You have so many Google Data Studio charts to choose from and visualization lets you and your viewers assess information easily. But the choice of charts can be a tricky and confusing issue. While multiple charts can express the same data, there are specific cases where one is better than the other.
In this blog, you will go through various Google Data Studio charts, and when you should use them.
Here is a broad overview of what you will cover:
Google Data Studio Charts
This is the list of Google Data Studio charts:
- Table with bars
- Table with heatmap
- Scorecard with compact numbers
- Time Series
- Time series chart
- Sparkline chart
- Smoothed time series chart
- Column chart
- Stacked column chart
- 100% stacked column chart
- Bar chart
- Stacked bar chart
- 100% stacked bar chart
- Geo map
- Google Maps
- Combo chart
- Stacked combo chart
- Line chart
- Smoothed line chart
- Stacked area chart
- 100% stacked area chart
- Area chart
- Scatter chart
- Bubble chart
- Pivot table
- Pivot table
- Pivot table with bars
- Pivot table with heatmap
- Bullet chart
Long list! Also, Google Data Studio keeps improving and bringing in new features.
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A table chart simply shows the metrics you want to look at for a given dimension in the form of plain numbers.
Table Chart With Bars
Numbers are obviously not the best ways to visualize data. A bar in the place of a number with its length representing the magnitude would be a good way to present data.
A table chart with bars merely replaces the numbers with bars.
We just saw how table charts can be created with bars to represent values. This allows viewers to visually consume information without staring at too many numbers.
Heat maps are also visual representations.
Just like bar charts use height to represent numbers, heat maps use color.
The variation in color might be by hue or by intensity.
A good dashboard is one where viewers can immediately view the most important metrics.
Think of a KPI report that you are measuring. Viewers shouldn’t have to search for the most important metrics. They should be seen in your report loud and clear.
The best way to do that is through a scorecard.
Time Series Chart
One variable that governs every aspect of our life is ‘time’. Often your data is paired with a period in time. The best way to represent time-related data is through a Time series chart.
Time series charts let you observe changes in trends, patterns in the occurrence of events, etc. visually.
A Sparkline chart shows the variation of some metric usually over time. It is usually very small, often small enough to embed in text, and doesn’t show axes or coordinates. They are sometimes called micro charts. Think of the visualization of the variation in stock prices of a list of companies.
Smoothed Time Series Chart
A smooth time-series graph helps you get rid of the roughness so that the trend can be seen more clearly.
This is one of the most widely used charts.
Column charts are used when you have categorical features having different numeric values and you want to visually compare their levels.
What is a categorical value? Simply put, it is a label. Countries, names of students in a class, years, products, etc. are all labels.
You put a categorical variable in the X-axis of a column chart. If you go with ‘Country’, then you will have names of different countries on the X-axis. If you are interested in studying rivers, you can put the names of various rivers on the X-axis.
On the Y-axis, however, there has to be a number. For example, if you take countries, Y-axis can be population or area or literacy rate. If you consider rivers, Y-axis can be the length of the rivers.
Stacked Column Chart
Stacked column charts look very similar to the column charts. But there is an important difference.
You are comparing the total values of a set of labels in a regular column chart. However, in a stacked column chart, you are also comparing various parts of the total. You use a stacked column chart when the parts of the total are also important.
For example, if you are comparing the number of clicks you get from various Google AdWords campaigns, you can use a regular column chart. But if you want to divide the clicks from each campaign based on the device used by the viewer, you can use a stacked column chart.
In the above case, the labels are various AdWord campaigns. They are broken down by the device used.
Horizontal Bar Chart
They are similar to their vertical cousins, but when you have a lot of labels or labels with long names, they won’t fit neatly on the screen. You don’t want to inconvenience your audience by making them scroll side-ways. Hence, in such cases, you should go for the horizontal bar charts.
When the X-axis is a time-related metric, such as ‘Year’ or ‘Quarter’ or ‘Month’, you should use a line chart, if showing the trend is more important to you than the total values.
Consider the below example.
This is the ‘Sessions’ data from Google Analytics. The dimension is ‘date’. On the Y-axis is the number of sessions. The sessions are broken down by the ‘Country’. Here, you may not be interested in the total number of sessions per day. You are more interested in looking at the trend for each country, while simultaneously comparing it with other countries. So you should use a line chart.
A line chart will also be a better choice when one part of the total overtakes another and conveying that to the viewers is important.
If you want to display different data on the same chart, you can use a combo chart. Use different graphs (a bar chart and a line chart for example) to avoid visual confusion, but you can put all the data in one chart.
A combo chart is generally used when you have mixed data that you want to put in a single chart. For example, when you have data on revenue and sales quantity you can use bars to represent revenue for a given month and a line to represent the sales for that month. Similarly, if you want to show actual sales and expected sales in a chart, you can use a combo chart.
A combo chart often, but not always contains two axes. One for each type of data.
The pie chart is a celebrity in the visualization world! But there are many cases where you should avoid using them.
It is well known that pie charts can be used when you want to represent each category of a dimension as a percentage/share of the total pie.
But here are some cases when you should totally avoid pie charts.
Suppose you want to show the difference between the totals, and the differences are small, it is better to use a column chart because length is easier to compare than area.
Also, never use pie charts when you have negative values or zeroes. It will either distort the whole thing or not show the label at all (in case of zero values).
Don’t use pie charts when you have many labels because the pie will look cluttered.
Avoid pie charts with more than 5 slices like the one above.
Stacked Area Chart
I don’t know how to put a stacked area chart into words.
OK, this is a stacked area chart.
In the area chart, you represent the change in one or more than one quantities along with the total in time. So, if you want to show how the total and its parts changed over time, you may use a stacked area chart. The area represents the quantity.
However, if you don’t care about the total and you want to show how the individual quantities changed with time and in comparison to each other (e.g. you want to show one quantity overtaking another), then you should use the line chart.
Use area charts when there is a large difference in the values. It is difficult to visually notice small differences in the area.
You can use a regular area chart when you are not interested in the cumulative values.
This is a regular area chart.
Scatter charts are usually used when you want to show a relationship between two variables. This is usually used in scientific research and is used to measure the correlation coefficients between two variables.
For example, one axis can represent height and another weight to plot a large number of data points and see if there is a general correlation between height and weight.
A scatter plot has two dimensions. If your data has three dimensions instead of two, you can represent the third dimension with size. So the points of the scatter chart become bubbles in a bubble chart, whose size is proportional to the magnitude of the third dimension.
On the surface pivot tables look similar to tables except that in case of pivot tables, there are two dimensions instead of one.
Just like in the case of tables, you can represent the values with a bar or a heat map.
A bullet chart is used to measure your performance with a metric against a target.
There are three layers you see in a Google Data Studio bullet chart. First, you see various shades that indicate ranges for poor, average, and good performance which are adjustable. There is a vertical bar which is the target you set and finally the central bar which represents the actual performance. Check out the best google data studio templates too.
Bullet charts would be great to showcase the performance of your KPIs in your report!
Usually, treemaps are used to present large amounts of hierarchical data using rectangles nested within larger rectangles.
In treemaps, you can show data that can look too cluttered in a pie chart.
Learn more about tree maps here.
Google Data Studio can be used to show geographical data on an actual map. That way you get a clearer picture and it makes for a great presentation. I mean who doesn’t love looking at maps!
You can also change the zoom settings and narrow down to your area of interest.
The latest chart addition in Google Data Studio is Google Maps.
In a Google Map, you can plot two metrics one using bubble size and another using color.
For example, in the map below, the bubble size represents the impressions and the bubble color from green to blue represents URL CTR.
So, Google Maps feature of Google Data Studio lets you plot two dimensions as opposed to one which is the case with Geo maps.
You can even change the style to ‘satellite’ in the panel on the left.
One thing to remember is, turning on ‘Apply filter’ in ‘Interactions’ and shifting to ‘View’ will let you interact with the charts. This is applicable to all charts.
You have covered quite a few Google Data Studio charts. The goal of this article was not just to introduce you to the charts but also to try to discuss which chart you should select and when. While using the wrong chart might hide important information or confuse the audience, the right one can drive the point home with no words.
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Share your thoughts on Google Data Studio charts in the comments section!