Apr 25, 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.

- 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.

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

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

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

- 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.

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

- Introduction to Data Manipulation with dplyr (Coursera course)

Free Audit Link Here

- R Data Manipulation - A Practical Guide (book)

- 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.

- Functions (R Programming) (W3schools website)

- Control Structures (R Documentation)

- Advanced R (book by Hadley Wickham)

- 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.

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

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

All 5 Courses Free Audit Link Here

- R Graphics Cookbook (book by Winston Chang)

- 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.

- Introduction to Statistical Inference (Coursera course)

Free to Audit Link Here

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

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

- 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.

- Machine Learning with R (Simplilearn course)

- Machine Learning with R (book)

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

- 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.

- 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)

- Web Scraping with R (book)

- Text Analytics Crash Course with R (Youtube Video)

- 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.

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

- R Projects (Coursera course)

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

- 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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