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What's Next for R?
21–30 of 72 posts
Re: What's Next for R?
#22Cannot comment from my personal impressions, as I have almost zero knowledge of R, compared to several years of using Python for writing apps and working with data. I like R's focus on functional programming, though. However, a couple of years ago, my wife tried to transition from business consulting to a data analytics / data science role. She started with taking an R course. She was put off by R's complexity and th…
I guess that's more an issue with the courses than the language per se. Sometimes it is a good idea to begin the course with direct application, instead of focusing on the language.
Re: What's Next for R?
#23Cannot comment from my personal impressions, as I have almost zero knowledge of R, compared to several years of using Python for writing apps and working with data. I like R's focus on functional programming, though. However, a couple of years ago, my wife tried to transition from business consulting to a data analytics / data science role. She started with taking an R course. She was put off by R's complexity and th…
You only have to go through the learning process once. You are able to use the language for a lifetime. I find it so strange how much emphasis we tend to put on things being simple to learn and pick up.
Re: What's Next for R?
#24I would highly recommend the use of the package data.table over tibble or the basic data.frame if you are doing any type of modeling in R with larger datasets. Yes R has many data structures but knowing how to use data.table will blow your mind in term of efficiency. Matt and other contributors have built something extremely fast and flexible. I get that R is not for everyone but used correctly it is a beast. Now thi…
This may be useful. I prefer dplyr's syntax. https://github.com/tidyverse/dtplyr
One of the reason we use data.table is that it reduces the depencies when building custom images and its stability has been better than the tidyverse in the past. It might not be the case in the future, but that is how we made our choice initially.
Re: What's Next for R?
#25Cannot comment from my personal impressions, as I have almost zero knowledge of R, compared to several years of using Python for writing apps and working with data. I like R's focus on functional programming, though. However, a couple of years ago, my wife tried to transition from business consulting to a data analytics / data science role. She started with taking an R course. She was put off by R's complexity and th…
I guess that's more an issue with the courses than the language per se. Sometimes it is a good idea to begin the course with direct application, instead of focusing on the language.
I have encountered a lot of really terrible R learning materials. One data viz course I took (a very, very reputable and widely-used course on a major MOOC platform) taught how to make several simple chart types in each of base R, a library called lattice that I've never encountered since, and ggplot2. I think a lot of it comes from R instructors who started out back before the tidyverse trying to teach the path they _took_ to learning the language, rather than the quickest path to being proficient in the language as it exists today.
The tidyverse is incredibly controversial in parts of the R community; it's essentially an opinionated set of packages that basically comes with its own "standard" library. But I think that wholeheartedly embracing it, and hiding the way to do things in R that you would do them without the affordances that the tidyverse offers, is absolutely the right way to teach R these days. Unfortunately, a lot of courses and books haven't caught up to that yet.
Re: What's Next for R?
#26This currently missing are better LSP (Language Server Protocol)[1] (it supports only some of the LSP features), better linter[2] and static analysis, better integration with GitHub[3], and so on. More on the tooling side, I believe.
[1] https://cran.r-project.org/web/packages/languageserver/readm...
Re: What's Next for R?
#27I would highly recommend the use of the package data.table over tibble or the basic data.frame if you are doing any type of modeling in R with larger datasets. Yes R has many data structures but knowing how to use data.table will blow your mind in term of efficiency. Matt and other contributors have built something extremely fast and flexible. I get that R is not for everyone but used correctly it is a beast. Now thi…
> I believe Julia to be the future but so far the adoption rate in house has been low.
Why do you believe it will be the future, and what do you see as the barriers to roll-out? I ask as someone who is curious about when/whether to start investing in Julia competence
Re: What's Next for R?
#28I know this is a dead horse, but I think R seriously shot itself in the foot with its data structures[1]. I don't really see a solution for this, as fixing it would never be backward compatible. I'll always pick Python over R because the data structures actually make sense to me as a programmer (objects that look like lists, dicts, matrices, etc. or any combination of the above, and they all behave in very predictabl…
It's possible if you provide the migration tool, something like Rust's `cargo fix`[1]. Apart from small obvious warnings, it can apply the migration from the Rust-2015 edition to Rust-2018 one[2]. Introducing the new R edition and a similar tool could help with this.
[1] https://github.com/rust-lang/rustfix
[2] https://doc.rust-lang.org/nightly/edition-guide/editions/tra...
Re: What's Next for R?
#29I would highly recommend the use of the package data.table over tibble or the basic data.frame if you are doing any type of modeling in R with larger datasets. Yes R has many data structures but knowing how to use data.table will blow your mind in term of efficiency. Matt and other contributors have built something extremely fast and flexible. I get that R is not for everyone but used correctly it is a beast. Now thi…
> I believe Julia to be the future but so far the adoption rate in house has been low. Why do you believe it will be the future, and what do you see as the barriers to roll-out? I ask as someone who is curious about when/whether to start investing in Julia competence
> We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled. (Did we mention it should be as fast as C?)
https://julialang.org/blog/2012/02/why-we-created-julia
I've been playing around with it. As a Python/MATLAB guy, the syntax is very friendly. I can see it displacing Python in production code where you need speed and might avoid some of the heavy Python DS libraries. Overall it seems like a thoughtful combo of a lot of good numerical programming features.
Re: What's Next for R?
#30When I used R in University (majored in Applied Mathematics and Statistics) I was always awestruck at how every sort of novel modeling technique from GLM, to Beta Regressions, to GARCH, is all easily accessible for free, with proper academic paper and documentation, and with a cohesive standard support.
It was really useful to be able to apply most theory I was learning to actual research datasets. This is what I miss the most since moving to Python.
What I don't miss is R's terrible packaging system and how it made collaborating with colleagues near impossible. I can't count the amount of times I had to debug dependencies on others' script just to be able to move forward with some team project.