There are a few reasons why someone might choose to use R over Python for data science. Here are a few possible reasons:
- R has a larger and more active community of users in the field of statistics and data science. This means that there are more resources available (such as libraries and tutorials) for people working with data in R.
- R has a number of specialized libraries and tools for working with statistical models and performing complex data analysis tasks. For example, R has extensive support for generalized linear models, mixed-effects models, and other types of statistical models that are commonly used in data science.
- R is a programming language that is specifically designed for working with data and statistics. This means that the syntax and features of the language are well-suited to tasks such as importing and cleaning data, exploring and visualizing data, and building statistical models.
That said, Python is also a popular choice for data science and has its own strengths and advantages. Some people might prefer to use Python because it is a general-purpose programming language that can be used for many different types of tasks, including data science. Python also has a large and active community of users, and there are many libraries and tools available for working with data in Python. Ultimately, the choice between R and Python for data science will depend on the specific needs and preferences of the individual or organization doing the work.