How to get at R source. I am sick of Googling this. I am writing it down this time.
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Accessing R Source

2017-07-31 update: Since I wrote this @jimhester has created the lookup package to automate this process. So if all you want is the result, just use that! If you want a bit more context, then keep reading. AFAIK this info is still fundamentally sound.

How to get at R source. I am sick of Googling this. I am writing it down this time.


getS3method("<S3_GENERIC>", "<CLASS>")


The definitive reference is this classic R News article:

Accessing the Sources

Uwe Ligges

Volume 6/4, October 2006. Go to page 43.

Another good reference is the help file for method():

Just print it

If you are lucky, just printing the function will work.

#> function (object = nm, nm) 
#> {
#>     names(object) <- nm
#>     object
#> }
#> <bytecode: 0x7fce93c98920>
#> <environment: namespace:stats>

But there are many ways this can fail.

vector             # .Internal
#> function (mode = "logical", length = 0L) 
#> .Internal(vector(mode, length))
#> <bytecode: 0x7fce94835c40>
#> <environment: namespace:base>
class              # .Primitive
#> function (x)  .Primitive("class")
subset             # S3 generic
#> function (x, ...) 
#> UseMethod("subset")
#> <bytecode: 0x7fce933c0178>
#> <environment: namespace:base>

What then?

Function is an S3 generic

These are characterized by UseMethod() in the printed result:

#> function (x, ...) 
#> UseMethod("subset")
#> <bytecode: 0x7fce933c0178>
#> <environment: namespace:base>

Print the default method by appending .default:

#> function (x, subset, ...) 
#> {
#>     if (!is.logical(subset)) 
#>         stop("'subset' must be logical")
#>     x[subset & !]
#> }
#> <bytecode: 0x7fce935f1228>
#> <environment: namespace:base>

Or list the methods for this generic:

#> [1] subset.default    subset.matrix    
#> see '?methods' for accessing help and source code

Then print the method you seek:

#> function (x, subset, select, drop = FALSE, ...) 
#> {
#>     if (missing(select)) 
#>         vars <- TRUE
#>     else {
#>         nl <- as.list(1L:ncol(x))
#>         names(nl) <- colnames(x)
#>         vars <- eval(substitute(select), nl, parent.frame())
#>     }
#>     if (missing(subset)) 
#>         subset <- TRUE
#>     else if (!is.logical(subset)) 
#>         stop("'subset' must be logical")
#>     x[subset & !, vars, drop = drop]
#> }
#> <bytecode: 0x7fce93314388>
#> <environment: namespace:base>

Sometimes the method definition is not exported from the package namespace. That's indicated by an asterisk * in the listing (pardon the way I do this, but I have to send through capture.output() if the asterisks are to survive tail():

mout <- capture.output(methods(print))
#> [1] "[195] print.vignette*                                   "
#> [2] "[196] print.warnings                                    "
#> [3] "[197] print.xgettext*                                   "
#> [4] "[198] print.xngettext*                                  "
#> [5] "[199] print.xtabs*                                      "
#> [6] "see '?methods' for accessing help and source code"

Good news: you've found the function you need. Bad news: you still can't read the source.

#> Error in eval(expr, envir, enclos): object 'print.xgettext' not found

Use getAnywhere() or getS3method() to close the deal:

#> A single object matching 'print.xgettext' was found
#> It was found in the following places
#>   registered S3 method for print from namespace tools
#>   namespace:tools
#> with value
#> function (x, ...) 
#> {
#>     cat(x, sep = "\n")
#>     invisible(x)
#> }
#> <bytecode: 0x7fce9787e150>
#> <environment: namespace:tools>

This printed the source AND we learned the associated namespace. If you know the namespace, you can also use ::: to see source:

#> function (x, ...) 
#> {
#>     cat(x, sep = "\n")
#>     invisible(x)
#> }
#> <bytecode: 0x7fce9787e150>
#> <environment: namespace:tools>

Compiled code

Whenever you see .C(), .Call(), .Fortran(), .External(), or .Internal() and .Primitive(), the source you seek is in underlying compiled code.

You need to locate the source code of R or the associated add-on package on the internet or download it locally. Then browse around or search.

Visit the source of R on the internet:

Download source of R itself:

  • Download source for current release from, e.g. R-3.2.2.tar.gz, and unpack it.

    tar xvf R-3.2.2.tar.gz
  • See more info there about getting the development version.

  • Or checkout from the official Subversion repository

How to find what you need in the R source (paraphrasing Ligges, in places):

  • For R and standard R packages, look in subdirs of $R_HOME/src/, most especially $R_HOME/src/main/.
  • If calling R function is .Primitive() or .Internal(), find the entry point $R HOME/src/main/names.c. Then try to find that function. Example below.
  • Use the GitHub search capabilities.

Example: I want the source for levels<-.

`levels<-`    # .Primitive()
#> function (x, value)  .Primitive("levels<-")

Search for levels<- in $R HOME/src/main/names.c and we find this line:

{"levels<-", do_levelsgets, 0, 1, 2, {PP_FUNCALL, PREC_LEFT, 1}}

which tells us we're looking for do_levelsgets. Now use your search capability (GitHub? grep?) to look for that within the files below $R_HOME/src/. I choose the GitHub option and use this query:

 do_levelsgets path:src/main

And finally arrive at my destination: lines 1242 through 1261 in $R_HOME/src/main/attrib.c.

Visit the source of an R package on the internet:

  • If developed on GitHub, go to package's official repo. Ideally, this will be provided as a URL on the package's CRAN page, but sadly not always the case.
  • If the package is on CRAN but not on GitHub, go to the read-only mirror of its source from the METACRAN project.

Download source of an add-on package:

How to find what you need in R package source:

  • Look in the src directory. If you are lucky, there will be a file that obviously contains the function of interest.
  • Otherwise, search with grep, your editor/IDE, or GitHub queries and follow the trail to rainbow's end.

Example: I want the source for dplyr::bind_rows. dplyr is developed on GitHub.

First I search within the R directory with this GitHub search query:

bind_rows path:R

GitHub search only shows you the first one or two matches within a file, but I gather that R/bind.r is where I want to look. I visit it in the browser and use the browser to search for bind_rows, which reveals the function definition. That reveals I actually need bind_rows_.

So I do another GitHub search with this query:


which reveals hits in


Reading the relevant bit of src/bind.cpp reveals I really need rbind__impl.

So I do another GitHub search with this query:


And finally arrive at my destination: lines 7 though 119 of src/bind.cpp.

Things I haven't covered

An incomplete list:

  • S4
  • Other places to put code, e.g. R-Forge or BitBucket