A bar chart represents data in rectangular bars with length of the bar proportional to the value of the variable. R uses the function barplot() to create bar charts. R can draw both vertical and Horizontal bars in the bar chart. In bar chart each of the bars can be given different colors.

Syntax

The basic syntax to create a bar-chart in R is −

barplot(H,xlab,ylab,main, names.arg,col)

Following is the description of the parameters used −

H is a vector or matrix containing numeric values used in bar chart.
xlab is the label for x axis.
ylab is the label for y axis.
main is the title of the bar chart.
names.arg is a vector of names appearing under each bar.
col is used to give colors to the bars in the graph.

Example

A simple bar chart is created using just the input vector and the name of each bar.

The below script will create and save the bar chart in the current R working directory.

# Create the data for the chart
H <- c(7,12,28,3,41)

# Give the chart file a name
png(file = "barchart.png")

# Plot the bar chart
barplot(H)

# Save the file
dev.off()

When we execute above code, it produces following result −

Bar Chart Labels, Title and Colors

The features of the bar chart can be expanded by adding more parameters. The main parameter is used to add title. The col parameter is used to add colors to the bars. The args.name is a vector having same number of values as the input vector to describe the meaning of each bar.

Example

The below script will create and save the bar chart in the current R working directory.

# Create the data for the chart
H <- c(7,12,28,3,41)
M <- c("Mar","Apr","May","Jun","Jul")

# Give the chart file a name
png(file = "barchart_months_revenue.png")

# Plot the bar chart
barplot(H,names.arg=M,xlab="Month",ylab="Revenue",col="blue",
main="Revenue chart",border="red")

# Save the file
dev.off()

When we execute above code, it produces following result −

Group Bar Chart and Stacked Bar Chart

We can create bar chart with groups of bars and stacks in each bar by using a matrix as input values.

More than two variables are represented as a matrix which is used to create the group bar chart and stacked bar chart.

# Create the input vectors.
colors = c("green","orange","brown")
months <- c("Mar","Apr","May","Jun","Jul")
regions <- c("East","West","North")

# Create the matrix of the values.
Values <- matrix(c(2,9,3,11,9,4,8,7,3,12,5,2,8,10,11), nrow = 3, ncol = 5, byrow = TRUE)

# Give the chart file a name
png(file = "barchart_stacked.png")

# Create the bar chart
barplot(Values, main = "total revenue", names.arg = months, xlab = "month", ylab = "revenue", col = colors)

# Add the legend to the chart
legend("topleft", regions, cex = 1.3, fill = colors)
# Save the file
dev.off()

Page 4 Image 3 11 — Group Bar &Stacked Bar Chart

So, this brings us to the end of blog. This Tecklearn ‘How to create Bar 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.

518