We all know about the increasing demand for machine learning, but what's new is exploring machine learning with new software that is R. Many of us have learned machine learning with python. However it is well said that one language is not enough, be it literature language or coding language. The first thing which comes to our mind is why R? Is R eases our job? R and python both have their benefits. Let us discuss machine learning with R in more detail.
R can be intimidating at some times as it involves a scripting language with an odd syntax. But it helps to work faster as it has hundreds of packages and thousands of functions to choose from, providing multiple ways to do each task.
1. Installing R and relevant packages.
2. Loading the dataset.
To date, R has primarily been used in academics and research. This is beginning to change, though, as R usage expands into the enterprise market. R was written by statisticians and it shows—basic data management tasks are very easy. Labeling data, filling missing values, and filtering are all simple and intuitive in R, which emphasizes user-friendly data analysis, statistics, and graphical models.
Since R was built as a statistical language, it has great statistical support overall. It represents the way statisticians think pretty well, so for anyone with a formal stats background it feels natural. Packages like StatsModels provide solid coverage for statistical models in Python, but the ecosystem of statistical model packages for R is much more robust. As far as beginner programmers are concerned, R makes exploratory work easier than Python because statistical models can be written with just a few lines of code.
R, like Python, has plenty of packages to boost its performance. When it comes to approaching parity with Python in machine learning, Nnet improves R by supplying the ability to easily model neural networks. Caret is another package that bolsters R's machine learning capabilities, in this case by offering a set of functions that increase the efficiency of predictive model creation.
But data analysis is R's domain, and there are packages to improve it beyond its already-stellar capabilities. Packages for the pre-modeling, modeling and post-modeling stages of data analysis are available. These packages are directed at specific tasks like data visualization, continuous regression, and model validation.
The R's closest answer to pandas is probably Dplyr, but it is more limited than pandas. That might sound negative, but Dplyr has the benefit of being more focused, which makes discovering how to perform a task much easier. Dplyr is also more readable than pandas. Both Python and R have great packages to maintain some kind of parity with the other, regardless of the problem you're trying to solve. There are so many distributions, modules, IDEs, and algorithms for each that you really can't go wrong with.
Therefore it is fair to say that Machine Learning in R can be just as easy and useful as Machine Learning in Python. There are some advantages and disadvantages for Machine Learning in R but its strengths in certain fields are so strong that using it for those purposes should be an afterthought rather than a consideration.