Hi Patrick
does this help?
dat <-
data.frame(x = rnorm(12*5,0,1),
y = rnorm(12*5,0,1),
gp = factor(1:12))
dat$NS = ifelse(sapply(dat$gp, pmatch,flN, nomatch = 0) > 0,
"Needles","Stems")
dat = dat[order(dat[,"NS"]),]
dat$GP = factor(1:6)
xyplot(y ~ x|gp, data
It's a vector of **length** 1, not **value** 1. In your case it gives
the index (1 to 12) of the level being drawn in the panel, which is
used to draw the strip according to other strip parameters, esp.
style.
You seem to be making this way more difficult than you should.
strip.default is the **fu
I’m inexperience but am trying to get my head around using functions to make a
number of ggplots easier to do.
I have a function that creates a ggplot taking one input variable as an
argument. The variable name is shorthand for the actual variable (variable name
= tue, Actual name = Tuesday).
Sarah, you make it sound as though everyone should be using matrices, even
though they have distinct disadvantages for many types of analysis.
You are right that rbind on data frames is slow, but dplyr::bind_rows
handles data frames almost as fast as your rbind-ing matrices solution.
And if y
I'm having difficulty following the help for those functions.
My plot has a single conditioning factor with 12 levels. My
factor.levels in a call to strip.default looks like this:
factor.levels = expression(Needles~ "::"~alpha -pinene,
Stems~ "::"~alpha -pinene,
On Mon, Jun 27, 2016 at 5:42 PM, Greg Snow <538...@gmail.com> wrote:
> You can use the grconvertX and grconvertY functions to find the
> coordinates (in user coordinates to pass to rect) of the figure region
> (or other regions).
>
> Probably something like:
> grconvertX(c(0,1), from='nfc', to='use
I want to call the subroutine init_random_seed() in R. The subroutine is
defined as an example in the following link.
https://gcc.gnu.org/onlinedocs/gfortran/RANDOM_005fSEED.html
subroutine init_random_seed()
use iso_fortran_env, only: int64
implicit none
integer, allocata
You can use the grconvertX and grconvertY functions to find the
coordinates (in user coordinates to pass to rect) of the figure region
(or other regions).
Probably something like:
grconvertX(c(0,1), from='nfc', to='user')
grconvertY(c(0,1), from='nfc', to='user')
On Fri, Jun 24, 2016 at 8:19 P
Hi,
Note that if your list of 200k data frames is the result of splitting
a big data frame, then trying to rbind the result of the split is
equivalent to reordering the orginal big data frame. More precisely,
do.call(rbind, unname(split(df, f)))
is equivalent to
df[order(f), , drop=FALSE]
That's not what I said, though, and it's not necessarily true. Growing
an object within a loop _is_ a slow process, but that's not the
problem here. The problem is using data frames instead of matrices.
The need to manage column classes is very costly. Converting to
matrices will almost always be e
Hi,
Just to add my tuppence, which might not even be worth that these days...
I found the following blog post from 2013, which is likely dated to some
extent, but provided some benchmarks for a few methods:
http://rcrastinate.blogspot.com/2013/05/the-rbinding-race-for-vs-docall-vs.html
Ther
Your description of the data frames as "approx" puts the solution to
considerable difficulty and speed penalty. If you want better performance you
need a better handle on the data you are working with.
For example, if you knew that every data frame had exactly three columns named
identically a
Hi Bert,
You are most likely right. I just thought that do.call("rbind", is
somehow more clever and allocates the memory up front. My error. After
more searching I did find rbind.fill from plyr which seems to do the
job (it computes the size of the result data.frame and allocates it
first).
best
There is a substantial overhead in rbind.dataframe() because of the
need to check the column types. Converting to matrix makes a huge
difference in speed, but be careful of type coercion.
testdf <- data.frame(matrix(runif(300), nrow=100, ncol=3))
testdf.list <- lapply(1:1, function(x)testdf)
The following might be nonsense, as I have no understanding of R
internals; but
"Growing" structures in R by iteratively adding new pieces is often
warned to be inefficient when the number of iterations is large, and
your rbind() invocation might fall under this rubric. If so, you might
try
Are accessors a fancy feature that do not work?
I wanted to use accessor functions in a R refclass to hide the classes
implementation where I am using sqlite.
What I did observe is, that if I access in a method any of the fields
(in the example below field .data in method printExample) all the
ac
I have a list (variable name data.list) with approx 200k data.frames
with dim(data.frame) approx 100x3.
a call
data <-do.call("rbind", data.list)
does not complete - run time is prohibitive (I killed the rsession
after 5 minutes).
I would think that merging data.frame's is a common operation. I
"Assign ... key to a value" defies my understanding of those terms, and
includes no context (API is a very vague term). We are not (necessarily)
subject area experts in your preferred domain of jargon.
Doing things when you start up your session is typically done as described in
?Startup
--
S
Please use Reply-All to keep the mailing list included in the conversation. I
don't do private consulting via the Internet, and others can correct me if I
give bad advice.
I doubt the maintaner function "doesn't work"... more likely you did not read
the help file to learn how to use it:
?main
All,
Is there a way to assign an API key to a value FREDAPI which is loaded and
available once a R session is has started?
Glenn
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https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do
Hi
On top of what Duncan wrote you can check results yourself
> str(iris[,"Sepal.Length"])
num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
here you get vector and data.frame class is lost. The result is same as
> str(iris$Sepal.Length)
num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
How
On 27/06/2016 3:52 AM, Christian Hoffmann wrote:
Since the change to R-3.2.1 I seem to be unable to compile and install
my package cwhmisc. One evidence is the appearance of th messages in R
CMD build and install:
* installing *source* package 'cwhmisc' ...
** R
** inst
** preparing package for
Since the change to R-3.2.1 I seem to be unable to compile and install
my package cwhmisc. One evidence is the appearance of th messages in R
CMD build and install:
* installing *source* package 'cwhmisc' ...
** R
** inst
** preparing package for lazy loading
Error in eval(expr, envir, enclos)
Hi All,
Petr, Bert, David, Ivan, Duncan and Rui helped me to develop a function
able to replace NA's in variables IF NEEDED:
#---
# Module: t_replace_na.R
# Author: Georg Maubach
# Date : 2016-06
On 27/06/2016 7:43 AM, g.maub...@weinwolf.de wrote:
Hi Petr,
many thanks for your reply and the examples.
My subscripting problems drive me nuts.
I have understood that dataset[variable] is semantically identical to
dataset[, variable] cause dataset[variable] takes all cases because no
other s
Hi Petr,
many thanks for your reply and the examples.
My subscripting problems drive me nuts.
I have understood that dataset[variable] is semantically identical to
dataset[, variable] cause dataset[variable] takes all cases because no
other subscripts are given.
Where can I lookup the rules w
Hi
see in line
> -Original Message-
> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of
> g.maub...@weinwolf.de
> Sent: Monday, June 27, 2016 10:45 AM
> To: David L Carlson ; Bert Gunter
>
> Cc: r-help@r-project.org
> Subject: [R] Antwort: Fw: Re: Subscripting problem with
Hi David,
Hi Bert,
many thanks for the valuable discussion on NA in R (please see extract
below). I follow your arguments leaving NA as they are for most of the
time. In special occasions however I want to replace the NA with another
value. To preserve the newly acquired knowledge for me I wrot
Hi
have you read something of these?
http://www.r-bloggers.com/five-ways-to-handle-big-data-in-r/
https://en.wikipedia.org/wiki/Programming_with_Big_Data_in_R
http://r-pbd.org/
http://www.columbia.edu/~sjm2186/EPIC_R/EPIC_R_BigData.pdf
I am not an expert in big data, however when reading your da
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