> On 15 Sep 2017, at 11:38, yadav neog wrote:
>
> hello to all. I am working on macroeconomic data series of India, which in
> a yearly basis. I am unable to convert my data frame into time series.
> kindly help me.
> also using zoo and xts packages. but they take only monthly observations.
>
>
This list is about R programming, not statistics, although they do often
intersect. Nevertheless, this discussion seems to be all about the latter,
not the former, so I think you would do better bringing it to a statistics
list like stats.stackexchange.com rather than here.
Cheers,
Bert
Bert Gu
thanks, eric../ actually I have the data which have not specify the months.
therefore i bound to declare is in yearly data. i also attached a sample
data set that may be helpful for you to providing suggestions. thank you
On Fri, Sep 15, 2017 at 5:23 PM, Ismail SEZEN wrote:
>
> > On 15 Sep 2017,
First, see the example at https://isezen.github.io/PCA/
> On 15 Sep 2017, at 13:43, Shylashree U.R wrote:
>
> Dear Sir/Madam,
>
> I am trying to do PCA analysis with "iris" dataset and trying to interpret
> the result. Dataset contains 150 obs of 5 variables
>
>Sepal.Length Sepal.Width P
Try this:
# supplied data
library(zoo) # need the 'na.locf' function
x <- structure(list(ScanDate = structure(c(16433, 16440, 16447, 16454,
16461, 16468, 16475, 16482,
16489, 16496, 16503, 16510, 16517,
> On 15 Sep 2017, at 16:35, Berend Hasselman wrote:
>
>>
>> On 15 Sep 2017, at 11:38, yadav neog wrote:
>>
>> hello to all. I am working on macroeconomic data series of India, which in
>> a yearly basis. I am unable to convert my data frame into time series.
>> kindly help me.
>> also using z
> On 15 Sep 2017, at 11:38, yadav neog wrote:
>
> hello to all. I am working on macroeconomic data series of India, which in
> a yearly basis. I am unable to convert my data frame into time series.
> kindly help me.
> also using zoo and xts packages. but they take only monthly observations.
>
>
On 09/15/2017 08:57 AM, Michael Dewey wrote:
In line
On 15/09/2017 13:30, Martin Møller Skarbiniks Pedersen wrote:
On 15 September 2017 at 14:13, Duncan Murdoch
wrote:
On 15/09/2017 8:11 AM, Martin Møller Skarbiniks Pedersen wrote:
Hi,
I am installing a lot of packages to a new R inst
In line
On 15/09/2017 13:30, Martin Møller Skarbiniks Pedersen wrote:
On 15 September 2017 at 14:13, Duncan Murdoch
wrote:
On 15/09/2017 8:11 AM, Martin Møller Skarbiniks Pedersen wrote:
Hi,
I am installing a lot of packages to a new R installation and it takes
a
long time.
However
On 15 September 2017 at 14:13, Duncan Murdoch
wrote:
> On 15/09/2017 8:11 AM, Martin Møller Skarbiniks Pedersen wrote:
>
>> Hi,
>>
>>I am installing a lot of packages to a new R installation and it takes
>> a
>> long time.
>>However the machine got 4 cpus and most of the packages are writ
On 15/09/2017 8:11 AM, Martin Møller Skarbiniks Pedersen wrote:
Hi,
I am installing a lot of packages to a new R installation and it takes a
long time.
However the machine got 4 cpus and most of the packages are written in
C/C++.
So is it possible to add a -j4 flag to the make command
Hi,
I am installing a lot of packages to a new R installation and it takes a
long time.
However the machine got 4 cpus and most of the packages are written in
C/C++.
So is it possible to add a -j4 flag to the make command when I use the
install.packages() function?
That will probably spee
Usually, PCA is used for a large number of features. FactoMineR [1]
package provides a couple of examples, check for temperature example.
But you may want to consult to basic PCA material as well, I suggest a
book from Chris Bishop [2].
[1] https://cran.r-project.org/web/packages/FactoMineR/vigne
> On 15 Sep 2017, at 12:38, yadav neog wrote:
>
> hello to all. I am working on macroeconomic data series of India, which in
> a yearly basis. I am unable to convert my data frame into time series.
Do you really need to convert your data to time series/xts/zoo? I don’t know
you try what kind
You did not provide the data frame so I will first create one and then use
it to create an xts
library(xts)
df <- data.frame( year=1980:2009, cnsm=sample(170:180,30,replace=TRUE),
incm=rnorm(30,53,1), wlth=rnorm(30,60,1))
dates <- as.Date(paste(df$year,"-01-01",sep=""))
myXts <-
Dear Sir/Madam,
I am trying to do PCA analysis with "iris" dataset and trying to interpret
the result. Dataset contains 150 obs of 5 variables
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.13.5 1.4
0.2 se
hello to all. I am working on macroeconomic data series of India, which in
a yearly basis. I am unable to convert my data frame into time series.
kindly help me.
also using zoo and xts packages. but they take only monthly observations.
'data.frame': 30 obs. of 4 variables:
$ year: int 1980 1981
Hi,
I have an input data
> dput (input)
structure(list(ScanDate = structure(c(16433, 16440, 16447, 16454,
16461, 16468, 16475, 16482, 16489, 16496, 16503, 16510, 16517,
16524, 16531, 16538, 16545, 16552, 16559, 16566, 16573, 16580,
16587, 16594, 16601, 16608, 16615, 16622), class = "Date"), OnPr
Hi Eric,
Bert's solution is very elegant. His final comment prompted me to check out
the aperm() function which I have never used.
The final step to complete his response is
prec_daily2 <- aperm(prec_daily, c(3,1,2))
Regards
On Wed, Sep 13, 2017 at 9:06 PM, Bert Gunter wrote:
> Thanks for the
I'm pleased to announce that asciiSetupReader is now on CRAN:
https://cran.r-project.org/web/packages/asciiSetupReader/index.html
This package allows users to read ASCII files that have an SPSS or SAS
setup file (.sps or .sas). Datasets that come in these txt-sps and txt-sas
paris can now be acces
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