[R] Linear regression and stand deviation at the Linux command line

[Keith Christian]

Hi Ivan,

Thanks for the suggestions.  Will try them.
-------- Keith

On Fri, Aug 23, 2024 at 1:57 AM Ivan Krylov <ikrylov using disroot.org> wrote:
>
> В Thu, 22 Aug 2024 13:07:37 -0600
> Keith Christian <keith1christian using gmail.com> пишет:
>
> > I'm interested in R construct(s) to be entered at the command
> > line that would output slope, y-intercept, and r-squared values read
> > from a csv or other filename entered at the command line, and the same
> > for standard deviation calculations, namely the standard deviation,
> > variance, and z-scores for every data point in the file.
>
> If you'd like to script R at the command line, consider the
> commandArgs() function (try entering ?commandArgs at the R prompt).
> This way you can pass a file path to an R process without unsafely
> interpolating it into the R expression itself. These arguments can be
> given to R --args or to Rscript (without the --args).
>
> Also consider the 'littler' scripting-oriented R front-end
> <https://CRAN.R-project.org/package=littler>, which puts the command
> line arguments into the 'argv' variable and has a very convenient -d
> option which loads CSV data from the standard input into a variable
> named 'X'.
>
> > Are line numbers, commas, etc. needed or no?
>
> Depends on how you read it. By default, the function read.table() will
> expect your data to be separated by a mixture of tabs and spaces and
> will recognise a header if the first line contains one less column than
> the rest of the file. Enter ?read.table at the R prompt to see the
> available options (which include read.csv).
>
> Good introductions to R include, well, "An Introduction to R" [1] (also
> available by typing RShowDoc('R-intro') into the R prompt) and "Visual
> Statistics" by Dr. A. Shipunov [2].
>
> Start with functions read.table(), lm(), scale(), sd(), summary(). Use
> str() to look at the structure of a variable: summary(lm(...)) will
> return a named list from which you can extract the values you are
> interested in (see ?summary.lm). When in doubt, call
> help(name_of_the_function).
>
> --
> Best regards,
> Ivan
>
> [1]
> https://cran.r-project.org/doc/manuals/R-intro.html
>
> [2]
> http://web.archive.org/web/20230106210646/http://ashipunov.info/shipunov/school/biol_240/en/visual_statistics.pdf