Python and R are the best programming languages for data science and machine learning. Still, a lot of people guide you to choose a language between Python and R. Basically what they do is whoever can teach your Python will always suggest you learn Python and whoever can teach R will suggest you learn R and to forget about Python.
At the end of the day, they seem very biased with the language they suggest you choose as if they are being paid to promote. As a Python expert, in this article, I’ll tell you why R for Machine Learning.
Also, Read – Machine Learning Full Course for free.
I already covered an article on why Python is best for machine learning which you can read here. But in this article, I’m assuming that if Python isn’t an option then why R for Machine Learning.
Introduction to the R Programming Language
R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical techniques (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, grouping) and graphing, and is highly extensible.
The best thing about R is that it was developed by statisticians and the worst thing about R is that it was developed by statisticians.
So why R for Machine Learning?
R is an extremely powerful language for manipulating and analyzing data. Its rapid rise within the data science and machine learning communities has made it as very much useful programming language for analysis.
The success of the R programming language in the data analysis community stems from two factors described in the previous epitaphs:
- R provides most of the technical power that statisticians need in the default language
- and R was supported by a community of statisticians who are also open source enthusiasts.
A language specially designed for statistical computation has many technical advantages. As the R project description notes, the language provides an open source bridge to S, which contains many highly specialized statistical operations as core functions.
For example, to perform a basic linear regression in R, you just need to pass the data to the lm function, which then returns an object with detailed information about the regression (coefficients, standard errors, residuals, etc.). This data can then be visualized by bypassing the results to the plot function, which is designed to visualize the results of this analysis.
In other languages with large scientific computing communities, such as Python, duplicating the functionality of lm requires the use of several third-party libraries to represent data (NumPy), perform analysis (SciPy), and visualize results ( matplotlib).
Also, as in other scientific computing environments, the fundamental data type in R is a vector. Vectors can be aggregated and organized in different ways, but at heart, all data is represented that way.
This relatively rigid perspective on data structures can be limiting, but it also makes sense given the application of the language. The most frequently used data structure in R is the dataframe, which can be thought of as a matrix with attributes, an internally defined “spreadsheet” structure, or a relational database-like structure at the heart of the software. language.
A data frame is simply a columnar aggregation of vectors to which R provides a specific functionality, making it ideal for working with any type of data.
Disadvantages of R for Machine Learning
Despite all its power, R also has its drawbacks. R does not cope well with big data, and while much effort has been put into fixing this issue, it remains a serious issue. The datasets you’ll be using are relatively small, and all of the systems we’re going to build are prototypes or proof of concept models.
This distinction is important because if you intend to build enterprise-level machine learning systems at Google or Facebook scale, then R is not the right solution.
Companies like Google and Facebook often use R as a “sandbox” to play with data and experiment with new ways of machine learning. If any of these experiments are successful, engineers will try to replicate the functionality designed in R in a more appropriate language, such as C.
So I hope you liked this article on why you might want to consider R for machine learning. I mentioned its downsides as well so as not to sound biased with R. Please feel free to ask your valuable questions in the comments section below.
