View the Source of an R Package

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/*.

View the source of an R package

Just enter the name of a function/method without parentheses:

R> base::rev.default 
function (x) 
if (length(x)) x[length(x):1L] else x
<environment: namespace:base>

See also R-Help Desk - Accessing the Sources in R News Volume 6/4, October 2006.

How to view the source code of an R function

Since it is a function within the vars package, you can look at the source code the same way that you did for irf.varest.

library(vars)
vars:::.irf

Output

function (x, impulse, response, y.names, n.ahead, ortho, cumulative) 
{
    if ((class(x) == "varest") || (class(x) == "vec2var")) {
        if (ortho) {
            irf <- Psi(x, nstep = n.ahead)
        }
        else {
            irf <- Phi(x, nstep = n.ahead)
        }
    }
    else if ((class(x) == "svarest") || (class(x) == "svecest")) {
        irf <- Phi(x, nstep = n.ahead)
    }
    dimnames(irf) <- list(y.names, y.names, NULL)
    idx <- length(impulse)
    irs <- list()
    for (i in 1:idx) {
        irs[[i]] <- matrix(t(irf[response, impulse[i], 1:(n.ahead + 
            1)]), nrow = n.ahead + 1)
        colnames(irs[[i]]) <- response
        if (cumulative) {
            if (length(response) > 1) 
                irs[[i]] <- apply(irs[[i]], 2, cumsum)
            if (length(response) == 1) {
                tmp <- matrix(cumsum(irs[[i]]))
                colnames(tmp) <- response
                irs[[i]] <- tmp
            }
        }
    }
    names(irs) <- impulse
    result <- irs
    return(result)
}
<bytecode: 0x7ff5bbb40138>
<environment: namespace:vars>

View the source of an builtin R package

This has nothing to do with reorder being compiled to bytecode and everything to do with it being a generic function.

My answer here elaborates on this.

But specifically for this situation if you want to see the code you can use

# Find what methods are available for reorder
methods(reorder)
# Attempt to check out the code for reorder.default
reorder.default
# Use getAnywhere to view code regardless of if it is exported
getAnywhere(reorder.default)

How can I read the source code for an R function?

What we see when you type summary is

> summary
function (object, ...) 
UseMethod("summary")
<bytecode: 0x0456f73c>
<environment: namespace:base>

This is telling us that summary is a generic function and has many methods attached to it. To see what those methods are actually called we can try

> methods(summary)
 [1] summary.aov             summary.aovlist         summary.aspell*        
 [4] summary.connection      summary.data.frame      summary.Date           
 [7] summary.default         summary.ecdf*           summary.factor         
[10] summary.glm             summary.infl            summary.lm             
[13] summary.loess*          summary.manova          summary.matrix         
[16] summary.mlm             summary.nls*            summary.packageStatus* 
[19] summary.PDF_Dictionary* summary.PDF_Stream*     summary.POSIXct        
[22] summary.POSIXlt         summary.ppr*            summary.prcomp*        
[25] summary.princomp*       summary.srcfile         summary.srcref         
[28] summary.stepfun         summary.stl*            summary.table          
[31] summary.tukeysmooth*   

   Non-visible functions are asterisked

Here we see all the methods associated with the summary function. What this means is that there is different code for when you call summary on an lm object than there is when you call summary on a data.frame. This is good because we wouldn't expect the summary to be conducted the same way for those two objects.

To see the code that is run when you call summary on a data.frame you can just type

summary.data.frame

as shown in the methods list. You'll be able to examine it and study it and do whatever you want with the printed code. You mentioned that you were interested in factors so you will probably want to examine the output of summary.factor. Now you might notice that some of the methods printed had an asterisk (*) next to them which implies that they're non-visible. This essentially means that you can't just type the name of the function to try to view the code.

> summary.prcomp
Error: object 'summary.prcomp' not found

However, if you're determined to see what the code actually is you can use the getAnywhere function to view it.

> getAnywhere(summary.prcomp)
A single object matching ‘summary.prcomp’ was found
It was found in the following places
  registered S3 method for summary from namespace stats
  namespace:stats
with value

function (object, ...) 
{
    vars <- object$sdev^2
    vars <- vars/sum(vars)
    importance <- rbind(`Standard deviation` = object$sdev, `Proportion of Variance` = round(vars, 
        5), `Cumulative Proportion` = round(cumsum(vars), 5))
    colnames(importance) <- colnames(object$rotation)
    object$importance <- importance
    class(object) <- "summary.prcomp"
    object
}
<bytecode: 0x03e15d54>
<environment: namespace:stats>

Hopefully this helps you explore the code in R much more easily in the future.

For even more details you can view Volume 6/4 of The R Journal (warning, pdf) and read Uwe Ligge's "R Help Desk" section which deals with viewing the source code of R functions.

How to view R source code

Try this:

getAnywhere(cooks.distance.glm)

From here: http://cran.r-project.org/doc/manuals/R-intro.html#Object-orientation

show source code for function in R

You have to ask using the corresponding method used by the function. Try this:

princomp # this is what you did without having a good enough answer
methods(princomp) # Next step, ask for the method: 'princomp.default'
getAnywhere('princomp.default') # this will show you the code

The code you are looking for is:

function (x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE, 
    nrow(as.matrix(x))), ...) 
{
    cl <- match.call()
    cl[[1L]] <- as.name("princomp")
    if (!missing(x) && !missing(covmat)) 
        warning("both 'x' and 'covmat' were supplied: 'x' will be ignored")
    z <- if (!missing(x)) 
        as.matrix(x)[subset, , drop = FALSE]
    if (is.list(covmat)) {
        if (any(is.na(match(c("cov", "n.obs"), names(covmat))))) 
            stop("'covmat' is not a valid covariance list")
        cv <- covmat$cov
        n.obs <- covmat$n.obs
        cen <- covmat$center
    }
    else if (is.matrix(covmat)) {
        cv <- covmat
        n.obs <- NA
        cen <- NULL
    }
    else if (is.null(covmat)) {
        dn <- dim(z)
        if (dn[1L] < dn[2L]) 
            stop("'princomp' can only be used with more units than variables")
        covmat <- cov.wt(z)
        n.obs <- covmat$n.obs
        cv <- covmat$cov * (1 - 1/n.obs)
        cen <- covmat$center
    }
    else stop("'covmat' is of unknown type")
    if (!is.numeric(cv)) 
        stop("PCA applies only to numerical variables")
    if (cor) {
        sds <- sqrt(diag(cv))
        if (any(sds == 0)) 
            stop("cannot use cor=TRUE with a constant variable")
        cv <- cv/(sds %o% sds)
    }
    edc <- eigen(cv, symmetric = TRUE)
    ev <- edc$values
    if (any(neg <- ev < 0)) {
        if (any(ev[neg] < -9 * .Machine$double.eps * ev[1L])) 
            stop("covariance matrix is not non-negative definite")
        else ev[neg] <- 0
    }
    cn <- paste("Comp.", 1L:ncol(cv), sep = "")
    names(ev) <- cn
    dimnames(edc$vectors) <- if (missing(x)) 
        list(dimnames(cv)[[2L]], cn)
    else list(dimnames(x)[[2L]], cn)
    sdev <- sqrt(ev)
    sc <- if (cor) 
        sds
    else rep(1, ncol(cv))
    names(sc) <- colnames(cv)
    scr <- if (scores && !missing(x) && !is.null(cen)) 
        scale(z, center = cen, scale = sc) %*% edc$vectors
    if (is.null(cen)) 
        cen <- rep(NA_real_, nrow(cv))
    edc <- list(sdev = sdev, loadings = structure(edc$vectors, 
        class = "loadings"), center = cen, scale = sc, n.obs = n.obs, 
        scores = scr, call = cl)
    class(edc) <- "princomp"
    edc
}
<environment: namespace:stats>

I think this what you were asking for.

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