# R - Pie Charts

In R the pie chart is created using the

**pie()**function which takes positive numbers as a vector input. The additional parameters are used to control labels, color, title etc.

## Syntax

The basic syntax for creating a pie-chart using the R is −pie(x, labels, radius, main, col, clockwise)Following is the description of the parameters used −

**x**is a vector containing the numeric values used in the pie chart.**labels**is used to give description to the slices.**radius**indicates the radius of the circle of the pie chart.(value between −1 and +1).**main**indicates the title of the chart.**col**indicates the color palette.**clockwise**is a logical value indicating if the slices are drawn clockwise or anti clockwise.

## Example

A very simple pie-chart is created using just the input vector and labels. The below script will create and save the pie chart in the current R working directory.# Create data for the graph. x <- c(21, 62, 10, 53) labels <- c("London", "New York", "Singapore", "Mumbai") # Give the chart file a name. png(file = "city.jpg") # Plot the chart. pie(x,labels) # Save the file. dev.off()When we execute the above code, it produces the following result −

## Pie Chart Title and Colors

We can expand the features of the chart by adding more parameters to the function. We will use parameter**main**to add a title to the chart and another parameter is

**col**which will make use of rainbow colour pallet while drawing the chart. The length of the pallet should be same as the number of values we have for the chart. Hence we use length(x).

## Example

The below script will create and save the pie chart in the current R working directory.# Create data for the graph. x <- c(21, 62, 10, 53) labels <- c("London", "New York", "Singapore", "Mumbai") # Give the chart file a name. png(file = "city_title_colours.jpg") # Plot the chart with title and rainbow color pallet. pie(x, labels, main = "City pie chart", col = rainbow(length(x))) # Save the file. dev.off()When we execute the above code, it produces the following result −

## Slice Percentages and Chart Legend

We can add slice percentage and a chart legend by creating additional chart variables.# Create data for the graph. x <- c(21, 62, 10,53) labels <- c("London","New York","Singapore","Mumbai") piepercent<- round(100*x/sum(x), 1) # Give the chart file a name. png(file = "city_percentage_legends.jpg") # Plot the chart. pie(x, labels = piepercent, main = "City pie chart",col = rainbow(length(x))) legend("topright", c("London","New York","Singapore","Mumbai"), cex = 0.8, fill = rainbow(length(x))) # Save the file. dev.off()When we execute the above code, it produces the following result −

## 3D Pie Chart

A pie chart with 3 dimensions can be drawn using additional packages. The package**plotrix**has a function called

**pie3D()**that is used for this.

# Get the library. library(plotrix) # Create data for the graph. x <- c(21, 62, 10,53) lbl <- c("London","New York","Singapore","Mumbai") # Give the chart file a name. png(file = "3d_pie_chart.jpg") # Plot the chart. pie3D(x,labels = lbl,explode = 0.1, main = "Pie Chart of Countries ") # Save the file. dev.off()When we execute the above code, it produces the following result −

*Table of contents:*1. R - Overview

2. R - Environment Setup

3. R - Basic Syntax

4. R - Data Types

5. R - Variables

6. R - Operators

7. R - Decision Making

8. R - Loops

9. R - Functions

10. R - Strings

11. R - Vectors

12. R - Matrices

13. R - Arrays

14. R - Factors

15. R - Data Frames

16. R - Packages

17. R - Data Reshaping

18. R - CSV Files

19. R - Excel Files

20. R - Binary Files

21. R - XML Files

22. R - JSON Files

23. R - Web Data

24. R - Database

25. R - Pie Charts

26. R - Bar Charts

27. R - Boxplots

28. R - Histograms

29. R - Line Graphs

30. R - Scatterplots

31. R - Mean, Median and Mode

32. R - Linear Regression

33. R - Multiple Regression

34. R - Logistic Regression

35. R - Normal Distribution

36. R - Binomial Distribution

37. R - Poisson Regression

38. R - Analysis of Covariance

39. R - Time Series Analysis

40. R - Nonlinear Least Square

41. R - Decision Tree

42. R - Random Forest

43. R - Survival Analysis

44. R - Chi Square Tests

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