This is a guide to the internal structures of R and coding standards for the core team working on R itself.
The current version of this document is 2.6.1 (2007-11-26).
ISBN 3-900051-14-3
This chapter is the beginnings of documentation about R internal structures. It is written for the R core team and others studying the code in the src/main directory.
It is a work-in-progress, first begun for R 2.4.0, and should be checked against the current version of the source code.
What R users think of as variables or objects are
symbols which are bound to a value. The value can be thought of as
either a SEXP (a pointer), or the structure it points to, a
SEXPREC (and there are alternative forms used for vectors, namely
VECSXP pointing to VECTOR_SEXPREC structures).
So the basic building blocks of R objects are often called
nodes, meaning SEXPRECs or VECTOR_SEXPRECs.
Note that the internal structure of the SEXPREC is not made
available to R Extensions: rather SEXP is an opaque pointer, and
the internals can only be accessed by the functions provided.
Both types of node structure have as their first three fields a 32-bit
sxpinfo header and then three pointers (to the attributes and the
previous and next node in a doubly-linked list), and then some further
fields. On a 32-bit platform a node1 occupies 28 bytes: on a 64-bit platform typically 56
bytes (depending on alignment constraints).
The first five bits of the sxpinfo header specify one of up to 32
SEXPTYPEs.
Currently SEXPTYPEs 0:10 and 13:25 are in use. Values 11 and 12 were
used for internal factors and ordered factors and have since been
withdrawn. Note that the SEXPTYPEs are stored in saved
objects and that the ordering of the types is used, so the gap cannot
easily be reused.
no SEXPTYPE Description 0NILSXPNULL1SYMSXPsymbols 2LISTSXPpairlists 3CLOSXPclosures 4ENVSXPenvironments 5PROMSXPpromises 6LANGSXPlanguage objects 7SPECIALSXPspecial functions 8BUILTINSXPbuiltin functions 9CHARSXPinternal character strings 10LGLSXPlogical vectors 13INTSXPinteger vectors 14REALSXPnumeric vectors 15CPLXSXPcomplex vectors 16STRSXPcharacter vectors 17DOTSXPdot-dot-dot object 18ANYSXPmake “any” args work 19VECSXPlist (generic vector) 20EXPRSXPexpression vector 21BCODESXPbyte code 22EXTPTRSXPexternal pointer 23WEAKREFSXPweak reference 24RAWSXPraw vector 25S4SXPS4 classes not of simple type
Many of these will be familiar from R level: the atomic vector types
are LGLSXP, INTSXP, REALSXP, CPLXSP,
STRSXP and RAWSXP. Lists are VECSXP and names
(also known as symbols) are SYMSXP. Pairlists (LISTSXP,
the name going back to the origins of R as a Scheme-like language)
are rarely seen at R level, but are for example used for argument
lists. Character vectors are effectively lists all of whose elements
are CHARSXP, a type that is rarely visible at R level.
Language objects (LANGSXP) are calls (including formulae and so
on). Internally they are pairlists with first element a
reference2 to the function to be called with remaining elements the
actual arguments for the call (and with the tags if present giving the
specified argument names). Although this is not enforced, many places
in the code assume that the pairlist is of length one or more, often
without checking.
Expressions are of type EXPRSXP: they are a vector of (usually
language) objects most often seen as the result of parse().
The functions are of types CLOSXP, SPECIALSXP and
BUILTINSXP: where SEXPTYPEs are stored in an integer
these are sometimes lumped into a pseudo-type FUNSXP with code
99. Functions defined via function are of type CLOSXP and
have formals, body and environment.
The SEXPTYPE S4SXP was introduced in R 2.4.0 for S4
classes which were previously represented as empty lists, that is
objects which do not consist solely of a simple type such as an atomic
vector or function.
The sxpinfo header is defined as a 32-bit C structure by
struct sxpinfo_struct {
SEXPTYPE type : 5; /* discussed above */
unsigned int obj : 1; /* is this an object with a class attribute? */
unsigned int named : 2; /* used to control copying */
unsigned int gp : 16; /* general purpose, see below */
unsigned int mark : 1; /* mark object as `in use' in GC */
unsigned int debug : 1;
unsigned int trace : 1;
unsigned int spare : 1; /* unused */
unsigned int gcgen : 1; /* generation for GC */
unsigned int gccls : 3; /* class of node for GC */
}; /* Tot: 32 */
The debug bit is used for closures and environments. For
closures it is set by debug() and unset by undebug(), and
indicates that evaluations of the function should be run under the
browser. For environments it indicates whether the browsing is in
single-step mode.
The trace bit is used for functions for trace() and for
other objects when tracing duplications (see tracemem).
The named field is set and accessed by the SET_NAMED and
NAMED macros, and take values 0, 1 and 2.
R has a `call by value' illusion, so an assignment like
b <- a
appears to make a copy of a and refer to it as b.
However, if neither a nor b are subsequently altered there
is no need to copy. What really happens is that a new symbol b
is bound to the same value as a and the named field on the
value object is set (in this case to 2). When an object is about
to be altered, the named field is consulted. A value of 2
means that the object must be duplicated before being changed. (Note
that this does not say that it is necessary to duplicate, only that it
should be duplicated whether necessary or not.) A value of 0
means that it is known that no other SEXP shares data with this
object, and so it may safely be altered. A value of 1 is used
for situations like
dim(a) <- c(7, 2)
where in principle two copies of a exist for the duration of the
computation as (in principle)
a <- `dim<-`(a, c(7, 2))
but for no longer, and so some primitive functions can be optimized to avoid a copy in this case.
The gp bits are by definition `general purpose'. As of version
2.4.0 of R, bit 4 (i.e., the fifth bit) is turned on to mark S4 objects.
Bits 0-3 and bits 14-15 have been used previously as described below
(from detective work on the sources).
The bits can be accessed and set by the LEVELS and
SETLEVELS macros, which names appear to date back to the internal
factor and ordered types and are now used in only a few places in the
code. The gp field is serialized/unserialized for the
SEXPTYPEs other than NILSXP, SYMSXP and
ENVSXP.
If we label the bits from 0, bits 14 and 15 of gp are used for
`fancy bindings'. Bit 14 is used to lock a binding or an environment,
and bit 15 is used to indicate an active binding. (For the definition
of an `active binding' see the header comments in file
src/main/envir.c.) Bit 15 is used for an environment to indicate
if it participates in the global cache.
Almost all other uses seem to be only of bits 0 and 1, although one reserves the first four bits.
The macros ARGUSED and SET_ARGUSED are used when matching
actual and formal function arguments, and take the values 0, 1 and 2.
The macros MISSING and SET_MISSING are used for pairlists
of arguments. Four bits are reserved, but only two are used (and
exactly what for is not explained). It seems that bit 0 is used by
matchArgs to mark missingness on the returned argument list, and
bit 1 is used to mark the use of a default value for an argument copied
to the evaluation frame of a closure.
Bit 0 is used by macros DDVAL and SET_DDVAL. This
indicates that a SYMSXP is one of the symbols ..n which
are implicitly created when ... is processed, and so indicates
that it may need to be looked up in a DOTSXP.
Bit 0 is used for PRSEEN, a flag to indicate if a promise has
already been seen during the evaluation of the promise (and so to avoid
recursive loops).
Bit 0 is used for HASHASH, on the PRINTNAME of the
TAG of the frame of an environment.
Bits 0 and 1 are used for weak references (to indicate 'ready to finalize', 'finalize on exit').
Bit 0 is used by the condition handling system (on a VECSXP) to
indicate a calling handler.
As from R 2.5.0, bits 2 and 3 for a CHARSXP are used to note
that it is known to be in Latin-1 and UTF-8 respectively. (These are not
usually set if it is also known to be in ASCII, since code does not need
to know the charset to handle ASCII strings.)
A SEXPREC is a C structure containing the 32-bit header as
described above, three pointers (to the attributes, previous and next
node) and the node data, a union
union {
struct primsxp_struct primsxp;
struct symsxp_struct symsxp;
struct listsxp_struct listsxp;
struct envsxp_struct envsxp;
struct closxp_struct closxp;
struct promsxp_struct promsxp;
} u;
All of these alternatives apart from the first (an int) are three
pointers, so the union occupies three words.
The vector types are RAWSXP, CHARSXP, LGLSXP,
INTSXP, REALSXP, CPLXSXP, STRSXP,
VECSXP, EXPRSXP and WEAKREFSXP. Remember that such
types are a VECTOR_SEXPREC, which again consists of the header
and the same three pointers, but followed by two integers giving the
length and `true length'3 of the vector, and then followed by the data (aligned as
required: on most 32-bit systems with a 24-byte VECTOR_SEXPREC
node the data can follow immediately after the node). The data are a
block of memory of the appropriate length to store `true length'
elements (rounded up to a multiple of 8 bytes, with the 8-byte blocks
being the `Vcells' referred in the documentation for gc()).
The `data' for the various types are given in the table below. A lot of this is interpretation, i.e. the types are not checked.
NILSXPNILSXP, R_NilValue, with
no data.
SYMSXPPRINTNAME (a CHARSXP), SYMVALUE and
INTERNAL. (If the symbol's value is a .Internal function,
the last is a pointer to the appropriate SEXPREC.) Many symbols
have SYMVALUE R_UnboundValue.
LISTSXPLISTSXP or NULL) and TAG
(usually a SYMSXP).
CLOSXPENVSXPNULL or a
VECSXP). A frame is a tagged pairlist with tag the symbol and
CAR the bound value.
PROMSXPNULL.
LANGSXPLISTSXP used for function calls. (The CAR
references the function (perhaps via a symbol or language object), and
the CDR the argument list with tags for named arguments.) R-level
documentation references to `expressions' / `language objects' are
mainly LANGSXPs, but can be symbols (SYMSXPs) or
expression vectors (EXPRSXPs).
SPECIALSXPBUILTINSXP.Internals.
CHARSXPlength, truelength followed by a block of bytes (allowing
for the nul terminator).
LGLSXPINTSXPlength, truelength followed by a block of C ints
(which are 32 bits on all R platforms).
REALSXPlength, truelength followed by a block of C doubles
CPLXSXPlength, truelength followed by a block of C99
double complexs, or equivalent structures.
STRSXPlength, truelength followed by a block of pointers
(SEXPs pointing to CHARSXPs).
DOTSXPLISTSXP for the value bound to a ...
symbol: a pairlist of promises.
ANYSXPVECSXPEXPRSXPlength, truelength followed by a block of pointers. These
are internally identical (and identical to STRSXP) but differ in
the interpretations placed on the elements.
BCODESXPEXTPTRSXPSYMSXP?).
WEAKREFSXPWEAKREFSXP is a special VECSXP of length 4, with
elements `key', `value', `finalizer' and `next'.
The `key' is NULL, an environment or an external pointer,
and the `finalizer' is a function or NULL.
RAWSXPlength, truelength followed by a block of bytes.
S4SXPAs we have seen, the field gccls in the header is three bits to
label up to 8 classes of nodes. Non-vector nodes are of class 0, and
`small' vector nodes are of classes 1 to 6, with `large' vector nodes
being of class 7. The `small' vector nodes are able to store vector
data of up to 8, 16, 32, 48, 64 and 128 bytes: larger vectors are
malloc-ed individually whereas the `small' nodes are allocated
from pages of about 2000 bytes.
What users think of as `variables' are symbols which are bound to
objects in `environments'. The word `environment' is used ambiguously
in R to mean either the frame of an ENVSXP (a pairlist
of symbol-value pairs) or an ENVSXP, a frame plus an
enclosure.
There are additional places that `variables' can be looked up, called `user databases' in comments in the code. These seem undocumented in the R sources, but apparently refer to the RObjectTable package at http://www.omegahat.org/RObjectTables/.
The base environment is special. There is an ENVSXP environment
with enclosure the empty environment R_EmptyEnv, but the frame of
that environment is not used. Rather its bindings are part of the
global symbol table, being those symbols in the global symbol table
whose values are not R_UnboundValue. When R is started the
internal functions are installed (by C code) in the symbol table, with
primitive functions having values and .Internal functions having
what would be their values in the field accessed by the INTERNAL
macro. Then .Platform and .Machine are computed and the
base package is loaded into the base environment followed by the system
profile.
The frames of environments (and the symbol table) are normally hashed for faster access (including insertion and deletion).
By default R maintains a (hashed) global cache of `variables' (that
is symbols and their bindings) which have been found, and this refers
only to environments which have been marked to participate, which
consists of the global environment (aka the user workspace), the base
environment plus environments4 which have been attached. When an environment is either
attached or detached, the names of its symbols are flushed
from the cache. The cache is used whenever searching for variables from
the global environment (possibly as part of a recursive search).
S has the notion of a `search path': the lookup for a `variable'
leads (possibly through a series of frames) to the `session frame' the
`working directory' and then along the search path. The search path is
a series of databases (as returned by search()) which contain the
system functions (but not necessarily at the end of the path, as by
default the equivalent of packages are added at the end).
R has a variant on the S model. There is a search path (also
returned by search()) which consists of the global environment
(aka user workspace) followed by environments which have been attached
and finally the base environment. Note that unlike S it is not
possible to attach environments before the workspace nor after the base
environment.
However, the notion of variable lookup is more general in R, hence the plural in the title of this subsection. Since environments have enclosures, from any environment there is a search path found by looking in the frame, then the frame of its enclosure and so on. Since loops are not allowed, this process will eventually terminate: until R 2.2.0 it always terminated at the base environment, but nowadays it can terminate at either the base environment or the empty environment. (It can be conceptually simpler to think of the search always terminating at the empty environment, but with an optimization to stop at the base environment.) So the `search path' describes the chain of environments which is taken once the search reaches the global environment.
Name spaces are environments associated with packages (and once again
the base package is special and will be considered separately). A
package pkg with a name space defines two environments
namespace:pkg and package:pkg: it is
package:pkg that can be attached and form part of
the search path.
The objects defined by the R code in the package are symbols with
bindings in the namespace:pkg environment. The
package:pkg environment is populated by selected symbols
fron the namespace:pkg environment (the exports). The
enclosure of this environment is an environment populated with the
explicit imports from other name spaces, and the enclosure of
that environment is the base name space. (So the illusion of the
imports being in the name space environment is created via the
environment tree.) The enclosure of the base name space is the global
environment, so the search from a package name space goes via the
(explicit and implicit) imports to the standard `search path'.
The base name space environment R_BaseNamespace is another
ENVSXP that is special-cased. It is effectively the same thing
as the base environment R_BaseEnv except that its
enclosure is the global environment rather than the empty environment:
the internal code diverts lookups in its frame to the global symbol
table.
As we have seen, every SEXPREC has a pointer to the attributes of
the node (default R_NilValue). The attributes can be accessed/set
by the macros/functions ATTRIB and SET_ATTRIB, but such
direct access is normally5 only
used to check if the attributes are NULL or to reset them.
Otherwise access goes through the functions getAttrib and
setAttrib which impose restrictions on the attributes. One thing
to watch is that if you copy attributes from one object to another you
may (un)set the "class" attribute and so need to copy the object
and S4 bits as well. There is a macro/function
DUPLICATE_ATTRIB to automate this.
The code assumes that the attributes of a node are either
R_NilValue or a pairlist of non-zero length (and this is checked
by SET_ATTRIB). The attributes are named (via tags on the
pairlist). The replacement function attributes<- ensures that
"dim" precedes "dimnames" in the pairlist. Attribute
"dim" is one of several that is treated specially: the values are
checked, and any "names" and "dimnames" attributes are
removed. Similarly, you cannot set "dimnames" without having set
"dim", and the value assigned must be a list of the correct
length and with elements of the correct lengths (and all zero-length
elements are replaced by NULL).
The other attributes which are given special treatment are
"names", "class", "tsp", "comment" and
"row.names". For pairlist-like objects the names are not stored
as an attribute but (as symbols) as the tags: however the R interface
makes them look like conventional attributes, and for one-dimensional
arrays they are stored as the first element of the "dimnames"
attribute. The C code ensures that the "tsp" attribute is an
REALSXP, the frequency is positive and the implied length agrees
with the number of rows of the object being assigned to. Classes and
comments are restricted to character vectors, and assigning a
zero-length comment or class removes the attribute. Setting or removing
a "class" attribute sets the object bit appropriately. Integer
row names are converted to and from the internal compact representation.
Care needs to be taken when adding attributes to objects of the types
with non-standard copying semantics. There is only one object of type
NILSXP, R_NilValue, and that should never have attributes
(and this is enforced in installAttrib). For environments,
external pointers and weak references, the attributes should be relevant
to all uses of the object: it is for example reasonable to have a name
for an environment, and also a "path" attribute for those
environments populated from R code in a package.
When should attributes be preserved under operations on an object?
Becker, Chambers & Wilks (1988, pp. 144–6) give some guidance. Scalar
functions (those which operate element-by-element on a vector and whose
output is similar to the input) should preserve attributes (except
perhaps class, and if they do preserve class they need to preserve the
OBJECT and S4 bits). Binary operations normally call
copyMostAttributes to copy most attributes from the longer
argument (and if they are of the same length from both, preferring the
values on the first). Here `most' means all except the names,
dim and dimnames which are set appropriately by the code
for the operator.
Subsetting (other than by an empty index) generally drops all attributes
except names, dim and dimnames which are reset as
appropriate. On the other hand, subassignment generally preserves such
attributes even if the length is changed. Coercion drops all
attributes. For example:
> x <- structure(1:8, names=letters[1:8], comm="a comment")
> x[]
a b c d e f g h
1 2 3 4 5 6 7 8
attr(,"comm")
[1] "a comment"
> x[1:3]
a b c
1 2 3
> x[3] <- 3
> x
a b c d e f g h
1 2 3 4 5 6 7 8
attr(,"comm")
[1] "a comment"
> x[9] <- 9
> x
a b c d e f g h
1 2 3 4 5 6 7 8 9
attr(,"comm")
[1] "a comment"
Contexts are the internal mechanism used to keep track of where a
computation has got to (and from where), so that control-flow constructs
can work and reasonable information can be produced on error conditions,
(such as via traceback) and otherwise (the sys.xxx
functions).
Execution contexts are a stack of C structs:
typedef struct RCNTXT {
struct RCNTXT *nextcontext; /* The next context up the chain */
int callflag; /* The context `type' */
JMP_BUF cjmpbuf; /* C stack and register information */
int cstacktop; /* Top of the pointer protection stack */
int evaldepth; /* Evaluation depth at inception */
SEXP promargs; /* Promises supplied to closure */
SEXP callfun; /* The closure called */
SEXP sysparent; /* Environment the closure was called from */
SEXP call; /* The call that effected this context */
SEXP cloenv; /* The environment */
SEXP conexit; /* Interpreted on.exit code */
void (*cend)(void *); /* C on.exit thunk */
void *cenddata; /* Data for C on.exit thunk */
char *vmax; /* Top of the R_alloc stack */
int intsusp; /* Interrupts are suspended */
SEXP handlerstack; /* Condition handler stack */
SEXP restartstack; /* Stack of available restarts */
struct RPRSTACK *prstack; /* Stack of pending promises */
} RCNTXT, *context;
plus additional fields for the future byte-code compiler. The `types' are from
enum {
CTXT_TOPLEVEL = 0, /* toplevel context */
CTXT_NEXT = 1, /* target for next */
CTXT_BREAK = 2, /* target for break */
CTXT_LOOP = 3, /* break or next target */
CTXT_FUNCTION = 4, /* function closure */
CTXT_CCODE = 8, /* other functions that need error cleanup */
CTXT_RETURN = 12, /* return() from a closure */
CTXT_BROWSER = 16, /* return target on exit from browser */
CTXT_GENERIC = 20, /* rather, running an S3 method */
CTXT_RESTART = 32, /* a call to restart was made from a closure */
CTXT_BUILTIN = 64 /* builtin internal function */
};
where the CTXT_FUNCTION bit is on wherever function closures are
involved.
Contexts are created by a call to begincontext and ended by a
call to endcontext: code can search up the stack for a
particular type of context via findcontext (and jump there) or
jump to a specific context via R_JumpToContext.
R_ToplevelContext is the `idle' state (normally the command
prompt), and R_GlobalContext is the top of the stack.
Note that whilst all calls to closures set a context, those to special internal functions never do, and those to builtin internal functions have done so only recently (and prior to that only when profiling).
Dispatching from a S3 generic (via UseMethod or its internal
equivalent) or calling NextMethod sets the context type to
CTXT_GENERIC. This is used to set the sysparent of the
method call to that of the generic, so the method appears to have
been called in place of the generic rather than from the generic.
The R sys.frame and sys.call work by counting calls to
closures (type CTXT_FUNCTION) from either end of the context
stack.
Note that the sysparent element of the structure is not the same
thing as sys.parent(). Element sysparent is primarily
used in managing changes of the function being evaluated, i.e. by
Recall and method dispatch.
CTXT_CCODE contexts are currently used in cat(),
load(), scan() and write.table() (to close the
connection on error), by PROTECT, serialization (to recover from
errors, e.g. free buffers) and within the error handling code (to
raise the C stack limit and reset some variables).
As we have seen, functions in R come in three types, closures
(SEXPTYPE CLOSXP), specials (SPECIALSXP) and
builtins (BUILTINSXP). In this section we consider when (and if)
the actual arguments of function calls are evaluated. The rules are
different for the internal (special/builtin) and R-level functions
(closures).
For a call to a closure, the actual and formal arguments are matched and
a matched call (another LANGSXP) is constructed. This process
first replaces the actual argument list by a list of promises to the
values supplied. It then constructs a new environment which contains
the names of the formal parameters matched to actual or default values:
all the matched values are promises, the defaults as promises to be
evaluated in the environment just created. That environment is then
used for the evaluation of the body of the function, and promises will
be forced (and hence actual or default arguments evaluated) when they
are encountered.
(Evaluating a promise sets NAMED = 2 on its value, so if the
argument was a symbol its binding is regarded as having multiple
references during the evaluation of the closure call.)
If the closure is an S3 generic (that is, contains a call to
UseMethod) the evaluation process is the same until the
UseMethod call is encountered. At that point the argument on
which to do dispatch (normally the first) will be evaluated if it has
not been already. If a method has been found which is a closure, a new
evaluation environment is created for it containing the matched
arguments of the method plus any new variables defined so far during the
evaluation of the body of the generic. (Note that this means changes to
the values of the formal arguments in the body of the generic are
discarded when calling the method, but actual argument promises
which have been forced retain the values found when they were forced.
On the other hand, missing arguments have values which are promises to
use the default supplied by the method and not the generic.) If the
method found is a special or builtin it is called with the matched
argument list of promises (possibly already forced) used for the generic.
The essential difference6 between special and builtin functions is
that the arguments of specials are not evaluated before the C code is
called, and those of builtins are. In each case positional matching of
arguments is used. Note that being a special/builtin is separate from
being primitive or .Internal: function is a special
primitive, + is a builtin primiitve, switch is a special
.Internal and grep is a builtin .Internal.
Many of the internal functions are internal generics, which for specials
means that they do not evaluate their arguments on call, but the C code
starts with a call to DispatchOrEval. The latter evaluates the
first argument, and looks for a method based on its class. (If S4
dispatch is on, S4 methods are looked for first, even for S3 classes.)
If it finds a method, it dispatches to that method with a call based on
promises to evaluate the remaining arguments. If no method is found,
the remaining arguments are evaluated before return to the internal
generic.
The other way that internal functions can be generic is to be group
generic. All such functions are builtins (so immediately evaluate all
their arguments), and contain a call to the C function
DispatchGeneric. There are some peculiarities over the number of
arguments for the "Math" group generic, with some members
allowing only one argument, some having two (with a default for the
second) and trunc allows one or more but the default only
accepts one.
Actual arguments to (non-internal) R functions can be fewer than are required to match the formal arguments of the function. Having unmatched formal arguments will not matter if the argument is never used (by lazy evaluation), but when the argument is evaluated, either its default value is evaluated (within the evaluation environment of the function) or an error is thrown with a message along the lines of
argument "foobar" is missing, with no default
Internally missingness is handled by two mechanisms. The object
R_MissingArg is used to indicate that a formal argument has no
(default) value. When matching the actual arguments to the formal
arguments, a new argument list is constructed from the formals all of
whose values are R_MissingArg with the first MISSING bit
set. Then whenever a formal argument is matched to an actual argument,
the corresponding member of the new argument list has its value set to
that of the matched actual argument, and if that is not
R_MissingArg the missing bit is unset.
This new argument list is used to form the evaluation frame for the function, and if named arguments are subsequently given a new value (before they are evaluated) the missing bit is cleared.
Missingness of arguments can be interrogated via the missing()
function. An argument is clearly missing if its missing bit is set or
if the value is R_MissingArg. However, missingness can be passed
on from function to function, for using a formal argument as an actual
argument in a function call does not count as evaluation. So
missing() has to examine the value (a promise) of a
non-yet-evaluated formal argument to see if it might be missing, which
might involve investigating a promise and so on ....
Dot-dot-dot arguments are convenient when writing functions, but complicate the internal code for argument evaluation.
The formals of a function with a ... argument represent that as a
single argument like any other argument, with tag the symbol
R_DotsSymbol. When the actual arguments are matched to the
formals, the value of the ... argument is of SEXPTYPE
DOTSXP, a pairlist of promises (as used for matched arguments)
but distinguished by the SEXPTYPE.
Recall that the evaluation frame for a function initially contains the
name=value pairs from the matched call, and hence
this will be true for ... as well. The value of ... is a
(special) pairlist whose elements are referred to by the special symbols
..1, ..2, ... which have the DDVAL bit set:
when one of these is encountered it is looked up (via ddfndVar)
in the value of the ... symbol in the evaluation frame.
Values of arguments matched to a ... argument can be missing.
Whether the returned value of a top-level R expression is printed is
controlled by the global boolean variable R_Visible. This is set
(to true or false) on entry to all primitive and internal functions
based on the eval column of the table in names.c: the
appropriate setting can be extracted by the macro PRIMPRINT.
The R primitive function invisible makes use of this
mechanism: it just sets R_Visibility = FALSE before entry and
returns its argument.
For most functions the intention will be that the setting of
R_Visible when they are entered is the setting used when they
return, but there need to be exceptions. The R functions
identify, options, system and writeBin
determine whether the result should be visible from the arguments or
user action. Other functions themselves dispatch functions which may
change the visibility flag: examples7 are
.Internal, do.call, eval,
eval.with.vis8, if,
NextMethod, Recall, recordGraphics,
standardGeneric, switch and UseMethod.
`Special' primitive and internal functions evaluate their arguments
internally after R_Visible has been set, and evaluation of
the arguments (e.g. an assignment as in PR#9263)) can change the value
of the flag. Prior to R 2.5.0, known instances of such functions
reset the flag after the internal evaluation of arguments: examples
include [, [[, $, c, cbind,
dump, rbind and unlist, as well as the language
constructs (which are primitives) for, while and
repeat.
The R_Visible flag can also get altered during the evaluation of
a function, with comments in the code about warning,
writeChar and graphics functions calling GText (PR#7397).
(Since the C-level function eval sets R_Visible, this
could apply to any function calling it. Since it is called when
evaluating promises, even object lookup can change R_Visible.)
From R 2.1.0 internal functions that were marked to set
R_Visible = FALSE enforced this when the function returned. As
from R 2.5.0 both internal and primitive functions force the
documented setting of R_Visible on return, unless the C code is
allowed to change it (the exceptions above are indicated by
PRIMPRINT having value 2).
The actual autoprinting is done by PrintValueEnv in
print.c. If the object to be printed has the S4 bit set and S4
methods dispatch is on, show is called to print the object.
Otherwise, if the object bit is set (so the object has a
"class" attribute), print is called to dispatch methods:
for objects without a class the internal code of print.default
is called.
R has since version 1.2.0 had a generational garbage collector, and
bit gcgen in the sxpinfo header is used in the
implementation of this. This is used in conjunction with the
mark bit to identify two previous generations.
There are three levels of collections. Level 0 collects only the
youngest generation, level 1 collects the two youngest generations and
level 2 collects all generations. After 20 level-0 collections the next
collection is at level 1, and after 5 level-1 collections at level 2.
Further, if a level-n collection fails to provide 20% free space
(for each of nodes and the vector heap), the next collection will be at
level n+1. (The R-level function gc() performs a level-2
collection.)
A generational collector needs to efficiently `age' the objects,
especially list-like objects (including STRSXPs). This is done
by ensuring that the elements of a list are regarded as at least as old
as the list when they are assigned. This is handled by the
functions SET_VECTOR_ELT and SET_STRING_ELT, which is why
they are functions and not macros. Ensuring the integrity of such
operations is termed the write barrier and is done by making the
SEXP opaque and only providing access via functions (which cannot
be used as lvalues in assignments in C).
All code in R extensions is by default behind the write barrier. The
only way to obtain direct access to the internals of the SEXPRECs
is to define `USE_RINTERNALS' before including Rinternals.h,
which is normally defined in Defn.h. To enable a check on the
way that the access is used, R can be compiled with flag
--enable-strict-barrier which ensures that Defn.h does
not define `USE_RINTERNALS' and hence that SEXP is opaque in
most of R itself. (There are some necessary exceptions: foremost
memory.c where the accessor functions are defined and also
size.c which needs access to the sizes of the internal
structures.)
For background papers see http://www.stat.uiowa.edu/~luke/R/barrier.html and http://www.stat.uiowa.edu/~luke/R/gengcnotes.html.
Serialized versions of R objects are used by load/save
and also at a lower level by .saveRDS/.readRDS and
serialize/unserialize. These differ in what they
serialize to (a file, a connection, a raw vector) and whether they are
intended to serialize a single object or a collection of objects
(typically a workspace). save writes a header indicating the
format at the beginning of the file (a single LF-terninated line) which
the lower-level versions do not.
R has used the same serialization format since R 1.4.0 in December
2001. Reading of earlier formats is still supported via load,
but they are not described here. (Files of most of these formats can
still be found in data directories of packages.) The current
serialization format is called `version 2', and has been expanded in
back-compatible ways since R 1.4.0, for example to support additional
SEXPTYPEs.
save() works by first creating a tagged pairlist of objects to be
saved, and then saving that single object preceded by a single-line
header (typically RDX2\n for a binary save). load() reads
the header line, unserializes a single object (a pairlist or a vector
list) and assigns the elements of the list in the appropriate
environment.
Serialization in R needs to take into account that objects may contain references to environments, which then have enclosing environments and so on. (Environments recognized as package or name space environments are saved by name.) Further, there are `reference objects' which are not duplicated on copy and should remain shared on unserialization. These are weak references, external pointers and environments other than those associated with packages, name spaces and the global environment. These are handled via a hash table, and references after the first are written out as a reference marker indexed by the table entry.
Serialization first writes a header indicating the format (normally
`X\n' for an XDR format binary save, but `A\n', ASCII, and
`B\n', native word-order binary9, can also occur) and the version number of the
format and of two R versions (as integers). (Unserialization
interprets the two versions as the version of R which wrote the file
followed by the minimal version of R needed to read the format.)
Serialization then writes out the object recursively using function
WriteItem in file src/main/serialize.c.
Some objects are written as if they were SEXPTYPEs: such
pseudo-SEXPTYPEs cover R_NilValue, R_EmptyEnv,
R_BaseEnv, R_GlobalEnv, R_UnboundValue,
R_MissingArg and R_BaseNamespace.
For all SEXPTYPEs except NILSXP, SYMSXP and
ENVSXP serialization starts with a integer with the
SEXPTYPE in bits 0:710
followed by the object bit, two bits indicating if there are any
attributes and if there is a tag (for the pairlist types), an unused bit
and then the gp field11 in bits 12:27. Pairlist-like objects write their
attributes (if any), tag (if any), CAR and then CDR (using tail
recursion): other objects write their attributes after themselves.
Atomic vector objects write their length followed by the data: generic
vector-list objects write the length followed by a call to
WriteItem for each element. The code for CHARSXPs
special-cases NA_STRING and writes it as length -1 with no
data.
Environments are treated in several ways: as we have seen, some are
written as specific pseudo-SEXPTYPEs. Package and name space
environments are written with pseudo-SEXPTYPEs followed by the
name. `Normal' environments are written out as ENVSXPs with an
integer indicating if the environment is locked followed by the
enclosure, frame, `tag' (the hash table) and attributes.
In the `XDR' format integers and doubles are written in bigendian
order: however the format is not fully XDR as defind in RFC 1832 as byte
quantities (such as the contents of CHARSXP and RAWSXP
types) are written as-is and not padded to a multiple of four bytes.
The `ASCII' format writes 7-bit characters. Integers are formatted with
%d (except that NA_integer_ is written as NA),
doubles formatted with %.16g (plus NA, Inf and
-Inf) and bytes with %02x. Strings are written using
standard escapes (e.g. \t and \013 for non-printing and
non-ASCII bytes.
Character data in R are stored in the sexptype CHARSXP. Until
R 2.1.0 it was assumed that the data were in the platform's native
8-bit encoding, and furthermore it was quite often assumed that the
encoding was ISO Latin-1 or a superset (such as Windows' CP1252 or
Latin-9).
As from R 2.1.0 there was support for other encodings, in particular
UTF-8 and the multi-byte encodings used on Windows for CJK languages.
However, there was no way of indicating which encoding had been used,
even if this was known (and e.g. scan would not know the
encoding of the file it was reading). This lead to packages with data
in French encoded in Latin-1 in .rda files which could not be
read in other locales (and they would be able to be displayed in a
French UTF-8 locale, if not in most Japanese locales).
R 2.5.0 introduced a limited means to indicate the encoding of a
CHARSXP via two of the `general purpose' bits which are used to
declare the encoding to be either Latin-1 or UTF-8. (Note that it is
possible for a character vector to contain elements in different
encodings.) Both printing and plotting notice the declaration and
convert the string to the current locale (possibly using <xx> to
display in hexadecimal bytes that are not valid in the current locale).
Many (but not all) of the character manipulation functions will either
preserve the declaration or re-encode the character string.
Eventually strings that refer to the OS such as file names will need to be passed through a wide-character interface on some OSes (e.g. Windows), but currently they are just recoded to the current locale.
When are character strings declared to be of known encoding? One way is
to do so directly via Encoding. The parser declares the encoding
if this is known, either via the encoding argument to
parse or from the locale within which parsing is being done at
the R command line. Functions scan, read.table,
readLines and source have an encoding argument, but
do not assume anything about files from the current locale. Also,
iconv marks character strings it converts to Latin-1 or UTF-8.
It is not necessary to declare the encoding of ASCII strings as they will work in any locale, but the overhead in doing so is small since they will never be passed to iconv for translation.
The rationale behind considering only UTF-8 and Latin-1 is that most
systems are capable of producing UTF-8 strings and this is the nearest
we have to a universal format. For those that do not (for example those
lacking a powerful enough iconv), it is likely that they work in
Latin-1, the old R assumption.
Each of warning and stop have two C-level equivalents,
warning, warningcall, error and errorcall.
The relationship between the pairs is similar: warning tries to
fathom out a suitable call, and then calls warningcall with that
call as the first argument if it succeeds, and with call =
R_NilValue it is does not. When warningcall is called, it
includes the deparsed call in its printout unless call =
R_NilValue.
warning and error look at the context stack. If the
topmost context is not of type CTXT_BUILTIN, it is used to
provide the call, otherwise the next context provides the call.
This means that when these function are called from a primitive or
.Internal, the imputed call will not be to
primitive/.Internal but to the function calling the
primitive/.Internal . This is exactly what one wants for a
.Internal, as this will give the call to the closure wrapper.
(Further, for a .Internal, the call is the argument to
.Internal, and so may not correspond to any R function.)
However, it is unlikely to be what is needed for a primitive.
The upshot is that that warningcall and errorcall should
normally be used for code called from a primitive, and warning
and error should be used for code called from a .Internal
(and necessarily from .Call, .C and so on, where the call
is not passed down). However, there are two complications. One is that
code might be called from either a primitive or a .Internal, in
which case probably warningcall is more appropriate. The other
involves replacement functions, where the call will be of the form
(fron R < 2.6.0)
> length(x) <- y ~ x
Error in "length<-"(`*tmp*`, value = y ~ x) : invalid value
which is unpalatable to the end user. For replacement functions there
will be a suitable context at the top of the stack, so warning
should be used. (The results for .Internal replacement functions
such as substr<- are not ideal.)
[This section is currently a preliminary draft and should not be taken as definitive. The description assumes that R_NO_METHODS_TABLES has not been set.]
[The internal representation of objects from S4 classes changed in R
2.4.0. It is possible that objects from earlier representations still
exist, but there is no guarantee that they will be handled correctly.
An attempt is made to detect old-style S4 objects and warn when binary
objects are loaded or a workspace is restored.]
S4 objects can be of any SEXPTYPE. They are either an object of
a simple type (such as an atomic vector or function) with S4 class
information or of type S4SXP. In all cases, the `S4 bit' (bit 4
of the `general purpose' field) is set, and can be tested by the
macro/function IS_S4_OBJECT.
S4 objects are created via new()12 and thence via the C
function R_do_new_object. This duplicates the prototype of the
class, adds a class attribute and sets the S4 bit. All S4 class
attributes should be character vectors of length one with an attribute
giving (as a character string) the name of the package (or
.GlobalEnv) containing the class definition. Since S4 objects
have a class attribute, the OBJECT bit is set.
It is currently unclear what should happen if the class attribute is removed from an S4 object, or if this should be allowed.
S4 classes are stored as R objects in the environment in which they
are created, with names .__C__classname: as such they are
not listed by default by ls.
The objects are S4 objects of class "classRepresentation" which
is defined in the methods package.
Since these are just objects, they are subject to the normal scoping
rules and can be imported and exported from name spaces like other
objects. The directives importClassesFrom and
exportClasses are merely convenient ways to refer to class
objects without needing to know their internal `metaname' (although
exportClasses does a little sanity checking via isClass).
Details of methods are stored in S4 objects of class
"MethodsList". They have a non-syntatic name of the form
.__M__generic:package for all methods defined in the
current environment for the named generic derived from a specific
package (which might be .GlobalEnv).
There is also environment .__T__generic:package which
has names the signatures of the methods defined, and values the
corresponding method functions. This is often referred to as a `methods
table'.
When a package without a name space is attached these objects become
visible on the search path. library calls
methods:::cacheMetaData to update the internal tables.
During an R session there is an environment associated with each
non-primitive generic containing objects .AllMTable,
.Generic, .Methods, .MTable, .SigArgs and
.SigLength. .MTable and AllMTable are merged
methods tables containing all the methods defined directly and via
inheritance respectively. .Methods is a merged methods list.
Exporting methods from a name space is more complicated than exporting a
class. Note first that you do not export a method, but rather the
directive exportMethods will export all the methods defined in
the name space for a specified generic: the code also adds to the list
of generics any that are exported directly. For generics which are
listed via exportMethods or exported themselves, the
corresponding "MethodsList" and environment are exported and so
will appear (as hidden objects) in the package environment.
Methods for primitives which are internally S4 generic (see below) are
always exported, whether mentioned in the NAMESPACE file or not.
Methods can be imported either via the directive
importMethodsFrom or via importing a namespace by import.
Also, if a generic is imported via importFrom, its methods are
also imported. In all cases the generic will be imported if it is in
the namespace, so importMethodsFrom is most appropriate for
methods defined on generics in other packages. Since methods for a
generic could be imported from several different packages, the methods
tables are merged.
When a package with a name space is attached
methods:::cacheMetaData is called to update the internal tables:
only the visible methods will be cached.
This subsection does not discuss how S4 methods are chosen: see http://developer.r-project.org/howMethodsWork.pdf.
For all but primitive functions, setting a method on an existing
function that is not itself S4 generic creates a new object in the
current environment which is a call to standardGeneric with the
old definition as the default method. Such S4 generics can also be
created via a call to setGeneric13 and are standard closures
in the R language, with environment the environment within which they
are created. With the advent of name spaces this is somewhat
problematic: if myfn was previously in a package with a name
space there will be two functions called myfn on the search
paths, and which will be called depends on which search path is in use.
This is starkest for functions in the base name space, where the
original will be found ahead of the newly created function from any
other package with a name space.
Primitive functions are treated quite differently, for efficiency
reasons: this results in different semantics. setGeneric is
disallowed for primitive functions. The methods namespace
contains a list .BasicFunsList named by primitive functions:
the entries are either FALSE or a standard S4 generic showing
the effective definition. When setMethod (or
setReplaceMethod) is called, it either fails (if the list entry
is FALSE) or a method is set on the effective generic given in
the list.
Actual dispatch of S4 methods for almost all primitives piggy-backs on
the S3 dispatch mechanism, so S4 methods can only be dispatched for
primitives which are internally S3 generic. When a primitive that is
internally S3 generic is called with a first argument which is an S4
object and S4 dispatch is on (that is, the methods name space is
loaded), DispatchOrEval calls R_possible_dispatch (defined
in src/main/objects.c). (Members of the S3 group generics, which
includes all the generic operators, are treated slightly differently:
the first two arguments are checked and DispatchGroup is called.)
R_possible_dispatch first checks an internal table to see if any
S4 methods are set for that generic (and S4 dispatch is currently
enabled for that generic), and if so proceeds to S4 dispatch using
methods stored in another internal table. All primitives are in the
base name space, and this mechanism means that S4 methods can be set for
(some) primitives and will always be used, in contrast to setting
methods on non-primitives.
The exception is %*%, which is S4 generic but not S3 generic as
its C code contains a direct call to R_possible_dispatch.
The primitive as.double is special, as as.numeric and
as.real are copies of it. The methods package code partly
refers to generics by name and partly by function, and was modified in
R 2.6.0 to map as.double and as.real to
as.numeric (since that is the name used by packages exporting
methods for it).
Some elements of the language are implemented as primitives, for example
}. This includes the subset and subassignment `functions' and
they are S4 generic, again piggybacking on S3 dispatch.
.BasicFunsList is generated when methods is installed, by
computing all primitives, initially disallowing methods on all and then
setting generics for members of .GenericArgsEnv, the S4 group
generics and a short exceptions list in BasicFunsList.R: this
currently contains the subsetting and subassignment operators and an
override for c.
R's memory allocation is almost all done via routines in
src/main/memory.c. It is important to keep track of where memory
is allocated, as the Windows port (by default) makes use of a memory
allocator that differs from malloc etc as provided by MinGW.
Specifically, there are entry points Rm_malloc, Rm_free,
Rm_calloc and Rm_free provided by
src/gnuwin32/malloc.c. This was done for two reasons. The
primary motivation was performance: the allocator provided by MSVCRT
via MinGW was far too slow at handling the many small allocations
that the current (since R 1.2.0) allocation system for
SEXPRECs uses. As a side benefit, we can set a limit on the
amount of allocated memory: this is useful as whereas Windows does
provide virtual memory it is relatively far slower than many other R
platforms and so limiting R's use of swapping is highly advantageous.
The high-performance allocator is only called from
src/main/memory.c, src/main/regex.c, src/extra/pcre
and src/extra/xdr: note that this means that it is not used in
packages.
The rest of R should where possible make use of the allocators made
available by src/main/memory.c, which are also the methods
recommended in
Memory allocation
for use in R packages, namely the use of R_alloc,
Calloc, Realloc and Free. Memory allocated by
R_alloc is freed by the garbage collector once the `watermark'
has been reset by calling
vmaxset. This is done automatically by the wrapper code calling
primitives and .Internal functions (and also by the wrapper code
to .Call and .External), but
vmaxget and vmaxset can be used to reset the watermark
from within internal code if the memory is only required for a short
time.
All of the methods of memory allocation mentioned so far are relatively
expensive. All R platforms support alloca, and in almost all
cases14 this is managed by the
compiler, allocates memory on the C stack and is very efficient.
There are two disadvantages in using alloca. First, it is
fragile and care is needed to avoid writing (or even reading) outside
the bounds of the allocation block returned. Second, it increases the
danger of overflowing the C stack. It is suggested that it is only
used for smallish allocations (up to tens of thousands of bytes), and
that
R_CheckStack();
is called immediately after the allocation (as R's stack checking
mechanism will warn far enough from the stack limit to allow for modest
use of alloca). (do_makeunique in src/main/unique.c
provides an example of both points.)
An alternative strategy has been used for various functions which require intermediate blocks of storage of varying but usually small size, and this has been consolidated into the routines in the header file src/main/RBufferUtils.h. This uses a structure which contains a buffer, the current size and the default size. A call to
R_AllocStringBuffer(size_t blen, R_StringBuffer *buf);
sets buf->data to a memory area of at least blen+1 bytes.
At least the default size is used, which means that for small
allocations the same buffer can be reused. A call to
R_FreeStringBufferL releases memory if more than the default has
been allocated whereas a call to R_FreeStringBuffer frees any
memory allocated.
The R_StringBuffer structure needs to be initialized, for example by
static R_StringBuffer ex_buff = {NULL, 0, MAXELTSIZE};
which uses a default size of MAXELTSIZE = 8192 bytes. Most
current uses have a static R_StringBuffer structure, which
allows the (default-sized) buffer to be shared between calls to e.g.
grep and even between functions: this will need to be changed if
R ever allows concurrent evaluation threads. So the idiom is
static R_StringBuffer ex_buff = {NULL, 0, MAXELTSIZE};
...
char *buf;
for(i = 0; i < n; i++) {
compute len
buf = R_AllocStringBuffer(len, &ex_buff);
use buf
}
/* free allocation if larger than the default, but leave
default allocated for future use */
R_FreeStringBufferL(&ex_buff);
The memory used by R_alloc is allocated as R vectors, of type
RAWSXP for `small' allocations (less than 2^31 - 1 bytes) and of
type REALSXP for allocations up to 2^34 - 1 bytes on 64-bit
machines. Thus the allocation is in units of 8 bytes, and is rounded
up. (Prior to R 2.6.0 CHARSXPs were used, and so one byte was
added prior to rounding up. This had the effect of over-allocating
areas for doubles by one and thereby masked several subtle
programming errors.)
The vectors allocated are protected via the setting of R_VStack,
as the garbage collector marks everything that can be reached from that
location. When a vector is R_allocated, its ATTRIB
pointer is set to the current R_VStack, and R_VStack is
set to the latest allocation. Thus R_VStack is a single-linked
chain of vectors currently allocated via R_alloc. Function
vmaxset resets the location R_VStack, and should be to a
value that has previously be obtained via vmaxget:
allocations after the value was obtained will no longer be protected and
hence available for garbage collection.
This section notes known use by the system of these environments: the intention is to minimize or eliminate them.
The graphics devices system maintains two variables .Device and
.Devices in the base environment: both are always set. The
variable .Devices gives a list of character vectors of the names
of open devices, and .Device is the element corresponding to the
currently active device. The null device will always be open.
There appears to be a variable .Options, a pairlist giving the
current options settings. But in fact this is just a symbol with a
value assigned, and so shows up as a base variable.
Similarly, the evaluator creates a symbol .Last.value which
appears as a variable in the base environment.
Errors can give rise to objects .Traceback and
last.warning in the base environment.
The seed for the random number generator is stored in object
.Random.seed in the global environment.
Some error handlers may give rise to objects in the global environment:
for example dump.frames by default produces last.dump.
The windows() device makes use of a variable .SavedPlots
to store display lists of saved plots for later display. This is
regarded as a variable created by the user.
R makes use of a number of shared objects/DLLs stored in the modules directory. These are parts of the code which have been chosen to be loaded `on demand' rather than linked as dynamic libraries or incorporated into the main executable/dynamic library.
For a few of these (e.g. vfonts) the issue is size: the
database for the Hershey fonts is included in the C code of the module
and was at one time an appreciable part of the codebase for a rarely
used feature. However, for most of the modules the motivation has been
the amount of (often optional) code they will bring in via libraries to
which they are linked.
internetlapackvfontsX11X11(), jpeg() and png() devices. These are
optional, and link to the X11, jpeg and libpng
libraries.
.Internal vs .PrimitiveC code compiled into R at build time can be called “directly” or
via the .Internal interface, which is very similar to the
.External interface except in syntax. More precisely, R
maintains a table of R function names and corresponding C functions to
call, which by convention all start with `do_' and return a SEXP.
Via this table (R_FunTab in file src/main/names.c) one can
also specify how many arguments to a function are required or allowed,
whether the arguments are to be evaluated before calling or not, and
whether the function is “internal” in the sense that it must be
accessed via the .Internal interface, or directly accessible in
which case it is printed in R as .Primitive.
R's functionality can also be extended by providing corresponding C code and adding to this function table.
In general, all such functions use .Internal() as this is safer
and in particular allows for transparent handling of named and default
arguments. For example, axis is defined as
axis <- function(side, at = NULL, labels = NULL, ...)
.Internal(axis(side, at, labels, ...))
However, for reasons of convenience and also efficiency (as there is
some overhead in using the .Internal interface wrapped in a
function closure), there are exceptions which can be accessed directly.
Note that these functions make no use of R code, and hence are very
different from the usual interpreted functions. In particular,
args, formals and body return NULL for such
objects, and argument matching is purely positional (with two exceptions
described below).
The list of these “primitive” functions is subject to change: currently, it includes the following.
{ ( if for while repeat break next
return function quote on.exit
foo(a, b, ...)) for subsetting, assignment,
arithmetic and logic. These are the following 1-, 2-, and
N-argument functions:
[ [[ $ @
<- <<- = [<- [[<- $<-
+ - * / ^ %% %*% %/%
< <= == != >= >
| || & && !
abs sign sqrt
floor ceiling
exp expm1
log2 log10 log1p
cos sin tan
acos asin atan
cosh sinh tanh
acosh asinh atanh
gamma lgamma digamma trigamma
cumsum cumprod cummax cummin
Im Re Arg Conj Mod
log is a function of one or two arguments, but was made
primitive as from R 2.6.0 and so has named rather than positional
matching for back compatibility.
trunc is a difficult case: it is a primitive that can have zero
or more arguments: the default method handled in the primitive has
only one.
nargs missing
interactive is.xxx
.Primitive .Internal
globalenv baseenv emptyenv pos.to.env
unclass
seq_along seq_len
(where xxx stands for 27 different notions, such as
function, vector, numeric, and so forth, but not
is.loaded).
debug undebug browser proc.time gc.time
tracemem retracemem untracemem
length length<-
class class<-
oldClass oldCLass<-
attr attr<-
attributes attributes<-
names names<-
dim dim<-
dimnames dimnames<-
environment<-
levels<-
storage.mode<-
Note that optimizing NAMED = 1 is only effective within a
primitive (as the closure wrapper of a .Internal will set
NAMED = 2 when the promise to the argument is evaluated) and
hence replacement functions should where possible be primitive to avoid
copying (at least in their default methods).
: ~ c list
call as.call as.character as.complex as.double
as.integer as.logical as.raw
expression substitute as.environment
UseMethod invisible standardGeneric
.C .Fortran .Call .External
.Call.graphics .External.graphics
.subset .subset2 .primTrace .primUntrace
rep seq.int
lazyLoadDBfetch
rep and seq.int manage their own argument matching and so
do work in the standard way.
A small number of primitives are specials rather than
builtins, that is they are entered with unevaluated arguments.
This is clearly necessary for the language constructs and the assignment
operators. && and || conditionally evaluate their second
argument, and ~, .Internal, call, expression
and missing do not evaluate their arguments.
rep and seq.int are special as they evaluate some of their
arguments conditional on which are non-missing. c is special to
allow it to be used with language objects.
The subsetting, subassignment and @ operators are all special.
(For both extraction and replacement forms, $ and @
take a symbol argument, and [ and [[ allow missing
arguments.)
UseMethod is special to avoid the additional contexts added to
calls to builtins when profiling (via Rprof).
There are also special .Internal functions: switch,
Recall, cbind, rbind (to allow for the
deparse.level argument), lapply, eapply and
NextMethod.
As from R 2.5.0, prototypes are available for the primitive functions
and operators, and there are used for printing, args and package
checking (e.g. by tools::checkS3methods and by package
codetools). There are two environments in the base package
(and name space), `.GenericArgsEnv' for those primitives which are
internal S3 generics, and `.ArgsEnv' for the rest. Those
environments contain closures with the same names as the primitives,
formal argumnts derived (manually) from the help pages, a body which
is a suitable call to UseMethod or NULL and environment the
base name space.
The C code for print.default and args uses the closures in
these environments in preference to the definitions in base (as
primitives).
The QC function undoc checks that all the functions prototyped in
these environments are currently primitive, and that the primitives not
included are better thought of as language elements (at the time of
writing
$ $<- && ( : @ [ [[ [[<- [<- { || ~ <- <<- =
break for function if next repeat return while
. One could argue about ~, but it is known to the parser and has
semantics quite unlike a normal function. And : is documented
with different argument names in its two meanings.)
The QC functions codoc and checkS3methods also make use of
these environments (effectively placing them in front of base in the
search path), and hence the formals of the functions they contain are
checked against the help pages by codoc. However, there are two
problems with the generic primitives. The first is that many of the
operators are part of the S3 group generic Ops and that defines
their arguments to be e1 and e2: although it would be very
unusual, an operator could be called as e.g. "+"(e1=a, e2=b)
and if method dispatch occurred to a closure, there would be an argument
name mismatch. So the definitions in environment .GenericArgsEnv
have to use argument names e1 and e2 even though the
traditional documentation is in terms of x and y:
codoc makes the appropriate adjustment via
tools:::.make_S3_primitive_generic_env. The second discrepancy
is with the Math group generics, where the group generic is
defined with argument list (x, ...), but most of the members only
allow one argument when used as the default method (and round and
signif allow two as default methods): again fix-ups are used.
Those primitives which are in .GenericArgsEnv are checked (via
tests/primitives.R to be generic via defining methods for
them, and a check is made that the remaining primitives are probably not
generic, by setting a method and checking it is not dispatched to (but
this can fail for other reasons). However, there is no certain way to
know that if other .Internal or primitive functions are not
internally generic except by reading the source code.
The process of marking messages (errors, warnings etc) for translation in an R package is described in Localization, and the standard packages included with R have (with an exception in grDevices) been internationalized in the same way as other packages.
Internationalization for R code is done in exactly the same way as
for extension packages. As all standard packages which have R code
also have a namespace, it is never necessary to specify domain,
but for efficiency calls to message, warning and
stop should include domain = NA when the message is
constructed via gettextf, gettext or
ngettext.
For each package, the extracted messages and translation sources are stored under package directory po in the source package, and compiled translations under inst/po for installation to package directory po in the installed package. This also applies to C code in packages.
The main C code (e.g. that in src/*/*.c and in
the modules) is where R is closest to the sort of application for
which `gettext' was written. Messages in the main C code are in
domain R and stored in the top-level directory po with
compiled translations under share/locale.
The list of files covered by the R domain is specified in file po/POTFILES.in.
The normal way to mark messages for translation is via _("msg")
just as for packages. However, sometimes one needs to mark passages for
translation without wanting them translated at the time, for example
when declaring string constants. This is the purpose of the N_
macro, for example
{ ERROR_ARGTYPE, N_("invalid argument type")},
from src/main/errors.c.
A macro
#ifdef ENABLE_NLS
#define P_(StringS, StringP, N) ngettext (StringS, StringP, N)
#else
#define P_(String, StringP, N) (N > 1 ? StringP: String)
#endif
as a wrapper for ngettext: however in some cases the preferred
approach has been to conditionalize (on ENABLE_NLS) code using
ngettext.
The macro _("msg") can safely be used in src/appl; the
header for standalone `nmath' skips possible translation. (This
does not apply to N_ or P_).
Messages for the Windows GUI are in a separate domain `RGui'. This was done for two reasons:
iconv we ported
works well under Windows, this is less important than anticipated.)
Messages for the `RGui' domain are marked by G_("msg"), a
macro that is defined in src/gnuwin32/win-nls.h. The list of
files that are considered is hardcoded in the RGui.pot-update
target of po/Makefile.in.in: note that this includes
devWindows.c as the menus on the windows device are
considered to be part of the GUI. (There is also GN_("msg"), the
analogue of N_("msg").)
The template and message catalogs for the `RGui' domain are in the top-level po directory.
This is handled separately: see http://developer.r-project.org/Translations.html.
See po/README for how to update the message templates and catalogs.
R is meant to run on a wide variety of platforms, including Linux and most variants of Unix as well as 32-bit Windows versions and on MacOS X. Therefore, when extending R by either adding to the R base distribution or by providing an add-on package, one should not rely on features specific to only a few supported platforms, if this can be avoided. In particular, although most R developers use GNU tools, they should not employ the GNU extensions to standard tools. Whereas some other software packages explicitly rely on e.g. GNU make or the GNU C++ compiler, R does not. Nevertheless, R is a GNU project, and the spirit of the GNU Coding Standards should be followed if possible.
The following tools can “safely be assumed” for R extensions.
VPATH mechanism.
Windows-specific makefiles can assume GNU make 3.75 or later, as no other make is viable on that platform.
There are POSIX standards for these tools, but these may not fully be supported. Baseline features could be determined from a book such as The UNIX Programming Environment by Brian W. Kernighan & Rob Pike. Note in particular that `|' in a regexp is an extended regexp, and is not supported by all versions of grep or sed. The Open Group Base Specifications, Issue 6, which is technically identical to ISO/IEC 9945 and IEEE Std 1003.1 (POSIX), 2004, are available at http://www.opengroup.org/onlinepubs/009695399/mindex.html.
Under Windows, most users will not have these tools installed, and you should not require their presence for the operation of your package. However, users who install your package from source will have them, as they can be assumed to have followed the instructions in “the Windows toolset” appendix of the “R Installation and Administration” manual to obtain them. Redirection cannot be assumed to be available via system as this does not use a standard shell (let alone a Bourne shell).
In addition, the following tools are needed for certain tasks.
It is also important that code is written in a way that allows others to
understand it. This is particularly helpful for fixing problems, and
includes using self-descriptive variable names, commenting the code, and
also formatting it properly. The R Core Team recommends to use a
basic indentation of 4 for R and C (and most likely also Perl) code,
and 2 for documentation in Rd format. Emacs users can implement this
indentation style by putting the following in one of their startup
files. (For GNU Emacs 20: for GNU Emacs 21 or later
use customization to set the c-default-style to "bsd" and
c-basic-offset to 4.)
;;; C
(add-hook 'c-mode-hook
(lambda () (c-set-style "bsd")))
;;; ESS
(add-hook 'ess-mode-hook
(lambda ()
(ess-set-style 'C++)
;; Because
;; DEF GNU BSD K&R C++
;; ess-indent-level 2 2 8 5 4
;; ess-continued-statement-offset 2 2 8 5 4
;; ess-brace-offset 0 0 -8 -5 -4
;; ess-arg-function-offset 2 4 0 0 0
;; ess-expression-offset 4 2 8 5 4
;; ess-else-offset 0 0 0 0 0
;; ess-close-brace-offset 0 0 0 0 0
(add-hook 'local-write-file-hooks
(lambda ()
(ess-nuke-trailing-whitespace)))))
(setq ess-nuke-trailing-whitespace-p 'ask)
;; or even
;; (setq ess-nuke-trailing-whitespace-p t)
;;; Perl
(add-hook 'perl-mode-hook
(lambda () (setq perl-indent-level 4)))
(The `GNU' styles for Emacs' C and R modes use a basic indentation of 2, which has been determined not to display the structure clearly enough when using narrow fonts.)
When you (as R developer) add new functions to the R base (all the packages distributed with R), be careful to check if make test-Specific or particularly, cd tests; make no-segfault.Rout still works (without interactive user intervention, and on a standalone computer). If the new function, for example, accesses the Internet, or requires GUI interaction, please add its name to the “stop list” in tests/no-segfault.Rin.
[To be revised: use make check-devel, check the write barrier if you change internal structures.]
.Device: Base environment.Devices: Base environment.Internal: .Internal vs .Primitive.Last.value: Base environment.Options: Base environment.Primitive: .Internal vs .Primitive.Random.seed: Global environment.SavedPlots: Global environment.Traceback: Base environmentalloca: Memory allocatorsARGSUSED: Rest of headerATTRIB: AttributesCalloc: Memory allocatorscopyMostAttributes: AttributesDDVAL: Rest of headerdebug bit: Rest of headerDispatchGeneric: Argument evaluationDispatchOrEval: Argument evaluationdump.frames: Global environmentDUPLICATE_ATTRIB: Attributesemacs: R coding standardserror: Warnings and errorserrorcall: Warnings and errorsFree: Memory allocatorsgp bits: Rest of headerinvisible: Autoprintinglast.warning: Base environmentLEVELS: Rest of headermake: R coding standardsmakeinfo: R coding standardsMISSING: MissingnessMISSING: Rest of headerNAMED: .Internal vs .PrimitiveNAMED: Rest of headerNAMED: Argument evaluationnamed bit: Rest of headerPerl: R coding standardsPRIMPRINT: AutoprintingPRSEEN: Rest of headerR_alloc: Memory allocatorsR_AllocStringBuffer: Memory allocatorsR_BaseNamespace: Name spacesR_CheckStack: Memory allocatorsR_FreeStringBuffer: Memory allocatorsR_FreeStringBufferL: Memory allocatorsR_MissingArg: MissingnessR_Visible: AutoprintingRealloc: Memory allocatorsSET_ARGUSED: Rest of headerSET_ATTRIB: AttributesSET_DDVAL: Rest of headerSET_MISSING: Rest of headerSET_NAMED: Rest of headerSETLEVELS: Rest of headertrace bit: Rest of headerUseMethod: Contextsvmaxget: Memory allocatorsvmaxset: Memory allocatorswarning: Warnings and errorswarningcall: Warnings and errors[1] strictly, a SEXPREC
node; VECTOR_SEXPREC nodes are slightly smaller but followed by
data in the node.
[2] a pointer to a function or a symbol to look up the function by name, or a language object to be evaluated to give a function.
[3] This is almost unused. The only
current use is for hash tables of environments (VECSXPs), where
length is the size of the table and truelength is the
number of primary slots in use, and for the reference hash tables in
serialization (VECSXPs), where truelength is the number of
slots in use.
[4] Remember that attaching a list or a saved image actually creates and populates an environment and attaches that.
[5] An exception is the internal code for
terms.formula which directly manipulates the attributes.
[6] There is currently one other difference: when profiling builtin functions are counted as function calls but specials are not.
[7] the other current example is left brace, which is implemented as a primitive.
[8] a .Internal-only function used in
source, withVisible and a few other places.
[9] there is no R-level interface to this format
[10] only 0:4 will currently be used for
SEXPTYPEs but values 241:255 are used for pseudo-SEXPTYPEs.
[11] Currently the only relevant bits are 0:1, 4, 14:15.
[12] This can also create
non-S4 objects, as in new("integer").
[13] although this is not recommended as it is less future-proof.
[14] but apparently not on Windows.