Dear friends - updated to R 3.3.2 - tried to install nls - got this sad
response
package ‘nls’ is not available (as a binary package for R version 3.3.2)
I'm on windows 7
Did I do something wrong? Will a binary appear eventually? Would I have
to make it myself?
Best wishes
Troels Ring
Aal
> On 29 Nov 2016, at 09:30, Troels Ring wrote:
>
> Dear friends - updated to R 3.3.2 - tried to install nls - got this sad
> response
>
> package ‘nls’ is not available (as a binary package for R version 3.3.2)
>
> I'm on windows 7
>
> Did I do something wrong? Will a binary appear eventuall
> On Nov 29, 2016, at 12:30 AM, Troels Ring wrote:
>
> Dear friends - updated to R 3.3.2 - tried to install nls - got this sad
> response
>
> package ‘nls’ is not available (as a binary package for R version 3.3.2)
>
> I'm on windows 7
I don't see an `nls` package on CRAN. Perhaps it has bee
Hi
probably not at all simpler
> dat2.p<-split(t(dat), rep(1:(ncol(dat)/4), each=4))
> dat3.p<-as.data.frame(do.call(rbind, lapply(dat2.p, function(x) t(matrix(x,
> 4,nrow(dat))
>
> all.equal(dat3.p, dat3)
[1] TRUE
Cheers
Petr
> -Original Message-
> From: R-help [mailto:r-help-bou
Presumably a problem at your end. Suspicion points to permission settings on
the target directory, or virus checkers "helpfully" moving recently created
files to a safe place for scrutiny.
(Do you _really_ get errors for "survival" when installing "Hmisc"? It could
happen via a dependency, I su
On 11/29/2016 05:00 AM, r-help-requ...@r-project.org wrote:
Independent censoring is one of the fundamental assumptions in the survival
analysis. However, I cannot find any test for it or any paper which discusses
how real that assumption is.
I would be grateful if anybody could point me to
You may also want to use tools that are more robust.
Package nlmrt uses analytic Jacobian where possible and a Marquardt solver.
Package minpack.lm uses a Marquardt solver, but the forward difference
derivatives of
nls() for its Jacobian.
alpha level work in https://r-forge.r-project.org/R/?gro
Greetings!
In an SQL table, I have a column that contains a JSON. I'd like easy
access to all (in an ideal world) of these JSON fields. I started out
trying to get all fields from the JSON and so I wrote this function.
unfold.json <- function (df, column)
{
library(jsonlite)
ret <- data
Two quick hints:
* use simplifyDataFrame = FALSE in fromJSON()
* read
https://jennybc.github.io/purrr-tutorial/ls02_map-extraction-advanced.html
(and https://jennybc.github.io/purrr-tutorial/)
Hadley
On Tue, Nov 29, 2016 at 8:06 AM, Daniel Bastos wrote:
> Greetings!
>
> In an SQL table, I hav
Hello,
I'm struggling with an unexpected interference between the two packages
dplyr and plm, or to be more concrete with the "lag(x, ...)" function of
both packages.
If dplyr is in the namespace the plm function uses no longer the
appropriate lag()-function which accounts for the panel stru
Hi,
It shouldn't be entirely unexpected: when I load dplyr, I get a series
of messages telling me that certain functions are masked.
The following object is masked from ‘package:plm’:
between
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are
Hi,
I am trying to transpose large datasets inexcel (44 columns and 57774 rows) but
it keeps giving me the message we can'tpaste because copy area and paste area
aren't the same size. Is there a way totranspose all the data at one time
instead of piece by piece? One dataset has agreat amount of
> On Nov 29, 2016, at 6:52 AM, Sarah Goslee wrote:
>
> Hi,
>
> It shouldn't be entirely unexpected: when I load dplyr, I get a series
> of messages telling me that certain functions are masked.
>
>
> The following object is masked from ‘package:plm’:
>
>between
>
> The following objects
> On Nov 29, 2016, at 9:22 AM, Elham - via R-help wrote:
>
> Hi,
>
> I am trying to transpose large datasets inexcel (44 columns and 57774 rows)
> but it keeps giving me the message we can'tpaste because copy area and paste
> area aren't the same size. Is there a way totranspose all the data
>The other option would be to load dplyr first (which would give the waring
that >stats::lag was masked) and then later load plm (which should give a
further >warning that dplyr::lag is masked). Then the plm::lag function
will be found
>first.
Another option is to write the package maintainers and
yes you have right about excel.by R,what should I do for transposing row and
column?
On Tuesday, November 29, 2016 9:13 PM, David Winsemius
wrote:
> On Nov 29, 2016, at 9:22 AM, Elham - via R-help wrote:
>
> Hi,
>
> I am trying to transpose large datasets inexcel (44 columns and 57
It's 'stringsAsFactors' = FALSE (without my added quotes) with an 's'
at the end of 'strings' .
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Tue, Nov
Try David's suggestion to spell the argument "stringsAsFactors" correctly.
Then:
data <- read.table("your_file_location", sep ="\t", comment.char = "",
stringsAsFactors = F, header = T)
transpose_data <- t(data)
-Dan
On Tue, Nov 29, 2016 at 9:56 AM, Elham - via R-help
wrote:
> yes you have rig
thank you all,it worked
On Tuesday, November 29, 2016 9:49 PM, "Dalthorp, Daniel"
wrote:
Try David's suggestion to spell the argument "stringsAsFactors" correctly.
Then:
data <- read.table("your_file_location", sep ="\t", comment.char = "",
stringsAsFactors = F, header = T)
transpose
It is probably worth mentioning that this (i.e. transposing a data
frame) can be a potentially disastrous thing to do in R, though the
explanation is probably more than you want to know at this point (see
?t and follow the 'as.matrix' link for details). But if you start
getting weird results and
On Tue, Nov 29, 2016 at 11:52 AM, William Dunlap wrote:
>>The other option would be to load dplyr first (which would give the waring
>> that >stats::lag was masked) and then later load plm (which should give a
>> further >warning that dplyr::lag is masked). Then the plm::lag function will
>> be fo
On Tue, Nov 29, 2016 at 12:39 PM, David Winsemius
wrote:
>
>
> The other option would be to load dplyr first (which would give the waring
> that stats::lag was masked) and then later load plm (which should give a
> further warning that dplyr::lag is masked). Then the plm::lag function will
> be
No, no. It *is* for transposing. But it is *what* you are transposing
-- a data frame -- that may lead to the problems. You will have to
read what I referred you to and perhaps spend time with an R tutorial
or two (there are many good ones on the web) if your R learning is not
yet sufficient to und
Hi,
To provide a [very] small example of what Bert is referring to:
DF <- data.frame(Letters = letters[1:4], int = 1:4)
> str(DF)
'data.frame': 4 obs. of 2 variables:
$ Letters: Factor w/ 4 levels "a","b","c","d": 1 2 3 4
$ int: int 1 2 3 4
> DF
Letters int
1 a 1
2 b
Dear all,
I am using the Gridded Population of the World (v4) for the year 2010. The
data is in GeoTiFF format.
Source:
http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals/data-download
I imported the data using:
library(raster)
library(maptoo
Hello,
I haven't downloaded the data, but a mock-up of your steps below does as you
ask. You can see the resolution of y is 1 x 1 and each is filled with the sum
of 120 x 120 original cells each of which had a value of 1.
In this case, the raster package faithfully interprets the fractional deg
Hello All,
I have a dataframe of about 1.5 million rows from this dataframe I need to
filter out identifiers. An example would be 07-07099, AD-AD0999, and
AL-AL, FN-FN. I am using grepl to identify those of interest
as follows:
grepl("^[FN]|[AD]{2}", Identifier)
Th
Hello everyone,
I have generated a dendrogram by applying a hierarchical clustering
technique to a graph. Given this dendrogram I am trying to efficiently
find/ map/ label the dendrogram branches to their corresponding graph
edges. Using dendextend I am able to partition the leaves, obtain
subgrap
> On Nov 29, 2016, at 11:26 AM, Hadley Wickham wrote:
>
> On Tue, Nov 29, 2016 at 11:52 AM, William Dunlap wrote:
>>> The other option would be to load dplyr first (which would give the waring
>>> that >stats::lag was masked) and then later load plm (which should give a
>>> further >warning tha
> On Nov 29, 2016, at 4:09 PM, David Winsemius wrote:
>
>
>> On Nov 29, 2016, at 11:26 AM, Hadley Wickham wrote:
>>
>> On Tue, Nov 29, 2016 at 11:52 AM, William Dunlap wrote:
The other option would be to load dplyr first (which would give the waring
that >stats::lag was masked) and
That is not a very selective regex.
Actually, a long "or" probably is best, but you don't have to type it in
directly.
prefixes <- c( "AD", "FN" )
pat <- paste0( "^(", paste( prefixes, collapse="|" ), ")[0-9]{4}$" )
grepl( pat, Identifier )
--
Sent from my phone. Please excuse my brevity.
On
Hi all,
In one folder I have several files and I want
combine/concatenate(rbind) based on some condition .
Here is the sample of the files in one folder
test.csv
test123.csv
test456.csv
Adat.csv
Adat123.csv
Adat456.csv
I want to create 2 files as follows
test_all = rbind(
Something like this:
filelist <- list.files(pattern="^test")
myfiles <- lapply(filelist, read.csv)
myfiles <- do.call(rbind, myfiles)
On Tue, Nov 29, 2016 at 9:11 PM, Val wrote:
> Hi all,
>
> In one folder I have several files and I want
> combine/concatenate(rbind) based on some condition
Thank you Sarah,
Some of the files are not csv but some are *txt with space delimited.
Bdat.txt
Bdat123.txt
Bdat456.txt
How do I do that?
On Tue, Nov 29, 2016 at 8:28 PM, Sarah Goslee wrote:
> Something like this:
>
> filelist <- list.files(pattern="^test")
> myfiles <- lapply(filel
Presumably using read.table instead of read.csv.
Get the import working with one sample file first, then do whatever you
had to do with one file over and over.
You still need to go read up on regex patterns... to get you started the
pattern for matching the csv files would be something like
Hello R Users,
I am trying to use the svyolr command and coming up with the following error:
Error in MASS::polr(formula, data = df, ..., Hess = TRUE, model = FALSE, :
attempt to find suitable starting values failed
>From what I have read online, a possible solution is to specify a value in th
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