R Programming language has numerous libraries to create charts and graphs. A pie-chart is a representation of values as slices of a circle with different colors. The slices are labeled and the numbers corresponding to each slice is also represented in the chart.

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.png")

# 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 −

Page 4 Image 3 13 — Percentages & Chart Legend

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 −

So, this brings us to the end of blog. This Tecklearn ‘How to create Pie Charts in R’ blog helps you with commonly asked questions if you are looking out for a job in Data Science. If you wish to learn R Language and build a career in Data Science domain, then check out our interactive, Data Science using R Language Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/data-science-training-using-r-language/

Data Science using R Language Training

About the Course

Tecklearn’s Data Science using R Language Training develops knowledge and skills to visualize, transform, and model data in R language. It helps you to master the Data Science with R concepts such as data visualization, data manipulation, machine learning algorithms, charts, hypothesis testing, etc. through industry use cases, and real-time examples. Data Science course certification training lets you master data analysis, R statistical computing, connecting R with Hadoop framework, Machine Learning algorithms, time-series analysis, K-Means Clustering, Naïve Bayes, business analytics and more. This course will help you gain hands-on experience in deploying Recommender using R, Evaluation, Data Transformation etc.

Why Should you take Data Science Using R Training?

The Average salary of a Data Scientist in R is $123k per annum – Glassdoor.com
A recent market study shows that the Data Analytics Market is expected to grow at a CAGR of 30.08% from 2020 to 2023, which would equate to $77.6 billion.
IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle & other MNCs worldwide are using data science for their Data analysis.

What you will Learn in this Course?

Introduction to Data Science

Need for Data Science
What is Data Science
Life Cycle of Data Science
Applications of Data Science
Introduction to Big Data
Introduction to Machine Learning
Introduction to Deep Learning
Introduction to R&R-Studio
Project Based Data Science

Introduction to R

Introduction to R
Data Exploration
Operators in R
Inbuilt Functions in R
Flow Control Statements & User Defined Functions
Data Structures in R

Data Manipulation

Need for Data Manipulation
Introduction to dplyr package
Select (), filter(), mutate(), sample_n(), sample_frac() & count() functions
Getting summarized results with the summarise() function,
Combining different functions with the pipe operator
Implementing sql like operations with sqldf()

Visualization of Data

Loading different types of datasets in R
Arranging the data
Plotting the graphs

Introduction to Statistics

Types of Data
Probability
Correlation and Co-variance
Hypothesis Testing
Standardization and Normalization

Introduction to Machine Learning

What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Supervised Learning algorithm: Linear Regression and Logistic Regression

Logistic Regression

Intro to Logistic Regression
Simple Logistic Regression in R
Multiple Logistic Regression in R
Confusion Matrix
ROC Curve

Classification Techniques

What are classification and its use cases?
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?
What is Naive Bayes?
Support Vector Machine: Classification

Decision Tree

Decision Tree in R
Information Gain
Gini Index
Pruning

Recommender Engines

What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Types of Recommendations
User-Based Recommendation
Item-Based Recommendation
Difference: User-Based and Item-Based Recommendation
Recommendation use cases

Time Series Analysis

What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Got a question for us? Please mention it in the comments section and we will get back to you.

658