Looker Studio and Looker Studio: Getting Started articles come before us, now we can move on to more detailed topics and applications related to Looker Studio.
According to the flow of the topic, in this article I will discuss the charts and tables we will use in the data visualization and listing process, specifically focusing on the chart and diagram elements available in the Tools bar.
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Continuing from the example I shared in the Looker Studio: Getting Started article, I will continue using the Looker Studio – Report Example #1 report. I will add new pages to this report and navigate between them. To move between pages, simply click on an empty area within the workspace and use the arrow keys (right/left) to navigate, or alternatively, use the page list located in the top-left corner (refer to the image).
Looker Studio: Charts
As you may have seen in other articles, Looker Studio focuses on three key steps: connect to your data sources, visualize the data by filtering and segmenting it, and share the resulting story. Data sources, data integration, fields, and calculated fields will be discussed in other articles for now. In this article, we will focus on visualizing data and creating a story. All the tools we will use—charts (Add Chart) and other elements (date range, filter control, data validation, etc.)—are available in the Tools bar.
First, let’s take a look at what’s available under Add Chart1, and then we’ll go into detail about each of these elements and how we’ll use them in our report.
| Charts | ||
|---|---|---|
| Table (table) | Score Card (score card) | Time Series (time series chart) |
| Bar (bar chart) | Pie (pie chart) | Geo (geo chart) |
| Line (combo chart) | Area (area chart) | Scatter (scatter) |
| Pivot Table (pivot table) | Bullet (bullet chart) |
For insights on how to use these charts and tables, you can visit the Looker Studio > Gallery page2. For getting started, you may examine the examples Nonprofit Web Data Template and Sports Viz Sunday – 2018 03 – F13.
By clicking on a chart or table you’ve added to your workspace, you can change it to another type of chart or table that appears on the right-hand side. Of course, in such a case, you may also need to reconfigure the metrics and dimensions.
Looker Studio: Add Chart
The operations mentioned below (Add Chart) can also be managed under the Add heading in the menu. I will also provide an in-depth discussion on chart models and related topics. Therefore, under the headings below, I have only included detailed information regarding the usage of the relevant chart. For data sample demonstration, I used the Master View under the Google Merchandise Store property. As an example, you can share the report to your own account and make the desired adjustments through this view.
Add Chart > Table
Let’s begin with the Table. The Table chart type allows for visualizations in columns, including Table, , Bar Table, and Heatmap Table. This chart type presents data openly through rows and columns. The core type is the Table, while the others are formatted variations. At least one metric or dimension is required to enable listing.
In the example above, you can observe different table types in Tables A, B, and C. Table D can be considered as a combination of all these types. Within Table D, the Users (metric) column (Column #1) includes the Number, the Number of Sessions per User (metric) column (Column #2) presents a Heatmap, and finally, the Pages/Session (metric) column (Column #3) is displayed as a Bar chart (with the “Show Number” option activated). When you deselect the “Show Number” option in the Pages/Session column, the data will be represented solely based on bar densities. As shown in the example, additional color definitions are possible during both Bar and Heatmap visualizations. After creating a Table, you can easily switch between other table views.
In the example above (page 2), a visual has also been used as a background. To achieve this, you simply follow the Insert > Image > Data steps, select the relevant image, and then use Arrange > To Back to place the image behind other elements.
Add Graphic > Score Card
The Score Card and the Score Card with Shortened Numbers can be positioned within a report as text fields. Some customizations are also possible in this usage. In a Score Card, only metrics can be used. Below, I illustrate the A, B, C, and D usage examples. In the A usage, users are assigned the default date range without any time comparison, and there are no additional styling features beyond defining the background. In the B usage, the highest values are defined for the primary metric, and the decimal precision is set to Auto.
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Add Chart > Time Series
First, we leverage time-based variations for time series. This is because each series will not be time-based, and if there is no time-dependent condition, the chart we use will fail to accurately represent the data. Therefore, in accordance with the rule that at least one of the parameters used must be time-related, we utilize the Time Dimension (on the X-axis) during the Dimensions definition phase. We use this to evaluate the changes over time. If the data represents a static, non-changing condition over time, then a time series is unnecessary. Time can be taken in units such as minutes, hours, days, weeks, months, seasons, or years. In the example below, a time-based (daily) session change is observed.
Upon closer inspection of this example, we can evaluate user behaviors over time, the impact of holidays on users, the traffic volume (and thus the associated technical requirements) of our website, campaign ideas, and many other contextual factors. If any outliers (anomalies) are present, we can generate hypotheses about their causes, perform predictions, impute missing data, and correct errors (data scrubbing). A separate article dedicated to time series and usage details would provide a more accurate explanation. For now, let’s proceed with the usage examples described above.
In the example shown, the A usage case employs the Time Series Chart in its most basic form. There are no dimensions, date comparisons, filters, or segment definitions; the metric defined is the session count. Stylistically, bars are selected, and a trend line is absent. In the B usage case, different from A, the Mini Chart variation is selected, and a periodic comparison is in place. During the comparison phase, the comparison area is defined based on the tone of the primary color defined in Data Studio, rather than through styling. In the C usage case, the “series” is represented as a line instead of bars, as in both A and B. In this C usage case, which also includes periodic comparisons, you can observe the rounded time series variation. A linear trend line allows us to interpret that session counts are increasing over time (specifically in November). In the D usage case, both lines and bars are displayed together. The dimension is gender (male and female), and the metric is session count. The data is filtered by the Google Analytics Segment “Converting Sessions.” This enables us to evaluate changes over time by gender and compare them through periodic comparisons, thereby generating hypotheses about potential outliers.
Add Chart > Bar & Column
Bar and column charts are very useful options for tracking data changes and/or comparing elements. In bar and column charts, values are sorted according to the X and Y axes. In the example below, you can see many customized usages used together.
In A, data for the Date dimension and Session metric is displayed in ascending date order. The chart, without any filter, segment, or period comparison, is defined as 30 Columns in the Style section. In B, the stacked column chart variation is selected. The dimension > Date, Tool. Metric > Session parameters, the feature that differentiates the chart from a regular column chart is that the bars are selected as stacked bars. As a result, the same historical values will be aggregated and placed along the Y-axis, rather than being displayed side by side along the X-axis. To view the difference, you can cancel the “Stacked Bars” selection in the Style section. Additionally, in B, the Date is displayed as a day in YYYYMMDD format, different from other usages. In C, the 100% Stacked Column Chart variation is used. Unlike B, with the 100% stacked option, values on the Y-axis are expressed as percentages. Thus, the bars represent the values within the 100% area. Apart from the Date and Tool dimensions and the Session metric, no filters or segments are defined. In D, you see the use of bars instead of columns (horizontal), with a column count defined as 10. No additional elements are added beyond the Date dimension and Session metric. In E, the Stacked Bar Chart variation is presented based on the values from B. In F, differently from C, the Color Measure is defined as a Single Color under Style. As a result, the splits are reflected through tonal variations based on the defined primary color.
Add Chart > Donut (Pie)
Pie charts are used to evaluate the proportions of individual quantities within the total amount (the percentage relationship between segments relative to the whole). Structurally, quantitative data is displayed as segments of a circle. The size of each segment on the circle corresponds to the proportion of the respective quantity. You can view examples through the styling customization options below.
In the example, A shows the Donut Chart displaying both size and session metrics. In the example with five segments (Donut Chart), the Color Measure is set to “Size Values.” The difference between the Donut Chart and the Ring Chart lies primarily in the addition of a gap in the central portion of the Donut Chart. In B, you can observe the effects of the defined empty space in the “Manage Size Value Colors” section. In C, since the Color Measure is set to a single color, segments are displayed using shades of the main color. In D, you can view the chart, which is expressed in a single color, transformed into a ring by the gap in the central portion.
Add Chart > Geographic Map
Geographic maps provide us with the ability to evaluate data in a location-based context. Using the latitude/longitude values we define, or predefined values such as country ISO code, country, city, etc., we can evaluate metrics by marking areas. Looker Studio offers the option to select “Zoom to Region” in addition to defining dimensions and metrics.
Data can be displayed on maps by zooming in and out, based on selections at global, regional, and sub-regional scales. In A, B, and C usage modes, representations are made using the country ISO code dimension value; in D and E usage modes, the dimension is defined through country selection. In A usage, the global scale is used, while in other usage modes, regional and sub-regional scales are available.
Add Chart > Line
With a line chart, you can compare different metrics based on a single dimension (e.g., date), allowing you to observe how events develop and continue over time. This enables a straightforward assessment of ongoing or time-bound “numerical” changes. Additionally, by grouping data, line charts can reveal trends and relationships among the data.
In the example application A, the Time dimension includes Session and Page Views metrics, providing us with information on how many pages were viewed during the sessions in November. Unlike other applications, application A also features axis labeling. This allows us to read the values of both metrics on the Y-axis. Application B differs from A by including an additional metric: the number of products added to the shopping cart. This enables us to incorporate an additional data point into our analysis process. Additionally, the Organic Traffic segment has been included in the graph. You can view the changes by deactivating the segment. For instance, by selecting a campaign, you can evaluate user sessions and other metrics based on the campaign. Another difference in this application is the date display. The date definition in the format YYYYMMDD is shown on a daily basis, resulting in the data being ordered in two-digit format on the X-axis. Application C differs from both A and B by presenting data in a line series format. Similarly, the data is ordered by the day of the month (GG). The Organic Traffic segment remains active. In Application D, the Source dimension and Browser breakdown dimension are present, with the Session metric selected. The sorting is performed based on the Source dimension. The graph reflects the browser data originating from the relevant sources. As observed, Chrome usage shows a general peak based on the source. The absence of continuous lines indicates no relationship between the dimensions. For example, if Safari were used by users from each source just as Chrome is, it would display a continuous line pattern similar to Chrome, reflecting the usage intensity.
Add Chart > Field
Area Chart shares similar features with Line Chart. The difference is that the line is filled with a specific color and/or texture. It is typically used to show the development (such as changes in values between data categories) of quantitative values (such as one or more data series) over a specific range or time period (trends).
In the app, the change in the Session metric is viewed through the Date time dimension and Source split dimension. In the Area Chart, the Stacked visual option is active, hence the chart variation is referred to as Stacked Area Chart. In this app, in addition to Stacking, a 100% Stacked option is also active. This enables the metric change to be interpreted by layers in percentage terms. A simple Area Chart is used in C usage. In D app, differently from the others, Gender is defined in the Source Dimension, the Session with Transformation selected, and styling adjustments have been made.
Add Chart > Distribution
Distribution Chart is also referred to as Scatter Plot, Point Chart, or X-Y Chart. Essentially, a Distribution Chart is a collection of points or a distribution. These points (for example, values of two variables) are positioned using Cartesian coordinates. The values of the variables distributed along the X and Y axes indicate whether a relationship or correlation exists between the variables.
In A usage, the relationship between internal promotion clicks (X and Y metrics) and the number of products added to the shopping cart is being observed across the time dimension. In B usage, the relationship between session duration and the number of products added to the shopping cart is being evaluated. Additionally, trend lines show time-based changes and comparisons against previous periods. In C usage, the relationship between e-commerce conversion rate and transaction volume is being displayed. Unlike in A and B usage, in this usage, an average order value is defined for bubble size. As a result, bubble sizes vary according to the average order value. In D usage, in addition to the time dimension, the gender dimension is also defined. As can be seen in the chart, male and female values are assigned different colors, and the relationship between the number of products added to the shopping cart and page views is reflected through these colors and bubble sizes.
Add Chart > Pivot Table
Pivot tables can be used to reduce a large dataset or to examine relationships among data points. For example, you can use a pivot table to analyze which source generated the highest revenue in a specific month. In Looker Studio reports, up to three pivot tables can be created on a single page. The following example showcases various variations of pivot tables.
In A usage, the Bar (1st metric: Bar) variation is employed, and the session logs obtained from the Agents are listed by the days of the week. In the Bar usage, in addition to the numbers, the corresponding number is also displayed as a bar in the predefined colors. The B usage is presented as a simple pivot table. While in A usage, the days of the week are ordered, in B usage, dates are ordered by month. In C usage, the visualization is achieved by using defined colors and tones based on the density of the value (cell) at the intersection of rows and columns, as indicated by the Heat Map.
Add Chart > Item Marker (Target Chart)
The Item Marker, or alternatively known as the Target Chart, is generally used in the evaluation process of performance data. Unlike a Bar Chart, it includes additional visual elements to provide more content and is typically developed as an alternative to less informative, less efficient, and more complex dashboard tables and gauges.
In the example, the metric “Sessions with Search” is displayed in A usage, while the metric “Sessions” is shown in B usage. Different Bar Colors are used in both usages, and axes are included in the charts.
Regarding Looker Studio Usage
In the coming time, I will discuss various sources and examples, including those originating from multiple alternatives, such as the charts mentioned above. Of course, I will also continue sharing reports created from different data sources and consolidated data. For now, you may review the simple examples I prepared for the provided charts and I can transfer the relevant report to your account.