Apr 26, 2023

How would I learn R Programming in 2023

Learning R programming language can be a valuable skill in today's data-driven world. R is a powerful tool for statistical analysis, data visualization, and machine learning. In this blog, we'll provide a detailed two-month plan to learn R programming for beginners. The plan includes monthly, weekly, and daily activities that will help you develop a solid foundation in R programming.

Month 1

Week 1:

  • Start with the basics of R programming language by studying its syntax and data types.
  • Learn how to install and set up RStudio, which is an Integrated Development Environment (IDE) for R.
  • Take online tutorials and practice exercises to get familiarized with R programming.

In the first week, you will learn the fundamentals of R programming language. You'll start by studying the syntax and data types in R. You'll also learn how to install and set up RStudio, which is a popular IDE for R programming. This week is all about getting comfortable with R and practicing basic coding skills.

Learning Resources

  • R for Data Science by Hadley Wickham and Garrett Grolemund (book)

Link Here

  • R Programming A-Z™: R For Data Science With Real Exercises! (Udemy course)

Certificate Link Here 

Free Torrent Link Here 

  • R Programming Tutorial for Beginners | R Programming Basics | R Language (YouTube video)

Link Here

Week 2:

  • Learn about data structures in R such as vectors, matrices, and data frames.
  • Practice data manipulation techniques such as sub-setting, merging, and transforming data.

In the second week, you will learn about data structures in R such as vectors, matrices, and data frames. You'll also learn how to manipulate data using sub-setting, merging, and transforming techniques. This week is all about understanding how to work with data in R.

Learning Resources

  • Data Structures (R Programming) (tutorial on the official R website)

Link Here

  • Introduction to Data Manipulation with dplyr (Coursera course)

Free Audit Link Here

  • R Data Manipulation - A Practical Guide (book)

Link Here

Week 3:

  • Learn about functions and control structures in R.
  • Practice writing and executing functions to perform various tasks.

In the third week, you will learn about functions and control structures in R. You'll practice writing and executing functions to perform various tasks. This week is all about learning how to create reusable code in R.

Learning Resources

  • Functions (R Programming) (W3schools website)

Link Here

  • Control Structures (R Documentation)

Link Here

  • Advanced R  (book by Hadley Wickham)

Link Here

Week 4:

  • Explore R packages and libraries to expand the functionality of R.
  • Learn how to install and use packages such as ggplot2, dplyr, and tidyr for data visualization and data analysis.

In the fourth week, you will explore R packages and libraries to expand the functionality of R. You'll learn how to install and use packages such as ggplot2, dplyr, and tidyr for data visualization and data analysis. This week is all about learning how to leverage the power of R packages to perform advanced data analysis tasks.

Learning Resources

  • ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham (book)

Link Here

  • Tidyverse Skills for Data Science in R Specialization (Coursera course)

All 5 Courses Free Audit Link Here

  • R Graphics Cookbook (book by Winston Chang)

Link Here

Month 2

Week 5:

  • Learn about statistical analysis and modeling techniques in R.
  • Practice performing descriptive statistics, hypothesis testing, and regression analysis.

In the fifth week, you will learn about statistical analysis and modeling techniques in R. You'll practice performing descriptive statistics, hypothesis testing, and regression analysis. This week is all about learning how to use R for statistical analysis.

Learning Resources

  • Introduction to Statistical Inference (Coursera course)

Free to Audit Link Here

  • Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (book)

Link Here

  • An Introduction to Statistical Learning: with Applications in R by Gareth James et al. (book)

Link Here

Week 6:

  • Explore machine learning algorithms in R.
  • Learn about supervised and unsupervised learning algorithms such as decision trees, k-means clustering, and linear regression.

In the sixth week, you will explore machine learning algorithms in R. You'll learn about supervised and unsupervised learning algorithms such as decision trees, k-means clustering, and linear regression. This week is all about learning how to use R for machine learning.

Learning Resources

  • Machine Learning with R (Simplilearn course)

Link Here

  • Machine Learning with R (book)

Link Here

  • Hands-On Machine Learning with R: Build, tune, and deploy predictive models with machine learning in R by Bradley Boehmke (book)

Link Here

Week 7:

  • Learn about web scraping and text analysis in R.
  • Practice extracting data from websites and analyzing text data using R.

In the seventh week, you will learn about web scraping and text analysis in R. You'll practice extracting data from websites and analyzing text data using R. This week is all about learning how to use R for web scraping and text analysis.

Learning Resources

  • Text Mining and Analytics in R (Coursera course)

Free to Audit Link Here

  • Text Mining with R: A Tidy Approach by Julia Silge and David Robinson (book)

Link Here

  • Web Scraping with R (book)

Link Here

  • Text Analytics Crash Course with R (Youtube Video)

Link Here

Week 8:

  • Consolidate your learning by working on a project or a problem statement that requires the use of R programming language.
  • Apply the skills and techniques you've learned over the past seven weeks to solve a real-world problem.

In the eighth and final week, you will consolidate your learning by working on a project or a problem statement that requires the use of R programming language. You'll apply the skills and techniques you've learned over the past seven weeks to solve a real-world problem. This week is all about putting your knowledge into practice and building your confidence in using R.

Learning Resources

  • R Programming 2023 For Data Science:5 Real World Projects!! (Udemy Course)

Link Here

  • R Projects (Coursera course)

Link Here

  • Data Science Projects with R (Edureka! Youtube video)

Link Here

Daily Activities:

  • Spend at least an hour every day practicing R programming.
  • Read relevant blogs, articles, and tutorials to deepen your understanding of R programming.
  • Participate in online forums, communities, and discussion groups to seek help and share your knowledge.

In addition to the weekly activities, it's important to dedicate at least an hour every day to practicing R programming. You can use this time to work on exercises, practice coding, or experiment with different R packages and libraries. Reading relevant blogs, articles, and tutorials can also help deepen your understanding of R programming. Participating in online forums, communities, and discussion groups can also help you seek help and share your knowledge with other R programmers.

Learning R programming language requires a lot of dedication, effort, and practice. By following this two-month plan, you can develop a solid foundation in R programming and gain the skills needed to perform advanced data analysis and modeling tasks. Remember to stay motivated, seek help when needed, and keep practicing to become a proficient R programmer.

Good Luck!

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