What are Pie Chart and Donut Chart and When to Use Them

Pie Chart

The Pie Chart is essentially a circle divided into sectors. The area of each item reflects its value's proportion of the sum of all values in one data set.

Pie Chart are useful when you need to display the share of each constituent part as compared to the whole volume. Sectors can be not only depicted within the whole circle, but also separated from the rest of the chart making it an exploded Pie Chart. This kind of circular graphic remains illustrative only when provided with a few constituent parts. Pie Chart with too many slices are hard to work with efficiently.

The following chart shows the breakup of Full Time Employees by region at HSBC (As per Annual Report 2015)

When to use it

The primary use of a pie chart is to compare a certain sector to the total. The pie chart is particularly useful when there are only two sectors, for example yes/no or queued/finished.

Donut Chart

A Doughnut Chart or Doughnut Graph is a variant of the pie chart, with a blank center allowing for additional information about the data as a whole to be included. Each point is specified by an arc that length is proportional to the circumference as the data value to the total sum of all values.

The same chart above can be presented as a donut chart as shown below:

Advantages

The pie chart provides an instant understanding of proportions when few sectors are used as dimensions. When you use 10 sectors, or less, the pie chart keeps its visual efficiency.

Disadvantages

It is often hard to compare the results of two pie charts with each other, and therefore you should not do it.

It may be difficult to compare different sectors of a pie chart, especially a chart with many sectors.

The pie chart takes up a lot of space in relation to the values it visualizes.

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Data Science in Finance: 9-Book Bundle

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.