# 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.

#### Course Downloads

- Overview of Data Visualization
- When to Use Bar Chart, Column Chart, and Area Chart
- What is Line Chart and When to Use It
- What are Pie Chart and Donut Chart and When to Use Them
- How to Read Scatter Chart and Bubble Chart
- What is a Box Plot and How to Read It
- Understanding Japanese Candlestick Charts and OHLC Charts
- Understanding Treemap, Heatmap and Other Map Charts
- Visualization in Data Science
- Graphic Systems in R
- Accessing Built-in Datasets in R
- How to Create a Scatter Plot in R
- Create a Scatter Plot in R with Multiple Groups
- Creating a Bar Chart in R
- Creating a Line Chart in R
- Plotting Multiple Datasets on One Chart in R
- Adding Details and Features to R Plots
- Introduction to ggplot2
- Grammar of Graphics in ggplot
- Data Import and Basic Manipulation in R - German Credit Dataset
- Create ggplot Graph with German Credit Data in R
- Splitting Plots with Facets in ggplots
- ggplot2 - Chart Aesthetics and Position Adjustments in R
- Creating a Line Chart in ggplot 2 in R
- Add a Statistical Layer on Line Chart in ggplot2
- stat_summary for Statistical Summary in ggplot2 R
- Facets for ggplot2 Charts in R (Faceting Layer)
- Coordinates in ggplot2 in R
- Changing Themes (Look and Feel) in ggplot2 in R

# R Programming Bundle: 25% OFF

**R Programming - Data Science for Finance Bundle**for just $29 $39.