Python is a bit better, Python is used by more people.

Both the 2, python and R, have a huge user support. a 2017 survey showed that nearly 45% of data scientists use Python as their primary programming language, on the other hand, 11.2% of data scientists use R language.

Difference between python and r language is as follows:

Advantages of Python:

1. Python contains richer data structures than R for more precise access and memory control of data, most of the deep learning research is done in python to accomplish this.

2. Python is faster than R. Python can directly process G data; R can not, R analysis of data needs to be first through the database to transform big data into small data (through groupby) to be handed over to R to do the analysis, so it is not possible for R to analyze the behavior of the detailed list directly, only to analyze the statistical results.

3. Another advantage that Python has over R is the deployment of the model to the rest of the software. python is a general purpose language, and the process of writing an application in python that includes a python based model is seamless.

4. Python is a more balanced set of languages in all respects, whether it's calls to other languages, connecting to and reading from data sources, manipulating systems, or regular expressions and word processing, Python has clear advantages, especially in computer programming and web crawlers.

Advantages of the R language:

1. R is a more efficient stand-alone data analysis tool for statistical analysis. There is a wider range of model classes to choose from for extensive statistical modeling studies in R. If you have questions about modeling, R is the way to go.

2. Another trick of R is the ease of creating dashboards using Shiny, Python also has Dash as an alternative, but it is less sophisticated.

3. R's functions were developed for statisticians, so it has domain-specific strengths, such as powerful features for data visualization, and ggplot2, created by R Studio's Chief Scientist Hadley Wickham, is now one of the most popular data visualization packages in R's history.

ggplot2 allows users to customize plotting components at a higher level of abstraction. I personally love the variety of features and customizations available in ggplot2. ggplot2 offers more than 50 images suitable for a variety of industries.