A somewhat obnoxious approach that nevertheless holds some truth:
If R-users are trashing all other software and aggrandizing R, tell them:
If you do awesome statistical work using R, it is not necessarily because
you are using R. It is because you are an awesome statistician! :)
On Wednesday,
I think there's really no need to do anything to annoy the R community. Those
who will be interested in Julia will come over time; many won't because Julia
won't have anything to offer them.
-- John
On May 28, 2014, at 9:33 AM, Ted Fujimoto tftur...@gmail.com wrote:
A somewhat obnoxious
I am sympathetic to the need of being able to delete vertices or edges from
a graph. However, Graphs.jl (and many other packages) is still very young,
and it takes some time to provide a complete set of functionalities
(especially when one have to make a tradeoff between efficiency,
If an arrant beginner can chime in with some non-technical opinion, permit
me to do so here just this once--
The idea of R vs Julia is, if I may say so, not the way forward. These
languages can and should help each other win. R, as of now, *needs* a speed
language, as we all know; an effective
I know that people really hate this approach, but can't you just write files to
disk and then do I/O to get data from Julia into R? I've done a lot of that
over the last two years.
-- John
On May 21, 2014, at 12:18 PM, Travis Porco see.the.gal...@gmail.com wrote:
If an arrant beginner can
Thanks Prof. Bates and Stefan. I have made myself more familiar with Julia
because I want to use Octave and R less. However, even though some of the
Julia resistance from the R community is not tenable, it seems a bit unfair
to ask R users to shift their work to Julia at this point. For
That's a very reasonable position and I tend to agree that it should be
possible even if not cheap to delete edges. That issue, however, is quite
specific to Graphs.jl, not Julia as a whole. You should open an issue
requesting the feature on the Graphs.jl GitHub repository and see what comes of
On Sunday, May 11, 2014 12:55:47 PM UTC-5, Stefan Karpinski wrote:
I find this kind of amusing:
You may have heard before that R is a vectorized language, but what do we
mean by that? One way to read that is to say that many functions in R can
operate efficiently on vectors (in addition to
Sigh. Still haven't learned to proofread *before* hitting send.
On Monday, May 12, 2014 9:20:13 AM UTC-5, Douglas Bates wrote:
I must admit that was my reaction too when I saw those posts. If I wanted
to come up with an example of a problem showing why forced vectorization (R
does not
Two corrections to a single posting. A sure sign of Monday morning.
On Monday, May 12, 2014 9:20:13 AM UTC-5, Douglas Bates wrote:
I think there is a possible enhancement in that once you know c[n] you can
fill in c[n]*2^k until that product is m.
And what I meant was c[n*2^k]?
A couple
I tried out the c[nk] optimization and it doesn't produce a speedup,
which kind of makes sense – either way, you're computing each collatz value
exactly once, and sharing that work, so whether you fill in n*2^k now or
later doesn't really matter since you'll get to it eventually and once done
it's
That unfortunately also doesn't seem to speed things up either.
On Mon, May 12, 2014 at 12:07 PM, Steven G. Johnson
stevenj@gmail.comwrote:
On Sunday, May 11, 2014 1:55:47 PM UTC-4, Stefan Karpinski wrote:
while nʹ length(c) || c[nʹ] 0
nʹ = iseven(nʹ) ? nʹ1 :
On 5/12/14, 10:03, Stefan Karpinski wrote:
I rather like using the prime symbol in names for this kind of thing –
Jeff introduced me to it – but I can see why it might be confusing.
Wow, very confusing. Using n_ or something like that seems to be less
confusing.
I'm curious: how did you
Special characters menu on OS X. And cut-and-paste once I've already got
one.
On Mon, May 12, 2014 at 12:39 PM, Jason Grout
jason-s...@creativetrax.comwrote:
On 5/12/14, 10:03, Stefan Karpinski wrote:
I rather like using the prime symbol in names for this kind of thing –
Jeff introduced me
I find this kind of amusing:
You may have heard before that R is a vectorized language, but what do we
mean by that? One way to read that is to say that many functions in R can
operate efficiently on vectors (in addition to singletons).
That's certainly one way to spin it. Following it up
Besides the performance, type system and multiple dispatch the Julia
language also has several nice conveniences.
1. List comprehension which makes it easy to construct vectors and matrices
of various kinds:
julia [ sqrt(exp(i))-j for i = 1:8, j = 1:8]
8x8 Array{Float64,2}:
0.648721
On Friday, May 9, 2014 4:45:00 PM UTC-5, Ted Fujimoto wrote:
Hi all,
In the first few minutes of this video (
https://www.youtube.com/watch?v=v9Io-p_iymI) Prof. Doug Bates skims
through why one should invest in Julia over R. However, I felt it wasn't
detailed enough. Why should
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