How to create bar chart in R

Hits: 35

How to create bar chart in R

A bar chart, also known as a bar plot, is a graphical representation of a dataset that shows the frequency or the relative frequency of different values. It is a useful tool for visualizing the distribution of a dataset, comparing different groups of data, and identifying patterns. In this blog post, we will discuss how to create bar charts in R.

The most basic way to create a bar chart in R is by using the barplot() function. This function takes a single vector of data as an argument and creates a bar chart of the data. The function also takes several other arguments that can be used to customize the appearance of the plot, such as the color of the bars and the main title.

Another way to create a bar chart in R is by using the ggplot2 package. This package provides a powerful and flexible way to create bar charts and other types of plots. To create a bar chart using ggplot2, you first need to create a ggplot() object and then add a geom_bar() layer to the object. The geom_bar() layer takes several arguments that can be used to customize the appearance of the plot, such as the color of the bars, the width of the bars, and the main title.

In addition to the above methods, you can also create bar charts using other packages like lattice and plotly which provide more advanced functionalities.

To create a bar chart in R, you need to provide the data in the form of a vector or a data frame. You can then use the barplot() function or the ggplot2 package to create the bar chart and customize the appearance using various arguments.


In this Applied Machine Learning Recipe, you will learn: How to create bar chart in R.

How to create bar chart in R

Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!