Pie Chart

What is Pie Chart?

A pie chart is a type of data visualization that represents data as a circular graph, divided into sectors or slices. Each sector in the chart corresponds to a particular category or data point, and its size is proportional to the quantity or percentage it represents in relation to the whole.

The circle of the chart represents the total or 100% of the data being analyzed. The individual sectors or slices within the circle are labeled and color-coded to differentiate between different categories or data points. The larger the slice, the larger the quantity or percentage it represents.

When to use Pie Chart?

They are best used in the following situations:

  • Showing relative proportions: Pie charts are useful when you want to illustrate the distribution or composition of a whole into its various parts. They are effective at displaying the relative proportions or percentages of different categories within a dataset.
  • Comparing categories: These charts make it easy to compare the sizes of different categories visually. By examining the differences in the slice sizes, you can quickly identify which categories are more significant or dominant compared to others.
  • Highlighting a single data point: If you have a single data point that stands out from the rest, it can be a good choice. The emphasized slice can draw attention to the exceptional value and provide a visual focus.
  • Simplified data representation: Pie charts are best suited for datasets with a small number of categories. If you have too many categories, the chart can become cluttered and difficult to interpret. It is generally recommended to limit the number of slices to 5-7 for optimal clarity.
  • Presenting data to a non-technical audience: Pie charts are intuitive and easy to understand, making them suitable for conveying information to a broad audience. Their visual appeal and simplicity make them accessible to individuals who may not have a strong background in data analysis.

Guidelines for correct usage of Pie Chart

  • Collect summary data or raw data
  • Summarized data can be a count or a calculated value (mean), while raw data consists of individual occurrences or defects
  • Raw data should be organized in one column with each observation in a separate row
  • Select sample data randomly to ensure accurate statistical inferences
  • Random samples are necessary to make generalizations about a population
  • Non-randomly collected data may not accurately represent the population

Alternatives: When not to use Pie Chart

  • Bar Charts: Pie charts face a primary limitation: accurately comparing slice sizes is challenging, especially with numerous or similar-sized categories. Distinguishing between slices becomes difficult, hampering precise data interpretation. In such cases, bar charts or stacked bar charts serve as superior alternatives. Bar charts utilize rectangular bars, simplifying comparisons between categories, while stacked bar charts additionally reveal category subdivisions.
  • Scatter Plot or Line Chart: When your focus is on showcasing data trends or patterns, a scatter plot or line chart with data points connected by lines can be more informative than a pie chart. These charts display individual data points, allowing you to identify correlations, clusters, or outliers more easily. Scatter plots are particularly useful for displaying relationships between two variables.

Example of Pie Chart?

The objective of an automotive supply company's quality engineer is to reduce the quantity of car door panels that are rejected due to paint imperfections. To begin the investigation, the engineer generates a chart to compare the occurrence of flaws across various categories. She has performed this in following steps:

  1. She worked all day and gathered the necessary data.
  2. Now, she analyzes the data with the help of https://qtools.zometric.com/
  3. Inside the tool, she feeds the data along with other inputs as follows:
  4. After using the above mentioned tool, she fetches the output as follows:

How to do Pie Chart

The guide is as follows:

  1. Login in to QTools account with the help of https://qtools.zometric.com/
  2. On the home page, you can see Pie Chart under Graphical Analysis.
  3. Click on Pie Chart and reach the dashboard.
  4. Next, update the data manually or can completely copy (Ctrl+C) the data from excel sheet and paste (Ctrl+V) it here.
  5. Next, you need to map the columns with the parameters.
  6. Finally, click on calculate at the bottom of the page and you will get desired results.

On the dashboard of Pie Chart, the window is separated into two parts.

On the left part, Data Pane is present. In the Data Pane, each row makes one subgroup. Data can be fed manually or the one can completely copy (Ctrl+C) the data from excel sheet and paste (Ctrl+V) it here.

On the right part, there are many options present as follows:

  • Sector Labels: Sector labels in a pie chart are the textual representations of the categories or data points being represented by each sector of the chart. In a pie chart, each sector corresponds to a specific category or data point, and the sector labels provide information about what each sector represents.
  • Sector values: Sector values in a pie chart represent the numerical data associated with each sector or category. Each sector in a pie chart is proportional to its corresponding value, indicating the relative size or proportion of that category within the whole data set.