R Programming language and environment designed for statistical computing and data analysis. Here are some key concepts:
Vectorization: R is particularly strong in vectorization, allowing operations on entire vectors or matrices at once, making it efficient for statistical computations.
Data Frames: R operates with data frames, a type of table that makes it easy to handle and analyze structured data.
Functions and Packages: R has a vast ecosystem of packages that extend its functionality. Functions are a fundamental part of R, and users can create their own functions as well.
Graphics and Visualization: R provides powerful tools for data visualization, making it easier to explore and understand data through various graphical representations.
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History of R Programming
R has its roots in the S programming language, developed at Bell Laboratories by John Chambers and his colleagues in the 1970s. Ross Ihaka and Robert Gentleman created R at the University of Auckland, New Zealand, in the early 1990s. It has since evolved into a widely-used language for statistical computing and data analysis.
Features of R Programming
Open Source: R is an open-source language, meaning that its source code is freely available, and users can modify and distribute it.
Statistical Analysis: R is specifically designed for statistical computing and analysis, offering a rich set of statistical functions.
Data Manipulation: With packages like dplyr and tidyr, R makes it easy to manipulate and clean data efficiently.
Community Support: R has a large and active user community, contributing to its extensive range of packages and resources.
Types of R Programming Courses on Udemy
1. “R Programming A-Z™: R For Data Science With Real Exercises!”
Description: This course covers R programming comprehensively with a focus on practical exercises. It includes real-world examples and projects to reinforce learning.
Instructor: The course might be instructed by data science professionals or experienced educators in the field.
Content: The curriculum likely covers R basics, data manipulation, statistical analysis, and data visualization with practical exercises.
2. “Data Science and Machine Learning Bootcamp with R”
Description: This course could be a comprehensive bootcamp covering data science and machine learning concepts using R.
Instructor: Expect an instructor with a strong background in data science and machine learning.
Content: The course might cover data exploration, machine learning algorithms, and model deployment using R.
