How to compare data using charts

Column chart comparing data

Are your charting skills beyond compare? If not, and you are interested in things that are comparable, here’s a quick tutorial on what chart types to use when you are comparing data.

Used correctly, charts are an effective method for gauging similarities or differences between two datasets or more. Which chart to use depends on the complexity and the nature of your data; let’s explore that in detail.

Line charts

Line charts are often used to compare quantitative data over time. Such as changes in temperature, population, prices, etc.

The following demo displays the world population by region using a line chart. The chart allows us to compare the population growth. We can see easily that the Asian population is the highest in terms of growth and number, followed by the African population, where the Oceanic population has the lowest growth and number.
All the regions have a similar sharp growth from 1950.

The chart shows the population growth trend every 50 years for each region, starting in 1750. But what the chart doesn’t show is the trend within the 50 years period. The population increase within 50 years could be steady thanks to a good economy or medical breakthroughs, but the population growth could also be sharp somewhere in the 50 years period, as a result of massive immigration, invading/including new territories, etc.

Column charts

Column charts, as the line charts are also used to compare data sets, but contrary to line charts, column charts are often used to compare categorical data, such as gender, jobs, grades, etc. The representation of the data value in length makes the column chart ideal charts to compare data.

The chart below compares the top five populations of Europe by gender in 2016. Germany has the largest population in Europe, followed by France and the United Kingdom. In all the top five European countries, the number of females is greater than the number of males; The biggest gap between the number of females and males is in France with 2,079,918, followed by Italy with 1,752,909.

In the case of close data values like the demo above, it is effective to add an interactive zooming capability or/and fix labels on each bar to futher differentiate the data values to the readers:

Bar charts

Bar charts like column charts are used to compare categorical data. Unlike the column charts, the bar charts are oriented vertically instead of horizontally. The main advantage of bar charts over column charts is that it is better with many categories, especially on a small screen, as the height can be adjusted to show all categories.

Here is the bar chart, with the same data from the previous demo, that displays the top five populations of Europe by gender in 2016.

Bubble chart

The bubble charts are very useful to compare data sets when each “subject of study” – in this case, countries, are to be evaluated across multiple dimensions (aka. variables) using any data type (quantitative or qualitative).

The following bubble chart allows for a 4-dimensional displaying of data:

  • The daily sugar intake on the Y-axis.
  • The daily fat intake on the X-axis.
  • Obesity among adults is represented by the bubbles’ size.
  • Safety fat/sugar intake using three colors:
    • Green, if a country respects at least one safe daily intake of sugar or fat.
    • Yellow, if a country’s daily intake of sugar or fat are close to one safe daily intake.
    • Red, if a country doesn’t respect any of the safe daily intakes.

From the chart, only three countries, in green, respect at least one of the safe daily intake of sugar or fat, and they are Portugal, New Zealand, and Russia. The United States and Hungaria (in yellow) are right on at least one of the safe daily intake of sugar or fat. The rest of the countries are above the safe daily intake. Finland, Sweden, and Netherlands have almost the same daily fat intake. The United States has the highest number of obese, where Norway has the lowest number.


Although not very common, the treemap is perhaps one of the best ways to compare quantitative data sets using the size. Treemap comes with few distractions as it contains no axes, category labels, or a legend.

In this demo, a treemap chart displays the countries with the most refugees per capita in 2014.
It is easy to see that Lebanon has the most significant number of refugees per capita, where Sweden has the smallest number of refugees per capita.

Other chart types could also be used to compare data. Bear in mind to always check the nature of your data first, then select the appropriate chart.
Feel free to share in the comment section below your favorite charts to compare data sets.