Re: [R] Alternative and more efficient data manipulation
Hi:
As previously mentioned, the reshape package (in particular, melt())
is an alternative to make.groups(), although they serve the same
purpose in this example:
dat <- data.frame(`x1`=runif(6, 0, 125),
`x2`=runif(6, 50, 75),
`x3`=runif(6, 0, 100),
`x4`=runif(6, 0, 200),
date = as.Date(c("2009-09-25","2009-09-28","2009-10-02",
"2009-10-07","2009-10-15","2009-10-21")),
yy= head(letters,2), check.names=FALSE)
# id variables are not melted, but carried along in parallel
library('reshape')
mdat <- melt(dat, id = c('date', 'yy'))
# Create a new variable in the data frame rather than try to
# create it in the plot call
mdat <- within(mdat, val = as.numeric(substr(variable, 2, 2)))
# Here are a couple of plot examples that appear to be close
# to what you want to do:
# library('lattice')
# library('ggplot2')
# Create a key for dates
mkey <- list(space = 'top', columns = 2,
title = 'Date', cex.title = 1,
text = list(as.character(unique(mdat$date))),
points = list(pch = 16, col = 1:6),
lines = list(lty = 1, col = 1:6))
xyplot(value ~ val | yy, data = mdat, groups = date,
pch = 16, col = 1:6, col.line = 1:6,
type = c('p', 'l'), key = mkey)
# Similar type of plot in ggplot2 (legend at right instead)
ggplot(mdat, aes(x = val, y = value, colour = factor(date))) +
geom_point(size = 2.5) + geom_line(aes(group = date), size = 1) +
facet_wrap( ~ yy, ncol = 2) +
scale_colour_brewer('Date', pal = 'Dark2')
HTH,
Dennis
On Mon, Aug 15, 2011 at 4:57 PM, Sam Albers wrote:
> Hello list,
>
> ## I have been doing the following process to convert data from one
> form to another for a while but it occurs to me that there is probably
> an easier way to do this. I am often given data that have column names
> which are actually data and I much prefer dealing with data that are
> sorted by factors. So to convert the columns I have previously made
> use of make.groups() in the lattice package which works completely
> satisfactorily. However, it is a bit clunky for what I am using it for
> and I have to carry the other variables forward. Can anyone suggest a
> better way of converting data like this?
>
> library(lattice)
>
> dat <- data.frame(`x1`=runif(6, 0, 125),
> `x2`=runif(6, 50, 75),
> `x3`=runif(6, 0, 100),
> `x4`=runif(6, 0, 200),
> date =
> as.Date(c("2009-09-25","2009-09-28","2009-10-02","2009-10-07","2009-10-15","2009-10-21")),
> yy= head(letters,2), check.names=FALSE)
> ## Here is an example of the type of data that NEED converting
> dat
>
> dat.group <- with(dat, make.groups(x1,x2,x3,x4))
> ## Carrying the other variables forward
> dat.group$date <- dat$date
> dat.group$yy <- dat$yy
> ## Here is an example of what I would like the data to look like
> dat.group
>
> ## The point of this all is so that I can used the data in a manner
> such as this:
> with(dat.group, xyplot(data ~ as.numeric(substr(which, 2,2))|yy, groups=date))
>
> ## So I suppose what I am asking is if there is a more efficient way
> of doing this?
>
> Thanks so much in advance!
>
> Sam
>
> __
> R-help@r-project.org mailing list
> 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
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] Alternative and more efficient data manipulation
?reshape
You have your data in a wide format, but you want it in a long format.
reshape can convert it both ways.
Mikhail
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On
> Behalf Of Sam Albers
> Sent: Monday, August 15, 2011 6:58 PM
> To: r-help@r-project.org
> Subject: [R] Alternative and more efficient data manipulation
>
> Hello list,
>
> ## I have been doing the following process to convert data from one
> form to another for a while but it occurs to me that there is probably
> an easier way to do this. I am often given data that have column names
> which are actually data and I much prefer dealing with data that are
> sorted by factors. So to convert the columns I have previously made
> use of make.groups() in the lattice package which works completely
> satisfactorily. However, it is a bit clunky for what I am using it for
> and I have to carry the other variables forward. Can anyone suggest a
> better way of converting data like this?
>
> library(lattice)
>
> dat <- data.frame(`x1`=runif(6, 0, 125),
> `x2`=runif(6, 50, 75),
> `x3`=runif(6, 0, 100),
> `x4`=runif(6, 0, 200),
> date =
> as.Date(c("2009-09-25","2009-09-28","2009-10-02","2009-10-07","2009-10-
> 15","2009-10-21")),
> yy= head(letters,2), check.names=FALSE)
> ## Here is an example of the type of data that NEED converting
> dat
>
> dat.group <- with(dat, make.groups(x1,x2,x3,x4))
> ## Carrying the other variables forward
> dat.group$date <- dat$date
> dat.group$yy <- dat$yy
> ## Here is an example of what I would like the data to look like
> dat.group
>
> ## The point of this all is so that I can used the data in a manner
> such as this:
> with(dat.group, xyplot(data ~ as.numeric(substr(which, 2,2))|yy,
> groups=date))
>
> ## So I suppose what I am asking is if there is a more efficient way
> of doing this?
>
> Thanks so much in advance!
>
> Sam
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-
> guide.html
> and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Alternative and more efficient data manipulation
Hello list,
## I have been doing the following process to convert data from one
form to another for a while but it occurs to me that there is probably
an easier way to do this. I am often given data that have column names
which are actually data and I much prefer dealing with data that are
sorted by factors. So to convert the columns I have previously made
use of make.groups() in the lattice package which works completely
satisfactorily. However, it is a bit clunky for what I am using it for
and I have to carry the other variables forward. Can anyone suggest a
better way of converting data like this?
library(lattice)
dat <- data.frame(`x1`=runif(6, 0, 125),
`x2`=runif(6, 50, 75),
`x3`=runif(6, 0, 100),
`x4`=runif(6, 0, 200),
date =
as.Date(c("2009-09-25","2009-09-28","2009-10-02","2009-10-07","2009-10-15","2009-10-21")),
yy= head(letters,2), check.names=FALSE)
## Here is an example of the type of data that NEED converting
dat
dat.group <- with(dat, make.groups(x1,x2,x3,x4))
## Carrying the other variables forward
dat.group$date <- dat$date
dat.group$yy <- dat$yy
## Here is an example of what I would like the data to look like
dat.group
## The point of this all is so that I can used the data in a manner
such as this:
with(dat.group, xyplot(data ~ as.numeric(substr(which, 2,2))|yy, groups=date))
## So I suppose what I am asking is if there is a more efficient way
of doing this?
Thanks so much in advance!
Sam
__
R-help@r-project.org mailing list
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.
