My data frame looks like below
dat = structure(list(a = c(66, 100, 100, 100, 100, 100, 100, 66, 100,
66), b = c(100, 50, 100, 100, 100, 100, 100, 100, 100, 100),
c = c(75, 25, 75, 50, 50, 50, 50, 50, 75, 25)), class =
"data.frame", row.names = c(NA,
-10L))
The values are basically categories for each column, however there may
be missing values present, which typically represented as NA.
My question is can I directly use as.matrix(na.omit(dat)) to convert
this to matrix?
On Thu, 26 Jun 2025 at 05:27, Rolf Turner <rolftur...@posteo.net> wrote:
>
>
> On Thu, 26 Jun 2025 03:45:50 +0530
> Daniel Lobo <danielobo9...@gmail.com> wrote:
>
> > Hi,
> >
> > I have a dataframe for which all columns are numeric but categorical.
>
> I don't understand what that means. Perhaps an example?
>
> > There are some missing values as well
> >
> > Typically, I have CSV file saved in drive, and then read it using
> > read.csv command
>
> Is that relevant?
>
> > Can I use as.matrix(na.omit(<<my dataframe loaded using read.csv>>))
> > to convert such dataframe to matrix?
> >
> > I there any data loss or change that may occur if I use as.matrix
> > command?
>
> I think your question is too vague for anyone to be able to answer this.
>
> > I remember that some experts recommends not to use as.matrix()
> > command to convert a dataframe to matrix.
>
> My guess is that the problem is that as.matrix() will coerce all of the
> columns of a data frame to a common class, which might yield unexpected
> results.
>
> > Any guidance will be very helpful.
>
> It's possible that data.matrix() might be useful.
>
> But basically you should think carefully about what the nature of the
> entries of your data frame could possibly be, and then design your code
> to accommodate all of these possibilities, throwing an error if any
> entry does not conform to any of the possibilities that you envisage.
>
> cheers,
>
> Rolf Turner
>
> --
> Honorary Research Fellow
> Department of Statistics
> University of Auckland
> Stats. Dep't. (secretaries) phone:
> +64-9-373-7599 ext. 89622
> Home phone: +64-9-480-4619
>
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