1. Starting smartly with charts
An intelligent start to talking about data story visualizations is identifying the charts that work best with each data type.
2. Match your data to a chart
Certain charts work better with certain data types. The correct selection of chart types is crucial when trying to promote understanding of how a certain metric is performing. It is important to choose the right chart type when attempting to tell a story.
Charts are the most commonly used type of visualization in a data story. They are used to display quantitative and qualitative data in a format that can be easily understood by the viewer.
3. Pie charts
The pie chart is a popular visualization of numerical data divided into equal portions and presented as a circle.
Pie charts are commonly used to display the distribution of quantitative data, such as the number of people in a particular age group.
This type of chart is also helpful for identifying trends in your data and can be used to compare two very distinct categories against each other. You might opt for this chart if your audience already frequently uses it.
However, there is a severe flaw in pie charts, especially obvious to data analysts. If two categories are almost equal in amount, it can be difficult for your audience to discriminate between the two. You can fix this by highlighting the actual numbers, but then what is the point of having the pie chart itself?
4. Bar charts
Bar charts are the most common type of chart, because they effectively show counts and frequencies that can be broken down by category.
Bar charts are the logical alternative to pie charts and can almost always communicate the same information better.
The chart on the right is the same information as the previous slide, but is much more clear.
You can quickly see the values that represent each category which makes direct comparison easier. This makes a visualization of multiple metrics divided by category intuitive.
5. Scatter plot
A scatter plot is a type of chart that is a representation of data that uses two quantitative variables to communicate information. The x-axis represents one quantitative variable, while the y-axis represents another.
The communication of these relationships in quantitative data is one of the chief advantages of a scatter plot. In the scatter plot shown, the x-axis represents the budget expenditure and the y-axis represents the household income. We can see that as household income increases, budget expenditures generally increase. Because they increase or decrease together, we would characterize this relationship as positive.
6. Line chart
Line charts work best when you have time series data that you are trying to analyze and compare elements over time. This chart shows how household budget expenses have changed since 2019. The line chart makes it easy to identify trends and see the impact.
Here you can see how tuition and medicine costs increase at greater rates than other expenses.
7. Area charts
Area charts are similar to line charts but fill the area between the line and the X-axis.
This can minimize confusion about relative size, emphasize the relationship between variables, or emphasize how all variables are connected to a whole.
Here you can see how tuition and medicine costs have increased over time while savings has decreased.
8. Let's practice!
Now visualize success in these exercises as you demonstrate your knowledge about data types and charts.