How to visualise a Dataset according to its Class variables in R

Hits: 97

How to visualise a Dataset according to its Class variables in R

Visualizing a dataset according to its class variables in R is a useful way to understand the distribution and relationship between different classes. A class variable is a categorical variable that can take on a limited number of values, also known as factors.

There are several ways to visualize a dataset according to its class variables in R, such as bar charts, stacked bar charts, and box plots.

Bar charts: Bar charts are a simple way to visualize the distribution of a class variable. They show the count or percentage of observations for each class. You can use different colors or patterns to distinguish the different classes.

Stacked bar charts: Stacked bar charts are used to visualize the distribution of multiple class variables. They show the count or percentage of observations for each class, stacked on top of each other. This can help to compare the distribution of different classes.

Box plots: Box plots are used to visualize the distribution of a numerical variable across different classes. They show the median, quartiles, and outliers of the numerical variable for each class. This can help to compare the distribution of the numerical variable across different classes.

Faceted plots: Faceted plots are a useful way to visualize how different class variables affect a numerical variable. They allow you to create different plots for different levels of a class variable.

It’s important to note that the type of visualization used will depend on the type of data and the question you’re trying to answer. Also, it’s important to keep the visualization simple and clear, this will make it easy to understand the insights from the data.

 

In this Data Science Recipe, you will learn: How to visualise a Dataset according to its Class variables in R.



How to visualise a Dataset according to its Class variables 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!