily compare their performance over the same period.

Tip 2: Add Context with Reference Lines

Is your performance meeting expectations? A reference line helps answer this. In the Edit menu, go to the Y tab. Under Reference Lines, you can add a static value (like a monthly target) or have Looker calculate a line dynamically (like the average or median). Adding a line for the average daily sign-ups can instantly show you which days were above or below average.

Tip 3: Smooth Out Daily Noise with Moving Averages

Daily data can often be "noisy," with lots of sharp peaks and valleys that hide the real underlying trend. A moving average can smooth this out.

From the Data pane, click the Add calculation button. Create a new "Table Calculation" with a formula like this:

Give it a name like "7-Day Moving Average," format it as a number, and hit Save. This formula calculates the average of the current day's count and the six previous days. You can then hide the original "Users Count" from the visualization (by clicking the gear icon on the column and selecting Hide from Visualization) to see only the smoothed trend line, making long-term growth much clearer.

Common Mistakes to Avoid

As you get started, watch out for a few common pitfalls that can make your charts confusing.

Choosing the Wrong Chart Type

A pie chart or a bar chart isn't suitable for showing a continuous trend over time. Stick with a line chart for most time series data. An area chart is a good alternative if you want to emphasize the volume or magnitude of the changes.

Not Filtering Your Timeframe

Querying months or years of daily data can result in an unreadable, tangled chart and slow-running queries. Always use a filter to narrow the timeframe to the period you are actually interested in analyzing.

The "Spaghetti" Chart Problem

When you pivot a dimension with too many unique values (like pivoting by country when you have 100+ countries), you'll get a "spaghetti" chart - a messy jumble of overlapping lines that is impossible to read. If you need to show that many categories, consider filtering to show only the top 5 or 10, or using a different chart type altogether.

Final Thoughts

Creating a time series plot in Looker is a powerful first step in turning raw data into a clear narrative about your business performance. By following these steps, you can move beyond simple numbers to understand your growth, spot important patterns, and make smarter, more informed decisions.

Getting your data into one place and setting up the foundations in Looker is often the hardest part - not creating the actual chart. With tools like Graphed, we simplify this process entirely. By connecting your marketing and sales tools like Google Analytics, Shopify, and Salesforce in just a few clicks, you can ask for the analysis you need in simple language. Instead of building plots manually, you could just ask, "Show me new Shopify sales by day for the last 90 days," and get an instant, real-time dashboard without any setup.