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.
There are many creative ways to solve problems and some may get you in trouble
if you present them in class while even in some work situations, they may be
hard for most to understand, let alone maintain and make changes.
This group is amorphous enough that we have people who want "help" who
are new to the language, but also people who know plenty and encounter a new
kind of problem, and of course people who want to make use of what they see as
free labor.
Rui presented a very interesting idea and I like some aspects. But if presented
to most people, they might have to start looking up things.?
But I admit I liked some of the ideas he uses and am adding them to my bag of
tricks. Some were overkill for this particular requirement but that also makes
them more general and useful.
First, was the use of locale-independent regular expressions like [[:alpha:]]
that match any combination of [:lower:] and [:upper:] and thus are not
restricted to ASCII characters. Since I do lots of my activities in languages
other than English and well might include names with characters not normally
found in English, or not even using an overlapping? alphabet, I can easily
encounter items in the Name column that might not match [A-Za-z] but will match
with [:alpha:].
I don't know if using [:digit:] has benefits over [0-9] and I do note there
was no requirement to match more complex numbers than integers so no need to
allow periods or scientific notation and so on.
Then there is the use of mapply. The more general version of the problem
presented would include a data.frame with any number of columns, where a subset
of the columns might need to be checked for conditions that vary across the
columns but may include some broad categories of conditions that might be
re-used. If all the conditions are regular expression matches you can build,
then you can extend the list Rui used to have more items and also include
expressions that always match so that some columns are effectively ignored:
? ?regex <- list("[[:digit:]]", "[[:alpha:]]",
"[[:alpha:]]", "[.*])
So this generalizes to N columns as long as you supply exactly N patterns in the
list, albeit mapply does recycle arguments if needed as in the simplest case
where you want all columns checked the same way.
Rui then uses an anonymous function to pass to mapply() and that is a newish
feature added recently to R, I think. It was perhaps meant specifically to be
used with the new pipe symbol, but can be used anywhere but perhaps not in older
versions of R.
? ?\(x, r) grepl(r, x)
I note Rui also uses grepl() which returns a logical vector. I will show my
first attempt at the end where I used grep() to return index numbers of matches
instead. For this context, though, he made use of the fact that mapply in this
case returns a matrix of type logical:
i <- mapply(\(x, r) grepl(r, x), dat1, regex)
> i
? ? ? Name? ?Age Weight[1,] FALSE FALSE? ?TRUE[2,] FALSE FALSE? FALSE[3,] FALSE
FALSE? FALSE[4,] FALSE? TRUE? FALSE[5,] FALSE FALSE? FALSE[6,]? TRUE FALSE?
FALSE
And since R treats TRUE as 1 and FALSE as 0, then summing the rows gives you a
small integer between 0 and the number of columns, inclusive, and only rows with
no TRUE in them are wanted for this purpose:
dat1[rowSums(i) == 0L, ]
All I all, nicely done, but not trivial to read without comments, LOL!
And, yes, it could be made even more obscure as a one-liner.
My first attempt was a bit more focused on the specific needs described. I am
not sure how the HTML destroyer in this mailing list might wreck it, but I made
it a two-statement version that is formatted on multiple lines. An explanation
first.
I looked at using grep() on one column at a time to look for what should NOT be
there and ask it to invert the answer so it effectively tells me which rows to
keep. So it tests column 1 ($Name) to see if it has digits in it and returns
FALSE if it finds them which later means toss this row. It returns TRUE if that
entry, so far, makes the row valid. But note since I am not using grepl() it
does not return TRUE/FALSE at all. Rather it returns index numbers of the ones
that now inverted are TRUE. What goes in is a vector of individual items from a
column of the data. What goes out is the indices of which ones I want to keep
that can be used to index the entire data.frame. Based on the ample data, it
returns 1:5 as row 6 has a digit in "Jack3".
? grep("[0-9]", dat1$Name, invert = TRUE)
Similarly, two other grep() statements test if the second and third columns
contain any characters in?"[a-zA-Z]" and return a similar index vector
if they are OK.
What I would then have are three numeric vectors, not a matrix. Each contains a
subset of all the indices:
> grep("[0-9]", dat1$Name, invert = TRUE)[1] 1 2 3 4 5>
grep("[a-zA-Z]", dat1$Age, invert = TRUE)[1] 1 2 3 5 6>
grep("[a-zA-Z]", dat1$Weight, invert = TRUE)[1] 2 3 4 5 6
This set of data was designed to toss out one of each column so they all are of
the same length but need not be. Like Rui, my condition for deciding which rows
to keep is that all three of the index vectors have a particular entry. He
summed them as logicals, but my choice has small integers so the way I combine
them to exclude any not in all three is to use a sort of set intersect method.
The one built-in to R only handles two at a time so I nested two calls to
intersect but in a more general case, I would use some package (or build my own
function) that handles intersecting any number of such items.
Here is the full code, minus the initialization.
rows.keep <-intersect(intersect(grep("[0-9]", dat1$Name, invert =
TRUE),? ? ? ? ? ? ? ? ? ? grep("[a-zA-Z]", dat1$Age, invert = TRUE)),?
? ? ? ? grep("[a-zA-Z]", dat1$Weight, invert = TRUE))result <-
dat1[rows.keep,]
-----Original Message-----
From: Rui Barradas <ruipbarradas at sapo.pt>
To: David Carlson <dcarlson at tamu.edu>; 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 3:46 am
Subject: Re: [R] Row exclude
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.
______________________________________________
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]]
Rui: You made my day! -- or at least considerably improved it. Your solution was clever and clear. IMHO, it is also a terrific example of why one should expend the effort to really learn the core features of the language before plunging into packages with alternative paradigms. (But lots of wise folks will disagree, so let's not debate that and just consider me a luddite if you like). A minor tweak would be to add punctuation characters to the regex's:> dig <- '[[:digit:][:punct:]]' ; nondig <- '[[:alpha:][:punct:]]' > mapply(\(r,x)grepl(r,x),list(dig, nondig, nondig), dat1)This of course would need to be modified for numeric columns with '.' or ',' as a decimal separator. Most examples I've seen were of contamination by a particular character or two (like ',' )) for numeric entries, which could be easily handled of course. As usual, one of the virtues of a nice solution like yours is that it can easily be generalized, say to the case of a data frame with 100's of columns. One just has to be a bit careful about details. A usual 'gotcha' will be to ensure that factor columns are read in or converted to character. Another is that you need to first remove any non-character -- typically non-polluted numeric -- columns from the data frame. This can be done by something like: dat <- dat[, sapply(dat, is.character)] Anyway, with those caveats and perhaps others that I either haven't thought of or may be data-specific, here is an example that illustrates how nicely your approach extends. I'll start from the OP's dat1 example. 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) ## now enlarge the table and add a gender column which should contain only upper or lower case 'm','f', 'o' ## but which I have corrupted with some 'g's (typos) set.seed(9901) genderAbb <- c('M','F','O','m','f','o','g') gender <- sample(genderAbb, 24,dim rep = TRUE) dat1 <- cbind(dat1[rep(1:6,4),], Gender = gender ) head(dat1, 8) Name Age Weight Gender 1 Alex 20 13X O 2 Bob 25 142 M 3 Carol 24 120 o 4 John 3BC 175 o 5 Katy 35 160 f 6 Jack3 34 140 g 1.1 Alex 20 13X M 2.1 Bob 25 142 f ## Now create a list of the different target 'types' for columns. ## Note that these types are user-created categories, not R data types. ## So one can use whatever names one wants. ## Or could use numeric values -- but that obfuscates the meaning and increases the risk of error, imo. type <- c('char', 'int', 'gend') ## obvious ## Now, using your idea, determine the regex's that identify bad entries for each type, badpat <- list( char = '[[:punct:][:digit:]]', ## added stray punctuation int = '[[:punct:][:alpha:]]', ## ditto gend = '[^MFOmfo]' ) ## the only gender abbreviations that will be accepted. ## The initial '^' is the regex symbol for 'anything *but* these in character classes ## Now identify what type of data each column should contain. This is the part that could be tedious ## for many columns, but I see no way of avoiding it. A smarter UI than I give would help! target_type <- c('char','int','int','gend') ## and create the corresponding list of regex patterns to use for mapply() target_pat <- badpat[target_type] ## Now do the Barradas trick result <- mapply(\(pat,x)if(is.character(x))grepl(pat, x) else rep(FALSE, NROW(x)), target_pat, dat1) head(result, 8) ## it's a matrix, not a data frame of course ## ... and then proceed as you showed. Cheers, Bert On Sat, Jan 29, 2022 at 12:46 AM Rui Barradas <ruipbarradas at sapo.pt> wrote:> > 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.