Base R. Regarding code improvements:
1. Personally I find (\(...) ...)() notation hard to read (although by
placing (\(x), the body and )() on 3 separate lines it can be improved
somewhat). Instead let us use a named function. The name of the
function can also serve to self document the code.
2. The use of dat both at the start of the pipeline and then again
within a later step of the pipeline goes against a strict left to
right flow. In general if this occurs it is either a sign that we need
to break the pipeline into two or that we need to find another
approach which is what we do here.
We can use the base R code below. Note that the column names produced
by transform(S = read.table(...)) are S.V1, S.V2, etc. so to fix the
column names remove .V from all column names as in the fix_colnames
function shown. It does no harm to apply that to all column names
since the remaining column names will not match.
fix_colnames <- function(x) {
setNames(x, sub("\\.V", "", names(x)))
}
dat |>
transform(S = read.table(text = string,
header = FALSE, fill = TRUE, na.strings = "")) |>
fix_colnames()
Another way to write this which does not use a separate defined
function nor the anonymous function notation is to box the output of
transform:
dat |>
transform(S = read.table(text = string,
header = FALSE, fill = TRUE, na.strings = "")) |>
list(x = _) |>
with( setNames(x, sub("\\.V", "", names(x))) )
dplyr. Alternately use dplyr in which case we can make use of
rename_with . In this case read.table(...) creates column names V1,
V2, etc. and mutate does not change them so simply replacing V with S
at the start of each column name in the output of read.table will do.
Also we can pipe the read.table output directly to rename_with using a
nested pipeline, i.e. the second pipe is entirely within mutate rather
than after it) since mutate won't change the column names. The win
here is because, unlike transform, mutate does not require the S= that
is needed with transform (although it allows it had we wanted it).
library(dplyr)
dat |>
mutate(read.table(text = string,
header = FALSE, fill = TRUE, na.strings = "") |>
rename_with(~ sub("^V", "S", .x))
)
On Sun, Jul 21, 2024 at 3:08?PM Bert Gunter <bgunter.4567 at gmail.com>
wrote:>
> As always, good point.
> Here's a piped version of your code for those who are pipe
> afficianados. As I'm not very skilled with pipes, it might certainly
> be improved.
> dat <-
> dat$string |>
> read.table( text = _, fill = TRUE, header = FALSE, na.strings =
"") |>
> (\(x)'names<-'(x,paste0("s",
seq_along(x))))() |>
> (\(x)cbind(dat, x))()
>
> -- Bert
>
>
> On Sun, Jul 21, 2024 at 11:30?AM Gabor Grothendieck
> <ggrothendieck at gmail.com> wrote:
> >
> > Fixing col.names=paste0("S", 1:5) assumes that there will be
5 columns and
> > we may not want to do that. If there are only 3 fields in string, at
the most,
> > we may wish to generate only 3 columns.
> >
> > On Sun, Jul 21, 2024 at 2:20?PM Bert Gunter <bgunter.4567 at
gmail.com> wrote:
> > >
> > > Nice! -- Let read.table do the work of handling the NA's.
> > > However, even simpler is to use the 'colnames' argument
of
> > > read.table() for the column names no?
> > >
> > > string <- read.table(text = dat$string, fill = TRUE,
header > > > FALSE, na.strings = "",
> > > col.names = paste0("s", 1:5))
> > > dat <- cbind(dat, string)
> > >
> > > -- Bert
> > >
> > > On Sun, Jul 21, 2024 at 10:16?AM Gabor Grothendieck
> > > <ggrothendieck at gmail.com> wrote:
> > > >
> > > > We can use read.table for a base R solution
> > > >
> > > > string <- read.table(text = dat$string, fill = TRUE,
header = FALSE,
> > > > na.strings = "")
> > > > names(string) <- paste0("S", seq_along(string))
> > > > cbind(dat[-3], string)
> > > >
> > > > On Fri, Jul 19, 2024 at 12:52?PM Val <valkremk at
gmail.com> wrote:
> > > > >
> > > > > Hi All,
> > > > >
> > > > > I want to extract new variables from a string and add
it to the dataframe.
> > > > > Sample data is csv file.
> > > > >
> > > > > dat<-read.csv(text="Year, Sex,string
> > > > > 2002,F,15 xc Ab
> > > > > 2003,F,14
> > > > > 2004,M,18 xb 25 35 21
> > > > > 2005,M,13 25
> > > > > 2006,M,14 ac 256 AV 35
> > > > > 2007,F,11",header=TRUE)
> > > > >
> > > > > The string column has a maximum of five variables.
Some rows have all
> > > > > and others may not have all the five variables. If
missing then fill
> > > > > it with NA,
> > > > > Desired result is shown below,
> > > > >
> > > > >
> > > > > Year,Sex,string, S1, S2, S3 S4,S5
> > > > > 2002,F,15 xc Ab, 15,xc,Ab, NA, NA
> > > > > 2003,F,14, 14,NA,NA,NA,NA
> > > > > 2004,M,18 xb 25 35 21,18, xb, 25, 35, 21
> > > > > 2005,M,13 25,13, 25,NA,NA,NA
> > > > > 2006,M,14 ac 256 AV 35, 14, ac, 256, AV, 35
> > > > > 2007,F,11, 11,NA,NA,NA,NA
> > > > >
> > > > > Any help?
> > > > > Thank you in advance.
> > > > >
> > > > > ______________________________________________
> > > > > R-help at 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.
> > > >
> > > >
> > > >
> > > > --
> > > > Statistics & Software Consulting
> > > > GKX Group, GKX Associates Inc.
> > > > tel: 1-877-GKX-GROUP
> > > > email: ggrothendieck at gmail.com
> > > >
> > > > ______________________________________________
> > > > R-help at 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.
> >
> >
> >
> > --
> > Statistics & Software Consulting
> > GKX Group, GKX Associates Inc.
> > tel: 1-877-GKX-GROUP
> > email: ggrothendieck at gmail.com
--
Statistics & Software Consulting
GKX Group, GKX Associates Inc.
tel: 1-877-GKX-GROUP
email: ggrothendieck at gmail.com
Excellent message, Gabor.
Many tools we use are quite flexible and I just want to mention dplyr does have
ways to use something like mutate to rename a column, albeit rename(0 is more
specifically designed to do the job.
Here is an example of how mutate() can rename by making a new column and
removing the old by using a sort of pipeline within mutate():
mydata <- data.frame(a=1, b=2)
mutate(mydata,
c=a,
a=NULL,
d=b,
b=NULL)
The result:
> mutate(mydata, c=a, a=NULL, d=b, b=NULL)
c d
1 1 2
It is effectively the same as following up with a select as an alternative:
mydata |>
mutate(c=a,
d=b) |>
select(c,d)
What people may not quite have grasped is that pipes are not a panacea and can
be used alongside all kinds of other methods. Much of dplyr, such as shown
above, but also in things like the filter() verb, does a sort of internal
pipelining and can apply successive transformations before returning a result
suitable for another part of a pipeline. Part of the philosophy was to make more
functions where the first argument was something like a data.frame object (but
it could be other things) that could be passed along in a pipeline. Trying to
shoehorn in other functions that want the item in other positions makes for less
intuitive code using place markers like period or underscore.
Pipelines are seen by many as a linear construct but as you point out, with
careful design, you can make bigger pipelines that are more like graphs with
some regions being a sub-pipeline and do fairly complex things, albeit hard for
people to read and understand.
Maybe later, we can discuss again why some people insist on some kind of purity
of using the base of languages that are not really expected to stay still but to
evolve.
-----Original Message-----
From: R-help <r-help-bounces at r-project.org> On Behalf Of Gabor
Grothendieck
Sent: Monday, July 22, 2024 7:49 AM
To: Bert Gunter <bgunter.4567 at gmail.com>
Cc: r-help at R-project.org (r-help at r-project.org) <r-help at
r-project.org>
Subject: Re: [R] Extract
Base R. Regarding code improvements:
1. Personally I find (\(...) ...)() notation hard to read (although by
placing (\(x), the body and )() on 3 separate lines it can be improved
somewhat). Instead let us use a named function. The name of the
function can also serve to self document the code.
2. The use of dat both at the start of the pipeline and then again
within a later step of the pipeline goes against a strict left to
right flow. In general if this occurs it is either a sign that we need
to break the pipeline into two or that we need to find another
approach which is what we do here.
We can use the base R code below. Note that the column names produced
by transform(S = read.table(...)) are S.V1, S.V2, etc. so to fix the
column names remove .V from all column names as in the fix_colnames
function shown. It does no harm to apply that to all column names
since the remaining column names will not match.
fix_colnames <- function(x) {
setNames(x, sub("\\.V", "", names(x)))
}
dat |>
transform(S = read.table(text = string,
header = FALSE, fill = TRUE, na.strings = "")) |>
fix_colnames()
Another way to write this which does not use a separate defined
function nor the anonymous function notation is to box the output of
transform:
dat |>
transform(S = read.table(text = string,
header = FALSE, fill = TRUE, na.strings = "")) |>
list(x = _) |>
with( setNames(x, sub("\\.V", "", names(x))) )
dplyr. Alternately use dplyr in which case we can make use of
rename_with . In this case read.table(...) creates column names V1,
V2, etc. and mutate does not change them so simply replacing V with S
at the start of each column name in the output of read.table will do.
Also we can pipe the read.table output directly to rename_with using a
nested pipeline, i.e. the second pipe is entirely within mutate rather
than after it) since mutate won't change the column names. The win
here is because, unlike transform, mutate does not require the S= that
is needed with transform (although it allows it had we wanted it).
library(dplyr)
dat |>
mutate(read.table(text = string,
header = FALSE, fill = TRUE, na.strings = "") |>
rename_with(~ sub("^V", "S", .x))
)
On Sun, Jul 21, 2024 at 3:08?PM Bert Gunter <bgunter.4567 at gmail.com>
wrote:>
> As always, good point.
> Here's a piped version of your code for those who are pipe
> afficianados. As I'm not very skilled with pipes, it might certainly
> be improved.
> dat <-
> dat$string |>
> read.table( text = _, fill = TRUE, header = FALSE, na.strings =
"") |>
> (\(x)'names<-'(x,paste0("s",
seq_along(x))))() |>
> (\(x)cbind(dat, x))()
>
> -- Bert
>
>
> On Sun, Jul 21, 2024 at 11:30?AM Gabor Grothendieck
> <ggrothendieck at gmail.com> wrote:
> >
> > Fixing col.names=paste0("S", 1:5) assumes that there will be
5 columns and
> > we may not want to do that. If there are only 3 fields in string, at
the most,
> > we may wish to generate only 3 columns.
> >
> > On Sun, Jul 21, 2024 at 2:20?PM Bert Gunter <bgunter.4567 at
gmail.com> wrote:
> > >
> > > Nice! -- Let read.table do the work of handling the NA's.
> > > However, even simpler is to use the 'colnames' argument
of
> > > read.table() for the column names no?
> > >
> > > string <- read.table(text = dat$string, fill = TRUE,
header > > > FALSE, na.strings = "",
> > > col.names = paste0("s", 1:5))
> > > dat <- cbind(dat, string)
> > >
> > > -- Bert
> > >
> > > On Sun, Jul 21, 2024 at 10:16?AM Gabor Grothendieck
> > > <ggrothendieck at gmail.com> wrote:
> > > >
> > > > We can use read.table for a base R solution
> > > >
> > > > string <- read.table(text = dat$string, fill = TRUE,
header = FALSE,
> > > > na.strings = "")
> > > > names(string) <- paste0("S", seq_along(string))
> > > > cbind(dat[-3], string)
> > > >
> > > > On Fri, Jul 19, 2024 at 12:52?PM Val <valkremk at
gmail.com> wrote:
> > > > >
> > > > > Hi All,
> > > > >
> > > > > I want to extract new variables from a string and add
it to the dataframe.
> > > > > Sample data is csv file.
> > > > >
> > > > > dat<-read.csv(text="Year, Sex,string
> > > > > 2002,F,15 xc Ab
> > > > > 2003,F,14
> > > > > 2004,M,18 xb 25 35 21
> > > > > 2005,M,13 25
> > > > > 2006,M,14 ac 256 AV 35
> > > > > 2007,F,11",header=TRUE)
> > > > >
> > > > > The string column has a maximum of five variables.
Some rows have all
> > > > > and others may not have all the five variables. If
missing then fill
> > > > > it with NA,
> > > > > Desired result is shown below,
> > > > >
> > > > >
> > > > > Year,Sex,string, S1, S2, S3 S4,S5
> > > > > 2002,F,15 xc Ab, 15,xc,Ab, NA, NA
> > > > > 2003,F,14, 14,NA,NA,NA,NA
> > > > > 2004,M,18 xb 25 35 21,18, xb, 25, 35, 21
> > > > > 2005,M,13 25,13, 25,NA,NA,NA
> > > > > 2006,M,14 ac 256 AV 35, 14, ac, 256, AV, 35
> > > > > 2007,F,11, 11,NA,NA,NA,NA
> > > > >
> > > > > Any help?
> > > > > Thank you in advance.
> > > > >
> > > > > ______________________________________________
> > > > > R-help at 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.
> > > >
> > > >
> > > >
> > > > --
> > > > Statistics & Software Consulting
> > > > GKX Group, GKX Associates Inc.
> > > > tel: 1-877-GKX-GROUP
> > > > email: ggrothendieck at gmail.com
> > > >
> > > > ______________________________________________
> > > > R-help at 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.
> >
> >
> >
> > --
> > Statistics & Software Consulting
> > GKX Group, GKX Associates Inc.
> > tel: 1-877-GKX-GROUP
> > email: ggrothendieck at gmail.com
--
Statistics & Software Consulting
GKX Group, GKX Associates Inc.
tel: 1-877-GKX-GROUP
email: ggrothendieck at gmail.com
______________________________________________
R-help at 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.
Thanks. I found this to be quite informative and a nice example of how useful R-Help can be as a resource for R users. Best, Bert On Mon, Jul 22, 2024 at 4:50?AM Gabor Grothendieck <ggrothendieck at gmail.com> wrote:> > Base R. Regarding code improvements: > > 1. Personally I find (\(...) ...)() notation hard to read (although by > placing (\(x), the body and )() on 3 separate lines it can be improved > somewhat). Instead let us use a named function. The name of the > function can also serve to self document the code. > > 2. The use of dat both at the start of the pipeline and then again > within a later step of the pipeline goes against a strict left to > right flow. In general if this occurs it is either a sign that we need > to break the pipeline into two or that we need to find another > approach which is what we do here. > > We can use the base R code below. Note that the column names produced > by transform(S = read.table(...)) are S.V1, S.V2, etc. so to fix the > column names remove .V from all column names as in the fix_colnames > function shown. It does no harm to apply that to all column names > since the remaining column names will not match. > > fix_colnames <- function(x) { > setNames(x, sub("\\.V", "", names(x))) > } > > dat |> > transform(S = read.table(text = string, > header = FALSE, fill = TRUE, na.strings = "")) |> > fix_colnames() > > Another way to write this which does not use a separate defined > function nor the anonymous function notation is to box the output of > transform: > > dat |> > transform(S = read.table(text = string, > header = FALSE, fill = TRUE, na.strings = "")) |> > list(x = _) |> > with( setNames(x, sub("\\.V", "", names(x))) ) > > dplyr. Alternately use dplyr in which case we can make use of > rename_with . In this case read.table(...) creates column names V1, > V2, etc. and mutate does not change them so simply replacing V with S > at the start of each column name in the output of read.table will do. > Also we can pipe the read.table output directly to rename_with using a > nested pipeline, i.e. the second pipe is entirely within mutate rather > than after it) since mutate won't change the column names. The win > here is because, unlike transform, mutate does not require the S= that > is needed with transform (although it allows it had we wanted it). > > library(dplyr) > > dat |> > mutate(read.table(text = string, > header = FALSE, fill = TRUE, na.strings = "") |> > rename_with(~ sub("^V", "S", .x)) > ) > > > On Sun, Jul 21, 2024 at 3:08?PM Bert Gunter <bgunter.4567 at gmail.com> wrote: > > > > As always, good point. > > Here's a piped version of your code for those who are pipe > > afficianados. As I'm not very skilled with pipes, it might certainly > > be improved. > > dat <- > > dat$string |> > > read.table( text = _, fill = TRUE, header = FALSE, na.strings = "") |> > > (\(x)'names<-'(x,paste0("s", seq_along(x))))() |> > > (\(x)cbind(dat, x))() > > > > -- Bert > > > > > > On Sun, Jul 21, 2024 at 11:30?AM Gabor Grothendieck > > <ggrothendieck at gmail.com> wrote: > > > > > > Fixing col.names=paste0("S", 1:5) assumes that there will be 5 columns and > > > we may not want to do that. If there are only 3 fields in string, at the most, > > > we may wish to generate only 3 columns. > > > > > > On Sun, Jul 21, 2024 at 2:20?PM Bert Gunter <bgunter.4567 at gmail.com> wrote: > > > > > > > > Nice! -- Let read.table do the work of handling the NA's. > > > > However, even simpler is to use the 'colnames' argument of > > > > read.table() for the column names no? > > > > > > > > string <- read.table(text = dat$string, fill = TRUE, header > > > > FALSE, na.strings = "", > > > > col.names = paste0("s", 1:5)) > > > > dat <- cbind(dat, string) > > > > > > > > -- Bert > > > > > > > > On Sun, Jul 21, 2024 at 10:16?AM Gabor Grothendieck > > > > <ggrothendieck at gmail.com> wrote: > > > > > > > > > > We can use read.table for a base R solution > > > > > > > > > > string <- read.table(text = dat$string, fill = TRUE, header = FALSE, > > > > > na.strings = "") > > > > > names(string) <- paste0("S", seq_along(string)) > > > > > cbind(dat[-3], string) > > > > > > > > > > On Fri, Jul 19, 2024 at 12:52?PM Val <valkremk at gmail.com> wrote: > > > > > > > > > > > > Hi All, > > > > > > > > > > > > I want to extract new variables from a string and add it to the dataframe. > > > > > > Sample data is csv file. > > > > > > > > > > > > dat<-read.csv(text="Year, Sex,string > > > > > > 2002,F,15 xc Ab > > > > > > 2003,F,14 > > > > > > 2004,M,18 xb 25 35 21 > > > > > > 2005,M,13 25 > > > > > > 2006,M,14 ac 256 AV 35 > > > > > > 2007,F,11",header=TRUE) > > > > > > > > > > > > The string column has a maximum of five variables. Some rows have all > > > > > > and others may not have all the five variables. If missing then fill > > > > > > it with NA, > > > > > > Desired result is shown below, > > > > > > > > > > > > > > > > > > Year,Sex,string, S1, S2, S3 S4,S5 > > > > > > 2002,F,15 xc Ab, 15,xc,Ab, NA, NA > > > > > > 2003,F,14, 14,NA,NA,NA,NA > > > > > > 2004,M,18 xb 25 35 21,18, xb, 25, 35, 21 > > > > > > 2005,M,13 25,13, 25,NA,NA,NA > > > > > > 2006,M,14 ac 256 AV 35, 14, ac, 256, AV, 35 > > > > > > 2007,F,11, 11,NA,NA,NA,NA > > > > > > > > > > > > Any help? > > > > > > Thank you in advance. > > > > > > > > > > > > ______________________________________________ > > > > > > R-help at 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. > > > > > > > > > > > > > > > > > > > > -- > > > > > Statistics & Software Consulting > > > > > GKX Group, GKX Associates Inc. > > > > > tel: 1-877-GKX-GROUP > > > > > email: ggrothendieck at gmail.com > > > > > > > > > > ______________________________________________ > > > > > R-help at 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. > > > > > > > > > > > > -- > > > Statistics & Software Consulting > > > GKX Group, GKX Associates Inc. > > > tel: 1-877-GKX-GROUP > > > email: ggrothendieck at gmail.com > > > > -- > Statistics & Software Consulting > GKX Group, GKX Associates Inc. > tel: 1-877-GKX-GROUP > email: ggrothendieck at gmail.com
I had missed that one can pass fix.empty.names = TRUE to transform and
if we do that then we can
put an unnamed data.frame in transform like we can with mutate so
making that change we have the following
base R solution where there is an inner nested pipeline within the
outer pipeline as with the dplyr example.
transform(dat,
read.table(text = string, header = FALSE, na.strings = "", fill
TRUE), fix.empty.names = TRUE) |>
list(x = _) |>
with( setNames(x, sub("V", "S", names(x)) )
)
On Mon, Jul 22, 2024 at 7:49?AM Gabor Grothendieck
<ggrothendieck at gmail.com> wrote:>
> Base R. Regarding code improvements:
>
> 1. Personally I find (\(...) ...)() notation hard to read (although by
> placing (\(x), the body and )() on 3 separate lines it can be improved
> somewhat). Instead let us use a named function. The name of the
> function can also serve to self document the code.
>
> 2. The use of dat both at the start of the pipeline and then again
> within a later step of the pipeline goes against a strict left to
> right flow. In general if this occurs it is either a sign that we need
> to break the pipeline into two or that we need to find another
> approach which is what we do here.
>
> We can use the base R code below. Note that the column names produced
> by transform(S = read.table(...)) are S.V1, S.V2, etc. so to fix the
> column names remove .V from all column names as in the fix_colnames
> function shown. It does no harm to apply that to all column names
> since the remaining column names will not match.
>
> fix_colnames <- function(x) {
> setNames(x, sub("\\.V", "", names(x)))
> }
>
> dat |>
> transform(S = read.table(text = string,
> header = FALSE, fill = TRUE, na.strings = "")) |>
> fix_colnames()
>
> Another way to write this which does not use a separate defined
> function nor the anonymous function notation is to box the output of
> transform:
>
> dat |>
> transform(S = read.table(text = string,
> header = FALSE, fill = TRUE, na.strings = "")) |>
> list(x = _) |>
> with( setNames(x, sub("\\.V", "", names(x))) )
>
> dplyr. Alternately use dplyr in which case we can make use of
> rename_with . In this case read.table(...) creates column names V1,
> V2, etc. and mutate does not change them so simply replacing V with S
> at the start of each column name in the output of read.table will do.
> Also we can pipe the read.table output directly to rename_with using a
> nested pipeline, i.e. the second pipe is entirely within mutate rather
> than after it) since mutate won't change the column names. The win
> here is because, unlike transform, mutate does not require the S= that
> is needed with transform (although it allows it had we wanted it).
>
> library(dplyr)
>
> dat |>
> mutate(read.table(text = string,
> header = FALSE, fill = TRUE, na.strings = "") |>
> rename_with(~ sub("^V", "S", .x))
> )
>
>
> On Sun, Jul 21, 2024 at 3:08?PM Bert Gunter <bgunter.4567 at
gmail.com> wrote:
> >
> > As always, good point.
> > Here's a piped version of your code for those who are pipe
> > afficianados. As I'm not very skilled with pipes, it might
certainly
> > be improved.
> > dat <-
> > dat$string |>
> > read.table( text = _, fill = TRUE, header = FALSE, na.strings
= "") |>
> > (\(x)'names<-'(x,paste0("s",
seq_along(x))))() |>
> > (\(x)cbind(dat, x))()
> >
> > -- Bert
> >
> >
> > On Sun, Jul 21, 2024 at 11:30?AM Gabor Grothendieck
> > <ggrothendieck at gmail.com> wrote:
> > >
> > > Fixing col.names=paste0("S", 1:5) assumes that there
will be 5 columns and
> > > we may not want to do that. If there are only 3 fields in
string, at the most,
> > > we may wish to generate only 3 columns.
> > >
> > > On Sun, Jul 21, 2024 at 2:20?PM Bert Gunter <bgunter.4567 at
gmail.com> wrote:
> > > >
> > > > Nice! -- Let read.table do the work of handling the
NA's.
> > > > However, even simpler is to use the 'colnames'
argument of
> > > > read.table() for the column names no?
> > > >
> > > > string <- read.table(text = dat$string, fill =
TRUE, header > > > > FALSE, na.strings = "",
> > > > col.names = paste0("s", 1:5))
> > > > dat <- cbind(dat, string)
> > > >
> > > > -- Bert
> > > >
> > > > On Sun, Jul 21, 2024 at 10:16?AM Gabor Grothendieck
> > > > <ggrothendieck at gmail.com> wrote:
> > > > >
> > > > > We can use read.table for a base R solution
> > > > >
> > > > > string <- read.table(text = dat$string, fill = TRUE,
header = FALSE,
> > > > > na.strings = "")
> > > > > names(string) <- paste0("S",
seq_along(string))
> > > > > cbind(dat[-3], string)
> > > > >
> > > > > On Fri, Jul 19, 2024 at 12:52?PM Val <valkremk at
gmail.com> wrote:
> > > > > >
> > > > > > Hi All,
> > > > > >
> > > > > > I want to extract new variables from a string and
add it to the dataframe.
> > > > > > Sample data is csv file.
> > > > > >
> > > > > > dat<-read.csv(text="Year, Sex,string
> > > > > > 2002,F,15 xc Ab
> > > > > > 2003,F,14
> > > > > > 2004,M,18 xb 25 35 21
> > > > > > 2005,M,13 25
> > > > > > 2006,M,14 ac 256 AV 35
> > > > > > 2007,F,11",header=TRUE)
> > > > > >
> > > > > > The string column has a maximum of five
variables. Some rows have all
> > > > > > and others may not have all the five variables. If
missing then fill
> > > > > > it with NA,
> > > > > > Desired result is shown below,
> > > > > >
> > > > > >
> > > > > > Year,Sex,string, S1, S2, S3 S4,S5
> > > > > > 2002,F,15 xc Ab, 15,xc,Ab, NA, NA
> > > > > > 2003,F,14, 14,NA,NA,NA,NA
> > > > > > 2004,M,18 xb 25 35 21,18, xb, 25, 35, 21
> > > > > > 2005,M,13 25,13, 25,NA,NA,NA
> > > > > > 2006,M,14 ac 256 AV 35, 14, ac, 256, AV, 35
> > > > > > 2007,F,11, 11,NA,NA,NA,NA
> > > > > >
> > > > > > Any help?
> > > > > > Thank you in advance.
> > > > > >
> > > > > > ______________________________________________
> > > > > > R-help at 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.
> > > > >
> > > > >
> > > > >
> > > > > --
> > > > > Statistics & Software Consulting
> > > > > GKX Group, GKX Associates Inc.
> > > > > tel: 1-877-GKX-GROUP
> > > > > email: ggrothendieck at gmail.com
> > > > >
> > > > > ______________________________________________
> > > > > R-help at 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.
> > >
> > >
> > >
> > > --
> > > Statistics & Software Consulting
> > > GKX Group, GKX Associates Inc.
> > > tel: 1-877-GKX-GROUP
> > > email: ggrothendieck at gmail.com
>
>
>
> --
> Statistics & Software Consulting
> GKX Group, GKX Associates Inc.
> tel: 1-877-GKX-GROUP
> email: ggrothendieck at gmail.com
--
Statistics & Software Consulting
GKX Group, GKX Associates Inc.
tel: 1-877-GKX-GROUP
email: ggrothendieck at gmail.com