From my experience this is not the case. In biomedicine and bioinformatics few people actually use tidyverse because the data is much better represented as a matrix, and not in the "tidy" form.
Outside of that corporations (well at least 2 I contracted with) used `data.table` explicitly. Join 3 ad-click dataframes matching by userID, sessionID and closest possible time-point - that's one line in `data.table`.
Tidyverse is well suited for learning and for managing (relatively) simple datasets. But becomes cumbersome for more complex data. It can be used for those data too of course, just that it will be adding ad-hoc solutions and maybe get in a way more than help.
I've only use base R for my medical data (subsetting dataframe and such). Very rarely do I need tidy and also I find the pipe operator makes debugging harder. If and when I need it I'll use it that's that.
I think R have much more packages in medical, especially statistical packages, where many fields within medical cares about inferences not just prediction/forecasting. So I disagree with the "essentially the only thing keeping R in the game". The breath of packages in R is one of the many things that keep R in the game.
The tribalism and highly bias comments makes it very toxic and harder to have an honest discord.
They are just tools, use what makes you happy and get the job done.
I think 90% of data science content is for beginners because anything more advanced isn't best described as data science. As soon as you get beyond the initial stages of data analysis (cleaning and processing data), you're doing something best described as some other word (statistics, machine learning, etc.) - although, granted, there isn't much content in these areas if you don't know _exactly_ what you're looking for.
Not an advantage if you ask me - exactly because data.frame is built in, people have been building their own versions (tibble, data.table) instead of improving it. That's how R ended up with 3 different structures that are similar but have inconsistent apis and behaviour.
> lots of domain-specific packages
That's true.
> more consistent interfaces for basic statistics and machine learning models
Can't disagree more - there is no one go-to library for ML in R (like sklearn in Python) and each package has it's own strange interface and implementation.
I've been fortunate to only work on projects that use built-in data frames, never encountered tibble or data.table in the wild.
> there is no one go-to library for ML in R (like sklearn in Python) and each package has it's own strange interface and implementation.
I still disagree here - one example being the unified interface for generalized linear models. Also, the vast majority of classifiers (RF, SVM, etc.) have similar or identical interfaces. Also, the unified `predict` interface as well. Granted, `sklearn` does have a consistent API as well.
That said, some of this is just a personal preference for the vaguely functional interface in R. The object-orientedness in Python feels a little forced for some tasks in `sklearn`.
To make what I think your point is more explicit, people build their own things in R because R must maintain compatibility with S. So by and large, changes happen in packages and not the base language. This does lead to a proliferation of solutions for the same kinds of problems.
Python has utility. But R is far superior in its the quality of the packages, their documentation, their ability to behave predictably on a given data type.
I run a machine learning shop. Right now all of the training, application, and data management is handled via R. R is simply superior in too many ways for us to be bothered with python for the scale of work we are doing.
Since we're moving some big applications to keras/ TF we do use python and will be using more in the future. However, for almost all data management, munging, movement visualization, reporting, its an R world.
> ...
> Since we're moving some big applications to keras/ TF we do use python and will be using more in the future.
Not sure if I misunderstood, or you're contradicting yourself there.
> R is far superior in its the quality of the packages, their documentation, their ability to behave predictably on a given data type.
I not only disagree but I think that the exact opposite is true for each one of these points. But if things are working well in our shop, I'm not going to try to convince you otherwise.
> I not only disagree but I think that the exact opposite is true for each one of these points. But if things are working well in our shop, I'm not going to try to convince you otherwise.
I partially agree with you here. I'm extremely careful about what non-standard packages I use in R. Code quality varies wildly outside of these, likewise for documentation. But outside of neural networks, I've never found a package in Python that I felt better about in terms of code quality or documentation than its equivalent in R.
The primary reason to moving these to python is due to convenience/ the community. Most new work is published in python. If we find a new/ interesting model we want to implement, its probably written in python. Rather than reskin the thing in its entirety, its easier here to work in python.
A couple disclaimers: my group works primarily in geospatial data, and principally in LiDAR and multispectral imagery.
The coarse division I see between R/ Python, is that if you come from a research/ academic background (non-engineering), you probably learned to program in R. If you were an engineer, you probably learned matlab. If you are self taught/ coursera/ youtube, you probably learned in python.
R libraries are generally more geared towards academic research, and specifically, working within existing frameworks (handling geospatial data as geospatial data rather then turning them into a numpy arrays). Working in python, there is far more re-invention of the wheel, and its always a pain the ass to get things back into the structures they came in as.
Python has huge utility and is an important tool for certain work. But its really really not faster than R (it def used to be, this isnt the case any more).
R has better support for more scientific programming than python.
Not as a point of argument, just additional information: R's support for keras and TF is a wrapper around the Python interface to those libraries.
How so?
For more sophisticated linear algebra algorithm, such as SVD, both will use typically LAPACK, and again, both will exhibit essential identical performance.
There is one important difference though: when R is compiled for 64-bit machines, it can only use 64-bit floats! While numpy can support 32 and even (through software emulation) 16 bit floats. This can halve memory usage, which in turn halves cache misses, which results in a significant speed up in cases where 64-bits of precision is not needed.
See the yellow benchmark (matrix multiply). I suspect it's memory-related.
List functionality within datatable is blazingly fast. Faster than anything else I've seen in python or R.
In all benchmarks I've seen data.table is faster than dplyr on all tasks. Curious to see other results.
https://github.com/Rdatatable/data.table/wiki/Benchmarks-:-G...
To be fair, the book advertises showing R and Python code side-by-side. And that’s what it does. But it does it unlike how the languages are most often used in industry.
As a quick example, I saw no tidyverse code, which is essentially the only thing keeping R in the game. Learning R from this book won’t prepare you for writing R in most R shops.
I don’t see the utility in knowing how to do the same thing in both python and R if you’re a beginner. This is even more true if you’re not taking advantage of the strengths/weaknesses of either language.
Instead, just learn one of the languages well, and then learn the other well. Shallow dives in both will make you weak in both.
Unfortunately, 90% of data science content seems to be geared at beginners.