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    • Image courtesy of r-craft.org

      r-craft.org

      • R is a free, open-source statistical software environment that was specifically designed for statistical computing and graphics. Ross Ihaka and Robert Gentleman developed R in 1993 at the University of Auckland, New Zealand. Since then, it has become one of the most popular languages for statistics, data analysis, and machine learning.
      www.coursera.org › articles › rstudio

      Data Analysis with RStudio: Understanding the Basics

  2. www.coursera.org › learn › data-analysis-with-rData Analysis with R | Coursera

    Examine data using descriptive statistics, data grouping, analysis of variance (ANOVA), and correlation statistics. Evaluate a model for overfitting and underfitting conditions and tune its performance using regularization and grid search.

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  4. www.coursera.org › learn › data-analysis-rData Analysis with R Programming - Coursera

    Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors. Describe the options for generating visualizations in R. Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content.

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  6. www.datacamp.com › courses › free-introduction-to-rIntroduction to R Programming Course - DataCamp

    • 1 Pennsylvania Plaza, New York, 10119
    • Intro to basics. Free. Take your first steps with R. In this chapter, you will learn how to use the console as a calculator and how to assign variables. You will also get to know the basic data types in R. Let's get started.
    • Vectors. We take you on a trip to Vegas, where you will learn how to analyze your gambling results using vectors in R. After completing this chapter, you will be able to create vectors in R, name them, select elements from them, and compare different vectors.
    • Matrices. In this chapter, you will learn how to work with matrices in R. By the end of the chapter, you will be able to create matrices and understand how to do basic computations with them.
    • Factors. Data often falls into a limited number of categories. For example, human hair color can be categorized as black, brown, blond, red, grey, or white—and perhaps a few more options for people who color their hair.

  7. mdsr-book.github.io › mdsr3eModern Data Science with R - GitHub Pages

    Jul 25, 2024 · Modern Data Science with R. 3rd edition (light edits and updates) A comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Author. Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton. Published. July 25, 2024. Welcome! 3rd edition.

  8. andrewproctor.github.io › rcourse › indexIntroduction to Data Analysis in R - GitHub Pages

    This is an introduction to the R statistical programming language, focusing on essential skills needed to perform data analysis from entry, to preparation, analysis, and finally presentation.

  9. www.datacamp.com › category › rR Programming Courses | Online Courses for All Levels - DataCamp

    Introduction to R. Beginner. 4 hours. 14.1K. Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets. course. Intermediate R. Beginner. 6 hours. 3.8K. Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions. course.

  10. link.springer.com › book › 10An Introduction to Data Analysis in R: Hands-on Coding, Data ...

    This undergraduate textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. It covers the fundamentals of programming, data collection and preprocessing, data visualization, and statistical methods, and includes numerous real-world applications.