Shulou(Shulou.com)06/03 Report--

With the development of programming languages, some languages have developed well, others are facing elimination, and the more popular ones are Python. As a general scripting language, Python has gradually become the most popular language in data science. According to some people in the IT industry, Python became famous with the R language as a stepping stone, which is now a dying language.

There is some evidence that the popularity of Python has led to lower and lower R utilization. According to TIOBE, Python is currently the third most popular language in the world, after Java and C. from August 2018 to August 2019, Python usage rose by more than 3 per cent, reaching a rating of 10 per cent (TIOBE's proprietary measure of search activity), making it the most prominent star of the 20 most popular languages.

By contrast, R has suffered many setbacks in the TIOBE rankings in recent years, falling from No. 8 in January 2018 to No. 20 now, second only to Perl, Swift and Go. At its peak in January 2018, R's approval rating was about 2.6%. But according to the TIOBE index, it has now fallen to 0.8 per cent.

A person related to TIOBE wrote:

The popularity of Python continues to grow, but at the expense of the decline in the popularity of other programming languages, one of which is R, while Perl is declining even faster.

Other data show that Python's success over the years has come at the expense of R and SAS (the popular proprietary analytical environment). According to Burtch Works's survey of preferred modeling environments, Python, R and SAS were neck-and-neck in 2018:

Dice Insight once published an article about "five languages doomed to decline", among which is R.

R is considered to be a language for statistical computing, although academia and data scientists still use R, but some data analysis companies are turning to Python, as Nick Kolakowski, a senior editor of Dice Insight, said: "relying on the use of a small number of scholars and others is not enough to maintain R's survival."

Comparing the various technical benefits of R and Python, it can be found that while users may be able to perform any statistical tasks natively in R or its libraries, the language is not as good as Python when working in Web browsers, so those who belittle R think that R's scalability is limited in this respect. The growth of Python ecosystem exceeds that of R.

All these phenomena show that the development momentum of Python is very strong, far away from R many streets. However, some people confirm that the R language shows no signs of declining or has declined. Martijn Theuwissen, founder of DataCamp, an online education platform for data analysis in the United States, said:

Reports of the decline in R have been greatly exaggerated. According to my findings, if you look closely at R, it is still growing, but Python is growing faster.

According to R Consortium, an organization created to promote the use of the open source language, there are now more than 2 million R users worldwide, and developers have written and open source more than 13000 libraries through CRAN (Integrated R Archive Network) to automate various statistical tasks and chart.

R Consortium said:

A wide range of organizations have adopted R as a data science platform, including biotechnology, finance, research and high-tech industries. The R language is usually integrated into third-party analysis, visualization, and reporting applications and runs on a variety of computing platforms.

One of the advantages of R is that it is used as a course in colleges and universities, where many graduate students pursue science degrees in their disciplines and study R for statistical modeling. With the growing demand for data scientists, many of these people who have been trained in "hard" science have applied their statistical skills to new data science industries, bringing with them R knowledge. Python is also taught in higher education, but it is more in computer science.

It is well known that investigating the popularity of languages is a difficult task. Languages have a natural life, and there is no absolute way to determine their exact life cycle, so there is no way to fully predict their future (even TIOBE). So it's too early to say that Python has replaced R.

At this particular time, the future of Python may be brighter than R, but that doesn't mean R has no future. For some data science jobs, Python may be the best tool, but for others, Python is hard to beat R.

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