Hi Hadley,
actually, I started with floating point numbers, ensured that the
respective numbers are equal in R but I still got strange behaviour with
dplyr's group_by:
https://github.com/hadley/dplyr/issues/482
If I had to guess, I would suppose the source of this error somewhere in
the C++
However,
format((0.1+0.2)) == format(0.3)
[1] TRUE
Which suggests that if you want to treat measured variables as categories,
one way to do it is to format them first.
Of course, one may have to control the format more carefully than above
(if necessary, see for example ?formatC).
merge() on
Hi,
I would like to ask for best practice advice on the design of data
structure and the connected analysis techniques.
In my particular case, I have measurements of several variables at
several, sometimes equal, heights. Following the tidy data approach of
Hadley Wickham, I want to put all data
Hi
I would keep height as numeric and created height.f as factor, maybe ordered.
hh-runif(50)
hh
[1] 0.116060220 0.447546370 0.433749570 0.006548963 0.425710667 0.328972894
[7] 0.091274539 0.271797166 0.007669982 0.208922146 0.168174196 0.227466231
...
hh.f-cut(hh, seq(0,1,.1))
hh.f
[1]
Why not just round the floating point numbers to ensure they're equal
with zapsmall, round or signif?
Hadley
On Fri, Jul 4, 2014 at 4:04 AM, Sebastian Schubert
schubert@gmail.com wrote:
Hi,
I would like to ask for best practice advice on the design of data
structure and the connected
Keep as numeric and group with cut(), Hmisc::cut2, or findInterval. The beauty
of the functional language design is that you do not need to create a new
factor variable.
--
David
Sent from my iPhone
On Jul 4, 2014, at 8:33 AM, Hadley Wickham h.wick...@gmail.com wrote:
Why not just round
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