{"id":35586,"date":"2019-09-26T16:07:20","date_gmt":"2019-09-26T20:07:20","guid":{"rendered":"http:\/\/www.andysowards.com\/blog\/?p=35586"},"modified":"2023-02-24T17:02:39","modified_gmt":"2023-02-24T21:02:39","slug":"r-vs-python-better-data-science","status":"publish","type":"post","link":"https:\/\/andysowards.com\/blog\/2019\/r-vs-python-better-data-science\/","title":{"rendered":"R vs Python: Which is Better for Data Science?"},"content":{"rendered":"

If you are a data scientist, two programming languages that might come to mind when you think of data analysis are R and Python. They are extremely popular and widely compared. However, the choice of the language should be dictated by the analytical challenge that you need to tackle.<\/p>\n

R was developed for statisticians and offers strong data visualization functionalities while Python was designed for beginners who need an easy-to-comprehend syntax. The two programming languages have a horde of differences, but they both have a place in the programming and data analysis world.<\/p>\n

Introducing R and Python<\/h2>\n

\"origin-of-r-and-its-application-in-machine-learning\"<\/a>
\nOrigin of R and It\u00e2\u20ac\u2122s Application In Machine Learning Part-1<\/em><\/p>\n

R was created in 1995 as an open-source language. The idea behind R was to have a programming language that could offer a better way to analyze data, statistics, and graphic models. While this technology was initially used mainly for academic research, it has recently found its way into enterprises. In the corporate world, R is the most popular programming language. The supportive community of R developers gives R an edge against Python in the programming world. Experienced programmers will find the syntax of R easy to comprehend.<\/p>\n

Python, on the other hand, was created in 1991. The language focuses on code readability and productivity, making it easier for beginner programmers to find their footing in data science. Python is preferred by programmers who are engineering-oriented. The language is flexible, and its simplicity ensures it has a relatively low learning curve. Python also has a community, just like R, where programmers can contribute and help each other.<\/p>\n

R and Python: Which Programming Language is Popular?<\/h2>\n

\"python-or-r-for-data-science-programming-language\"<\/a>
\nR vs. Python: Which is a better programming language for data science?<\/em><\/p>\n

R and Python have been adopted by many programmers and are widely used. You might find comparisons between the two languages online, showing usage and popularity. These technologies evolve along with computer capacities. Although numbers online might show which technology is more popular, it might be challenging to compare the two programming languages side by side.<\/p>\n

R is dominant in data science environments \u00e2\u20ac\u201c most programmers in data science prefer R thanks to its excellent visuals. Python, on the other hand, as a general-purpose language, is used in many fields, including data science. In the end, you might find that\u00c2\u00a0Python is more popular<\/a>\u00c2\u00a0than R, but R is more prevalent in data science.<\/p>\n

R and Python: Usage<\/h2>\n

R<\/h3>\n

\"R-for-data-science\"<\/a>
\nR Programming for Data Science Reference<\/em><\/p>\n

R comes in handy when the data analysis is done on individual servers, or it requires standalone computing. The language is instrumental for exploratory work or any other type of data analysis thanks to the wide selection of packages and practical tests. The packages and practical tests provide you with the needed tools to kickstart your programming. This technology can even be used as part of a Big Data solution. When getting started with R, you will need first to install RStudio IDE. After installation, check packages such as:<\/p>\n