Given that you know which columns should be numeric and which should be
character, finding characters in numeric columns or numbers in character
columns is not difficult. Your data frame consists of three character
columns so you can use regular expressions as Bert mentioned. First you
should strip the whitespace out of your data:
dat1 <-read.table(text="Name, Age, Weight
Alex, 20, 13X
Bob, 25, 142
Carol, 24, 120
John, 3BC, 175
Katy, 35, 160
Jack3, 34, 140",sep=",", header=TRUE, stringsAsFactors=FALSE,
strip.white=TRUE)
Now check to see if all of the fields are character as expected.
sapply(dat1, typeof)
# Name Age Weight
# "character" "character" "character"
Now identify character variables containing numbers and numeric variables
containing characters:
BadName <- which(grepl("[[:digit:]]", dat1$Name))
BadAge <- which(grepl("[[:alpha:]]", dat1$Age))
BadWeight <- which(grepl("[[:alpha:]]", dat1$Weight))
Next remove those rows:
(dat2 <- dat1[-unique(c(BadName, BadAge, BadWeight)), ])
# Name Age Weight
# 2 Bob 25 142
# 3 Carol 24 120
# 5 Katy 35 160
You still need to convert Age and Weight to numeric, e.g. dat2$Age <-
as.numeric(dat2$Age).
David Carlson
On Fri, Jan 28, 2022 at 11:59 PM Bert Gunter <bgunter.4567 at gmail.com>
wrote:
> As character 'polluted' entries will cause a column to be read in
(via
> read.table and relatives) as factor or character data, this sounds like a
> job for regular expressions. If you are not familiar with this subject,
> time to learn. And, yes, ZjQcmQRYFpfptBannerStart
> This Message Is From an External Sender
> This message came from outside your organization.
> ZjQcmQRYFpfptBannerEnd
>
> As character 'polluted' entries will cause a column to be read in
(via
> read.table and relatives) as factor or character data, this sounds like a
> job for regular expressions. If you are not familiar with this subject,
> time to learn. And, yes, some heavy lifting will be required.
> See ?regexp for a start maybe? Or the stringr package?
>
> Cheers,
> Bert
>
>
>
>
> On Fri, Jan 28, 2022, 7:08 PM Val <valkremk at gmail.com> wrote:
>
> > Hi All,
> >
> > I want to remove rows that contain a character string in an integer
> > column or a digit in a character column.
> >
> > Sample data
> >
> > dat1 <-read.table(text="Name, Age, Weight
> > Alex, 20, 13X
> > Bob, 25, 142
> > Carol, 24, 120
> > John, 3BC, 175
> > Katy, 35, 160
> > Jack3, 34,
140",sep=",",header=TRUE,stringsAsFactors=F)
> >
> > If the Age/Weight column contains any character(s) then remove
> > if the Name column contains an digit then remove that row
> > Desired output
> >
> > Name Age weight
> > 1 Bob 25 142
> > 2 Carol 24 120
> > 3 Katy 35 160
> >
> > Thank you,
> >
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$
> > PLEASE do read the posting guide
> >
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
> [[alternative HTML version deleted]]
>
> ______________________________________________R-help at r-project.org
mailing list -- To UNSUBSCRIBE and more,
seehttps://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$
> PLEASE do read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$
> and provide commented, minimal, self-contained, reproducible code.
>
>
[[alternative HTML version deleted]]
Looks like David decided to supply a detailed answer.
I came up with a very different solution that calculates which row indices to
keep using three grep statements and intersecting those to use as an index into
the original data.
To each their own
-----Original Message-----
From: David Carlson via R-help <r-help at r-project.org>
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>
Sent: Sat, Jan 29, 2022 1:30 am
Subject: Re: [R] Row exclude
Given that you know which columns should be numeric and which should be
character, finding characters in numeric columns or numbers in character
columns is not difficult. Your data frame consists of three character
columns so you can use regular expressions as Bert mentioned. First you
should strip the whitespace out of your data:
dat1 <-read.table(text="Name, Age, Weight
? Alex,? 20,? 13X
? Bob,? 25,? 142
? Carol, 24,? 120
? John,? 3BC,? 175
? Katy,? 35,? 160
? Jack3, 34,? 140",sep=",", header=TRUE, stringsAsFactors=FALSE,
strip.white=TRUE)
Now check to see if all of the fields are character as expected.
sapply(dat1, typeof)
#? ? ? ? Name? ? ? ? Age? ? ? Weight
# "character" "character" "character"
Now identify character variables containing numbers and numeric variables
containing characters:
BadName <- which(grepl("[[:digit:]]", dat1$Name))
BadAge <- which(grepl("[[:alpha:]]", dat1$Age))
BadWeight <- which(grepl("[[:alpha:]]", dat1$Weight))
Next remove those rows:
(dat2 <- dat1[-unique(c(BadName, BadAge, BadWeight)), ])
#? ? Name Age Weight
#? 2? Bob? 25? ? 142
#? 3 Carol? 24? ? 120
#? 5? Katy? 35? ? 160
You still need to convert Age and Weight to numeric, e.g. dat2$Age <-
as.numeric(dat2$Age).
David Carlson
On Fri, Jan 28, 2022 at 11:59 PM Bert Gunter <bgunter.4567 at gmail.com>
wrote:
> As character 'polluted' entries will cause a column to be read in
(via
> read.table and relatives) as factor or character data, this sounds like a
> job for regular expressions. If you are not familiar with this subject,
> time to learn. And, yes, ZjQcmQRYFpfptBannerStart
> This Message Is From an External Sender
> This message came from outside your organization.
> ZjQcmQRYFpfptBannerEnd
>
> As character 'polluted' entries will cause a column to be read in
(via
> read.table and relatives) as factor or character data, this sounds like a
> job for regular expressions. If you are not familiar with this subject,
> time to learn. And, yes, some heavy lifting will be required.
> See ?regexp for a start maybe? Or the stringr package?
>
> Cheers,
> Bert
>
>
>
>
> On Fri, Jan 28, 2022, 7:08 PM Val <valkremk at gmail.com> wrote:
>
> > Hi All,
> >
> > I want to remove rows that contain a character string in an integer
> > column or a digit in a character column.
> >
> > Sample data
> >
> > dat1 <-read.table(text="Name, Age, Weight
> >? Alex,? 20,? 13X
> >? Bob,? 25,? 142
> >? Carol, 24,? 120
> >? John,? 3BC,? 175
> >? Katy,? 35,? 160
> >? Jack3, 34,?
140",sep=",",header=TRUE,stringsAsFactors=F)
> >
> > If the Age/Weight column contains any character(s) then remove
> > if the Name? column contains an digit then remove that row
> > Desired output
> >
> >? ? Name? Age weight
> > 1? Bob? ? 25? ? 142
> > 2? Carol? 24? ? 120
> > 3? Katy? ? 35? ? 160
> >
> > Thank you,
> >
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$
> > PLEASE do read the posting guide
> >
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
> ??? [[alternative HTML version deleted]]
>
> ______________________________________________R-help at r-project.org
mailing list -- To UNSUBSCRIBE and more,
seehttps://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$
> PLEASE do read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$
> and provide commented, minimal, self-contained, reproducible code.
>
>
??? [[alternative HTML version deleted]]
______________________________________________
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.
[[alternative HTML version deleted]]
Hello,
Getting creative, here is another way with mapply.
regex <- list("[[:digit:]]", "[[:alpha:]]",
"[[:alpha:]]")
i <- mapply(\(x, r) grepl(r, x), dat1, regex)
dat1[rowSums(i) == 0L, ]
#? Name Age Weight
#2?? Bob ? 25 ?? ?? 142
#3 Carol ? 24?? ? ? 120
#5? Katy?? 35?????? 160
Hope this helps,
Rui Barradas
?s 06:30 de 29/01/2022, David Carlson via R-help
escreveu:> Given that you know which columns should be numeric and which should be
> character, finding characters in numeric columns or numbers in character
> columns is not difficult. Your data frame consists of three character
> columns so you can use regular expressions as Bert mentioned. First you
> should strip the whitespace out of your data:
>
> dat1 <-read.table(text="Name, Age, Weight
> Alex, 20, 13X
> Bob, 25, 142
> Carol, 24, 120
> John, 3BC, 175
> Katy, 35, 160
> Jack3, 34, 140",sep=",", header=TRUE,
stringsAsFactors=FALSE,
> strip.white=TRUE)
>
> Now check to see if all of the fields are character as expected.
>
> sapply(dat1, typeof)
> # Name Age Weight
> # "character" "character" "character"
>
> Now identify character variables containing numbers and numeric variables
> containing characters:
>
> BadName <- which(grepl("[[:digit:]]", dat1$Name))
> BadAge <- which(grepl("[[:alpha:]]", dat1$Age))
> BadWeight <- which(grepl("[[:alpha:]]", dat1$Weight))
>
> Next remove those rows:
>
> (dat2 <- dat1[-unique(c(BadName, BadAge, BadWeight)), ])
> # Name Age Weight
> # 2 Bob 25 142
> # 3 Carol 24 120
> # 5 Katy 35 160
>
> You still need to convert Age and Weight to numeric, e.g. dat2$Age <-
> as.numeric(dat2$Age).
>
> David Carlson
>
>
> On Fri, Jan 28, 2022 at 11:59 PM Bert Gunter <bgunter.4567 at
gmail.com> wrote:
>
>> As character 'polluted' entries will cause a column to be read
in (via
>> read.table and relatives) as factor or character data, this sounds like
a
>> job for regular expressions. If you are not familiar with this subject,
>> time to learn. And, yes, ZjQcmQRYFpfptBannerStart
>> This Message Is From an External Sender
>> This message came from outside your organization.
>> ZjQcmQRYFpfptBannerEnd
>>
>> As character 'polluted' entries will cause a column to be read
in (via
>> read.table and relatives) as factor or character data, this sounds like
a
>> job for regular expressions. If you are not familiar with this subject,
>> time to learn. And, yes, some heavy lifting will be required.
>> See ?regexp for a start maybe? Or the stringr package?
>>
>> Cheers,
>> Bert
>>
>>
>>
>>
>> On Fri, Jan 28, 2022, 7:08 PM Val <valkremk at gmail.com> wrote:
>>
>>> Hi All,
>>>
>>> I want to remove rows that contain a character string in an integer
>>> column or a digit in a character column.
>>>
>>> Sample data
>>>
>>> dat1 <-read.table(text="Name, Age, Weight
>>> Alex, 20, 13X
>>> Bob, 25, 142
>>> Carol, 24, 120
>>> John, 3BC, 175
>>> Katy, 35, 160
>>> Jack3, 34,
140",sep=",",header=TRUE,stringsAsFactors=F)
>>>
>>> If the Age/Weight column contains any character(s) then remove
>>> if the Name column contains an digit then remove that row
>>> Desired output
>>>
>>> Name Age weight
>>> 1 Bob 25 142
>>> 2 Carol 24 120
>>> 3 Katy 35 160
>>>
>>> Thank you,
>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more,
see
>>>
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$
>>> PLEASE do read the posting guide
>>>
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________R-help at r-project.org
mailing list -- To UNSUBSCRIBE and more,
seehttps://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$
>> PLEASE do read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> 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.
David, this is a nice presentation of the resolution. Good job. *Stephen Dawson, DSL* /Executive Strategy Consultant/ Business & Technology +1 (865) 804-3454 http://www.shdawson.com On 1/29/22 01:30, David Carlson via R-help wrote:> Given that you know which columns should be numeric and which should be > character, finding characters in numeric columns or numbers in character > columns is not difficult. Your data frame consists of three character > columns so you can use regular expressions as Bert mentioned. First you > should strip the whitespace out of your data: > > dat1 <-read.table(text="Name, Age, Weight > Alex, 20, 13X > Bob, 25, 142 > Carol, 24, 120 > John, 3BC, 175 > Katy, 35, 160 > Jack3, 34, 140",sep=",", header=TRUE, stringsAsFactors=FALSE, > strip.white=TRUE) > > Now check to see if all of the fields are character as expected. > > sapply(dat1, typeof) > # Name Age Weight > # "character" "character" "character" > > Now identify character variables containing numbers and numeric variables > containing characters: > > BadName <- which(grepl("[[:digit:]]", dat1$Name)) > BadAge <- which(grepl("[[:alpha:]]", dat1$Age)) > BadWeight <- which(grepl("[[:alpha:]]", dat1$Weight)) > > Next remove those rows: > > (dat2 <- dat1[-unique(c(BadName, BadAge, BadWeight)), ]) > # Name Age Weight > # 2 Bob 25 142 > # 3 Carol 24 120 > # 5 Katy 35 160 > > You still need to convert Age and Weight to numeric, e.g. dat2$Age <- > as.numeric(dat2$Age). > > David Carlson > > > On Fri, Jan 28, 2022 at 11:59 PM Bert Gunter <bgunter.4567 at gmail.com> wrote: > >> As character 'polluted' entries will cause a column to be read in (via >> read.table and relatives) as factor or character data, this sounds like a >> job for regular expressions. If you are not familiar with this subject, >> time to learn. And, yes, ZjQcmQRYFpfptBannerStart >> This Message Is From an External Sender >> This message came from outside your organization. >> ZjQcmQRYFpfptBannerEnd >> >> As character 'polluted' entries will cause a column to be read in (via >> read.table and relatives) as factor or character data, this sounds like a >> job for regular expressions. If you are not familiar with this subject, >> time to learn. And, yes, some heavy lifting will be required. >> See ?regexp for a start maybe? Or the stringr package? >> >> Cheers, >> Bert >> >> >> >> >> On Fri, Jan 28, 2022, 7:08 PM Val <valkremk at gmail.com> wrote: >> >>> Hi All, >>> >>> I want to remove rows that contain a character string in an integer >>> column or a digit in a character column. >>> >>> Sample data >>> >>> dat1 <-read.table(text="Name, Age, Weight >>> Alex, 20, 13X >>> Bob, 25, 142 >>> Carol, 24, 120 >>> John, 3BC, 175 >>> Katy, 35, 160 >>> Jack3, 34, 140",sep=",",header=TRUE,stringsAsFactors=F) >>> >>> If the Age/Weight column contains any character(s) then remove >>> if the Name column contains an digit then remove that row >>> Desired output >>> >>> Name Age weight >>> 1 Bob 25 142 >>> 2 Carol 24 120 >>> 3 Katy 35 160 >>> >>> Thank you, >>> >>> ______________________________________________ >>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >>> https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$ >>> PLEASE do read the posting guide >>> https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$ >>> and provide commented, minimal, self-contained, reproducible code. >>> >> [[alternative HTML version deleted]] >> >> ______________________________________________R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, seehttps://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$ >> PLEASE do read the posting guide https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$ >> and provide commented, minimal, self-contained, reproducible code. >> >> > [[alternative HTML version deleted]] > > ______________________________________________ > 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. >
Thank you David for your help.
I just have one question on this. What is the purpose of using the
"unique" function on this?
(dat2 <- dat1[-unique(c(BadName, BadAge, BadWeight)), ])
I got the same result without using it.
(dat2 <- dat1[-(c(BadName, BadAge, BadWeight)), ])
My concern is when I am applying this for the large data set the
"unique"
function may consume resources(time and memory).
Thank you.
On Sat, Jan 29, 2022 at 12:30 AM David Carlson <dcarlson at tamu.edu>
wrote:
> Given that you know which columns should be numeric and which should be
> character, finding characters in numeric columns or numbers in character
> columns is not difficult. Your data frame consists of three character
> columns so you can use regular expressions as Bert mentioned. First you
> should strip the whitespace out of your data:
>
> dat1 <-read.table(text="Name, Age, Weight
> Alex, 20, 13X
> Bob, 25, 142
> Carol, 24, 120
> John, 3BC, 175
> Katy, 35, 160
> Jack3, 34, 140",sep=",", header=TRUE,
stringsAsFactors=FALSE,
> strip.white=TRUE)
>
> Now check to see if all of the fields are character as expected.
>
> sapply(dat1, typeof)
> # Name Age Weight
> # "character" "character" "character"
>
> Now identify character variables containing numbers and numeric variables
> containing characters:
>
> BadName <- which(grepl("[[:digit:]]", dat1$Name))
> BadAge <- which(grepl("[[:alpha:]]", dat1$Age))
> BadWeight <- which(grepl("[[:alpha:]]", dat1$Weight))
>
> Next remove those rows:
>
> (dat2 <- dat1[-unique(c(BadName, BadAge, BadWeight)), ])
> # Name Age Weight
> # 2 Bob 25 142
> # 3 Carol 24 120
> # 5 Katy 35 160
>
> You still need to convert Age and Weight to numeric, e.g. dat2$Age <-
> as.numeric(dat2$Age).
>
> David Carlson
>
>
> On Fri, Jan 28, 2022 at 11:59 PM Bert Gunter <bgunter.4567 at
gmail.com>
> wrote:
>
>> As character 'polluted' entries will cause a column to be read
in (via
>> read.table and relatives) as factor or character data, this sounds like
a
>> job for regular expressions. If you are not familiar with this subject,
>> time to learn. And, yes, ZjQcmQRYFpfptBannerStart
>> This Message Is From an External Sender
>> This message came from outside your organization.
>> ZjQcmQRYFpfptBannerEnd
>>
>> As character 'polluted' entries will cause a column to be read
in (via
>> read.table and relatives) as factor or character data, this sounds like
a
>> job for regular expressions. If you are not familiar with this subject,
>> time to learn. And, yes, some heavy lifting will be required.
>> See ?regexp for a start maybe? Or the stringr package?
>>
>> Cheers,
>> Bert
>>
>>
>>
>>
>> On Fri, Jan 28, 2022, 7:08 PM Val <valkremk at gmail.com> wrote:
>>
>> > Hi All,
>> >
>> > I want to remove rows that contain a character string in an
integer
>> > column or a digit in a character column.
>> >
>> > Sample data
>> >
>> > dat1 <-read.table(text="Name, Age, Weight
>> > Alex, 20, 13X
>> > Bob, 25, 142
>> > Carol, 24, 120
>> > John, 3BC, 175
>> > Katy, 35, 160
>> > Jack3, 34,
140",sep=",",header=TRUE,stringsAsFactors=F)
>> >
>> > If the Age/Weight column contains any character(s) then remove
>> > if the Name column contains an digit then remove that row
>> > Desired output
>> >
>> > Name Age weight
>> > 1 Bob 25 142
>> > 2 Carol 24 120
>> > 3 Katy 35 160
>> >
>> > Thank you,
>> >
>> > ______________________________________________
>> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more,
see
>> >
https://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$
>> > PLEASE do read the posting guide
>> >
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$
>> > and provide commented, minimal, self-contained, reproducible code.
>> >
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________R-help at r-project.org
mailing list -- To UNSUBSCRIBE and more,
seehttps://urldefense.com/v3/__https://stat.ethz.ch/mailman/listinfo/r-help__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVXhZB_0c$
>> PLEASE do read the posting guide
https://urldefense.com/v3/__http://www.R-project.org/posting-guide.html__;!!KwNVnqRv!QW1WPKY5eSNT7sMW28dnAKV7IXWvIc4UwOwUHkJgJ8uuGUrIAXvRjZWVRmZSfcI$
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
[[alternative HTML version deleted]]