I used to do mostly data analysis in my day-to-day work and R was my go-to and absolute favorite language for years in terms of usability for data analysis. Doing actual software development in R is quirky at best, to be honest.
Nowadays I write code for research that requires 'actual' software development, so I've been using python almost exclusively (with pytorch under the hood, which I love.) No doubt, python is a better language for software engineering.
Nevertheless, for analysis I just cannot warm up to numpy/pandas/matplotlib _at all_. When it's time to analyze results of my experiments or produce publication level graphics, I write my python results to disk and use the tidyverse as a last mile solution.
I used to do mostly data analysis in my day-to-day work and R was my go-to and absolute favorite language for years in terms of usability for data analysis. Doing actual software development in R is quirky at best, to be honest.
Nowadays I write code for research that requires 'actual' software development, so I've been using python almost exclusively (with pytorch under the hood, which I love.) No doubt, python is a better language for software engineering.
Nevertheless, for analysis I just cannot warm up to numpy/pandas/matplotlib _at all_. When it's time to analyze results of my experiments or produce publication level graphics, I write my python results to disk and use the tidyverse as a last mile solution.