Posted February 03, 2020 03:29:00 Google Analytics is a great tool for building data-driven healthcare applications, but its primary benefit lies in providing a platform to connect healthcare organizations and clients to the most relevant data.
In this post, we’ll walk through how to use it to build data-rich visualizations that help healthcare organizations to identify the most appropriate data sources for their clinical trials.
The goal of this article is to walk you through the process of building a data visualization using Google Analytics, and to show you how to get started with the tools and data you need to create it.
If you’re a Google Analytics user, you’ve likely already built some visualization tools, or are considering building one.
But if you’ve never used Google Analytics before, we’ve written a quick introduction to the tool.
This article is also available in: Chinese, Dutch, English, German, Italian, Portuguese, Spanish, Swedish, Turkish, and Vietnamese.
You can also access the complete source code and a live demo of this visualization here.
Let’s get started!
First, let’s build a data visualizer using Google.
First, create a new Google Analytics account for your clinical trials using the link below: Create an account with Google Analytics If you’ve already created an account, go ahead and create a free account for yourself.
Once you’ve created a free Google Analytics Account, you’ll have access to the tools that you need, and will have a chance to sign up for Google Analytics.
If not, you can sign up with a paid account here.
To do this, open the Google Analytics dashboard, and select the “Analytics” tab: You should now see a new tab labeled “Tools” next to the “Google Analytics” tab.
Click the “+” icon to add the tools to your dashboard.
Select “Analytic” and add the data visualization tools that were added to your account.
Now click “Add” to add this visualization to your Google Analytics profile.
You’ll see a dialog asking you to confirm the new settings.
Click “Add”, and then click “Continue” to proceed.
Next, add the visualizations you created using Google’s Analytics tools.
The first visualization that you added should be the “Data Visualizer”.
Select the “Visualizer” tab, and then select “Add Visualization”.
Choose the visualization from the drop-down menu that appears: Select “Data Sources” from the “Add Source” drop-box, and choose the data source that you want to visualize.
Click next to continue.
This visualization should be available in your dashboard: This visualization looks like this: Now that you’ve added the visualization, click the “Edit” button to edit it.
You should see this dialog: Select the visualization that was added to the dashboard, click “Edit”, and check the box next to “Edit Data Sources”.
Click “Save”.
Next, you should see the “Next Visualization” dialog.
You’re now ready to add a new visualization.
Select the new visualization, and click “Next” to begin the visualization process: Select a visualization type, and the visualization should now appear in your visualization editor.
Click in the “View” area of the editor, and you should now have a list of available visualizations: The first visualizer that you have selected should be “Data Source”: Now you can go ahead with the next visualization: Select another visualization type and select “Visualization”: This visualization appears in your visualizer: The next visualization should appear in the editor: Click “Finish” to save your visualization.
Finally, you might notice that the visualization has an “Authorized Users” box: Click this box, and a pop-up window should appear: You can now remove this box to add your own visualization to the visualization: Now, you have a visualization with your own data, and it should be visible to everyone in your Google analytics dashboard.
You’ve now created a new data visualization, but the real work is done when you add a visualization to Google Analytics using Google Maps.
If your visualization is displayed in Google Maps, you will see the following pop-ups: The visualization should automatically update when it updates with new data:
