Programming Language R: Learning Path & Resources.

R is an open-source programming language that is widely used for statistical computing and data analysis. It was created in the 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. R has become popular among data scientists, statisticians and researchers due to its powerful statistical analysis capabilities, ease of use and extensive libraries for data analysis.

Prerequisite:

Before learning R, it is recommended that you have a good understanding of statistics and probability theory. Some familiarity with programming concepts such as variables, functions, loops and conditional statements is also helpful. However, R is a beginner-friendly language and you can start learning it even if you have no prior programming experience.

Learning Path Steps:

1. Basic Concepts: The first step in learning R is to understand the basic concepts such as data types, variables, operators and control structures. You can start with online tutorials or books that cover the basics of R.
2. Data Analysis: Once you have a good understanding of the basic concepts, you can move on to data analysis. This involves learning how to import, manipulate and visualize data in R. You can use various R packages such as dplyr, ggplot2 and tidyr to perform data analysis.
3. Statistical Analysis: R is widely used for statistical analysis and there are many packages available for statistical modeling, hypothesis testing and machine learning. You can start with learning linear regression, logistic regression and ANOVA.
4. Advanced Topics: Once you have mastered the basics of R and data analysis, you can move on to more advanced topics such as time-series analysis, Bayesian statistics and deep learning.
5. Project Work: The best way to master R is to work on real-world projects. You can participate in online competitions such as Kaggle or work on your own data analysis projects.

What to Learn After R:

After learning R, you can expand your skill set by learning other programming languages such as Python, SQL and Java. You can also learn data visualization tools such as Tableau and PowerBI, cloud computing platforms such as AWS and Azure and big data technologies such as Hadoop and Spark.

Top 5 Advantages of R:

1. Open-source and Free: R is an open-source language, which means it is free to use and distribute. This makes it accessible to everyone, regardless of their financial resources.
2. Extensive Libraries: R has a vast library of packages that make it easy to perform various data analysis and statistical modeling tasks. This saves time and effort in developing custom solutions.
3. Visualization Capabilities: R has powerful visualization capabilities, with many packages available for creating high-quality charts and graphs. This makes it easy to present data in an understandable format.
4. Cross-Platform Compatibility: R is compatible with various operating systems such as Windows, Mac and Linux, making it easy to share code and collaborate with others.
5. Community Support: R has a large and active community of developers and users, which means there are many resources available for learning, sharing knowledge and getting help.

Top 5 Disadvantages of R:

1. Steep Learning Curve: R has a steep learning curve, especially for those with no prior programming experience. However, there are many resources available to help beginners learn R.

2. Memory Management: R uses a lot of memory and managing memory can be challenging, especially when working with large datasets. However, there are various techniques and packages available for managing memory in R.
3. Performance Issues: R is not known for its speed and some operations can be slow, especially when dealing with large datasets. However, there are techniques and packages available to improve performance in R.
4. Limited Applications: R is primarily used for statistical computing and data analysis and its applications are somewhat limited compared to general-purpose programming languages such as Python.
5. Lack of Standardization: R lacks standardization and different packages and libraries may have different syntax and conventions, making it challenging to learn and use.

Conclusion:

R is a powerful language for statistical computing and data analysis. It has an extensive library of packages and visualization capabilities, making it a popular choice among data scientists and researchers. Learning R requires some knowledge of statistics and probability theory, but it is a beginner-friendly language. After mastering R, you can expand your skill set by learning other programming languages and technologies. While R has some disadvantages, its benefits outweigh its limitations, making it a valuable tool for data analysis and research.

Top 20 Learning Resources:

1. R Documentation: https://www.rdocumentation.org/
2. R for Data Science (book): https://r4ds.had.co.nz/
3. R Graphics Cookbook (book): http://www.cookbook-r.com/Graphs/
4. Advanced R (book): http://adv-r.had.co.nz/
5. Coursera – Data Science Specialization: https://www.coursera.org/specializations/jhu-data-science
6. edX – Data Science Essentials: https://www.edx.org/course/data-science-essentials-2
7. DataCamp: https://www.datacamp.com/courses/free-introduction-to-r
8. Codecademy – Learn R: https://www.codecademy.com/learn/learn-r
9. RStudio Webinars: https://resources.rstudio.com/webinars
10. Kaggle Kernels: https://www.kaggle.com/kernels
11. Stack Overflow: https://stackoverflow.com/questions/tagged/r
12. R-bloggers: https://www.r-bloggers.com/
13. The R Journal: https://journal.r-project.org/
14. Quick-R: https://www.statmethods.net/
15. RStudio Cheat Sheets: https://www.rstudio.com/resources/cheatsheets/
16. RStudio Community: https://community.rstudio.com/
17. Rseek: https://rseek.org/
18. GitHub – R Programming Language: https://github.com/topics/r
19. Shiny Web Applications for R: https://shiny.rstudio.com/
20. Data Visualization with ggplot2: https://ggplot2-book.org/