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I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
There's a bunch of comments below which can be summed up with 'use R because doesn't have a direct python equivalent' but they're all missing the point that the Python data science ecosystem is evolving at a much faster pace than R and will completely supersede it in a few years.
R, like SAS, is a tool for non-programmers. And there it shall remain. The only demographic where R makes sense long term are pure mathematicians/statisticians who are not proficient in programming. But that demographic is rapidly declining in size.
I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
I use both Python and R. tidyverse/ggplot2 alone are enough reason to use R, and are substantially faster for tasks that utilize those packages than the equivalent in Python (in my opinion). Although I haven't had as much reason to use base R. For more ML-related tasks I do go back to Python.
The overall api of tidyverse packages is such a joy, and recent improvements in purrr/tidyr allow me to construct nested data analysis workflows I couldn’t even dream of in python.
I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
Is there anything comparable to tidyverse and ggplot2 in python? If so I will switch immediately.
ggplot2 : plotnine is quite good ggplot2 clone based on matplotlib. I feel like ggplot2 is a bit better and more complete, but if you want to do something that isn't supported it's harder for me to hack than matplotlib.
Tidyverse: To me, ggplot2 is the only essential part of the tidyverse. Lubridate is also good. Most others seem like semantics and syntax sugar. I prefer data.table, which is similar to Pandas. DT is super fast but imho Pandas has a more intuitive and consistent API (and if you want a speed up for large N then dask might work).
I use both R and Python on a regular basis. I choose Python for lower-level stuff, automation, parallelism / concurrency, and R for bespoke statistics. I use both for everyday statistics and plotting, but I feel that R has light advantages. I feel like if you're comfortable switching languages there are good reasons to use both. It's also important for me because I work with different teams that have different practices and preferences.
I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
I don't think there's any reason to learn R for anyone who is already proficient at programming. Despite being proficient with R, the only times I used it in the last two years were for ggplot. And even for data vis, I'm increasingly using Python and JS. There's a bunch of comments below which can be summed up with 'use R because doesn't have a direct python equivalent' but they're all missing the point that the Pyth…
The point is R is a very good language for statistic because of the packages not data science. Data science can do their own thing it's okay. It's also okay for data science to use statistic models from statistic too.
> R, like SAS, is a tool for non-programmers.
I respect and love data science and machine learning but this behavior of generalization is terrible. There are many wondeful programmers contribute to R and uses R as I am sure there are many wonderful statisticians that use Python. They're just tools.
> And there it shall remain. The only demographic where R makes sense long term are pure mathematicians/statisticians who are not proficient in programming. But that demographic is rapidly declining in size.
What is up with these generalizations? R is not going anywhere in the statistic community. It's doing fine. Also from my experiences in academia most math people use matlab and if any R.
It's okay to have both R and Python doing their thing.
There is no need to conflate data science and statistic or have this weird tribalism.
I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
Although I like R and often use R to quickly order tabulated data, there are a few things to take into account that in recent times are building a strong case for me not to use R habitually.
Development in R is frustrating. If you don't need to do dev, then on this point you are home free. Testing things that you deploy in R is not simple.
Scripting in R can be frustrating. I have a script that traverses Excel files and using tryCatch() is just so much more complicated with it being a function. In Python the try-catch functionality is part of the design syntax.
There are scenarios where R is better. If you are in actuarial science, research or academics then often you'll find R libraries that just work.
R treats tabular data with grace. Everything in R is an array.
The takeaway for me is that I should use R less and Python more. I personally can't deal with something like tryCatch() being overcomplicated, but for people who don't do dev anyway and maybe need to analyse DNA sequences for a living, R can be rewarding. For me: the ggplot2 library is great; stay away from Shiny and dev in R.
I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
Earlier quoted context omitted.
I use both Python and R. tidyverse/ggplot2 alone are enough reason to use R, and are substantially faster for tasks that utilize those packages than the equivalent in Python (in my opinion). Although I haven't had as much reason to use base R. For more ML-related tasks I do go back to Python.
This. I’ve contributed code to popular libraries in both languages, and while I (overall) have a preference for python (mostly due to it being general purpose), I find R code unparalleled when it comes to raw data manipulation/analysis. The overall api of tidyverse packages is such a joy, and recent improvements in purrr/tidyr allow me to construct nested data analysis workflows I couldn’t even dream of in python.
This is an interesting concept. It makes perfect sense to learn both simultaneously. On the other hand, it must be confusing at times. Imagine learning two languages at the same time, from the same book. It's an experiment I haven't tried, but i'm curious about the outcome.
Also interesting to learn two languages, in parallel, where neither is particularly good at parallelism :)
I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.