Guide to Data Studio for Beginners

Originally Posted on Interhacktives

So you want to learn how to use Google Data Studio, but aren’t sure where to start? I was in the same boat a couple of months ago. While it took some failed attempts to wrap my head around the ins and outs of the platform, it was well worth the effort.

Data Studio is a platform for data analysis, data visualization and reporting. You can connect it to tons of data sources, from Bigquery to social media channels, to your personal website metrics. 

In this tutorial, I’ll be working from my presentation called “Women in Government for Strong Institutions, Peace & Justice” which was part of Google’s Visualize 2030 Competition. You can scroll through the full presentation and the other winners’ presentations here.

First things first, access data studio. Then, once you’re in the platform, you’ll want to connect your data source. 


Connect Your Data Source

For this example, the key sources of data I used were World Development Indicators (WDIs) provided by the World Bank and Social Development Goals (SDGs) from the UN, both accessible via BigQuery. However, you can connect to virtually any kind of data source imaginable, including Google Analytics, Google Sheets, Github, Kaggle, Twitter and Facebook.

Once you’ve connected your data, it will be visible in your Data Sources. Now you can get cracking on your report.


Start a New Report

You may notice that there aren’t any tools visible at the top of your window, this is because you need to connect your report to a data source before the tools can populate in your window. Start by linking to the data source you already added to your repository. 

I’m going to recreate Slide 8 of my presentation. Slide 8 only uses the world_bank_wdi_indicators data source. That’s the only data source I’ll connect to for this tutorial. However, it’s worth noting that it’s possible to connect to as many data sources as you like, from multiple platforms, and even blend the data as you please, throughout a report.


Creating a Map 

Now I’ll get started on creating a map. Select Add a Chart Geo Map. Drag the map to it’s desired location and resize as needed. The panel to the right is where you can adjust your map’s dimensions, metrics and regional display. I plan to show intentional homicides around the world, color-coded by density. I need to make sure my map filter reflects that particular metric.


Because the World Bank’s dataset is so massive, you need to know the specific indicator in order to call up your desired subset of data. Taking a look through WB’s indicator cheat sheet that I found on their website, I’ve located the indicator that calls up the global data for intentional homicide, VC.IHR.PSRC.P5.


Using Filters 

Having located the indicator, I now select Create a Filter at the bottom of the right panel. The filter tool is probably the most important part of Google Data Studio. It allows you to form arguments that shape your data visualizations.


My argument will be straightforward. I only want to call up the data that falls under the umbrella of my chosen indicator, VC.IHR.PSRC.P5. You can see my argument below, but there are a lot of different filters you can make for your data based on this various arguments. Once you save the filter, it will show up in your panel and your map will update to reflect the intentional homicide rate in each country. 


However, we still have a couple more things to fix before the map is complete. 

Right now, the homicide rate displayed in the map is actually just a sum of the number of recorded data entries. For example, if homicide data wascollected 3 times in India over the past 25 years, the number that shows up on the map in India is 3. To remedy this, you simply click on Record Count, and switch it to Value. Then next to Value Click the edit icon, and switch to Average instead of Sum. This should update your data to reflect the correct intentional homicide rate as an average of the rates collected over the years.


To double check what your map is displaying, you can toggle between view and edit modes to see what your data looks. 

Add a Filter Button for Interactivity

By now, your map is done! But there are a few extra touches you can add to make your report more interactive. Let’s add a button to allow viewers to toggle the data by year, in order to see how the intentional homicide rate has changed over time, rather than being restricted to the averaged view. 

Click on the button that looks like a funnel called Filter Control.


Move and size the new drop down as you please and then take a look at the panel to the right again. Just like we did for the map, we want to add a filter using our code for homicide, VC.IHR.PSRC.P5. This time, our data filter is already saved, and we can simply select it, rather than making a new filter. Under Dimension select year, so that we can toggle the data by year. Remove any other selected metrics. Your filter button settings should look like this….


and this is what your report should now look like in view mode:


Duplicating a Graph

Now, I also want include a graph that compares Rwanda’s intentional homicide rate with the world’s. Here’s how to quickly duplicate the data used in our map into a small column graph.

Simply select the map and copy and paste it. Move and scale the new map. Then, at the top of the right panel, select the drop down and change the chart type to a Column Chart.


To display only Rwanda and the World average set the Dimension as Country_name, Metric as AVG Value, and Sort as Country_name. Finally, we’ll need to create a new Filter that includes an extra AND clause so that only the data for Rwanda and the World are called up. It will look like this:


Now you should have a beautiful column graph! 

In view mode, you’ll also notice that the year filter button affects all the data on the page, which is just what we want. The only issue is that there are many years where neither the world nor Rwanda’s intentional homicide was collected, which sometimes leaves the column graph empty depending on the year selected.


Layout, Theme and Colors

Now that the hard part is done, you can change the look and feel of your report by using the Layout and Theme tool and by changing the Style of your graphs.


Once you’re done, your report will look something like this:


I hope this helped you begin your journey with Google Data Studio. There are many public datasets that you can use to learn Data Studio, so enjoy exploring!

To see some exemplary uses of Google Data Studio, you can check out the projects of the other winners of Google’s Visualize 2030 project, and check out some of the other reports in Google’s Data Studio Report Gallery

Feel free to get in touch if you have any questions!

Visualize 2030

I’m really excited to announce I was chosen as one of the winners of Google Cloud’s Visualize 2030 competition! The challenge was to use Google Data Studio and data from the World bank and the United Nations to tell a data-driven story that explores how at least two sustainable development goals influence each other, and how they can be achieved by 2030.

As someone with an interest in development studies and a love for data, this project was a dream come true. Not only was the material interesting, but it challenged me to learn how to use Google Data Studio, which was completely new to me when I started working on my submission. If you want to learn more about Google Data Studio, my impressions and my guide for beginners, I’ll be sharing an article on interhacktives that you can check out shortly!

As for my project, inspired by the record high number of women in Congress in the US, I wanted to tell a data story related to the positive impact women in government can have on a nation.

With this in mind, my data story was centered on goal 5 (gender equality), and goal 16 (peace, justice, & strong institutions). Using the WDI and SDI datasets, I attempted to show the relationship between increased female participation and reduced corruption and stronger institutions, using Rwanda as a paradigm of this phenomenon. Although the datasets had a wide range of data available, I chose to display the indicators with the best, most complete data (some had missing countries), and used those to build the argument for my case.

You can take a look at my project below. And you should also check out the other 4 winners and their amazing submissions here :)


Data vizualisation checklist: 7 things to check before you click ‘publish’

Originally posted on Interhacktives

We all know that data visualizations can really bring an article to life. However, I’ve learned from experience that it’s easy to become so engrossed in making a data vis that you compromise the quality by overlooking key elements. Learn from my mistakes and make sure to knock these 7 things off your checklist before you click “publish!”

1. Pick the right chart or graph

A great visualization should be able to tell a story on its own, so it’s essential to make sure you’ve chosen the right type of graph or chart. Here’s a guide if you need help:

2. Make sure your numbers add up

It goes without saying that you should double, even triple check your data before finalizing your corresponding visualization. And, if you’re visualizing a composition, like a pie chart, make sure your values add up to 100%.

Also, remember that occasionally, even when your data is accurate, rounding to the nearest whole number can lead to a total sum of over 100. In that case, just round your values to the nearest 10th to avoid confusion.


3. Check your chart axes, labels and intervals

Label your axes for maximum clarity and readability. Inconsistent labels and intervals can be extremely misleading for readers.

This graph from USA Today seems to show that the number of Americans receiving federal welfare is skyrocketing. Closer inspection shows the Y-axis is cropped to start at 94 million, creating a distortion.

4. Annotate your graph when necessary

If your data visualization is more complex, annotation can be tremendously useful. Is there something noticeable that bears further explanation or scrutiny? Point it out on your graph to provide readers with a bit of extra context.

For example, in this dataset, data from November 16th is missing. Without pointing out the absent data, the reader would assume the value was simply 0 on that day.

Annotation on makes this graph by FiveThirtyEight more intuitive, which means that we don’t even need a legend.

Also, always remember to include your data source!

5. Balance aesthetics and functionality

Aesthetics and form should work hand in hand to complement each other, rather than to distract. So, if you’re making a chart that compares the growth rate of apple trees to orange trees, you probably shouldn’t color code the orange trees with red and the apple trees with orange. Keep it simple.

6. Do a squint test

Consider hierarchy first. Do a squint test – squint your eyes and see what part of your graph pops out first. If the graph is well designed, the main trend of the graph should be the first thing to grab your attention, even when the image is blurred. Any extra elements or design flourishes that detract from the central message should be diminished or removed altogether.

Next, consider readability. Is your color scheme simple and easy on the eyes? Try picking two key colors, and add additional colors by either lightening or darkening the originals. Or, if you need several colors, try this palette generator for ideas. Additionally, try to pick a simple, sans-serif font for easy readability on screens.

7. Get a second opinion

When in doubt, ask a friend for their opinion. Sometimes you are so deep in your project that the most obvious of mistakes completely escape your notice!

Any questions? Feel free to tweet me at @a_c_holmes!