Find the Source File Containing R Function Definition

Find the source file containing R function definition

Digging into the srcref attribute of one of the loaded functions appears to work, if you go deep enough ...

source("tmp/tmpsrc.R")
str(util.add)
## function (a, b)  
##  - attr(*, "srcref")=Class 'srcref'  atomic [1:8] 1 13 1 31 13 31 1 1
##   .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x8fffb18> 
srcfile <- attr(attr(util.add,"srcref"),"srcfile")
ls(srcfile)
## [1] "Enc"           "filename"      "fixedNewlines" "isFile"       
## [5] "lines"         "parseData"     "timestamp"     "wd"    
srcfile$filename
## [1] "tmp/tmpsrc.R"

How to find the file where a function is defined?

The environment()-function will return the package in which a function is "located" after it is loaded.

> environment(facet_grid)
<environment: namespace:ggplot2>

After downloading ggplot2_version_whatever.tag.gz from CRAN (or perhaps github) and expanding it, you can find (using your system text search facilities) a file named facet-grid.r that has this definiton starting at line 125:

facet_grid <- function(facets, margins = FALSE, scales = "fixed", space = "fixed", shrink = TRUE, labeller = "label_value", as.table = TRUE, switch = NULL, drop = TRUE) {

You should find more comments. Comments are dropped during compiling unless you make special efforts to retain them.

How can I view the source code for a function?

UseMethod("t") is telling you that t() is a (S3) generic function that has methods for different object classes.

The S3 method dispatch system

For S3 classes, you can use the methods function to list the methods for a particular generic function or class.

> methods(t)
[1] t.data.frame t.default    t.ts*       

   Non-visible functions are asterisked
> methods(class="ts")
 [1] aggregate.ts     as.data.frame.ts cbind.ts*        cycle.ts*       
 [5] diffinv.ts*      diff.ts          kernapply.ts*    lines.ts        
 [9] monthplot.ts*    na.omit.ts*      Ops.ts*          plot.ts         
[13] print.ts         time.ts*         [<-.ts*          [.ts*           
[17] t.ts*            window<-.ts*     window.ts*      

   Non-visible functions are asterisked

"Non-visible functions are asterisked" means the function is not exported from its package's namespace. You can still view its source code via the ::: function (i.e. stats:::t.ts), or by using getAnywhere(). getAnywhere() is useful because you don't have to know which package the function came from.

> getAnywhere(t.ts)
A single object matching ‘t.ts’ was found
It was found in the following places
  registered S3 method for t from namespace stats
  namespace:stats
with value

function (x) 
{
    cl <- oldClass(x)
    other <- !(cl %in% c("ts", "mts"))
    class(x) <- if (any(other)) 
        cl[other]
    attr(x, "tsp") <- NULL
    t(x)
}
<bytecode: 0x294e410>
<environment: namespace:stats>

The S4 method dispatch system

The S4 system is a newer method dispatch system and is an alternative to the S3 system. Here is an example of an S4 function:

> library(Matrix)
Loading required package: lattice
> chol2inv
standardGeneric for "chol2inv" defined from package "base"

function (x, ...) 
standardGeneric("chol2inv")
<bytecode: 0x000000000eafd790>
<environment: 0x000000000eb06f10>
Methods may be defined for arguments: x
Use  showMethods("chol2inv")  for currently available ones.

The output already offers a lot of information. standardGeneric is an indicator of an S4 function. The method to see defined S4 methods is offered helpfully:

> showMethods(chol2inv)
Function: chol2inv (package base)
x="ANY"
x="CHMfactor"
x="denseMatrix"
x="diagonalMatrix"
x="dtrMatrix"
x="sparseMatrix"

getMethod can be used to see the source code of one of the methods:

> getMethod("chol2inv", "diagonalMatrix")
Method Definition:

function (x, ...) 
{
    chk.s(...)
    tcrossprod(solve(x))
}
<bytecode: 0x000000000ea2cc70>
<environment: namespace:Matrix>

Signatures:
        x               
target  "diagonalMatrix"
defined "diagonalMatrix"

There are also methods with more complex signatures for each method, for example

require(raster)
showMethods(extract)
Function: extract (package raster)
x="Raster", y="data.frame"
x="Raster", y="Extent"
x="Raster", y="matrix"
x="Raster", y="SpatialLines"
x="Raster", y="SpatialPoints"
x="Raster", y="SpatialPolygons"
x="Raster", y="vector"

To see the source code for one of these methods the entire signature must be supplied, e.g.

getMethod("extract" , signature = c( x = "Raster" , y = "SpatialPolygons") )

It will not suffice to supply the partial signature

getMethod("extract",signature="SpatialPolygons")
#Error in getMethod("extract", signature = "SpatialPolygons") : 
#  No method found for function "extract" and signature SpatialPolygons

Functions that call unexported functions

In the case of ts.union, .cbindts and .makeNamesTs are unexported functions from the stats namespace. You can view the source code of unexported functions by using the ::: operator or getAnywhere.

> stats:::.makeNamesTs
function (...) 
{
    l <- as.list(substitute(list(...)))[-1L]
    nm <- names(l)
    fixup <- if (is.null(nm)) 
        seq_along(l)
    else nm == ""
    dep <- sapply(l[fixup], function(x) deparse(x)[1L])
    if (is.null(nm)) 
        return(dep)
    if (any(fixup)) 
        nm[fixup] <- dep
    nm
}
<bytecode: 0x38140d0>
<environment: namespace:stats>

Functions that call compiled code

Note that "compiled" does not refer to byte-compiled R code as created by the compiler package. The <bytecode: 0x294e410> line in the above output indicates that the function is byte-compiled, and you can still view the source from the R command line.

Functions that call .C, .Call, .Fortran, .External, .Internal, or .Primitive are calling entry points in compiled code, so you will have to look at sources of the compiled code if you want to fully understand the function. This GitHub mirror of the R source code is a decent place to start. The function pryr::show_c_source can be a useful tool as it will take you directly to a GitHub page for .Internal and .Primitive calls. Packages may use .C, .Call, .Fortran, and .External; but not .Internal or .Primitive, because these are used to call functions built into the R interpreter.

Calls to some of the above functions may use an object instead of a character string to reference the compiled function. In those cases, the object is of class "NativeSymbolInfo", "RegisteredNativeSymbol", or "NativeSymbol"; and printing the object yields useful information. For example, optim calls .External2(C_optimhess, res$par, fn1, gr1, con) (note that's C_optimhess, not "C_optimhess"). optim is in the stats package, so you can type stats:::C_optimhess to see information about the compiled function being called.

Compiled code in a package

If you want to view compiled code in a package, you will need to download/unpack the package source. The installed binaries are not sufficient. A package's source code is available from the same CRAN (or CRAN compatible) repository that the package was originally installed from. The download.packages() function can get the package source for you.

download.packages(pkgs = "Matrix", 
                  destdir = ".",
                  type = "source")

This will download the source version of the Matrix package and save the corresponding .tar.gz file in the current directory. Source code for compiled functions can be found in the src directory of the uncompressed and untared file. The uncompressing and untaring step can be done outside of R, or from within R using the untar() function. It is possible to combine the download and expansion step into a single call (note that only one package at a time can be downloaded and unpacked in this way):

untar(download.packages(pkgs = "Matrix",
                        destdir = ".",
                        type = "source")[,2])

Alternatively, if the package development is hosted publicly (e.g. via GitHub, R-Forge, or RForge.net), you can probably browse the source code online.

Compiled code in a base package

Certain packages are considered "base" packages. These packages ship with R and their version is locked to the version of R. Examples include base, compiler, stats, and utils. As such, they are not available as separate downloadable packages on CRAN as described above. Rather, they are part of the R source tree in individual package directories under /src/library/. How to access the R source is described in the next section.

Compiled code built into the R interpreter

If you want to view the code built-in to the R interpreter, you will need to download/unpack the R sources; or you can view the sources online via the R Subversion repository or Winston Chang's github mirror.

Uwe Ligges's R news article (PDF) (p. 43) is a good general reference of how to view the source code for .Internal and .Primitive functions. The basic steps are to first look for the function name in src/main/names.c and then search for the "C-entry" name in the files in src/main/*.

R: source() and path to source files

If you are distributing a script to colleagues, you should really not be writing a script that sources other scripts. What if you want to rename or move functions.R in the future? What if you need to modify a function in functions.R, but wrapper.R relies on the older version of that function? It's a flimsy solution that will cause headache. I would recommend either of the following instead.

1. Put everything needed into a single, self-contained script and distribute that.

2. If you really want to separate code into different files, write a package. Might sound like overkill, but packages can actually be very simple and lightweight. In the simplest form a package is just a directory with a DESCRIPTION and NAMESPACE file along with an R/ directory. Hadley breaks this down nicely: https://r-pkgs.org/whole-game.html.

How to find out which .c file contains the .c functions of R internals, on Windows?

As a Windows user, here are a couple of options. The first one is preferable, but the second one is OK for occasional use:

• Download grepwin, which will allow you to search Windows directories using the powerful grep command that both Joshua and Gavin have mentioned. It (or some equivalent) is indispensable if you'll be doing much poking around in program source directories.

• Use the search bar at this site to search the R source directory for the definition of do_matchcall. Clicking on the result it returns will tell you that do_matchcall is "[defined] at line 1193 of file unique.c", and will provide a hyperlink to the code in unique.c.

Like I said, though, you'll ultimately be much happier if you equip your Windows box with some implementation of grep.

Is there an Rstudio keyboard shortcut to open up the file that contains the source code to a function you've written?

If you are within a package then F2 will navigate to the source file of functions defined within that package (it would be nice if you could also go to other packages but that doesn't work yet). You can also use Ctrl+. to do a typeahead search of all functions in the package (and navigate from the list).

How to examine the code of a function in R that's object class sensitive

When you say

the function did do other things
depending on the class of the object
thrown at it

you are already at the heart of the S3 dispatch mechanism! So
I would recommend reading a programming book on R as e.g.

• (classic but dated) Venables/Ripley "S Programming",
• Gentleman "Bioinformatics with R",
• Brown/Murdoch "First Course in Statistical Programming with R",
• Chambers "Software for Data Analysis: Programming with R",

or other resources from this SO question on R books along with an example package or two from the rich set of CRAN packages.

Find top level functions in R file

I think sourcing will be necessary, but you don't need to clutter you global environment. I tested this locally and it seems to work:

find_functions = function(file) {
    search_env = new.env()
    source(file = file, local = search_env)
    objects = ls(envir = search_env)
    functions = objects[sapply(ls(envir = search_env), FUN = function(x) {
        is.function(get(x, envir = search_env))
    })]
    return(functions)
}

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