Variables and Data Frames in R

Variables and Data Frames in R

In statistical analysis, a variable is a characteristic or value that can change or take on different values. For example, a person’s age, height, or weight are all variables. In computer programming, a variable is a container for a value that can change during the execution of a program.

A data frame is a tabular data structure in which data is organized into rows and columns, similar to a spreadsheet or a database table. Each column represents a variable, and each row represents an observation or a sample.

In statistical analysis, data frames are used to store and manage data sets. They provide a way to organize, manipulate and analyze large amounts of data in a structured way. Data frames can be created by reading data from a file or database, or by creating them manually in a program.

Each variable in a data frame has a name, a data type and a set of values. The data type can be numeric, categorical, or character. The values can be numbers, text, or dates.

It’s important to note that in a data frame, the columns represent variables and the rows represent observations or samples, so each variable should have only one value for each observation. Having duplicate values for the same variable in different rows, makes it difficult to analyze the data and can lead to incorrect conclusions.


In this Data Science Recipe, you will learn: How to use Variables and Data Frames in R .

Variables and Data Frames 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!