Dig deeper into your data with our “Data explorer” template
A blend of scatter plots, maps and bubble charts, this template lets you combine multiple variables into different views
08 August 2022
by Mafe Callejón
Our new “Data explorer” template makes it easy to mix and match the variables in a dataset to discover hidden links and to create stunning visuals. It brings together the power of scatter plots, bubble charts, maps and cartograms, allowing you to show your data in different ways.
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The Data explorer is the perfect tool to delve into complex datasets with multiple variables. The template takes numeric, categorical and geographical data to build all those different kinds of visuals. The more metrics, the merrier!
Let’s explore the settings and combinations that are possible with the template. The dataset we’ve used in the following examples combines the following variables – and some others – per country: population, GDP (total and per capita), CO2 emissions (total and per capita), poverty rate and surface temperature anomaly. So, in a nutshell, we have data to understand who produces more carbon emissions, where in the world temperatures are rising more and who owns the world’s wealth. Here’s a sample of the dataset. The blue columns contain categorical data and the yellow ones contain numeric data:
Now let’s look at that data in a visualization!
Each unit of data is represented by a bubble. In our case, each country is a bubble in the chart.
We can size the bubbles based on a numeric value, like we've done here with the country's population.
We can also color the bubbles based on a categorical value, such as income group...
... or region.
Numerical values, like GDP per capita, can also be used to color the bubbles.
Split and group the data to spot trends within clusters or to spot outliers. Here we can see how the Europe and Central Asia regions have a higher proportion of countries with a higher GDP per capita.
Here, by sizing the bubbles based on the population, we can see how more populated countries, like India, China, Indonesia or Brazil, don't have such high levels of wealth per person.
Combine several variables to get more complex narratives. This chart shows how wealthier countries with large populations are also the biggest polluters.
Or, change the bubble chart for a map and see where the biggest polluters are.
Switch the boundaries for tiles to get a fairer representation of all countries.
Lastly, add a filter to focus on a specific subset of the data. Here we're centering our attention on Europe and Central Asia.
Create your own “Data explorer” visualization now →
Features and further customization
As with most of our templates, the magic starts in the Data tab with the column selection. In the “Data explorer” template you can select columns to represent categorical and numerical values, which will determine things like the position of the bubbles, their size and the color, as well as the color of the map regions. Bind a column to the Filter option if you want to get smaller subsets of the data.
Variables can be selected and manipulated using the side panel in the Preview tab
This is where you need to set the key columns to match the Data tab to the Geo Regions and Geo Points tab to switch from chart mode to map mode.
Once the data has been uploaded and the columns selected, a side panel within the visualization itself will give the user all possible options to combine the different variables. This logic is similar to that of our “Survey” template . For best results and loading time, we suggest working with between 100 and 1000 rows of data .
One unique trait from this template is that you can set two simultaneous color palettes: one categorical and one numerical. The correct one will be applied to the chart once a variable has been selected to color the bubbles or regions.
Check out our help doc to learn more.
Interviewing your data
The true power of the “Data explorer” is its flexibility. Quickly selecting variables to see how they relate to each other makes it easier to discover hidden patterns, not to mention that it can be a good opportunity to give users a chance to experiment with the data themselves.
Using the same dataset as before, let’s see how we can combine the different variables to understand our data better.
Let's start with something simple...
Let's see the relationship between a country's wealth (GDP) ...
... and its carbon emissions, effectively creating a scatter plot.
See how the bubbles cluster in an a tight ascending line? That shows us that our two variables (GDP and emissions) are closely related. Roughly speaking, the wealthier the country, the more emissions it generates.
If we color by income group, we can confirm how that is mostly true: most bubbles closer to the top right corner belong to the Upper-middle income and High income brackets.
Now, let's size the bubbles by the country's population. Unsurprisingly, countries with larger populations are also higher up in the emissions scale.
The sized bubbles gives us some perspective as to who are the big players: India, China, the US, Brazil, Indonesia, Pakistan and Nigeria are countries with large populations, large emissions and a sizeable GDP.
Now, let's focus on CO2 emissions per capita. This measures how much average citizens per country contribute to pollution. Big players are Qatar, Trinidad and Tobago, Brunei and Mongolia.
If we position the bubbles based on their total emissions but size them by their emissions per capita, we can see that big overall polluters are not the same as countries wigh high rates of pollution per person, like it's the case of India.
Shading the bubbles by the surface temperature anomaly value, meaning how much hotter a country is compared to the past, reveals that most countries are warming up.
Finally, after grouping them by income bracket, we can see how emisisons are generated disproportionately by high-income countries and upper-middle income ones.
Create your own “Data explorer” visualization now →
Overall, the “Data explorer” template is an effective and quick way to explore, test and interview data, either to show results or to make new discoveries within the numbers. Tortoise Media were one of the earliest adopters of the template. Check out their Global AI Index project to see an example in the wild.
Now it’s your turn! Combine the variables in the side panel of the visualization below to experiment with the visualization and to reach your own conclusions.