Re: [R] require help
> 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. > > 'data.frame': 30 obs. of 4 variables: > $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... > $ cnsm: num 174 175 175 172 173 ... > $ incm: num 53.4 53.7 53.5 53.2 53.3 ... > $ wlth: num 60.3 60.5 60.2 60.1 60.7 ... > -- Second try to do what you would like (I hope and think) Using Eric's sample data zdf <- data.frame(year=2001:2010, cnsm=sample(170:180,10,replace=TRUE), incm=rnorm(10,53,1), wlth=rnorm(10,60,1)) zdf # R ts zts <- ts(zdf[,-1], start=zdf[1,"year"]) zts # turn data into a zoo timeseries and an xts timeseries library(zoo) z.zoo <- as.zoo(zts) z.zoo library(xts) z.xts <- as.xts(zts) z.xts Berend Hasselman > Yadawananda Neog > Research Scholar > Department of Economics > Banaras Hindu University > Mob. 9838545073 > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Regarding Principal Component Analysis result Interpretation
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 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 Fri, Sep 15, 2017 at 5:12 AM, Ismail SEZEN wrote: > 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 Petal.Length Petal.Width Species > > 1 5.13.5 1.4 > >0.2 setosa > > 2 4.93.0 1.4 > > 0.2 setosa > > . > > . > >150 5.93.0 5.1 > 18 > > verginica > > > > now I used 'prcomp' function on dataset and got result as following: > >> print(pc) > > Standard deviations (1, .., p=4): > > [1] 1.7083611 0.9560494 0.3830886 0.1439265 > > > > Rotation (n x k) = (4 x 4): > >PC1 PC2PC3PC4 > > Sepal.Length 0.5210659 -0.37741762 0.7195664 0.2612863 > > Sepal.Width -0.2693474 -0.92329566 -0.2443818 -0.1235096 > > Petal.Length 0.5804131 -0.02449161 -0.1421264 -0.8014492 > > Petal.Width 0.5648565 -0.06694199 -0.6342727 0.5235971 > > > > I'm planning to use PCA as feature selection process and remove variables > > which are corelated in my project, I have interpreted the PCA result, but > > not sure is my interpretation is correct or wrong. > > > You want to “remove variables which are correlated”. Correlated among > themselves? If so, why don’t you create a pearson correlation matrix (see > ?cor) and define a threshold and remove variables which are correlated > according to this threshold? Perhaps I did not understand you correctly, > excuse me. > > for iris dataset, each component will be as much as correlated with PC1 > and remaining part will be correlated PC2 and so on. Hence, you can > identify which variables are similar in terms of VARIANCE. You can > understand it if you examine the example that I gave above. > > In PCA, you can also calculate the correlations between variables and PCs > but this shows you how PCs are affected by this variables. I don’t know how > you plan to accomplish feature selection process so I hope this helps you. > Also note that resources part at the end of example. > > isezen > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] require help
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, 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 of an analysis but perhaps you don’t have to. > > > kindly help me. > > also using zoo and xts packages. but they take only monthly observations. > > If you really have to convert to xts/zoo, why don’t yo set each year to > first day of January and use it as is? For instance, > > index, cnsm, incm, wlth > 1980-01-01, 174, 53.4, 60.3 > 1981-01-01, 175, 53.7, 60.5 > 1982-01-01, 175, 53.5, 60.2 > ….. > > > > > 'data.frame': 30 obs. of 4 variables: > > $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... > > $ cnsm: num 174 175 175 172 173 ... > > $ incm: num 53.4 53.7 53.5 53.2 53.3 ... > > $ wlth: num 60.3 60.5 60.2 60.1 60.7 ... > > -- > > Yadawananda Neog > > Research Scholar > > Department of Economics > > Banaras Hindu University > > Mob. 9838545073 > > > > -- Yadawananda Neog Research Scholar Department of Economics Banaras Hindu University Mob. 9838545073 __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Regarding Principal Component Analysis result Interpretation
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 Petal.Length Petal.Width Species > 1 5.13.5 1.4 >0.2 setosa > 2 4.93.0 1.4 > 0.2 setosa > . > . >150 5.93.0 5.1 18 > verginica > > now I used 'prcomp' function on dataset and got result as following: >> print(pc) > Standard deviations (1, .., p=4): > [1] 1.7083611 0.9560494 0.3830886 0.1439265 > > Rotation (n x k) = (4 x 4): >PC1 PC2PC3PC4 > Sepal.Length 0.5210659 -0.37741762 0.7195664 0.2612863 > Sepal.Width -0.2693474 -0.92329566 -0.2443818 -0.1235096 > Petal.Length 0.5804131 -0.02449161 -0.1421264 -0.8014492 > Petal.Width 0.5648565 -0.06694199 -0.6342727 0.5235971 > > I'm planning to use PCA as feature selection process and remove variables > which are corelated in my project, I have interpreted the PCA result, but > not sure is my interpretation is correct or wrong. You want to “remove variables which are correlated”. Correlated among themselves? If so, why don’t you create a pearson correlation matrix (see ?cor) and define a threshold and remove variables which are correlated according to this threshold? Perhaps I did not understand you correctly, excuse me. for iris dataset, each component will be as much as correlated with PC1 and remaining part will be correlated PC2 and so on. Hence, you can identify which variables are similar in terms of VARIANCE. You can understand it if you examine the example that I gave above. In PCA, you can also calculate the correlations between variables and PCs but this shows you how PCs are affected by this variables. I don’t know how you plan to accomplish feature selection process so I hope this helps you. Also note that resources part at the end of example. isezen __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Calculating Weeks Since Last Event
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,
16524, 16531, 16538, 16545,
16552, 16559, 16566, 16573, 16580,
16587, 16594, 16601, 16608,
16615, 16622), class = "Date"), OnPromotion =
c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1)), .Names = c("ScanDate",
"OnPromotion"), sorted =
"ScanDate", class = c("data.table",
"data.frame"), row.names = c(NA, -28L))
# find where the promotions start and then create a flag that indicates when
# the previous promotion started
indx <- which(x$OnPromotion == 1)[1] # get initial promotion
if (length(indx) == 0) stop('no promtions') # make sure there is one
in the data
# add a column with the running total of promotions
x$count <- c(rep(0, indx - 1), seq(0, length = nrow(x) - indx + 1))
x$flag <- x$count # save a copy
# now replace no promotions with NAs so we can use 'na.locf'
indx <- (x$OnPromotion == 0) & (x$count != 0)
x$flag[indx] <- NA
x$flag <- zoo::na.locf(x$flag)
# determine weeks since
x$weeks_since <- ifelse(x$count != 0,
x$count - x$flag + 1,
0
)
x # print out the result
##
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
On Fri, Sep 15, 2017 at 5:02 AM, Abhinaba Roy wrote:
> 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"), OnPromotion =
> c(0,
> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
> 0, 0, 1, 1, 1, 1)), .Names = c("ScanDate", "OnPromotion"), sorted =
> "ScanDate", class = c("data.table",
> "data.frame"), row.names = c(NA, -28L))
>
> I am looking for an output
>
>> dput(output)
>
> 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"), OnPromotion =
> c(0,
> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
> 0, 0, 1, 1, 1, 1), Weeks_Since_Last_Promo = c(0, 0, 0, 0, 0,
> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 3, 4, 1,
> 1, 1)), .Names = c("ScanDate", "OnPromotion", "Weeks_Since_Last_Promo"
> ), sorted = "ScanDate", class = c("data.table", "data.frame"), row.names =
> c(NA,
> -28L))
>
> The logic :
>
> The data is weekly.
>
> I want to calculate the number of weeks elapsed since the last promotion
> (OnPromotion : 1 indicates promotion for that week and 0 indicates no
> promotion).
>
> As, there are no promotion initially we set the value for
> 'Weeks_Since_Last_Promo' to 0 (zero). The first promo occurs on
> '2015-03-02' and 'Weeks_Since_Last_Promo' is still 0. Moving to
> '2015-03-09' there was a promotion the week before and so 1 week elapsed
> after the last promo.
>
> If we look at '2015-06-15' then there was a promo 4 weeks back in the week
> of '2015-05-18' and so 'Weeks_Since_Last_Promo' = 4.
>
> How can we do it in R?
>
> Thanks,
> Abhinaba
>
> [[alternative HTML version deleted]]
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
__
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Re: [R] require help
> 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 zoo and xts packages. but they take only monthly observations. >> >> 'data.frame': 30 obs. of 4 variables: >> $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... >> $ cnsm: num 174 175 175 172 173 ... >> $ incm: num 53.4 53.7 53.5 53.2 53.3 ... > > > It shouldn't be difficult. > Example: > > tsdata <- data.frame(year=c(2000,2002,2003), x=c(1,2,3),y=c(10,11,12)) > xy.ts <- as.ts(tsdata) > > library(zoo) > > as.zoo(xy.ts) Ignore my suggestion. Doesn't do what you need. Berend __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] require help
> 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. > > 'data.frame': 30 obs. of 4 variables: > $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... > $ cnsm: num 174 175 175 172 173 ... > $ incm: num 53.4 53.7 53.5 53.2 53.3 ... It shouldn't be difficult. Example: tsdata <- data.frame(year=c(2000,2002,2003), x=c(1,2,3),y=c(10,11,12)) xy.ts <- as.ts(tsdata) library(zoo) as.zoo(xy.ts) Berend Hasselman > $ wlth: num 60.3 60.5 60.2 60.1 60.7 ... > -- > Yadawananda Neog > Research Scholar > Department of Economics > Banaras Hindu University > Mob. 9838545073 > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to add make option to package compilation?
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 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 speed up the package installation process 390%.
See the Ncpus argument in ?install.packages.
Thanks.
However it looks like Ncpus=4 tries to compile four R packages at the
same
time using one cpu for each packages.
The variable MAKE is defined in ${R_HOME}/etc/Renviron, and can be
over-written with ~/.Renviron
MAKE=make -j
There is further discussion in
https://cran.r-project.org/doc/manuals/r-release/R-admin.html#Configuration-variables
and ?Renviron.
One could configure a source installation to always compile with make
-j, something like ./configure MAKE="make -j"
Martin
But you said you had lots to install so would that not speed things up too?
From the documentation:
"
Ncpus: the number of parallel processes to use for a parallel
install of more than one source package. Values greater than
one are supported if the ‘make’ command specified by
‘Sys.getenv("MAKE", "make")’ accepts argument ‘-k -j Ncpus’
"
[[alternative HTML version deleted]]
__
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http://www.R-project.org/posting-guide.html
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to add make option to package compilation?
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 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 speed up the package installation process 390%.
See the Ncpus argument in ?install.packages.
Thanks.
However it looks like Ncpus=4 tries to compile four R packages at the same
time using one cpu for each packages.
But you said you had lots to install so would that not speed things up too?
From the documentation:
"
Ncpus: the number of parallel processes to use for a parallel
install of more than one source package. Values greater than
one are supported if the ‘make’ command specified by
‘Sys.getenv("MAKE", "make")’ accepts argument ‘-k -j Ncpus’
"
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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--
Michael
http://www.dewey.myzen.co.uk/home.html
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to add make option to package compilation?
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 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 speed up the package installation process 390%.
>>
>
> See the Ncpus argument in ?install.packages.
Thanks.
However it looks like Ncpus=4 tries to compile four R packages at the same
time using one cpu for each packages.
From the documentation:
"
Ncpus: the number of parallel processes to use for a parallel
install of more than one source package. Values greater than
one are supported if the ‘make’ command specified by
‘Sys.getenv("MAKE", "make")’ accepts argument ‘-k -j Ncpus’
"
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to add make option to package compilation?
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 when I use the install.packages() function? That will probably speed up the package installation process 390%. See the Ncpus argument in ?install.packages. Duncan Murdoch __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] How to add make option to package compilation?
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 speed up the package installation process 390%. Regards Martin M. S. Pedersen [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Regarding Principal Component Analysis result Interpretation
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/vignettes/clustering.pdf [2] http://www.springer.com/de/book/9780387310732?referer=www.springer.de __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] require help
> 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 of an analysis but perhaps you don’t have to. > kindly help me. > also using zoo and xts packages. but they take only monthly observations. If you really have to convert to xts/zoo, why don’t yo set each year to first day of January and use it as is? For instance, index, cnsm, incm, wlth 1980-01-01, 174, 53.4, 60.3 1981-01-01, 175, 53.7, 60.5 1982-01-01, 175, 53.5, 60.2 ….. > > 'data.frame': 30 obs. of 4 variables: > $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... > $ cnsm: num 174 175 175 172 173 ... > $ incm: num 53.4 53.7 53.5 53.2 53.3 ... > $ wlth: num 60.3 60.5 60.2 60.1 60.7 ... > -- > Yadawananda Neog > Research Scholar > Department of Economics > Banaras Hindu University > Mob. 9838545073 > __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] require help
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 <- xts(df,order.by=dates) On Fri, Sep 15, 2017 at 12:38 PM, 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. > > 'data.frame': 30 obs. of 4 variables: > $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... > $ cnsm: num 174 175 175 172 173 ... > $ incm: num 53.4 53.7 53.5 53.2 53.3 ... > $ wlth: num 60.3 60.5 60.2 60.1 60.7 ... > -- > Yadawananda Neog > Research Scholar > Department of Economics > Banaras Hindu University > Mob. 9838545073 > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Regarding Principal Component Analysis result Interpretation
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 setosa 2 4.93.0 1.4 0.2 setosa . . 150 5.93.0 5.1 18 verginica now I used 'prcomp' function on dataset and got result as following: >print(pc) Standard deviations (1, .., p=4): [1] 1.7083611 0.9560494 0.3830886 0.1439265 Rotation (n x k) = (4 x 4): PC1 PC2PC3PC4 Sepal.Length 0.5210659 -0.37741762 0.7195664 0.2612863 Sepal.Width -0.2693474 -0.92329566 -0.2443818 -0.1235096 Petal.Length 0.5804131 -0.02449161 -0.1421264 -0.8014492 Petal.Width 0.5648565 -0.06694199 -0.6342727 0.5235971 I'm planning to use PCA as feature selection process and remove variables which are corelated in my project, I have interpreted the PCA result, but not sure is my interpretation is correct or wrong. If you can correct me it will be of great help. If i notice the PCs result, I found both positive and negative data. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] require help
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 1982 1983 1984 1985 1986 1987 1988 1989 ... $ cnsm: num 174 175 175 172 173 ... $ incm: num 53.4 53.7 53.5 53.2 53.3 ... $ wlth: num 60.3 60.5 60.2 60.1 60.7 ... -- Yadawananda Neog Research Scholar Department of Economics Banaras Hindu University Mob. 9838545073 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Calculating Weeks Since Last Event
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"), OnPromotion =
c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1)), .Names = c("ScanDate", "OnPromotion"), sorted =
"ScanDate", class = c("data.table",
"data.frame"), row.names = c(NA, -28L))
I am looking for an output
> dput(output)
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"), OnPromotion =
c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1), Weeks_Since_Last_Promo = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 3, 4, 1,
1, 1)), .Names = c("ScanDate", "OnPromotion", "Weeks_Since_Last_Promo"
), sorted = "ScanDate", class = c("data.table", "data.frame"), row.names =
c(NA,
-28L))
The logic :
The data is weekly.
I want to calculate the number of weeks elapsed since the last promotion
(OnPromotion : 1 indicates promotion for that week and 0 indicates no
promotion).
As, there are no promotion initially we set the value for
'Weeks_Since_Last_Promo' to 0 (zero). The first promo occurs on
'2015-03-02' and 'Weeks_Since_Last_Promo' is still 0. Moving to
'2015-03-09' there was a promotion the week before and so 1 week elapsed
after the last promo.
If we look at '2015-06-15' then there was a promo 4 weeks back in the week
of '2015-05-18' and so 'Weeks_Since_Last_Promo' = 4.
How can we do it in R?
Thanks,
Abhinaba
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Re: [R] compounding precipitation based on whether falls within a day
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 reprex. Wouldn't have bothered without it.
>
> The following is I believe **almost** what you want. It seems a bit clumsy
> to me, so others may provide you something neater. But anyway...
>
> ## Convert POSIXct vector to dates
> ## There are 22 different days, not 21
> date <- as.Date(prec_idx)
>
> ## Sum results by date at each i,j of the last 2 array dimensions
> z <- lapply(unique(date),function(d)
>apply(prec[date==d,,],2:3,sum)
>)
>
> ## This gives a list with 22 3x4 matrices of sums.
> ## Convert to 3x4x22 array with
>
> prec_daily <- array(unlist(z),dim=c(3,4,22))
>
> ## This is the **almost** part. You can use the aperm() function to reshape
> the array if you like. I leave those pleasures to you.
>
> HTH.
>
> Cheers,
> 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 Wed, Sep 13, 2017 at 9:52 AM, Morway, Eric wrote:
>
> > Using the small reproducible example below, I'm wondering how best to
> > complete the following task:
> >
> > In the small reproducible example below, the 3D array prec has indexes
> that
> > correspond to time, x, y (i.e., prec[time, x, y]). In this case, the
> time
> > index is hours since some predefined start time. I'd like to add up all
> > the time indexes in 'prec' based on whether or not the corresponding
> hours
> > fall within a given day. So, at the end of the small example below,
> there
> > are two variables that I'm left with, prec_idx (an hourly sequence from
> > beg_time to end_time) whose length is equal to the first index (the time
> > index) of the 3D array of precipitation called prec. That is, I'd like
> to
> > get a 3D array called prec*_daily* that has dimension prec*_daily*[21, 3,
> > 4],
> > where 21 is the number of days and the value in say prec*_daily*[1,x,y]
> is
> > equal to prec[1,x,y] + prec[2,x,y] + ... + prec[24,x,y]
> >
> >
> > ndays <- 21
> > base_time <- as.character('2001-12-31T23:00:00Z')
> > hrs_since_base <- 1
> >
> > # adding an extra second to the end b/c I'm paranoid about the midnight
> > time stamp not being explicit
> > beg_time <- as.POSIXct(base_time, format = "%Y-%m-%dT%H:%M:%SZ") +
> > (hrs_since_base * 60 * 60) + 1
> >
> > max_hr_since <- 24 * ndays
> > end_time <- as.POSIXct(base_time, format = "%Y-%m-%dT%H:%M:%SZ") +
> > (max_hr_since * 60 * 60) + 1
> >
> > prec_idx <- seq(beg_time, end_time, by='hour')
> >
> > prec <- array(abs(rnorm((24*ndays) * 3 * 4)) , dim=c(24*ndays, 3, 4))
> >
> > length(prec_idx)
> > # 504
> > dim(prec)
> > # 504 3 4
> >
> > # How do I aggregate prec to get daily sums of precipitation based on the
> > prec_idx array?
> >
> > [[alternative HTML version deleted]]
> >
> > __
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> > posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
> [[alternative HTML version deleted]]
>
> __
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> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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[R] [R-pkgs] New CRAN Package Announcement: asciiSetupReader
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 accessible through R. The function has the option of correcting value labels (e.g. 1 to Male, 2 to Female) and column names (e.g. V1 to Sex). You may also select only certain columns to read in which is helpful when dealing with very large data sets. A vignette is available explaining how to use the package with SPSS setup files. The process is the same as for SAS setup files. Please let me know if you if you find any bugs or problems in the package. https://github.com/jacobkap/asciiReader/issues Jacob [[alternative HTML version deleted]] ___ R-packages mailing list r-packa...@r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
