The first step of making a data visualization is choosing a chart type. Chart type isn’t our only tool when it comes to visualizing data, but it’s an important one for communicating about the relationship we want to show.

In this context, a “relationship” in the data could mean something like…

  1. “the shop’s sales of Gouda were higher in 2021 than any year since 2006”
  2. “30% of people ordered pizza with pineapple”
  3. “most people in the sample have a shoe size between 6 and 10.5 ”
  4. “as temperature increases, ice cream sales increase”

The first example is a change over time – that can be perfect for a line chart or a bar chart.

The second example compares a part to the whole: 30% of people got pizza with pineapple, out of 100% of people who ordered pizza. A pie chart is the classic (sometimes controversial) choice, but newer options include waffle and donut charts. Yum!

The third example is a distribution – the spread of data points in one variable. A histogram is the classic choice for visualizing a distribution.

The fourth example is a direct comparison of two variables to help understand a trend. This is perfect for a scatterplot, with or without a trend line.

There’s often more than one possible chart we can use for a dataset. But different charts emphasize different questions, arguments, or relationships in the data, and whichever we choose should help translate that data relationship into a visual relationship.

Next up, we’ll get to know these charts a little better.

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