9 ms·

The R learning curve

58 points by sonabinu 14y ago

dnc 14y ago
Maybe it's just me, but I find it difficult not to make analogies between R and JavaScript. Both are dynamic, "script" languages with C-based syntax and with LISP-like nature that lurks underneath it. Both have functions as first class citizens. Both are inconsistent in different ways. Just to name a few ... I think that these analogies reduced frustration that I had felt about R once when I "discovered" them and helped me to adopt and learn R.
andrewcooke 14y ago
for learning more about plotting, the gplot2 book is a tad expensive, but worthwhile. http://www.amazon.com/gp/product/0387981403 http://www.amazon.com/gp/product/0387981403 (it helped me understand the structure behind the library, which i found confusing from cookbooks etc)
- jasonpbecker 14y ago
  I have found this [site] (http://www.cookbook-r.com/ http://www.cookbook-r.com/) to be more helpful (plus the online documentation for ggplot2 which is really good). Hadley's book is really good and a solid treatment (from the author of so many clear, powerful package it's not a surprise), but there have been a lot of changes to ggplot2 since 2009. I have personally found ggplot2 to be so powerful because it lends itself very well to actually learning through using Cookbook-style examples.
  - hadley 14y ago
    The cookbook is now an O'Reilly book: http://amzn.com/1449316956 http://amzn.com/1449316956
xijuan 14y ago
Taking the exact same course and going the same process of learning!
Pitarou 14y ago
This sounds like the experience of a beginning programmer learning pretty-much any popular scripting language. > I found that regular expressions ... are a great way to isolate the string that one is looking for... Well I never! I'm not criticising the guy who wrote this, but I don't know why this has been voted up on Hacker News.
- capex 14y ago
  This is a good place for 'And I am no man' from Lord of the Rings. The author appears to be a female, not a guy.
- Schwolop 14y ago
  To be honest, I found the entire coursera course on programming for data analytics to be in the same vein. So much time was spent explaining what each optional parameter to a function did that I skipped at least 50% of each lecture.
  - aheilbut 14y ago
    A big problem with R is that much too much semantics gets buried in critical optional parameters to functions.
  - bcbrown 14y ago
    Same here, I gave up. I'd rather read about the syntax. Save the lectures for concepts that are hard to get across in text. I thought part of the problem was that the course was geared towards grad students for whom this might be their first programming language, as opposed to programmer who already know a couple languages.
    - mogrim 14y ago
      Not convinced, the non-programmers on the discussion boards were having real problems... Certainly wasn't the best course I've done on Coursera, although it did "force" me to learn R, so in that sense it achieved its aim.
    - 14y ago
gklitt 14y ago
I'm currently learning R and was hoping for a more detailed blog post. I found this to be a pretty bland report that would have been made much more interesting and useful if the author had included some concrete examples.
mkhattab 14y ago
> Cleaning the data - This takes time and it can be annoying. Have you tried using Google Refine[1]? It's an excellent tool for cleaning datasets and a useful addition to your workflow. I was miffed I didn't know about it when I had to collect and analyze data from surveys awhile ago. I was using Python for cleanup (Google Refine supports Jython). [1]: http://code.google.com/p/google-refine/ http://code.google.com/p/google-refine/
- jasonpbecker 14y ago
  Google-refine does some really cool stuff, but one of the great developments in the last couple of years within the R community is a big push toward reproducible research and building a powerful toolchain to assist to that end. The problem with Google-Refine is that it is very hard to recreate precisely the same steps to get from raw to processed data.
banachtarski 14y ago
3 packages: plyr ggplot2 reshape Learn those 3 and use them for everything you do in R.
- jasonpbecker 14y ago
  plyr, while having awesome syntax, is really slow for anything beyond even the most modest dataset. Try using it with 50k rows, not to mention true "big data". data.table is much faster. It's an extension to data.frames that adds some additional constrains/rules that allows for much faster operations including aggregating, subsetting, and merging data. Cleaning data, however, does not have a steep learning curve or high difficulty level in R-- it has a steep learning curve and high difficulty level period. Implementing good procedures for data munging is 80% of the job.
  - EzGraphs 14y ago
    Yeah - for the small data sets, Ruby or Python is often easier easier to use to whip a data set into a form that can be simply slurped into R as a dataframe.
    - mogrim 14y ago
      Has to be said I cheated a bit on the Coursera course, I used Ruby to clean up some of the data. Seemed a lot easier, and it's a lot more likely I'd use some kind of scripting language (be it Perl, Ruby, Grep, ...) to do a first clean up than head directly to R to do the job.
    - aheilbut 14y ago
      With pandas / statsmodels / patsy, it's getting easier to stick with python for everything.
      - GrumpySimon 14y ago
        Don't forget Bokeh[1] - a replacement for ggplot2 [1] https://github.com/ContinuumIO/Bokeh https://github.com/ContinuumIO/Bokeh
EzGraphs 14y ago
I spent about a year blogging about R as I learned it (http://www.r-chart.com/ http://www.r-chart.com/). It is not a general purpose sort of language but is great for math / charting tasks. A couple of ways that software developer types can minimize time to get up and running: 1) Check out http://www.r-bloggers.com/ http://www.r-bloggers.com/. The site gives you a good sense of the state of R and Tal (who runs the site) has done a great job of promoting R and encouraging the community. 2) Pick one graphics package and stick with it. The standard functions are sufficient but ggplot2 has a more finished appearance. 3) Review available libraries. If you want to do something, someone else likely already has and has posted a package to CRAN. 4) One way for SQL developers to limit the need of learning the idiosyncrasies of the language is to use the sqldf package to manipulate data frames and use ggplot2 (which generally takes data frames as arguments) to display charts.
abraxasz 14y ago
I've had to learn R during my master (in statistics), and found the process very unpleasant. There are different ways in which a language can be difficult to learn. Haskell has a steep learning curve because it has a lot of unusual concepts (for those who come from an imperative background) and requires a shift in the way we think about code. J is also difficult to learn because the syntax is crazy, and again, the language is very different from what we're used to. I found R difficult to learn because it is seems inconsistent to me. Now one reason might be that I picked up bits of R here and there without taking the time to learn the syntax from a book (unlike what I did for other languages), but it took me quite some time to be able to make sense of the different structures (vector, list, matrix, dataframe), their differences, and in particular how functions operate differently on these structures. I also have a deep hatred for the system of attributes (why would anyone want to give attributes to a vector...), and find the indexing system (especially for lists) to be nonsensical. In fact, I think that lists themselves are terrible to work with. The general impression that I've had learning R is the language is not coherent and systematic in its design in the way other languages are (I know python, c, common lisp, ...), and I find myself spending a lot of time in the interpreter simply trying out things because I'm not sure that it will return what I want, in the format that I want (which never happens to me in any other language). Now don't get me wrong, I use R daily and it's a useful tool. It has lots of great libraries, including the fantastic ggplot2, and the remarkable Rcpp (best C++ interface I've ever seen.. ok I haven't seen any other, but this one is really great). But learning it was no fun, and if the statistics community decided to move to a cleaner language, I'd definitely be running ahead..
- EzGraphs 14y ago
  In R, I often can write a dozen line program that will do something incredible, but it might take an hour to hammer out those lines and cannot reconstruct them from memory because of inconsistencies in syntax. It sort of feels like a statistics DSL built on Lisp.
  - pseut 14y ago
    R is exactly like a statistics DSL built on Lisp. The first sentences of a talk by Ihaka [1] are: > R began as an experiment in trying to use the methods of Lisp implementors to build a small testbed which could be used to trial some ideas on how a statistical environment might be built. Early on, the decision was made to use an S-like syntax. Once that decision was made, the move toward being more and more like S has been irresistible. Since I find it basically impossible to remember how 'eval', 'quote', 'substitute', etc. work in R, I suspect that the Lispers are onto something when they say that the lack of syntax in Lisp is an important feature. [1] http://www.stat.auckland.ac.nz/%7Eihaka/downloads/Interface98.pdf http://www.stat.auckland.ac.nz/%7Eihaka/downloads/Interface9...
wiradikusuma 14y ago
Don't forget the free http://tryr.codeschool.com/ http://tryr.codeschool.com/ from Code School and O'Reilly.
capkutay 14y ago
I don't know why a programmer would limit them self with static data tools when d3 and processing provide beautiful, dynamic, and interactive solutions with a similar learning curve. The only difference is you can use the JavaScript chops you pick up learning d3 in any front end work you do.
- hogu 14y ago
  people use R for much more than data visualization, doing regressions, fitting SVMs, decisions trees, GARCH and ARMA time series models, isn't really trivial in javascript. also ggplot, while more limited is way easier and faster than d3 for doing the specific things that ggplot is good at. But for statistics, that's usually what you want
- pseut 14y ago
  Sounds great! What's the MCMC package for javascript?
  - billwilliams 14y ago
    https://npmjs.org/package/mcmc https://npmjs.org/package/mcmc
  - disgruntledphd2 14y ago
    I worked with a computer scientist once, and he tested out lots of languages for replacing his VB setup (I know, I was surprised too). JS turned out to be the fastest language for a lot of his tests. If the libraries ever get there, it will be lethal. Presumably there might be a way to compile java libraries to JS using Rhino, but its not something I've ever investigated.
RockyMcNuts 14y ago
A few resources that might speed the learning curve Survival guide http://www.win-vector.com/blog/2009/09/survive-r/ http://www.win-vector.com/blog/2009/09/survive-r/ Tutorials http://www.statmethods.net/index.html http://www.statmethods.net/index.html http://heather.cs.ucdavis.edu/~matloff/r.old.html http://heather.cs.ucdavis.edu/~matloff/r.old.html http://www.twotorials.com/ http://www.twotorials.com/ http://en.wikibooks.org/wiki/R_Programming http://en.wikibooks.org/wiki/R_Programming http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf Docs http://cran.r-project.org/manuals.html http://cran.r-project.org/manuals.html StackOverflow http://stackoverflow.com/questions/tagged/r?sort=votes&pagesize=15 http://stackoverflow.com/questions/tagged/r?sort=votes&p... Cheat sheeets http://cran.r-project.org/doc/contrib/Short-refcard.pdf http://cran.r-project.org/doc/contrib/Short-refcard.pdf http://bit.ly/VHPA3G http://bit.ly/VHPA3G R Journal http://journal.r-project.org/ http://journal.r-project.org/ R News (predecessor to R Journal) http://cran.r-project.org/doc/Rnews/ http://cran.r-project.org/doc/Rnews/ Rseek search engine http://rseek.org/ http://rseek.org/ R could use something like the Python ecosystem intro, explaining where to find stuff, not sure if any of the tutorials are as canonical as for instance Dive Into Python or Learn Python The Hard Way.
- aheilbut 14y ago
  one more: http://www.burns-stat.com/pages/Tutor/R_inferno.pdf http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
- pseut 14y ago
  > R could use something like the Python ecosystem intro, explaining where to find stuff, not sure if any of the tutorials are as canonical as for instance Dive Into Python or Learn Python The Hard Way. It only became popular recently; the expectation used to be that you'd learn R while enrolled in a statistics class. The canonical references are books: Modern Applied Statistics with S, R Graphics, etc. I assume that will change soon.
  - RockyMcNuts 14y ago
    Would be great if the CRAN package repositories had ratings and download counts. It can be hard to find the 'right way' to do something.
billwilliams 14y ago
R is my mistress and my wife. Hadley Wickham held the ceremony. Data.Table is the glue that holds together our tenuous marriage. cats[color="brown",summary(tooth_length),by=list(breed,age)]. This line of code will efficiently grab all the brown cats in your data and summarize their tooth length by mean, median and other quartiles then break it out by the cats breed and age. Every time I use a language I can't lapply I despise the language and all those involved. R is hugely inconsistent and terribly inefficient. But its also the most irresistible blend of lispy and object oriented for when you actually need to get data analysis done.
zmmmmm 14y ago
R is probably the most frustrating language I've ever had to learn. For me, R doesn't so much have a learning "curve" as a learning "gradient". After a long, long time the rate of frustrations is not really leveling off much. Each time I have to negotiate a new library or module that I'm not familiar with I'm guaranteed hours of frustration as I try to grapple with what all the data types are, how they are subtly overridden to behave differently from the normal data types they inherit from, what things are going to be magically coerced into what other things without me realising it. Even after 2 years of involvement with it, I regularly meet problems that frustrate me for hours, because they are hard to express in R's functional style. Even when I figure out the final solution, it often performs very poorly. In between these frustrations R behaves almost magically. It manipulates huge data sets with an ease and simplicity that defies logic. I've eventually come to the conclusion that an important aspect of R is knowing when NOT to use it. If your task is inherently stateful, involves random access to lots of sparse relational data that is associated in complex ways and with complicated logic - R is going to make your life hell. Write a quick script to pre-process your data and get things into more like a straight data table form and then proceed with R to analyse and visualize it. This is my experience, anyway!
grn 14y ago
Wouldn't it be better to have a Python statistics library instead of creating a new language? When I looked at the documentation of R it looked very convoluted and it seems that my feeling is confirmed by other comments in this thread.
- billwilliams 14y ago
  Python is inherently not conducive to data analysis the same way R is. Python is not a lisp. R is more lispy, and its functional attributes make it incredibly useful for producing data analysis based code. For fully production ready systems R is not the ideal choice. For off the cuff analysis, python is a meager second.
- shared4you 14y ago
  Of course, these days Pandas is _the_ Python library for statistics. There is also scikit-learn. http://pandas.pydata.org/ http://pandas.pydata.org/
  - grn 14y ago
    I haven't used R nor Pandas. How does the two compare? Why do people use R and not Python?
    - reyan 14y ago
      > Why do people use R and not Python? R style DataFrames was raison d'etre of Pandas in the first place. (http://pandas.pydata.org/#why-not-r http://pandas.pydata.org/#why-not-r)
    - maxerickson 14y ago
      People started using R about the same time they started using Python (that is, the first half of the 1990s). Since then, R has gained a huge number of statistics packages. Python has fewer of them.
mbq 14y ago
My personal observation (after 6 years of using and 3 years of teaching R) is that people misunderstand R by skipping the array paradigm part. If you don't get it, others' code looks like magic and either your code is slow and ugly or you seek rescue in "more-magic" wrappers like sqldf, plyr or data.table. It's a bit like JavaScript; if you treat JS like "Java script" or "C without types", you'll suffer. Exploit its true nature and it will flourish.
- shared4you 14y ago
  I disagree. I've used Matlab for 4 years and switched to Numpy/Scipy 2 years ago -- so I understand the array paradigm part. The problem with R is the inconsistency and verbosity in syntax and semantics of the array language underlying it -- as other comments have pointed out. Some differences in ease of use: [1] Construct a matrix MATLAB: y = [1 1; 2 2; 6 6]; R: y <- matrix(c(1, 2, 6, 1, 2, 6), 3) [2] Insert a new row r = [3 3]; MATLAB: x = [y; r]; R: x <- rbind(y, c(r)); Which is intuitive and concise? ;)
  - mbq 14y ago
    So you also think that mean(iris(iris(:,5)==2,2)) is more intuitive than mean(iris[iris$Species=="versicolor","Sepal.Width"])? Or that constructing matrix by mapping f on 1:10 like M=[] for not_i=1:10 M[:,not_i]=f(not_i) is more concise that sapply(1:10,f)? (I know there is arrayfun, but I have never seen it used except in wow-MATLAB-is-functional blog posts)
    - pseut 14y ago
      Ironically, your "iris" object can't be a matrix the way it's written. I've had lots of students get confused about the distinction between matrices and data frames. And sapply will sometimes give (IIRC) matrices of lists with a single element if f is constructed incorrectly (requiring an explicit 'unlist' in the return statement of f). So I generally agree with you, but I think you made shared4you's point.
      - 14y ago
pvaldes 14y ago
Is useful, and yes when something don't go in your code can be very frustrating... but not more than other languages. It is frustrating its own complex ways. The documentation, for example. Often very abstract. Not to forget, the dificulty of the subjacent statistics for a lot of people. Some good Books can help with this. The wow factor of R is very hight in any case. There is a lot of things currently that I can do only with R, thus... keep hammering _and_ take a look to as many R code as you can
sonabinu 14y ago
I've compiled all the resources from the comments here http://datagrad.blogspot.com/2013/02/some-learning-resources-for-r.html http://datagrad.blogspot.com/2013/02/some-learning-resources...

58 points by sonabinu 14y ago

dnc 14y ago

Maybe it's just me, but I find it difficult not to make analogies between R and JavaScript. Both are dynamic, "script" languages with C-based syntax and with LISP-like nature that lurks underneath it. Both have functions as first class citizens. Both are inconsistent in different ways. Just to name a few ... I think that these analogies reduced frustration that I had felt about R once when I "discovered" them and helped me to adopt and learn R.

andrewcooke 14y ago

for learning more about plotting, the gplot2 book is a tad expensive, but worthwhile. http://www.amazon.com/gp/product/0387981403 http://www.amazon.com/gp/product/0387981403 (it helped me understand the structure behind the library, which i found confusing from cookbooks etc)

jasonpbecker 14y ago
I have found this [site] (http://www.cookbook-r.com/ http://www.cookbook-r.com/) to be more helpful (plus the online documentation for ggplot2 which is really good). Hadley's book is really good and a solid treatment (from the author of so many clear, powerful package it's not a surprise), but there have been a lot of changes to ggplot2 since 2009. I have personally found ggplot2 to be so powerful because it lends itself very well to actually learning through using Cookbook-style examples.
- hadley 14y ago
  The cookbook is now an O'Reilly book: http://amzn.com/1449316956 http://amzn.com/1449316956

xijuan 14y ago

Taking the exact same course and going the same process of learning!

Pitarou 14y ago

This sounds like the experience of a beginning programmer learning pretty-much any popular scripting language. > I found that regular expressions ... are a great way to isolate the string that one is looking for... Well I never! I'm not criticising the guy who wrote this, but I don't know why this has been voted up on Hacker News.

capex 14y ago
This is a good place for 'And I am no man' from Lord of the Rings. The author appears to be a female, not a guy.
Schwolop 14y ago
To be honest, I found the entire coursera course on programming for data analytics to be in the same vein. So much time was spent explaining what each optional parameter to a function did that I skipped at least 50% of each lecture.
- aheilbut 14y ago
  A big problem with R is that much too much semantics gets buried in critical optional parameters to functions.
- bcbrown 14y ago
  Same here, I gave up. I'd rather read about the syntax. Save the lectures for concepts that are hard to get across in text. I thought part of the problem was that the course was geared towards grad students for whom this might be their first programming language, as opposed to programmer who already know a couple languages.
  - mogrim 14y ago
    Not convinced, the non-programmers on the discussion boards were having real problems... Certainly wasn't the best course I've done on Coursera, although it did "force" me to learn R, so in that sense it achieved its aim.
  - 14y ago

gklitt 14y ago

I'm currently learning R and was hoping for a more detailed blog post. I found this to be a pretty bland report that would have been made much more interesting and useful if the author had included some concrete examples.

mkhattab 14y ago

> Cleaning the data - This takes time and it can be annoying. Have you tried using Google Refine[1]? It's an excellent tool for cleaning datasets and a useful addition to your workflow. I was miffed I didn't know about it when I had to collect and analyze data from surveys awhile ago. I was using Python for cleanup (Google Refine supports Jython). [1]: http://code.google.com/p/google-refine/ http://code.google.com/p/google-refine/

jasonpbecker 14y ago
Google-refine does some really cool stuff, but one of the great developments in the last couple of years within the R community is a big push toward reproducible research and building a powerful toolchain to assist to that end. The problem with Google-Refine is that it is very hard to recreate precisely the same steps to get from raw to processed data.

banachtarski 14y ago

3 packages: plyr ggplot2 reshape Learn those 3 and use them for everything you do in R.

jasonpbecker 14y ago
plyr, while having awesome syntax, is really slow for anything beyond even the most modest dataset. Try using it with 50k rows, not to mention true "big data". data.table is much faster. It's an extension to data.frames that adds some additional constrains/rules that allows for much faster operations including aggregating, subsetting, and merging data. Cleaning data, however, does not have a steep learning curve or high difficulty level in R-- it has a steep learning curve and high difficulty level period. Implementing good procedures for data munging is 80% of the job.
- EzGraphs 14y ago
  Yeah - for the small data sets, Ruby or Python is often easier easier to use to whip a data set into a form that can be simply slurped into R as a dataframe.
  - mogrim 14y ago
    Has to be said I cheated a bit on the Coursera course, I used Ruby to clean up some of the data. Seemed a lot easier, and it's a lot more likely I'd use some kind of scripting language (be it Perl, Ruby, Grep, ...) to do a first clean up than head directly to R to do the job.
  - aheilbut 14y ago
    With pandas / statsmodels / patsy, it's getting easier to stick with python for everything.
    - GrumpySimon 14y ago
      Don't forget Bokeh[1] - a replacement for ggplot2 [1] https://github.com/ContinuumIO/Bokeh https://github.com/ContinuumIO/Bokeh

EzGraphs 14y ago

I spent about a year blogging about R as I learned it (http://www.r-chart.com/ http://www.r-chart.com/). It is not a general purpose sort of language but is great for math / charting tasks. A couple of ways that software developer types can minimize time to get up and running: 1) Check out http://www.r-bloggers.com/ http://www.r-bloggers.com/. The site gives you a good sense of the state of R and Tal (who runs the site) has done a great job of promoting R and encouraging the community. 2) Pick one graphics package and stick with it. The standard functions are sufficient but ggplot2 has a more finished appearance. 3) Review available libraries. If you want to do something, someone else likely already has and has posted a package to CRAN. 4) One way for SQL developers to limit the need of learning the idiosyncrasies of the language is to use the sqldf package to manipulate data frames and use ggplot2 (which generally takes data frames as arguments) to display charts.

abraxasz 14y ago

I've had to learn R during my master (in statistics), and found the process very unpleasant. There are different ways in which a language can be difficult to learn. Haskell has a steep learning curve because it has a lot of unusual concepts (for those who come from an imperative background) and requires a shift in the way we think about code. J is also difficult to learn because the syntax is crazy, and again, the language is very different from what we're used to. I found R difficult to learn because it is seems inconsistent to me. Now one reason might be that I picked up bits of R here and there without taking the time to learn the syntax from a book (unlike what I did for other languages), but it took me quite some time to be able to make sense of the different structures (vector, list, matrix, dataframe), their differences, and in particular how functions operate differently on these structures. I also have a deep hatred for the system of attributes (why would anyone want to give attributes to a vector...), and find the indexing system (especially for lists) to be nonsensical. In fact, I think that lists themselves are terrible to work with. The general impression that I've had learning R is the language is not coherent and systematic in its design in the way other languages are (I know python, c, common lisp, ...), and I find myself spending a lot of time in the interpreter simply trying out things because I'm not sure that it will return what I want, in the format that I want (which never happens to me in any other language). Now don't get me wrong, I use R daily and it's a useful tool. It has lots of great libraries, including the fantastic ggplot2, and the remarkable Rcpp (best C++ interface I've ever seen.. ok I haven't seen any other, but this one is really great). But learning it was no fun, and if the statistics community decided to move to a cleaner language, I'd definitely be running ahead..

EzGraphs 14y ago
In R, I often can write a dozen line program that will do something incredible, but it might take an hour to hammer out those lines and cannot reconstruct them from memory because of inconsistencies in syntax. It sort of feels like a statistics DSL built on Lisp.
- pseut 14y ago
  R is exactly like a statistics DSL built on Lisp. The first sentences of a talk by Ihaka [1] are: > R began as an experiment in trying to use the methods of Lisp implementors to build a small testbed which could be used to trial some ideas on how a statistical environment might be built. Early on, the decision was made to use an S-like syntax. Once that decision was made, the move toward being more and more like S has been irresistible. Since I find it basically impossible to remember how 'eval', 'quote', 'substitute', etc. work in R, I suspect that the Lispers are onto something when they say that the lack of syntax in Lisp is an important feature. [1] http://www.stat.auckland.ac.nz/%7Eihaka/downloads/Interface98.pdf http://www.stat.auckland.ac.nz/%7Eihaka/downloads/Interface9...

wiradikusuma 14y ago

Don't forget the free http://tryr.codeschool.com/ http://tryr.codeschool.com/ from Code School and O'Reilly.

capkutay 14y ago

I don't know why a programmer would limit them self with static data tools when d3 and processing provide beautiful, dynamic, and interactive solutions with a similar learning curve. The only difference is you can use the JavaScript chops you pick up learning d3 in any front end work you do.

hogu 14y ago
people use R for much more than data visualization, doing regressions, fitting SVMs, decisions trees, GARCH and ARMA time series models, isn't really trivial in javascript. also ggplot, while more limited is way easier and faster than d3 for doing the specific things that ggplot is good at. But for statistics, that's usually what you want
pseut 14y ago
Sounds great! What's the MCMC package for javascript?
- billwilliams 14y ago
  https://npmjs.org/package/mcmc https://npmjs.org/package/mcmc
- disgruntledphd2 14y ago
  I worked with a computer scientist once, and he tested out lots of languages for replacing his VB setup (I know, I was surprised too). JS turned out to be the fastest language for a lot of his tests. If the libraries ever get there, it will be lethal. Presumably there might be a way to compile java libraries to JS using Rhino, but its not something I've ever investigated.

RockyMcNuts 14y ago

A few resources that might speed the learning curve Survival guide http://www.win-vector.com/blog/2009/09/survive-r/ http://www.win-vector.com/blog/2009/09/survive-r/ Tutorials http://www.statmethods.net/index.html http://www.statmethods.net/index.html http://heather.cs.ucdavis.edu/~matloff/r.old.html http://heather.cs.ucdavis.edu/~matloff/r.old.html http://www.twotorials.com/ http://www.twotorials.com/ http://en.wikibooks.org/wiki/R_Programming http://en.wikibooks.org/wiki/R_Programming http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf Docs http://cran.r-project.org/manuals.html http://cran.r-project.org/manuals.html StackOverflow http://stackoverflow.com/questions/tagged/r?sort=votes&pagesize=15 http://stackoverflow.com/questions/tagged/r?sort=votes&p... Cheat sheeets http://cran.r-project.org/doc/contrib/Short-refcard.pdf http://cran.r-project.org/doc/contrib/Short-refcard.pdf http://bit.ly/VHPA3G http://bit.ly/VHPA3G R Journal http://journal.r-project.org/ http://journal.r-project.org/ R News (predecessor to R Journal) http://cran.r-project.org/doc/Rnews/ http://cran.r-project.org/doc/Rnews/ Rseek search engine http://rseek.org/ http://rseek.org/ R could use something like the Python ecosystem intro, explaining where to find stuff, not sure if any of the tutorials are as canonical as for instance Dive Into Python or Learn Python The Hard Way.

aheilbut 14y ago
one more: http://www.burns-stat.com/pages/Tutor/R_inferno.pdf http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
pseut 14y ago
> R could use something like the Python ecosystem intro, explaining where to find stuff, not sure if any of the tutorials are as canonical as for instance Dive Into Python or Learn Python The Hard Way. It only became popular recently; the expectation used to be that you'd learn R while enrolled in a statistics class. The canonical references are books: Modern Applied Statistics with S, R Graphics, etc. I assume that will change soon.
- RockyMcNuts 14y ago
  Would be great if the CRAN package repositories had ratings and download counts. It can be hard to find the 'right way' to do something.

billwilliams 14y ago

R is my mistress and my wife. Hadley Wickham held the ceremony. Data.Table is the glue that holds together our tenuous marriage. cats[color="brown",summary(tooth_length),by=list(breed,age)]. This line of code will efficiently grab all the brown cats in your data and summarize their tooth length by mean, median and other quartiles then break it out by the cats breed and age. Every time I use a language I can't lapply I despise the language and all those involved. R is hugely inconsistent and terribly inefficient. But its also the most irresistible blend of lispy and object oriented for when you actually need to get data analysis done.

zmmmmm 14y ago

R is probably the most frustrating language I've ever had to learn. For me, R doesn't so much have a learning "curve" as a learning "gradient". After a long, long time the rate of frustrations is not really leveling off much. Each time I have to negotiate a new library or module that I'm not familiar with I'm guaranteed hours of frustration as I try to grapple with what all the data types are, how they are subtly overridden to behave differently from the normal data types they inherit from, what things are going to be magically coerced into what other things without me realising it. Even after 2 years of involvement with it, I regularly meet problems that frustrate me for hours, because they are hard to express in R's functional style. Even when I figure out the final solution, it often performs very poorly. In between these frustrations R behaves almost magically. It manipulates huge data sets with an ease and simplicity that defies logic. I've eventually come to the conclusion that an important aspect of R is knowing when NOT to use it. If your task is inherently stateful, involves random access to lots of sparse relational data that is associated in complex ways and with complicated logic - R is going to make your life hell. Write a quick script to pre-process your data and get things into more like a straight data table form and then proceed with R to analyse and visualize it. This is my experience, anyway!

grn 14y ago

Wouldn't it be better to have a Python statistics library instead of creating a new language? When I looked at the documentation of R it looked very convoluted and it seems that my feeling is confirmed by other comments in this thread.

billwilliams 14y ago
Python is inherently not conducive to data analysis the same way R is. Python is not a lisp. R is more lispy, and its functional attributes make it incredibly useful for producing data analysis based code. For fully production ready systems R is not the ideal choice. For off the cuff analysis, python is a meager second.
shared4you 14y ago
Of course, these days Pandas is _the_ Python library for statistics. There is also scikit-learn. http://pandas.pydata.org/ http://pandas.pydata.org/
- grn 14y ago
  I haven't used R nor Pandas. How does the two compare? Why do people use R and not Python?
  - reyan 14y ago
    > Why do people use R and not Python? R style DataFrames was raison d'etre of Pandas in the first place. (http://pandas.pydata.org/#why-not-r http://pandas.pydata.org/#why-not-r)
  - maxerickson 14y ago
    People started using R about the same time they started using Python (that is, the first half of the 1990s). Since then, R has gained a huge number of statistics packages. Python has fewer of them.

mbq 14y ago

My personal observation (after 6 years of using and 3 years of teaching R) is that people misunderstand R by skipping the array paradigm part. If you don't get it, others' code looks like magic and either your code is slow and ugly or you seek rescue in "more-magic" wrappers like sqldf, plyr or data.table. It's a bit like JavaScript; if you treat JS like "Java script" or "C without types", you'll suffer. Exploit its true nature and it will flourish.

shared4you 14y ago
I disagree. I've used Matlab for 4 years and switched to Numpy/Scipy 2 years ago -- so I understand the array paradigm part. The problem with R is the inconsistency and verbosity in syntax and semantics of the array language underlying it -- as other comments have pointed out. Some differences in ease of use: [1] Construct a matrix MATLAB: y = [1 1; 2 2; 6 6]; R: y <- matrix(c(1, 2, 6, 1, 2, 6), 3) [2] Insert a new row r = [3 3]; MATLAB: x = [y; r]; R: x <- rbind(y, c(r)); Which is intuitive and concise? ;)
- mbq 14y ago
  So you also think that mean(iris(iris(:,5)==2,2)) is more intuitive than mean(iris[iris$Species=="versicolor","Sepal.Width"])? Or that constructing matrix by mapping f on 1:10 like M=[] for not_i=1:10 M[:,not_i]=f(not_i) is more concise that sapply(1:10,f)? (I know there is arrayfun, but I have never seen it used except in wow-MATLAB-is-functional blog posts)
  - pseut 14y ago
    Ironically, your "iris" object can't be a matrix the way it's written. I've had lots of students get confused about the distinction between matrices and data frames. And sapply will sometimes give (IIRC) matrices of lists with a single element if f is constructed incorrectly (requiring an explicit 'unlist' in the return statement of f). So I generally agree with you, but I think you made shared4you's point.
    - 14y ago

pvaldes 14y ago

Is useful, and yes when something don't go in your code can be very frustrating... but not more than other languages. It is frustrating its own complex ways. The documentation, for example. Often very abstract. Not to forget, the dificulty of the subjacent statistics for a lot of people. Some good Books can help with this. The wow factor of R is very hight in any case. There is a lot of things currently that I can do only with R, thus... keep hammering _and_ take a look to as many R code as you can

sonabinu 14y ago

I've compiled all the resources from the comments here http://datagrad.blogspot.com/2013/02/some-learning-resources-for-r.html http://datagrad.blogspot.com/2013/02/some-learning-resources...