4 ms·

Python is still over shadowed by R in data science. I stopped using Python for data science and use R and it really worked out great for me. R has gained a lot

by baldfat 12y ago

Python is still over shadowed by R in data science. I stopped using Python for data science and use R and it really worked out great for me. R has gained a lot of momentum and I think that Pandas with Wes leaving isn't being developed as quickly. (Most of it is re-doing what R has done). I am amazed at all the FUD I was told to not use R and just use Python but after hitting so many walls I tired it and I am so glad I did. Your millage may vary.

jules 12y ago
<-- Maturity -- Fundamentals well designed --> R Python Julia
- bsg75 12y ago
  The relationship between maturity and design with respect to usefulness is not linear, with respect to "usefulness in production".
  - jules 12y ago
    Right, but it does depend on how long term you view it. Problems in the fundamentals of the core language can't be fixed, a mature library ecosystem can be developed.
PaulHoule 12y ago
R is an awful language with great libraries. My main beef about R (and similar tools) is that I summarize my data I get from Hadoop and find it is still too big for R to handle and I then have to summarize it some more.
- Lofkin 12y ago
  Have you tried data.table?
- baldfat 12y ago
  Have you tried the Hadley Universe? Seriously there isn't a data set that Python can do that R can't (Lots of ways to do things as opposed to Python's one correct way). Using dplyr has been a huge change in the way I do any data munging. Panda's has started using some of dplyr with new function assign. Dplyr really handles large data sets well like data.tables (data.table is fast and handles larger sets but I like dplyr's syntax more) By the way i should have stated that Python is a good choice for doing Data Science it is just that R works better for me.
- chubot 12y ago
  As others have said, try data.table. You assessment is pretty accurate. R is almost the opposite of Python -- you HAVE to use lots of external libraries. Whereas in Python, the core is super solid, but the libraries you get are of varying quality. R took me the longest to learn out of any language. I currently have coworkers struggling with R, and cursing it. But the learning pays off... it's pretty much the only option out there right now (and yes I use Python for almost everything, but you need R too.)
- baldfat 12y ago
  R does not deserve to be called awful. It is a language that works for many people, people make a living from it and is one of the fastest growing languages. [1] https://blog.javascripting.com/2014/07/28/fastest-growing-new-languages-on-github-are-r-rust-and-typescript-and-swift/ https://blog.javascripting.com/2014/07/28/fastest-growing-ne... [2] http://www.pcworld.com/article/2480920/r-programming-language-gaining-ground-on-traditional-statistics-packages.html http://www.pcworld.com/article/2480920/r-programming-languag... [3] http://redmonk.com/sogrady/2015/01/14/language-rankings-1-15/ http://redmonk.com/sogrady/2015/01/14/language-rankings-1-15... Yes I have read [R Inferno] http://www.burns-stat.com/pages/Tutor/R_inferno.pdf http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
Lofkin 12y ago
Thanks for your insight. I have some questions for your, if you don't mind. What walls and why did you switch? Did you find the shift in syntax and mindset difficult? Have you used rpy2? There definitely seems to be more innovation in R world, but blaze, numba and bokeh have no R equal.
- baldfat 12y ago
  Walls: I work at a Microsoft Office Universe (They refuse to know of any tools besides Word and you are a tech god if you use Excel) So when I finish my reports in R I actually give them Office Documents. The presentation is actually Power Point and the documents are in Word. I left Python and Pandas and the visualization was far behind what R had in its tool chain. I still feel that R does visualizations best. RStudio (A true IDE) was better for my work flow than IPython/Juypter. I actually really missed IPython at first and than slowly I grew to love RStudio. Not a wall, maybe a paper cut. A 1 index (R) over a 0 index (Python) is pretty smart for doing statistics, less errors. I also liked the flexibility to use whatever I wanted to do my work. Was difficult at first than learned to love the fact that there were 3 good ways to do something. Functional Programming. You can do some good functional programming with R. Functions are first class citizens. Biggest reason for anyone to switch is Hadley Wickham and his work with RStudio. It really is amazing everything that he has done for R in the last 5 years. Once again Python will get the job done but these are some of the reasons why I switched.
  - Lofkin 12y ago
    Interesting thanks for sharing. Hopefully bokeh might be able to address the visualization space with its upcoming statistical glyphs. When you say "flexibility to use whatever I wanted to do my work", do you mean data munging and manipulation or statistical modeling packages?
    - baldfat 12y ago
      Programming I mean that I have 5 different ways of doing anything and everything. SO if I want to do loops (I never do and people shouldn't) I can. If I want to do strict funtional programming, no problem. If I want to use a SQL type syntax I can use data.table. This is frustrating at first since stackoverflow will have a bunch of solutions and it will be something new. I end up really using a few libraries and sticking with them. I use dplyr, stringr, httr, ggvis or ggplot2 and tidyr. Graphics I can use just about anything. I can use plottly, ggplot2, D3.js, PDF report, HTML, send to a blogging site, interactive graphics with ggvisand Shiney, even PowerPoint slides.

by baldfat 12y ago

jules 12y ago

<-- Maturity -- Fundamentals well designed --> R Python Julia

bsg75 12y ago
The relationship between maturity and design with respect to usefulness is not linear, with respect to "usefulness in production".
- jules 12y ago
  Right, but it does depend on how long term you view it. Problems in the fundamentals of the core language can't be fixed, a mature library ecosystem can be developed.

PaulHoule 12y ago

R is an awful language with great libraries. My main beef about R (and similar tools) is that I summarize my data I get from Hadoop and find it is still too big for R to handle and I then have to summarize it some more.

Lofkin 12y ago
Have you tried data.table?
baldfat 12y ago
Have you tried the Hadley Universe? Seriously there isn't a data set that Python can do that R can't (Lots of ways to do things as opposed to Python's one correct way). Using dplyr has been a huge change in the way I do any data munging. Panda's has started using some of dplyr with new function assign. Dplyr really handles large data sets well like data.tables (data.table is fast and handles larger sets but I like dplyr's syntax more) By the way i should have stated that Python is a good choice for doing Data Science it is just that R works better for me.
chubot 12y ago
As others have said, try data.table. You assessment is pretty accurate. R is almost the opposite of Python -- you HAVE to use lots of external libraries. Whereas in Python, the core is super solid, but the libraries you get are of varying quality. R took me the longest to learn out of any language. I currently have coworkers struggling with R, and cursing it. But the learning pays off... it's pretty much the only option out there right now (and yes I use Python for almost everything, but you need R too.)
baldfat 12y ago
R does not deserve to be called awful. It is a language that works for many people, people make a living from it and is one of the fastest growing languages. [1] https://blog.javascripting.com/2014/07/28/fastest-growing-new-languages-on-github-are-r-rust-and-typescript-and-swift/ https://blog.javascripting.com/2014/07/28/fastest-growing-ne... [2] http://www.pcworld.com/article/2480920/r-programming-language-gaining-ground-on-traditional-statistics-packages.html http://www.pcworld.com/article/2480920/r-programming-languag... [3] http://redmonk.com/sogrady/2015/01/14/language-rankings-1-15/ http://redmonk.com/sogrady/2015/01/14/language-rankings-1-15... Yes I have read [R Inferno] http://www.burns-stat.com/pages/Tutor/R_inferno.pdf http://www.burns-stat.com/pages/Tutor/R_inferno.pdf

Lofkin 12y ago

Thanks for your insight. I have some questions for your, if you don't mind. What walls and why did you switch? Did you find the shift in syntax and mindset difficult? Have you used rpy2? There definitely seems to be more innovation in R world, but blaze, numba and bokeh have no R equal.

baldfat 12y ago
Walls: I work at a Microsoft Office Universe (They refuse to know of any tools besides Word and you are a tech god if you use Excel) So when I finish my reports in R I actually give them Office Documents. The presentation is actually Power Point and the documents are in Word. I left Python and Pandas and the visualization was far behind what R had in its tool chain. I still feel that R does visualizations best. RStudio (A true IDE) was better for my work flow than IPython/Juypter. I actually really missed IPython at first and than slowly I grew to love RStudio. Not a wall, maybe a paper cut. A 1 index (R) over a 0 index (Python) is pretty smart for doing statistics, less errors. I also liked the flexibility to use whatever I wanted to do my work. Was difficult at first than learned to love the fact that there were 3 good ways to do something. Functional Programming. You can do some good functional programming with R. Functions are first class citizens. Biggest reason for anyone to switch is Hadley Wickham and his work with RStudio. It really is amazing everything that he has done for R in the last 5 years. Once again Python will get the job done but these are some of the reasons why I switched.
- Lofkin 12y ago
  Interesting thanks for sharing. Hopefully bokeh might be able to address the visualization space with its upcoming statistical glyphs. When you say "flexibility to use whatever I wanted to do my work", do you mean data munging and manipulation or statistical modeling packages?
  - baldfat 12y ago
    Programming I mean that I have 5 different ways of doing anything and everything. SO if I want to do loops (I never do and people shouldn't) I can. If I want to do strict funtional programming, no problem. If I want to use a SQL type syntax I can use data.table. This is frustrating at first since stackoverflow will have a bunch of solutions and it will be something new. I end up really using a few libraries and sticking with them. I use dplyr, stringr, httr, ggvis or ggplot2 and tidyr. Graphics I can use just about anything. I can use plottly, ggplot2, D3.js, PDF report, HTML, send to a blogging site, interactive graphics with ggvisand Shiney, even PowerPoint slides.