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Copy pathcloc-recognized-languages.r
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75 lines (65 loc) · 1.98 KB
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#' Return a data frame of `cloc` recognized languages and associated extensions
#'
#' Some file extensions map to multiple languages:
#'
#' - `.cl` files could be Lisp or OpenCL
#' - `.d` files could be D or dtrace
#' - `.f` or `.for` files could be Fortran or Forth
#' - `.fs` files could be Forth or F#
#' - `.inc` files could be PHP or Pascal
#' - `.jl` files could be Lisp or Julia
#' - `.m` files could be MATLAB, Mathematica, Mercury, MUMPS, or Objective C
#' - `.pl` files could be Perl or Prolog
#' - `.pp` files could be Pascal or Puppet
#' - `.pro` files could be IDL, Prolog, or a Qt Project
#' - `.ts` files could be TypeScript or Qt Linguist
#' - `.v` files Coq or Verilog/SystemVerilog
#'
#' `cloc` has subroutines that attempt to identify the correct language based
#' on the file's contents for these special cases. Language identification
#' accuracy is a function of how much code the file contains; .m files with
#' just one or two lines for example, seldom have enough information to
#' correctly distinguish between MATLAB, Mercury, MUMPS, or Objective C.
#' @md
#' @return tibble
#' @export
#' @examples
#' cloc_recognized_languages()
cloc_recognized_languages <- function() {
perl <- find_perl()
c(
system.file("bin/cloc.pl", package = "cloc"),
"--show-lang"
) -> args
processx::run(
command = perl,
args = args
) -> res
dat <- res$stdout
dat <- unlist(strsplit(dat, "\r?\n"))
# sprintf(
# "%s %s --show-lang",
# perl,
# shQuote(system.file("bin/cloc.pl", package = "cloc")),
# source
# ) -> cmd
#
# dat <- system(cmd, intern = TRUE)
do.call(rbind.data.frame,
lapply(
strsplit(dat, "\\("),
function(.x) {
lang <- trimws(.x[1])
extensions <- trimws(.x[2])
extensions <- sub("\\)", "", extensions)
data.frame(
lang = lang,
extensions = extensions,
stringsAsFactors = FALSE
)
}
)
) -> out
class(out) <- c("tbl_df", "tbl", "data.frame")
out
}