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Great news for R users who largely have been left behind by the deep learning community. The main downside to this is that we will now start seeing all sorts o

by cs702 10y ago

Great news for R users who largely have been left behind by the deep learning community. The main downside to this is that we will now start seeing all sorts of experimental models from R users who know a lot more about statistics than about software engineering and therefore often write code that is not very elegant nor easy to understand.[1] [1] Don't take it from me. Quoting Hadley Wickham: "Much of the R code you’ll see in the wild is written in haste to solve a pressing problem. As a result, code is not very elegant, fast, or easy to understand. Most users do not revise their code to address these shortcomings. Compared to other programming languages, the R community tends to be more focussed on results instead of processes. Knowledge of software engineering best practices is patchy." http://adv-r.had.co.nz/Introduction.html http://adv-r.had.co.nz/Introduction.html

RA_Fisher 10y ago
Surely we're better off if statisticians and software engineers work together. That we're worse off when the two groups come together doesn't seem probable ...
- cs702 10y ago
  Of course. Overall this is good news.
azuajef 10y ago
This is a key point, well said. However, we need more people who are proficient in both "worlds" or at least, as another user points out below, more people willing to collaborate.
wodenokoto 10y ago
As long as the model is well described I'm all for more statistician experimenting with deep learning. I don't mind if initial code isn't great from a software perspective. If it is needed in production someone else would have to reimplement it.
- huac 10y ago
  My experience is that if the R code works, then somebody will rewrite it in Python/Scala in a better way. I think there are plenty of data scientists capable of writing R code well enough that other data scientists or engineers can understand and translate it. Remember that the core value here is the model and insights being developed, rather than the implementation.
max_ 10y ago
>Great news for R users who largely have been left behind by the deep learning community. No one often mentions that TensorFlow lacks native windows support.
Noseshine 10y ago
> code that is not very elegant nor easy to understand That was my impression when I started learning R while learning statistics - again, but this time with an actual interest in the subject and not just for the grade :) On the other hand, the whole R-learning experience, which was aided by having to do practical "statistical" things in the language and working with actual data, gave me a new perspective on programming, a much more data-oriented perspective if I may call it that. Before (learning R and statistics) I was interested in the code and what it could do, afterwards (but only for those kinds of applications) I was interested in the data and what I could do with it, the "how" was secondary. From that perspective I understood a lot of the (IMHO) "hacky" nature of R and some of its libraries.
Houshalter 10y ago
I recently started writing some R code and found it much more elegant than previous code. R has a bunch of functions for handling data and statistics related stuff, that really reduces complexity. But also it has a lot nice iteration functions that make it possible to do a lot of stuff with a single line. I only needed to use a for loop once. I never needed to do anything complex with data structures or nested loops or branching code, everything seemed to reduce to a few functions. It's a really elegant language, at least for its specialized domain.
makeset 10y ago
Unfortunately a lot of R's internal standard library code isn't much better, rife with assumptions of minuscule data, gratuitous copying of input data many times over and pointlessly returning them in results, glacial I/O, grossly inefficient implementations even at the C level, etc. Using R in a production environment is a slow journey through reimplementing R itself.

by cs702 10y ago

RA_Fisher 10y ago

Surely we're better off if statisticians and software engineers work together. That we're worse off when the two groups come together doesn't seem probable ...

cs702 10y ago
Of course. Overall this is good news.

azuajef 10y ago

This is a key point, well said. However, we need more people who are proficient in both "worlds" or at least, as another user points out below, more people willing to collaborate.

wodenokoto 10y ago

As long as the model is well described I'm all for more statistician experimenting with deep learning. I don't mind if initial code isn't great from a software perspective. If it is needed in production someone else would have to reimplement it.

huac 10y ago
My experience is that if the R code works, then somebody will rewrite it in Python/Scala in a better way. I think there are plenty of data scientists capable of writing R code well enough that other data scientists or engineers can understand and translate it. Remember that the core value here is the model and insights being developed, rather than the implementation.

max_ 10y ago

>Great news for R users who largely have been left behind by the deep learning community. No one often mentions that TensorFlow lacks native windows support.

Noseshine 10y ago

> code that is not very elegant nor easy to understand That was my impression when I started learning R while learning statistics - again, but this time with an actual interest in the subject and not just for the grade :) On the other hand, the whole R-learning experience, which was aided by having to do practical "statistical" things in the language and working with actual data, gave me a new perspective on programming, a much more data-oriented perspective if I may call it that. Before (learning R and statistics) I was interested in the code and what it could do, afterwards (but only for those kinds of applications) I was interested in the data and what I could do with it, the "how" was secondary. From that perspective I understood a lot of the (IMHO) "hacky" nature of R and some of its libraries.

Houshalter 10y ago

I recently started writing some R code and found it much more elegant than previous code. R has a bunch of functions for handling data and statistics related stuff, that really reduces complexity. But also it has a lot nice iteration functions that make it possible to do a lot of stuff with a single line. I only needed to use a for loop once. I never needed to do anything complex with data structures or nested loops or branching code, everything seemed to reduce to a few functions. It's a really elegant language, at least for its specialized domain.

makeset 10y ago

Unfortunately a lot of R's internal standard library code isn't much better, rife with assumptions of minuscule data, gratuitous copying of input data many times over and pointlessly returning them in results, glacial I/O, grossly inefficient implementations even at the C level, etc. Using R in a production environment is a slow journey through reimplementing R itself.