Simmer your data science recipe with R language(Part-1)

R language became a trend for analyzing the data a long ago. Though, it was developed in 1993. R has a wide range for catalogs for statistical and graphical methods. It includes all data analyzing tools by computing the problem. Most of the R libraries are written in R, but for the heavy computational task, C, C++ and Fortran codes are preferred. The R tool consists of a machine learning algorithm, linear regression, statistical approach towards your data.

40% of the data science practitioner use R for their data

Why learn R?

R is an interpreted language. Hence we can run Code without any compiler. R interprets the Code and makes the development of code easier. R is a vector language, so anyone can add functions to a single Vector without putting in a loop. Hence, R is powerful and faster than other languages.

Why choose R?

Importance Of R language

1. R is simpler and effective, has user-defined recursive functions.
2. R supports extensions. R performs a wide variety of functions for e.g., statistical modeling, data manipulation and graphical representations of data.
3. Developers can easily write their own software and distribute it in the form of add-on packages.
4. R has huge facilities for data analysis and data visualization that can use for representing the data in various forms.

Why use R?

Key features of R

1. R is used for statistical analysis. Every data says a story and to bring on the conclusion data scientist, data analyst, and statistician use R which is a data analysis software. The data types in R (vectors, data frames, matrices, etc.) make it simple to perform complex tasks. Standard statistical methods are easy to implement in R, newly developed techniques are often available in R first.
2. R is object-oriented programming. R provides operators, functions, and objects that allow data to explore and visualize.
3. R could be used in the statistical computations, data analysis and graphical representation of data. It helps in creating predictive models in data science.
4. Few machines learning algorithms are written in R. Machine learning is the subset of Artificial Intelligence (AI) that allows training the computer to work as a human.
5. R is an open-source software project: R is free and, scrutiny and tinkering by users and developers, has a high standard of quality and numerical accuracy. R’s open interfaces allow it to integrate with other applications and systems.
6. Last but not least, R toolbox has especially for beginners. So, one with less prior knowledge of programming could easily work.

Conclusion

Pros:

R is a great toolbox for data science and machine learning. The analysis which is frequently used like regression(Linear/ logistics), correlation, clustering can be done with R. R has a huge variety of statistical packages and if any statistical packages exist the odds are there that it already exists in R packages out before. R is free open source software that works for all.

Cons:

R has far more capabilities as compared to earlier tools but has the cons of memory management. Capabilities such as security were not built into the R language. This programming is not meant for advanced programs.

Top courses available for R language

• Introduction to R & Intermediate with R by data camp.
• Introduction to R by Cognitive class.ai of IBM.
• R language toolbox with Statistics.com.
• R language courses with edX.
• Data science specialization by John Hopkins University from coursera.