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R is an open-source language that has generated a substantial impact on the data scientist as well as the statistical world. Created by Ross Ihaka and Robert Gentleman in the early nineties, the R was intended as an open-source language to replace S.
This made it gain ground within no time due to factors such as flexibility, very good statistical power and most of all, the existence of a very vibrant user base. However, the usage of this language in web development has been gradually fading in recent years, primarily because scripts such as Python are now more popular. Let’s take a look at how and why R, the once-dominant data science language, is losing ground in the tech landscape.
In the initial stage of its business, R was a perfect market solution due to the lack of competitors. It provided statisticians, researchers as well as data scientists with a free, user-friendly tool that allows carrying out advanced statistical analysis and data visualization. The language was especially helpful with regard to colleges and universities adopting better and cheaper solutions.
This increase was supported by R’s vast package network. Tens of thousands of packages were created by a diverse open-source community, helping R focus on everything from biology to finance. We have seen packages like ggplot2 that transformed the way data visualizations were done alongside the dplyr and tidyr that boosted the data manipulator. This provided flexibility that enabled users to perform unique custom functions as well as create varied analysis visualization patterns.
It was in the mid-2000s that R had turned out to be one of the most usable tools for data analysis. It started being used by major corporations and institutions making it a mainstay for data science endeavors. The commercial support for R was commercially provided by an organization known as Revolution Analytics in 2009, which was later affiliated with Microsoft.
Academic support in particular can be identified as one of the major driving sources for the dominance of R during the analysed period of time. Almost all statistics and data science programs started using R as the go-to language for data analysis. It is for the same reasons that the principles of open science too helped R to succeed.
RStudio, an IDE specifically for R made writing R code easier. R has expanded towards data lovers and booted professionals due to the 2011 release of RStudio which facilitated code writing and debugging for new users.
For some time R was popular and another language, Python, only started receiving attention at the beginning of the 2010s. While Python was a general-purpose language to work on unlike R. Due to this flexibility it could be used in areas such as website development, machine learning, AI, etc. This flexibility made Python an attractive bet for data scientists who did not only need to do statistics but also a host of other things.
The effectively evolving use of AI and predictive modelling in the data science process was followed by the great importance of such Python tools as sci-kit-learn, TensorFlow, and PyTorch. R lost more ground to Python due to its ability to seamlessly integrate with these tools which in turn makes it the preferred language for machine learning.
Another reason that Python is preferred over R is that the language itself is easier to understand and learn for non-statistical people if compared to R. This not only helped make Python the kind of language that can appeal to engineers and software developers. Furthermore, the increasing utilization of Python for data analysis created more demand for Python developers which in turn pushed the newcomers to switch to Python from R.
In the last several years the popularity and the usage of R started to diminish as a leading programming language among data scientists. Well, there are several reasons; one of them is the rising popularity of machine learning and artificial intelligence – both of which belong to Python. However, R has not gained the same level of pace and popularity as the Python ecosystem with its packages such as caret or more.
The other couple of issues faced by R is that it has some constraints on its performance capabilities. R may be slower especially when dealing with significant data or engaging in intensive computations compared to Python. Here, too, Python has some edge over other languages because of its ability to link with high-performance libraries such as NumPy and pandas.
Nevertheless, such trends have kept alive the usefulness of R among statisticians especially for academic users. Its package system is still diverse, and the visualization features, especially in ggplot2 is still commendable.
They are not going out of business, so the future of R is blurry, but it definitely has not faded into oblivion. It continues to have a valid follower base, especially in fields such as genomics, econometrics, as well as biostatistics. It further continued that R’s development continually happens at RStudio to retain competitiveness in the market and Science has noted that R is particularly attractive to statisticians and academic scholars.
Nonetheless, for the general big data solution using data science, it is highly likely to see Python continuing to be the dominant language. As the novel developments in machine learning along with AI proceed, the functionality of data science tools is experimented with in different domains which may as well put Python over R.
R has had a significant experience whereby it graduated from a mere academic language to one of the most popular languages in data science. But it has declined rather slowly, toppled by Python, and the increasing focus on machine learning and AI. Despite that situation, R is still highly relevant and has immense use for statisticians and researchers in some specific fields.
