[R] Converting dataframe to matrix

[Jeff Newmiller]

Yes you can. Whether that will yield useful or misleading results depends what analytical tools you intend to apply to the resulting matrix. Categorical data in matrices tends to be kind of a dead end in my experience... but ymmv.

On June 25, 2025 5:23:47 PM PDT, Daniel Lobo <danielobo9976 using gmail.com> wrote:
>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 <rolfturner using posteo.net> wrote:
>>
>>
>> On Thu, 26 Jun 2025 03:45:50 +0530
>> Daniel Lobo <danielobo9976 using 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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>
>______________________________________________
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-- 
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