Is this a different question from the original post? It would be better to keep 
threads separate.
Always pre-process the data. Clean the data of obvious mistakes. This can be 
simple typographical errors or complicated like an author that wrote too when 
they intended two or to. In old English texts spelling was not standardized and 
the same word could have multiple spellings within one book or chapter. 
Removing punctuation is probably a part of this, though a program like 
Grammarly would not work very well if it removed punctuation.

After that it depends on what you are trying to accomplish. Are you interested 
in the number of times an author used the word “a” or “the” and is “The” 
different from “the?” Are you modeling word use frequency or comparing 
vocabulary between texts.

Too many choices.

Tim

From: Neha gupta <neha.bologn...@gmail.com>
Sent: Wednesday, April 13, 2022 2:49 PM
To: Bill Dunlap <williamwdun...@gmail.com>
Cc: Ebert,Timothy Aaron <teb...@ufl.edu>; r-help mailing list 
<r-help@r-project.org>
Subject: Re: Error with text analysis data

[External Email]
Someone just told me that you need to pre process the data before model 
construction. For instance, make the text to lower case, remove punctuation, 
symbols etc and tokenize the text (give number to each word). Then create word 
of bags model (not sure about it), and then create a model.

Is it true to perform all these steps?

Best regards

On Wednesday, April 13, 2022, Bill Dunlap 
<williamwdun...@gmail.com<mailto:williamwdun...@gmail.com>> wrote:
>  I would always suggest working until the model works, no errors and no NA 
> values

We agree on that.  However, the error gives you no hint about which variables 
are causing the problem.  If it did, then it could only tell about the first 
variable with the problem.  I think you would get to your working model faster 
if you got NA's for the constant columns and then could drop them all at once 
(or otherwise deal with them).

-Bill

On Wed, Apr 13, 2022 at 9:40 AM Ebert,Timothy Aaron 
<teb...@ufl.edu<mailto:teb...@ufl.edu>> wrote:
I suspect that it is because you are looking at two types of error, both 
telling you that the model was not appropriate. In the “error in contrasts” 
there is nothing to contrast in the model. For a numerical constant the program 
calculates the standard deviation and ends with a division by zero. Division by 
zero is undefined, or NA.

I would always suggest working until the model works, no errors and no NA 
values. The reason is that I can get NA in several ways and I need to 
understand why. If I just ignore the NA in my model I may be assuming the wrong 
thing.

Tim

From: Bill Dunlap <williamwdun...@gmail.com<mailto:williamwdun...@gmail.com>>
Sent: Wednesday, April 13, 2022 12:23 PM
To: Ebert,Timothy Aaron <teb...@ufl.edu<mailto:teb...@ufl.edu>>
Cc: Neha gupta <neha.bologn...@gmail.com<mailto:neha.bologn...@gmail.com>>; 
r-help mailing list <r-help@r-project.org<mailto:r-help@r-project.org>>
Subject: Re: [R] Error with text analysis data

[External Email]
Constant columns can be the model when you do some subsetting or are exploring 
a new dataset.  My objection is that constant columns of numbers and logicals 
are fine but those of characters and factors are not.

-Bill

On Wed, Apr 13, 2022 at 9:15 AM Ebert,Timothy Aaron 
<teb...@ufl.edu<mailto:teb...@ufl.edu>> wrote:
What is the goal of having a constant in the model? To me that seems pointless. 
Also there is no variability in sexCode regardless of whether you call it 
integer or factor. So the model y ~ sexCode is just a strange way to look at 
the variability in y and it would be better to do something like summarize(y) 
or mean(y) if that was the goal.

Tim

-----Original Message-----
From: R-help 
<r-help-boun...@r-project.org<mailto:r-help-boun...@r-project.org>> On Behalf 
Of Bill Dunlap
Sent: Wednesday, April 13, 2022 9:56 AM
To: Neha gupta <neha.bologn...@gmail.com<mailto:neha.bologn...@gmail.com>>
Cc: r-help mailing list <r-help@r-project.org<mailto:r-help@r-project.org>>
Subject: Re: [R] Error with text analysis data

[External Email]

This sounds like what I think is a bug in stats::model.matrix.default(): a 
numeric column with all identical entries is fine but a constant character or 
factor column is not.

> d <- data.frame(y=1:5, sex=rep("Female",5)) d$sexFactor <-
> factor(d$sex, levels=c("Male","Female")) d$sexCode <-
> as.integer(d$sexFactor) d
  y    sex sexFactor sexCode
1 1 Female    Female       2
2 2 Female    Female       2
3 3 Female    Female       2
4 4 Female    Female       2
5 5 Female    Female       2
> lm(y~sex, data=d)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
  contrasts can be applied only to factors with 2 or more levels
> lm(y~sexFactor, data=d)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
  contrasts can be applied only to factors with 2 or more levels
> lm(y~sexCode, data=d)

Call:
lm(formula = y ~ sexCode, data = d)

Coefficients:
(Intercept)      sexCode
          3           NA

Calling traceback() after the error would clarify this.

-Bill


On Tue, Apr 12, 2022 at 3:12 PM Neha gupta 
<neha.bologn...@gmail.com<mailto:neha.bologn...@gmail.com>> wrote:

> Hello everyone, I have text data with output variable have three subgroups.
> I am using the following code but getting the error message (see error
> after the code).
>
> d=read.csv("SONAR_RULES.csv", stringsAsFactors = FALSE)
> d$REMEDIATION_FUNCTION=NULL d$DEF_REMEDIATION_GAP_MULT=NULL
> d$REMEDIATION_BASE_EFFORT=NULL
>
> index <- createDataPartition(d$TYPE, p = .70,list = FALSE) tr <-
> d[index, ] ts <- d[-index, ]
>
> ctrl <- trainControl(method = "cv",number=3, index = index, classProbs
> = TRUE, summaryFunction = multiClassSummary)
>
> ran <- train(TYPE ~ ., data = tr,
>                     method = "rpart",
>                     ## Will create 48 parameter combinations
>                     tuneLength = 3,
>                     na.action= na.pass,
>                     metric = "Accuracy",
>                     preProc = c("center", "scale", "nzv"),
>                     trControl = ctrl)
> getTrainPerf(ran)
>
> *It gives me error:*
>
>
> *Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
> contrasts can be applied only to factors with 2 or more levels*
>
>
> *My data is as follow*
>
> Rows: 1,819
> Columns: 14
> $ PLUGIN_RULE_KEY             <chr> "InsufficientBranchCoverage",
> "InsufficientLin~
> $ PLUGIN_CONFIG_KEY           <chr> "", "", "", "", "", "", "", "", "", "",
> "S1120~
> $ PLUGIN_NAME                 <chr> "common-java", "common-java",
> "common-java", "~
> $ DESCRIPTION                 <chr> "An issue is created on a file as soon
> as the ~
> $ SEVERITY                    <chr> "MAJOR", "MAJOR", "MAJOR", "MAJOR",
> "MAJOR", "~
> $ NAME                        <chr> "Branches should have sufficient
> coverage by t~
> $ DEF_REMEDIATION_FUNCTION    <chr> "LINEAR", "LINEAR", "LINEAR",
> "LINEAR_OFFSET",~
> $ REMEDIATION_GAP_MULT        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA~
> $ DEF_REMEDIATION_BASE_EFFORT <chr> "", "", "", "10min", "", "",
> "5min", "5min", "~
> $ GAP_DESCRIPTION             <chr> "number of uncovered conditions",
> "number of l~
> $ SYSTEM_TAGS                 <chr> "bad-practice", "bad-practice",
> "convention", ~
> $ IS_TEMPLATE                 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> 0, 0, 0~
> $ DESCRIPTION_FORMAT          <chr> "HTML", "HTML", "HTML", "HTML", "HTML",
> "HTML"~
> $ TYPE                        <chr> "CODE_SMELL", "CODE_SMELL",
> "CODE_SMELL", "COD~
>
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>
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