.Internal vs .Primitive
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 2.12.1 (2010-12-16).
ISBN 3-900051-14-3
This chapter is the beginnings of documentation about R internal structures. It is written for the 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withdrawn.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 SEXPTYPE numbers 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'. We label these
from 0 to 15. Bits 0–5 and bits 14–15 have been used as described below
(mainly 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.
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 of version 2.4.0 of R, bit 4 is turned on to mark S4 objects.
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. From R 2.8.0 it is
guaranteed that they will not be set for CHARSXPs created by R
itself.) As from R 2.8.0 bit 5 is used to indicate that a
CHARSXP is hashed by its address, that is NA_STRING or in
the CHARSXP cache: this is not serialized.
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 (a SYMSXP or NULL).
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 traversed 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
from 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.
Environments in R usually have a hash table, although that is not the
default in new.env(). It is stored as a VECSXP where
length is used for the allocated size of the table and
truelength is the number of primary slots in use—the pointer to
the VECSXP is part of the header of a SEXP of type
ENVSXP, and this points to R_NilValue if the environment
is not hashed.
For the pros and cons of hashing, see a basic text on Computer Science.
The code to implement hashed environments is in src/main/envir.c.
Unless set otherwise (e.g. by the size argument of
new.env()) the initial table size is 29. The table will
be resized by a factor of 1.2 once the load factor (the proportion of
primary slots in use) reaches 85%.
The hash chains are stored as pairlist elements of the VECSXP:
items are inserted at the front of the pairlist. Hashing is principally
designed for fast searching of environments, which are from time to time
added to but rarely deleted from, so items are not actually deleted but
have their value set to R_UnboundValue.
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 normally 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.
Note that the `attributes' of a CHARSXP are used as part of the
management of the CHARSXP cache: of course CHARSXP's are
not user-visible but C-level code might look at their attributes.
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 calls to closures and builtins set a context, those to special internal functions never do.
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 functions 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 by the generic.) If the
method found is a primitive it is called with the matched argument list
of promises (possibly already forced) used for the generic.
The essential difference5 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. Note that being a special/builtin is
separate from being primitive or .Internal: quote is a
special primitive, + is a builtin primitive, cbind 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. Most such functions are builtins (so immediately evaluate all
their arguments), and all 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 method 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 ....
Special primitives also need to handle missing arguments, and in some
case (e.g. log) that is why they are special and not
builtin. This is usually done by testing if an argument's value is
R_MissingArg.
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 ddfindVar)
in the value of the ... symbol in the evaluation frame.
Values of arguments matched to a ... argument can be missing.
Special primitives may need to handle ... arguments: see for
example the internal code of switch in file
src/main/builtin.c.
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 file
src/main/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_Visible = 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: examples6 are
.Internal, do.call, eval,
eval.with.vis7, 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 file
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 header file
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 header
Defn.h does not define ‘USE_RINTERNALS’ and hence that
SEXP is opaque in most of R itself. (There are some necessary
exceptions: foremost in file memory.c where the accessor
functions are defined and also in file 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-terminated 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 binary8, 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 an integer with the
SEXPTYPE in bits 0:79
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 field10 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 defined 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 near-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 non-UTF-8 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.
Strings that refer to the OS such as file names need to be passed through a wide-character interface on some OSes (e.g. Windows), which is to a large extent done as from R 2.7.0.
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. (Other ways are recorded on the help page for
Encoding.)
It is not necessary to declare the encoding of ASCII strings as they
will work in any locale. As from R 2.8.0, ASCII strings should never
have a marked encoding, as any encoding will be ignored when entering
such strings into the CHARSXP cache.
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.
This was taken further in R 2.7.0. There the parser can return a UTF-8-encoded string if it encounters a ‘\uxxx’ escape for a Unicode point that cannot be represented in the current charset. (This needs MBCS support, and was only enabled11 on Windows.) This was taken further in R 2.10.0, where it is enabled for all platforms with MBCS support, and a ‘\uxxx’ or ‘\Uxxxxxxxx’ escape ensures that the parsed string will be marked as UTF-8.
Most of the character manipulation functions now preserve UTF-8 encodings: there are some notes as to which at the top of file src/main/character.c and in file src/library/base/man/Encoding.Rd.
Graphics devices are offered the possibility of handing UTF-8-encoded
strings without re-encoding to the native character set, by setting
hasTextUTF8 to be ‘TRUE’12 and
supplying functions textUTF8 and strWidthUTF8 that expect
UTF-8-encoded inputs. Normally the symbol font is encoded in Adobe
Symbol encoding, but that can be re-encoded to UTF-8 by setting
wantSymbolUTF8 to ‘TRUE’.
Windows has no UTF-8 locales, but rather expects to work with
UCS-213 strings.
R (being written in standard C) would not work internally with UCS-2
without extensive changes. As from R 2.7.0 the Rgui
console14 uses UCS-2 internally,
but communicates with the R engine in the native encoding. To allow
UTF-8 strings to be printed in UTF-8 in Rgui.exe, an escape
convention is used (see header file rgui_UTF8.h) which is used by
cat, print and autoprinting.
`Unicode' (UCS-2LE) files are common in the Windows world, and
readLines and scan will read them into UTF-8 strings on
Windows if the encoding is declared explicitly on an unopened
connection passed to those functions.
A global cache for CHARSXPs created by mkChar was
introduced in R 2.6.0 – the cache ensures that most CHARSXPs
with the same contents share storage (`contents' including any declared
encoding). Not all CHARSXPs are part of the cache – notably
‘NA_STRING’ is not.
In R 2.6.x and 2.7.x character strings created by mkCharLen
were not part of the cache: these were intended to be those containing
embedded nuls. As from R 2.8.0 the cache can handle any content,
although embedded nuls are now disallowed.
There are a few other ways in which CHARSXPs could or can escape
the cache. CHARSXPs reloaded from the save formats of
R prior to 0.99.0 are not cached (since the code used is frozen and
few examples still exist). Prior to R 2.8.0, CHARSXPs were
used to hold the finalizer function of a C finalizer (uncached) – now
RAWSXPs are used. Finally, user code could create uncached
CHARSXPs via allocString (removed in R 2.8.0) and
allocVector(CHARSXP ...) (deprecated in R 2.8.0, removed in R
2.9.0).
The cache records the encoding of the string as well as the bytes: all
requests to create a CHARSXP should be via a call to
mkCharLenCE. As from R 2.8.0 any encoding given in
mkCharLenCE call will be ignored if the string's bytes are all
ASCII characters.
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 functions 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
(from 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()15 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-syntactic 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 setGeneric16 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 file 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 file 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 file
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 file
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 file 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
cases17 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 file 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 such uses.
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(), png() and
tiff() devices. These are optional, and links to some or all of
the X11, pango, cairo, jpeg, libpng
and libtiff libraries.
We make use of the visibility mechanisms discussed in
Controlling visibility,
C entry points not needed outside the main R executable/dynamic
library (and in particular in no package nor module) should be prefixed
by attribute_hidden.
Minimizing the visibility of symbols in the R dynamic library will
speed up linking to it (which packages will do) and reduce the
possibility of linking to the wrong entry points of the same name. In
addition, on some platforms reducing the number of entry points allows
more efficient versions of PIC to be used: somewhat over half the entry
points are hidden. A convenient way to hide variables (as distinct from
functions) is to declare them extern0 in header file Defn.h.
The visibility mechanism used is only available with some compilers and platforms, and in particular not on Windows, where an alternative mechanism is used. Entry points will not be made available in R.dll if they are listed in the file src/gnuwin32/Rdll.hide. Entries in that file start with a space and must be strictly in alphabetic order in the C locale (use sort on the file to ensure this if you change it). It is possible to hide Fortran as well as C entry points via this file: the former are lower-cased and have an underline as suffix, and the suffixed name should be included in the file. Some entry points exist only on Windows or need to be visible only on Windows, and some notes on these are provided in file src/gnuwin32/Maintainters.notes.
Because of the advantages of reducing the number of visible entry
points, they should be declared attribute_hidden where possible.
Note that this only has an effect on a shared-R-library build, and so
care is needed not to hide entry points that are legitimately used by
packages. So it is best if the decision on visibility is made when a
new entry point is created, including the decision if it should be
included in header file Rinternals.h. A list of the visible
entry points on shared-R-library build on a reasonably standard
Unix-alike can be made by something like
nm -g libR.so | grep ' [BCDT] ' | cut -b20-
Windows is unique in that it conventionally treats importing variables differently from functions: variables that are imported from a DLL need to be specified by a prefix (often ‘_imp_’) when being linked to (`imported') but not when being linked from (`exported'). The details depend on the compiler system, and have changed for MinGW during the lifetime of that port. They are in the main hidden behind some macros defined in header file R_ext/libextern.h.
A (non-function) variable in the main R sources that needs to be
referred to outside R.dll (in a package, module or another DLL
such as Rgraphapp.dll) should be declared with prefix
LibExtern. The main use is in Rinternals.h, but it needs
to be considered for any public header and also Defn.h.
It would nowadays be possible to make use of the `auto-import' feature of the MinGW port of ld to fix up imports from DLLs (and if R is built for the Cygwin platform this is what happens). However, this was not possible when the MinGW build of R was first constructed in ca 1998, allows less control of visibility and would not work for other Windows compiler suites.
It is only possible to check if this has been handled correctly by compiling the R sources on Windows.
Lazy loading was introduced in R 2.0.0, for code in packages and for datasets in packages (both are optional, but it is the default for code). When a package/name space which uses it is loaded, the package/name space environment is populated with promises for all the named objects: when these promises are evaluated they load the actual code from a database.
There are separate databases for code and data, stored in the R
and data subdirectories. The database consists of two files,
name.rdb and name.rdx. The .rdb file
is a concatenation of serialized objects, and the .rdx file
contains an index. The objects are stored in (usually) a
gzip-compressed format with a 4-byte header giving the
uncompressed serialized length (in XDR, that is big-endian, byte order)
and read by a call to the primitive lazyLoadDBfetch. (Note that
this makes lazy-loading unsuitable for really large objects: the
unserialized length of an R object can exceed 4GB.)
The index or `map' file name.rdx is a compressed serialized
R object to be read by .readRDS. It is a list with three
elements variables, references and compressed. The
first two are named lists of integer vectors of length 2 giving the
offset and length of the serialized object in the name.rdb
file. Element variables has an entry for each named object:
references serializes a temporary environment used when named
environments are added to the database. compressed is a logical
indicating if the serialized objects were compressed: compression is
always used nowadays. R 2.10.0 adds the values compressed = 2
and 3 for bzip2 and xz compression (with the
possibility of future expansion to other methods): these formats add a
fifth byte to the header for the type of compression, and stores
serialized objects uncompressed if compression expands them.
The loader for a lazy-load database of code or data is function
lazyLoad in the base package, but note that there is a
separate copy to load base itself in file
R_HOME/base/R/base.
Lazy-load databases are created by the code in
src/library/tools/R/makeLazyLoad.R: the main tool is the
unexported function makeLazyLoadDB and the insertion of database
entries is done by calls to .Call("R_lazyLoadDBinsertValue",
...).
Lazy-load databases of less than 10MB are cached in memory at first use: this was found necessary when using file systems with high latency (removable devices and network-mounted file systems on Windows).
The same database mechanism is used to store parsed Rd files as
from R 2.10.0. One or all of the parsed objects is fetched by a call
to tools:::fetchRdDB.
.Internal vs .PrimitiveC code compiled into R at build time can be called directly in what
are termed primitives 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. This table (R_FunTab in
file src/main/names.c) also specifies how many arguments to a
function are required or allowed, whether or not the arguments are to be
evaluated before calling, 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.
Functions using .Internal() wrapped in a closure are in general
preferred as this ensures standard 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), the primitive functions are exceptions that can be
accessed directly. And of course, primitive functions are needed for
basic operations—for example .Internal is itself a primitive.
Note that primitive functions make no use of R code, and hence are
very different from the usual interpreted functions. In particular,
formals and body return NULL for such objects, and
argument matching can be handled differently. For some primitives
(including call, switch, .C and .subset)
positional matching is important to avoid partial matching of the first
argument.
The list of primitive functions is subject to change; currently, it includes the following.
{ ( if for while repeat break next
return function quote switch
foo(a, b, ...)) for subsetting, assignment,
arithmetic and logic:
[ [[ $ @
<- <<- = [<- [[<- $<-
+ - * / ^ %% %*% %/%
< <= == != >= >
| || & && !
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 in R 2.6.0 with named argument matching.
trunc is a difficult case: it is a primitive that can have one
or more arguments: the default method handled in the primitive has
only one.
nargs missing on.exit interactive
as.call as.character as.complex as.double
as.environment as.integer as.logical as.raw
is.array is.atomic is.call is.character
is.complex is.double is.environment is.expression
is.finite is.function is.infinite is.integer
is.language is.list is.logical is.matrix
is.na is.name is.nan is.null
is.numeric is.object is.pairlist is.raw
is.real is.recursive is.single is.symbol
baseenv emptyenv globalenv pos.to.env
unclass invisible seq_along seq_len
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 expression substitute
UseMethod standardGeneric
.C .Fortran .Call .External
round signif rep seq.int
as well as the following internal-use-only functions
.Primitive .Internal
.Call.graphics .External.graphics
.subset .subset2
.primTrace .primUntrace
lazyLoadDBfetch
The multi-argument primitives
call switch
.C .Fortran .Call .External
intentionally use positional matching, and need to do so to avoid partial matching to their first argument. They do check that the first argument (partially) matched the formal argument name. On the other hand,
attr attr<- browser rememtrace substitute UseMethod
log round signif rep seq.int
manage their own argument matching and do work in the standard way.
All the one-argument primitives check that if they are called with a
named argument that this (partially) matches the name given in the
documentation: this is also done for replacement functions with one
argument plus value.
The net effect is that from R 2.11.0 argument matching for primitives intended for end-user use is done in the same way as for interpreted functions except for the six exceptions where positional matching is required.
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, as well as for && and || which conditionally
evaluate their second argument, and ~, .Internal,
call, expression, missing, on.exit,
quote and substitute which do not evaluate some of 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.
log, round and signif are special to allow default
values to be given to missing arguments.
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.
There are also special .Internal functions: NextMethod
Recall, withVisible, cbind, rbind (to allow
for the deparse.level argument), eapply, lapply and
vapply.
Prototypes are available for the primitive functions and operators, and
these 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 arguments 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
Internationalization,
and the standard packages included with R have (with an exception in
grDevices for the menus of the windows() device) 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 files 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 file src/main/errors.c.
The P_ macro
#ifdef ENABLE_NLS
#define P_(StringS, StringP, N) ngettext (StringS, StringP, N)
#else
#define P_(StringS, StringP, N) (N > 1 ? StringP: StringS)
#endif
may be used
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 directory
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 header file src/gnuwin32/win-nls.h. The
list of files that are considered is hardcoded in the
RGui.pot-update target of file 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 file po/README for how to update the message templates and catalogs.
The structure of a source packages is described in Creating R packages: this chapter is concerned with the structure of installed packages.
The description here is for R 2.10.0 and later: the way help was installed differed in earlier versions.
An installed package has a top-level file DESCRIPTION, a copy of the file of that name in the package sources with a ‘Built’ field appended, and file INDEX, usually describing the objects on which help is available, a file NAMESPACE is the package has a name space, files such as CITATION, LICENCE and NEWS, and any other files copied in from inst. It will have directories Meta, help and html (even if the package has no help), almost always has a directory R and often has a directory libs to contain compiled code. Other directories with known meaning to R are data, demo, doc and po.
Function library looks for a name space and if one is found
passes control to loadNamespace. Then library or
loadNamespace looks for file R/pkgname, warns if it
is not found and otherwise sources the code (using sys.source)
into the package's environment, then lazy-loads a database
R/sysdata if present. So how R code gets loaded depends on
the contents of R/pkgname: standard templates to load
lazy-load databases are provided in share/R/[ns]packloader.R.
How (and if) compiled code is loaded is down to the package's start-code
such as .First.lib or .onLoad, although a useDynlib
directive in a name space provides an alternative.
Conventionally compiled code is loaded by a call to
library.dynam and this looks in directory libs (and in an
appropriate sub-directory if sub-architectures are in use) for a shared
object (Unix-alike) or DLL (Windows).
Subdirectory data serves two purposes. In a package using
lazy-loading of data, it contains a lazy-load database Rdata,
plus a file Rdata.rds which contain a named character vector used
by data in the (unusual) event that it is used for such a
package. Otherwise it is copy of the data directory in the
sources, with saved images re-compressed if R CMD INSTALL
--resave-data was used. If R CMD INSTALL --use-data-zip was
specified or selected via --auto-zip, the contents of
the directory are moved to a zip file Rdata.zip and a listing
written in file filelist.
Subdirectory demo supports the demo function, and is
copied from the sources.
Subdirectory po contains (in subdirectories) compiled message catalogs.
Directory Meta contains several files in .rds format, that
is serialized R objects. All packages have Rd.rds,
hsearch.rds, links.rds and package.rds. Packages
with name spaces have a file nsInfo.rds, and those with data,
demos or vignettes have data.rds, demo.rds or
vignette.rds files.
The structure of these files (and their existence and names) is private to R, so the description here is for those trying to follow the R sources: there should be no reference to these files in non-base packages.
File package.rds is a dump of information extracted from the
DESCRIPTION file. It is a list of several components. The first,
‘DESCRIPTION’, is a character vector, the DESCRIPTION file
as read by read.dcf. Further elements ‘Depends’,
‘Suggests’, ‘Imports’, ‘Rdepends’ and ‘Rdepends2’
record the ‘Depends’, ‘Suggests’ and ‘Imports’ fields.
These are all lists, and can be empty. The first three have an entry
for each package named, each entry being a list of length 1 or 3, which
element ‘name’ (the package name) and optional elements ‘op’
(a character string) and ‘version’ (an object of class
‘"package_version"’). Element ‘Rdepends’ is used for the
first version dependency on R, and ‘Rdepends2’ is a list of zero
or more R version dependencies—each is a three-element list of the
form described for packages. Element ‘Rdepends’ is no longer used,
but it is still needed so R < 2.7.0 can detect that the package was not
installed for it.
File nsInfo.rds records a list, a parsed version of the NAMESPACE file.
File Rd.rds records a data frame with one row for each help file. The (character) columns are ‘File’ (the file name with extension), ‘Name’ (the ‘\name’ section), ‘Type’ (from the optional ‘\docType’ section), ‘Title’, ‘Encoding’, ‘Aliases’, ‘Concepts’ and ‘Keywords’. The last three are character strings with zero or more entries separated by ‘, ’.
File hsearch.rds records the information to be used by ‘help.search’. This is a list of four unnamed elements which are character vectors for help files, aliases, keywords and concepts. All the matrices have columns ‘ID’ and ‘Package’ which are used to tie the aliases, keywords and concepts (the remaining column of the last three elements) to a particular help file. The first element has further columns ‘LibPath’ (empty since R 2.3.0), ‘name’, ‘title’, ‘topic’ (the first alias, used when presenting the results as ‘pkgname::topic’) and ‘Encoding’.
File links.rds records a named character vector, the names being aliases and the values character strings of the form
"../../pkgname/html/filename.html"
File data.rds records a two-column character matrix with columns of dataset names and titles from the corresponding help file. File demo.rds has the same structure for package demos.
File vignette.rds records a dataframe with one row for each `vignette' (.[RS]nw file in inst/doc) and with columns ‘File’ (the full file path in the sources), ‘Title’, ‘PDF’ (the pathless file name of the installed PDF version, if present), ‘Depends’, ‘Keywords’ and ‘R’ (the pathless file name of the installed R code, if present).
All installed packages, whether they had an .Rd files or not, have help and html directories. The latter normally only contains the single file 00Index.html, the package index which has hyperlinks to the help topics (if any).
Directory help contains files AnIndex, paths.rds
and pkgname.rd[bx]. The latter two files are a lazy-load
database of parsed .Rd files, accessed by
tools:::fetchRdDB. File paths.rds is a saved character
vector of the original path names of the .Rd files, used when
updating the database.
File AnIndex is a two-column tab-delimited file: the first column
contains the aliases defined in the help files and the second the
basename (without the .Rd or .rd extension) of the file
containing that alias. It is read by utils:::index.search to
search for files matching a topic (alias), and read by scan in
utils:::matchAvailableTopics, part of the completion system.
File aliases.rds is the same information as AnIndex as a named character vector (names the topics, values the file basename), for faster access by R >= 2.11.0.
R's graphics internals were revised for version 1.4.0 (and tidied up for 2.7.0). This was to enable multiple graphics systems to be installed on top on the graphics `engine' – currently there are two such systems, one supporting `base' graphics (based on that in S and whose R code18 is in package graphics) and one implemented in package grid.
Some notes on the changes for 1.4.0 can be found at http://www.stat.auckland.ac.nz/~paul/R/basegraph.html and http://www.stat.auckland.ac.nz/~paul/R/graphicsChanges.html.
At the lowest level is a graphics device, which manages a plotting surface (a screen window or a representation to be written to a file). This implements a set of graphics primitives, to `draw'
as well as requests for information such as
and requests/opportunities to take action such as
The device also sets a number of variables, mainly Boolean flags indicating its capabilities. Devices work entirely in `device units' which are up to its developer: they can be in pixels, big points (1/72 inch), twips, ..., and can differ19 in the ‘x’ and ‘y’ directions.
The next layer up is the graphics `engine' that is the main interface to
the device (although the graphics subsystems do talk directly to
devices). This is responsible for clipping, converting the pch
values 0...26 to sets of lines/circles, centring (and otherwise
adjusting) text, rendering mathematical expressions (`plotmath') and
mapping colour descriptions such as names to the internal
representation.
Another function of the engine is to manage display lists and snapshots.
Some but not all instances of graphics devices maintain display lists, a
`list' of operations that have been performed on the device to produce
the current plot (since the device was opened or the plot was last
cleared, e.g. by plot.new). Screen devices generally maintain
a display list to handle repaint and resize events where as file-based
formats do not—display lists are also used to implement
dev.copy() and friends. The display list is a pairlist of
.Internal (base graphics) or .Call.graphics (grid
graphics) calls, which means that the C code implementing a graphics
operation will be re-called when the display list is replayed: apart
from the part which records the operation if successful.
Snapshots of the current graphics state are taken by
GEcreateSnapshot and replayed later in the session by
GEplaySnapshot. These are used by recordPlot(),
replayPlot() and the GUI menus of the windows() device.
The `state' includes the display list.
The top layer comprises the graphics subsystems. Although there is
provision for 24 subsystems, after 6 years only two exist, `base' and
`grid'. The base subsystem is registered with the engine when R is
initialized, and unregistered (via KillAllDevices) when an R
session is shut down. The grid subsystem is registered in its
.onLoad function and unregistered in the .onUnload
function. The graphics subsystem may also have `state' information
saved in a snapshot (currently base does and grid does not).
Package grDevices was originally created to contain the basic
graphics devices (although X11 is in a separate load-on-demand
module because of the volume of external libraries it brings in). Since
then it has been used for other functionality that was thought desirable
for use with grid, and hence has been transferred from package
graphics to grDevices. This is principally concerned with
the handling of colours and recording and replaying plots.
R ships with several graphics devices, and there is support for third-party packages to provide additional devices—several packages now do. This section describes the device internals from the viewpoint of a would-be writer of a graphics device.
There are two types used internally which are pointers to structures related to graphics devices.
The DevDesc type20 is a structure defined in the header file
R_ext/GraphicsDevice.h (which is included by
R_ext/GraphicsEngine.h). This describes the physical
characteristics of a device, the capabilities of the device driver and
contains a set of callback functions that will be used by the graphics
engine to obtain information about the device and initiate actions
(e.g. a new page, plotting a line or some text). Type pDevDesc
is a pointer to this type.
The relationship of device units to physical dimensions is set by the
element ipr of the DevDesc structure: a ‘double’
array of length 2.
The GEDevDesc type is a structure defined in
R_ext/GraphicsEngine.h (with comments in the file) as
typedef struct _GEDevDesc GEDevDesc;
struct _GEDevDesc {
pDevDesc dev;
Rboolean displayListOn;
SEXP displayList;
SEXP DLlastElt;
SEXP savedSnapshot;
Rboolean dirty;
Rboolean recordGraphics;
GESystemDesc *gesd[MAX_GRAPHICS_SYSTEMS];
Rboolean ask;
}
So this is essentially a device structure plus information about the
device maintained by the graphics engine and normally21 visible to the engine
and not to the device. Type pGEDevDesc is a pointer to this
type.
The graphics engine maintains an array of devices, as pointers to
GEDevDesc structures. The array is of size 64 but the first
element is always occupied by the "null device" and the final
element is kept as NULL as a sentinel.22 This array is reflected in the R variable
‘.Devices’. Once a device is killed its element becomes available
for reallocation (and its name will appear as "" in
‘.Devices’). Exactly one of the devices is `active': this is the
the null device if no other device has been opened and not killed.
Each instance of a graphics device needs to set up a GEDevDesc
structure by code very similar to
pGEDevDesc gdd;
R_GE_checkVersionOrDie(R_GE_version);
R_CheckDeviceAvailable();
BEGIN_SUSPEND_INTERRUPTS {
pDevDesc dev;
/* Allocate and initialize the device driver data */
if (!(dev = (pDevDesc) calloc(1, sizeof(DevDesc))))
return 0; /* or error() */
/* set up device driver or free 'dev' and error() */
gdd = GEcreateDevDesc(dev);
GEaddDevice2(gdd, "dev_name");
} END_SUSPEND_INTERRUPTS;
The DevDesc structure contains a void * pointer
‘deviceSpecific’ which is used to store data specific to the
device. Setting up the device driver includes initializing all the
non-zero elements of the DevDesc structure.
Note that the device structure is zeroed when allocated: this provides some protection against future expansion of the structure since the graphics engine can add elements that need to be non-NULL/non-zero to be `on' (and the structure ends with 64 reserved bytes which will be zeroed and allow for future expansion).
Rather more protection is provided by the version number of the
engine/device API, R_GE_version defined in
R_ext/GraphicsEngine.h together with access functions
int R_GE_getVersion(void);
void R_GE_checkVersionOrDie(int version);
If a graphics device calls R_GE_checkVersionOrDie(R_GE_version)
it can ensure it will only be used in versions of R which provide the
API it was designed for and compiled against.
The following `capabilities' can be defined for the device's
DevDesc structure.
Rboolean: can the display gamma be adjusted? This is not true
for any current device, and as from R 2.8.0 there is no user-level
function to change gamma. So ‘FALSE’ is the only useful value.
integer: can the device do horizontal adjustment of text
via the text callback, and if so, how precisely? 0 = no
adjustment, 1 = {0, 0.5, 1} (left, centre, right justification) or 2 =
continuously variable (in [0,1]) between left and right justification.
Rboolean: can the device handle mouse down events? This
flag and the next three are not currently used by R, but are maintained
for back compatibility.
Rboolean: ditto for mouse move events.
Rboolean: ditto for mouse up events.
Rboolean: ditto for keyboard events.
Rboolean: should non-symbol text be sent (in UTF-8) to the
textUTF8 and strWidthUTF8 callbacks, and sent as Unicode
points (negative values) to the metricInfo callback?
Rboolean: should symbol text be handled in UTF-8 in the same way
as other text? Requires textUTF8 = TRUE.
Handling text is probably the hardest task for a graphics device, and the design allows for the device to optionally indicate that it has additional capabilities. (If the device does not, these will if possible be handled in the graphics engine.)
The three callbacks for handling text that must be in all graphics
devices are text, strWidth and metricInfo with
declarations
void text(double x, double y, const char *str, double rot, double hadj,
pGgcontext gc, pDevDesc dd);
double strWidth(const char *str, pGEcontext gc, pDevDesc dd);
void metricInfo(int c, pGEcontext gc,
double* ascent, double* descent, double* width,
pDevDesc dd);
The ‘gc’ parameter provides the graphics context, most importantly the current font and fontsize, and ‘dd’ is a pointer to the active device's structure.
The text callback should plot ‘str’ at ‘(x,
y)’23 with an anti-clockwise rotation of
‘rot’ degrees. (For ‘hadj’ see below.) The interpretation
for horizontal text is that the baseline is at y and the start is
a x, so any left bearing for the first character will start at
x.
The strWidth callback computes the width of the string which it
would occupy if plotted horizontally in the current font. (Width here
is expected to include both (preferably) or neither of left and right
bearings.)
The metricInfo callback computes the size of a single
character: ascent is the distance it extends above the baseline
and descent how far it extends below the baseline.
width is the amount by which the cursor should be advanced when
the character is placed. For ascent and descent this is
intended to be the bounding box of the `ink' put down by the glyph and
not the box which might be used when assembling a line of conventional
text (it needs to be for e.g. hat(beta) to work correctly).
However, the width is used in plotmath to advance to the next
character, and so needs to include left and right bearings.
The interpretation of ‘c’ depends on the locale. Using
c = 0 used to give an indication of the size of the font: it
often returned the measurements for character "M"—however it is
no longer used as from R 2.7.0. In a single-byte locale values
32...255 indicate the corresponding character in the locale (if
present). For the symbol font (as used by ‘graphics::par(font=5)’,
‘grid::gpar(fontface=5’) and by `plotmath'), values 32...126,
161...239, 241...254 indicate glyphs in the Adobe Symbol encoding. In
a multibyte locale, c represents a Unicode point (except in the
symbol font). So the function needs to include code like
Rboolean Unicode = mbcslocale && (gc->fontface != 5);
if (c < 0) { Unicode = TRUE; c = -c; }
if(Unicode) UniCharMetric(c, ...); else CharMetric(c, ...);
In addition, if device capability hasTextUTF8 (see below) is
true, Unicode points will be passed as negative values: the code snippet
above shows how to handle this. (This applies to the symbol font only
if device capability wantSymbolUTF8 is true.)
If possible, the graphics device should handle clipping of text. It
indicates this by the structure element canClip which if true
will result in calls to the callback clip to set the clipping
region. If this is not done, the engine will clip very crudely (by
omitting any text that does not appear to be wholly inside the clipping
region).
The device structure has an integer element canHadj, which
indicates if the device can do horizontal alignment of text. If this is
one, argument ‘hadj’ to text will be called as 0 ,0.5,
1 to indicate left-, centre- and right-alignment at the indicated
position. If it is two, continuous values in the range [0, 1]
are assumed to be supported.
A new capability in R 2.7.0 (graphics API version 4) is
hasTextUTF8. If this is true, it has two consequences. First,
there are callbacks textUTF8 and strWidthUTF8 that should
behave identically to text and strWidth except that
‘str’ is assumed to be in UTF-8 rather than the current locale's
encoding. The graphics engine will call these for all text except in
the symbol font. Second, Unicode points will be passed to the
metricInfo callback as negative integers. If your device would
prefer to have UTF-8-encoded symbols, define wantSymbolUTF8 as
well as hasTextUTF8. In that case text in the symbol font is
sent to textUTF8 and strWidthUTF8.
Some devices can produce high-quality rotated text, but those based on
bitmaps often cannot. Those which can should set
useRotatedTextInContour to be true from graphics API version 4.
Several other elements relate to the precise placement of text by the graphics engine:
double xCharOffset;
double yCharOffset;
double yLineBias;
double cra[2];
These are more than a little mysterious. Element cra provides an
indication of the character size, par("cra") in base graphics, in
device units. The mystery is what is meant by `character size': which
character, which font at which size? Some help can be obtained by
looking at what this is used for. The first element, `width', is not
used by R except to set the graphical parameters. The second,
`height', is use to set the line spacing, that is the relationship
between par("mai") and par("mai") and so on. It is
suggested that a good choice is
dd->cra[0] = 0.9 * fnsize;
dd->cra[1] = 1.2 * fnsize;
where ‘fnsize’ is the `size' of the standard font (cex=1)
on the device, in device units. So for a 12-point font (the usual
default for graphics devices), ‘fnsize’ should be 12 points in
device units.
The remaining elements are yet more mysterious. The postscript()
device says
/* Character Addressing Offsets */
/* These offsets should center a single */
/* plotting character over the plotting point. */
/* Pure guesswork and eyeballing ... */
dd->xCharOffset = 0.4900;
dd->yCharOffset = 0.3333;
dd->yLineBias = 0.2;
It seems that xCharOffset is not currently used, and
yCharOffset is used by the base graphics system to set vertical
alignment in text() when pos is specified, and in
identify(). It is occasionally used by the graphic engine when
attempting exact centring of text, such as character string values of
pch in points() or grid.points()—however, it is
only used when precise character metric information is not available or
for multi-line strings.
yLineBias is used in the base graphics system in axis() and
mtext() to provide a default for their ‘padj’ argument.
The aim is to make the (default) output from graphics devices as similar
as possible, and further steps were taken in that direction in R
2.7.0. Generally people follow the model of the postscript and
pdf devices (which share most of their internal code).
The following conventions have become established:
lwd = 1 should correspond to a line width of 1/96 inch. This
will be a problem with pixel-based devices, and generally there is a
minimum line width of 1 pixel (although this may not be appropriate
where anti-aliasing of lines is used, and cairo prefers a minimum
of 2 pixels).
These conventions are less clear-cut for bitmap devices, especially where the bitmap format does not have a design resolution.
The interpretation of the line texture (par("lty") is described
in the header GraphicsEngine.h and in the help for par: note that the
`scale' of the pattern should be proportional to the line width (at
least for widths above the default).
One of the device callbacks is a function mode, documented in
the header as
* device_Mode is called whenever the graphics engine
* starts drawing (mode=1) or stops drawing (mode=0)
* GMode (in graphics.c) also says that
* mode = 2 (graphical input on) exists.
* The device is not required to do anything
Since mode = 2 has only recently been documented at device level,
it is not surprising that was it not used by any device: prior to R
2.7.0 it was not set by grid::grid.locator. It could be used to
change the graphics cursor, but devices currently do that in the
locator callback. (In base graphics the mode is set for the
duration of a locator call, but if type != "n" is switched
back for each point whilst annotation is being done.)
Many devices do indeed do nothing on this call, but some screen devices
ensure that drawing is flushed to the screen when called with mode
= 0. It is tempting to use it for some sort of buffering, but note
that `drawing' is interpreted at quite a low level and a typical single
figure will stop and start drawing many times.
Graphics devices may be designed to handle user interaction. The current model is similar to the one introduced in R 2.1.0 for the Windows screen device, but the design was changed in R 2.12.0 to be more open ended.
Users may use grDevices::setGraphicsEventEnv to set the
eventEnv environment in the device driver to hold event
handlers. When the user calls grDevices::getGraphicsEvent, R
will take three steps. First, it sets the device driver member
gettingEvent to true for each device with a
non-NULL eventEnv entry, and calls initEvent(dd,
true) if the callback is defined. It then enters an event loop. Each
time through the loop R will process events once, then check whether
any device has set the result member of eventEnv to a
non-NULL value, and will save the first such value found to be
returned. C functions doMouseEvent and doKeybd are
provided to call the R event handlers onMouseDown,
onMouseMove, onMouseUp, and onKeybd and set
eventEnv$result during this step. Finally, initEvent is
called again with init=false to inform the the devices that the
loop is done, and the result is returned to the user.
Specific devices are mostly documented by comments in their sources, although for devices of many years' standing those comments can be in need of updating. This subsection is a repository of notes on design decisions.
The X11(type="Xlib") device dates back to the mid 1990's and was
written then in Xlib, the most basic X11 toolkit. It has since
optionally made use of a few features from other toolkits: libXt
is used to read X11 resources, and libXmu is used in the handling
of clipboard selections.
Using basic Xlib code makes drawing fast, but is limiting. There
is no support of translucent colours (that came in the Xrender
toolkit of 2000) nor for rotated text (which R implements by
rendering text to a bitmap and rotating the latter).
The hinting for the X11 window asks for backing store to be used, and some windows managers may use it to handle repaints, but it seems that most repainting is done by replaying the display list (and here the fast drawing is very helpful).
There are perennial problems with finding fonts. Many users fail to realize that fonts are a function of the X server and not of the machine that R is running on. After many difficulties, R tries first to find the nearest size match in the sizes provided for Adobe fonts in the standard 75dpi and 100dpi X11 font packages—even that will fail to work when users of near-100dpi screens have only the 75dpi set installed. The 75dpi set allows sizes down to 6 points on a 100dpi screen, but some users do try to use smaller sizes and even 6 and 8 point bitmapped fonts do not look good.
Introduction of UTF-8 locales has caused another wave of difficulties.
X11 has very few genuine UTF-8 fonts, and produces composite fontsets
for the iso10646-1 encoding. Unfortunately these seem to have
low coverage apart from a few monospaced fonts in a few sizes (which are
not suitable for graph annotation), and where glyphs are missing what is
plotted is often quite unsatisfactory.
The approach being taken for R 2.7.0 is to make use of more modern
toolkits, namely cairo for rendering and Pango for font
management—because these are associated with Gtk+2 they are
widely available. Cairo supports translucent colours and alpha-blending
(via Xrender), and anti-aliasing for the display of lines
and text. Pango's font management is based on fontconfig and
somewhat mysterious, but it seems mainly to use Type 1 and TrueType
fonts on the machine running R and send grayscale bitmaps to cairo.
The windows() device is a family of devices: it supports plotting
to Windows (enhanced) metafiles, BMP, JPEG, PNG and
TIFF files as well as to Windows printers.
In most of these cases the primary plotting is to a bitmap: this is used for the (default) buffering of the screen device, which also enables the current plot to be saved to BMP, JPEG, PNG or TIFF (it is the internal bitmap which is copied to the file in the appropriate format).
The device units are pixels (logical ones on a metafile device).
The code was originally written by Guido Masarotto with extensive use of macros, which can make it hard to disentangle.
For a screen device, xd->gawin is the canvas of the screen, and
xd->bm is the off-screen bitmap. So macro DRAW arranges
to plot to xd->bm, and if buffering is off, also to
xd->gawin. For all other device, xd->gawin is the canvas,
a bitmap for the jpeg() and png() device, and an internal
representation of a Windows metafile for the win.metafile() and
win.print device. Since `plotting' is done by Windows GDI calls
to the appropriate canvas, its precise nature is hidden by the GDI
system.
Buffering on the screen device is achieved by running a timer, which when it fires copies the internal bitmap to the screen. This is set to fire every 500ms (by default) and is reset to 100ms after plotting activity.
Repaint events are handled by copying the internal bitmap to the screen canvas (and then reinitializing the timer), unless there has been a resize. Resizes are handled by replaying the display list: this might not be necessary if a fixed canvas with scrollbars is being used, but that is the least popular of the three forms of resizing.
Text on the device has moved to `Unicode' (UCS-2) in recent years. As
from R 2.7.0, UTF-8 is requested (hasTextUTF8 = TRUE) for
standard text, and converted to UCS-2 in the plotting functions in file
src/extra/graphapp/gdraw.c. However, GDI has no support for
Unicode symbol fonts, and symbols are handled in Adobe Symbol encoding.
Support for translucent colours (with alpha channel between 0 and 255)
was introduced in R 2.6.0 for the screen device only, and extended to
the bitmap devices in R 2.7.0.24 This is done by drawing on a further
internal bitmap, xd->bm2, in the opaque version of the colour
then alpha-blending that bitmap to xd->bm. The alpha-blending
routine is in a separate DLL, msimg32.dll, which is loaded on
first use.25 As small a rectangular region as
reasonably possible is alpha-blended (this is rectangle r in the
code), but things like mitre joins make estimation of a tight bounding
box too much work for lines and polygonal boundaries.
Translucent-coloured lines are not common, and the performance seems
acceptable.
The support for a transparent background in png() predates full
alpha-channel support in libpng (let alone in PNG viewers), so
makes use of the limited transparency support in earlier versions of
PNG. Where 24-bit colour is used, this is done by marking a single
colour to be rendered as transparent. R chose ‘#fdfefd’, and
uses this as the background colour (in GA_NewPage if the
specified background colour is transparent (and all non-opaque
background colours are treated as transparent). So this works by
marking that colour in the PNG file, and viewers without transparency
support see a slightly-off-white background, as if there were a
near-white canvas. Where a palette is used in the PNG file (if less
than 256 colours were used) then this colour is recorded with full
transparency and the remaining colours as opaque. If 32-bit colour were
available then we could add a full alpha channel, but this is dependent
on the graphics hardware and undocumented properties of GDI.
Devices receive colours as an unsigned int (in the GPar
structure and some of the devices as the typedef rcolor):
the comments in file R_ext/GraphicsDevice.h are the primary
documentation. The 4 bytes in the unsigned int are
R,G,B and alpha from least to most
significant. So each of RGB has 256 levels of luminosity from 0 to 255.
The alpha byte represents (from R 2.0.0) opacity, so value 255 is
fully opaque and 0 fully transparent: many but not all devices handle
semi-transparent colours.
Colors can be created in C via the macro R_RGBA, and a set of
macros are defined in R_ext/GraphicsDevice.h to extract the
various components.
Colours in the base graphics system were originally adopted from S (and
before that the GRZ library from Bell Labs), with the concept of a
(variable-sized) palette of colours referenced by numbers
‘1...N’ plus ‘0’ (the background colour). R
introduced the idea of referring to colours by character strings, either
in the forms ‘#RRGGBB’ or ‘#RRGGBBAA’ (representing the bytes
in hex) as given by function rgb() or via names: the 657 known
names are given in the character vector colors and in a table in
file colors.c. Note that semi-transparent colours are not
`premultiplied', so 50% transparent white is ‘#ffffff80’.
What is not clear is how the RGB values are to be mapped to display colours in the graphics device. There is a proposal (http://developer.r-project.org/sRGB-RFC.html) to regard the mapping as the colorspace `sRGB', but this is not currently done. The sRGB colorspace is an industry standard: it is used by Web browsers and JPEGs from all but high-end digital cameras. The interpretation is a matter for graphics devices and for code that manipulates colours, but not for the graphics engine or subsystems.
R uses a painting model similar to PostScript and PDF. This means that where shapes (circles, rectangles and polygons) can both be filled and have a stroked border, the fill should be painted first and then the border (or otherwise only half the border will be visible). Where both the fill and the border are semi-transparent there is some room for interpretation of the intention. Most devices first paint the fill and then the border, alpha-blending at each step. However, PDF does some automatic grouping of objects, and when the fill and the border have the same alpha, they are painted onto the same layer and then alpha-blended in one step. (See p. 569 of the PDF Reference Sixth Edition, version 1.7. Unfortunately, although this is what the PDF standard says should happen, it is not correctly implemented by some viewers.)
The base graphics system is likely to move to package graphics at some stage, but it currently implemented in files in src/main.
For historical reasons it is largely implemented in two layers.
Files plot.c, plot3d.c and par.c contain the code
for the around 30 .Internal calls that implement the basic
graphics operations. This code then calls functions with names starting
with G and declared in header Rgraphics.h in file
graphics.c, which in turn call the graphics engine (whose
functions almost all have names starting with GE).
A large part of the infrastructure of the base graphics subsystem are
the graphics parameters (as set/read by par()). These are stored
in a GPar structure declared in the private header
Graphics.h. This structure has two variables (state and
valid) tracking the state of the base subsystem on the device,
and many variables recording the graphics parameters and functions of
them.
The base system state is contained in baseSystemState structure
defined in R_ext/GraphicsBase.h. This contains three GPar
structures and a Boolean variable used to record if plot.new()
(or persp) has been used successfully on the device.
The three copies of the GPar structure are used to store the
current parameters (accessed via gpptr), the `device copy'
(accessed via dpptr) and space for a saved copy of the `device
copy' parameters. The current parameters are, clearly, those currently
in use and are copied from the `device copy' whenever plot.new()
is called (whether or not that advances to the next `page'). The saved
copy keeps the state when the device was last completely cleared (e.g.
when plot.new() was called with par(new=TRUE)), and is
used to replay the display list.
The separation is not completely clean: the `device copy' is altered if
a plot with log scale(s) is set up via plot.window().
There is yet another copy of most of the graphics parameters in
static variables in graphics.c which are used to preserve
the current parameters across the processing of inline parameters in
high-level graphics calls (handled by ProcessInlinePars).
Snapshots of the base subsystem record the `saved device copy' of the
GPar structure.
There remain quite a number of anomalies. For example, function
GEcontourLines is (despite its name) coded in file
plot3d.c and used to support function contourLines in
package grDevices.
[At least pointers to documentation.]
The behavior of R CMD check can be controlled through a variety of command line arguments and environment variables.
There is an internal --install=value command line argument not shown by R CMD check --help, with possible values
check:filefakeskipnoThe following environment variables can be used to customize the operation of check: a convenient place to set these is the file ~/.R/check.Renviron.
_R_CHECK_ALL_NON_ISO_C__R_CHECK_FORCE_SUGGESTS__R_CHECK_LATEX_VIGNETTES_R_check_weave_vignettes is also true),
latex package vignettes in the process of checking them: this
will show up Sweave source errors, including missing source
files. Default: true.
_R_CHECK_RD_CONTENTS__R_CHECK_RD_STYLE_\method markup.
Default: true.
_R_CHECK_RD_XREFS__R_CHECK_SUBDIRS_NOCASE__R_CHECK_SUBDIRS_STRICT__R_CHECK_USE_INSTALL_LOG__R_CHECK_WEAVE_VIGNETTES__R_CHECK_USE_CODETOOLS__R_CHECK_CODOC_S4_METHODS_codoc() testing is also performed on S4 methods.
This was off by default in R versions prior to 2.10.0 as it gave false
positives.
Default: true.
_R_CHECK_DOT_INTERNAL_.Internal calls.
Default: false.
_R_CHECK_EXECUTABLES__R_CHECK_EXECUTABLES_EXCLUSIONS__R_CHECK_PERMISSIONS_.Platform$OS.type == "unix".
_R_CHECK_FF_CALLS_checkFF() testing. Legacy mostly.
Default: true.
_R_CHECK_LICENSE__R_CHECK_RD_EXAMPLES_T_AND_F_check_T_and_F() also looks for “bad” (global)
‘T’/‘F’ uses in examples.
Off by default because this can result in false positives.
_R_CHECK_RD_CHECKRD_MINLEVEL_checkRd.
Default: -1.
_R_CHECK_XREFS_REPOSITORIES__R_CHECK_SRC_MINUS_W_IMPLICIT__R_CHECK_SRC_MINUS_W_UNUSED__R_CHECK_WALL_FORTRAN__R_CHECK_ASCII_CODE__R_CHECK_ASCII_DATA__R_CHECK_SKIP_ARCH__R_CHECK_SKIP_TESTS_ARCH__R_CHECK_SKIP_EXAMPLES_ARCH_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 Mac OS 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 3.79 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 (21 or later) users can
implement this indentation style by putting the following in one of
their startup files, and using customization to set the
c-default-style to "bsd" and c-basic-offset to
4.)
;;; ESS
(add-hook 'ess-mode-hook
(lambda ()
(ess-set-style 'C++ 'quiet)
;; 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.]
Various dialects of TeX and used for different purposes in R. The policy is that manuals be written in ‘texinfo’, and for convenience the main and Windows FAQs are also. This has the advantage that is is easy to produce HTML and plain text versions as well as typeset manuals.
LaTeX is not used directly, but rather as an intermediate format for typeset help documents and for vignettes.
Care needs to be taken about the assumptions made about the R user's
system: it may not have either ‘texinfo’ or a TeX system
installed. We have attempted to abstract out the cross-platform
differences, and almost all the setting of typeset documents is done by
tools::texi2dvi. This is used for offline printing of help
documents, preparing vignettes and for package manuals via R
CMD Rd2dvi. It is not currently used for the R manuals created in
directory doc/manual.
tools::texi2dvi makes use of a system command texi2dvi
where available. On a Unix-alike this is usually part of
‘texinfo’, whereas on Windows if it exists at all it would be an
executable, part of MiKTeX. If none is available, the R code runs
a sequence of (pdf)latex, bibtex and
makeindex commands.
This process has been rather vulnerable to the versions of the external software used: particular issues have been texi2dvi and texinfo.tex updates, mismatches between the two26, versions of the LaTeX package ‘hyperref’ and quirks in index production. The licenses used for LaTeX and latterly ‘texinfo’ prohibit us from including `known good' versions in the R distribution.
On a Unix-alike configure looks for the executables for TeX and friends and if found records the absolute paths in the system Renviron file. This used to record ‘false’ if no command was found, but it nowadays records the name for looking up on the path at run time. The latter can be important for binary distributions: one does not want to be tied to, for example, TeXLive 2007.
This chapter is for notes about possible future changes to R: there is no commitment to make such changes, let alone to a timescale.
Vectors in R are limited to a length of 2^31 - 1 elements (about 2
billion), as the length is stored in the SEXPREC as a C
int, and that type is used extensively to record lengths and
element numbers, including in packages. (It is the latter that
precludes a simple change to unsigned int).
There is also some desire to be able to store larger integers in R,
although the possibility of storing these as double is often
overlooked (and e.g. file pointers as returned by seek are
already stored as double).
A single object with 2^31 or more elements will take up at least 8GB of memory if integer or logical and 16GB if numeric or character, so routine use of such objects is in 2010 still some way off, but it is time to start to note some of the considerations in allowing longer vectors. Note that longer vectors are impossible under 32-bit platforms, so one possibility is to make changes only on 64-bit platforms.
double to store lengths
and element numbers, since this can hold integers up to 2^53 exactly.
VECTOR_SEXPREC has a
number of consequences. Most immediately, the serialization format
would have to be changed to accommodate longer lengths – and it might
well be prudent to store lengths up to 2^31-1 in the same way as before
to avoid unnecessary padding and for backwards compatibility. (Note that
the XDR format has a type ‘hyper’ for 64-bit integers, but it is
not currently implemented in the code in src/extra/xdr.)
Rlongint. It would
be cleanest if this were a type defined by R, and it may need to be.
C99 guarantees via stdint.h a number of types which are
64-bit or at least 64-bit and which would be suitable, but regrettably
people try to use R internals in C++, whereas C99 is not a subset
of C++ and C++ compilers are not required to support these types. It
would need to be tested if stdint.h was widely enough usable.
integer type is no
longer necessarily suitable. Much code has been written assuming that
it is! Different routes have been proposed:
longint. R's usual
implicit coercion rules would ensure that supplying an integer
vector for indexing or length<- would work.
integer type
to be 64-bit on 64-bit platforms (which was the approach taken by S-PLUS
for DEC/Compaq Alpha systems). Or even on all platforms.
integer or double values for lengths and
indices, and return double only when necessary.
The third has the advantages of minimal disruption to existing code and
not increasing memory requirements, but the disadvantage that the code
branches for long vectors would be tested rarely. In the first and
third scenarios both R's own code and user code would have to be
adapted for lengths that were not of type integer.
Some FORTRAN compilers (e.g. gfortran) have flags to compile
code with INTEGER as 64-bit (but not all), and even amongst those
which do care is needed that any external functions which are called
have versions for 64-bit integers and the correct versions are called.
However, it does seem that for the foreseeable future anything but the most trivial matrix algebra on gigabyte matrices will be too slow to be contemplated, not least under the single-thread implementation supplied by R.
.C, .Fortran, .Call and .External
interfaces need consideration (as well as the .Internal and
.Primitive interfaces used by R itself).
Most users of the .C and .Fortran interfaces use
as.integer for lengths and element numbers, but a few omit these
in the knowledge that these were of type integer. It might be
reasonable to suppose that these interfaces were not intended to be used
with long vectors and make passing a long vector to them an error.
The remaining interfaces will need to cope with the changed
VECTOR_SEXPREC types. It seems likely that in most cases lengths
are accessed by the length and LENGTH
functions27 So one approach is to keep these returning 32-bit lengths and
introduce `long' versions.
See also http://www.cs.uiowa.edu/~luke/talks/useR10.pdf.
.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 environment_R_CHECK_ALL_NON_ISO_C_: Tools_R_CHECK_ASCII_CODE_: Tools_R_CHECK_ASCII_DATA_: Tools_R_CHECK_CODOC_S4_METHODS_: Tools_R_CHECK_DOT_INTERNAL_: Tools_R_CHECK_EXECUTABLES_: Tools_R_CHECK_EXECUTABLES_EXCLUSIONS_: Tools_R_CHECK_FF_CALLS_: Tools_R_CHECK_FORCE_SUGGESTS_: Tools_R_CHECK_LATEX_VIGNETTES_: Tools_R_CHECK_LICENSE_: Tools_R_CHECK_PERMISSIONS_: Tools_R_CHECK_RD_CHECKRD_MINLEVEL_: Tools_R_CHECK_RD_CONTENTS_: Tools_R_CHECK_RD_EXAMPLES_T_AND_F_: Tools_R_CHECK_RD_STYLE_: Tools_R_CHECK_RD_XREFS_: Tools_R_CHECK_SKIP_ARCH_: Tools_R_CHECK_SKIP_EXAMPLES_ARCH_: Tools_R_CHECK_SKIP_TESTS_ARCH_: Tools_R_CHECK_SRC_MINUS_W_IMPLICIT_: Tools_R_CHECK_SRC_MINUS_W_UNUSED_: Tools_R_CHECK_SUBDIRS_NOCASE_: Tools_R_CHECK_SUBDIRS_STRICT_: Tools_R_CHECK_USE_CODETOOLS_: Tools_R_CHECK_USE_INSTALL_LOG_: Tools_R_CHECK_WALL_FORTRAN_: Tools_R_CHECK_WEAVE_VIGNETTES_: Tools_R_CHECK_XREFS_REPOSITORIES_: Toolsalloca: Memory allocatorsARGSUSED: Rest of headerATTRIB: Attributesattribute_hidden: Hiding C entry pointsCalloc: 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 headermkChar: The CHARSXP cachemkCharLenCE: The CHARSXP cacheNAMED: .Internal vs .PrimitiveNAMED: Argument evaluationNAMED: Rest of headernamed 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: AutoprintingRdll.hide: Hiding C entry pointsRealloc: 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] There is currently one other difference: when profiling builtin functions are counted as function calls but specials are not.
[6] the other current example is left brace, which is implemented as a primitive.
[7] a .Internal-only function used in
source, withVisible and a few other places.
[8] there is no R-level interface to this format
[9] only 0:4 will currently be used for
SEXPTYPEs but values 241:255 are used for pseudo-SEXPTYPEs.
[10] Currently the only relevant bits are 0:1, 4, 14:15.
[11] See define
USE_UTF8_IF_POSSIBLE in file src/main/gram.c.
[12] To maximize back-compatibility with devices installed prior to R 2.7.0 it has to be exactly that value: all others are equivalent to ‘FALSE’.
[13] or UTF-16 if support for surrogates is enabled in the OS, which it is not normally so at least for Western versions of Windows, despite some claims to the contrary on the Microsoft website.
[14] but not the GraphApp toolkit.
[15] This can also create
non-S4 objects, as in new("integer").
[16] although this is not recommended as it is less future-proof.
[17] but apparently not on Windows.
[18] The C code is in files base.c,
graphics.c, par.c, plot.c and plot3d.c in
directory src/main.
[19] although that needs to be
handled carefully, as for example the xspline functions used
prior to R 2.7.0 to depend on the aspect ratio of the pixels, and the
circle callback is given a radius (and that should be interpreted
as in the x units).
[20] NewDevDesc from R 1.4.0,
renamed in R 2.8.0.
[21] It is
possible for the device to find the GEDevDesc which points to its
DevDesc, and this is done often enough that there is a
convenience function desc2GEDesc to do so.
[22] Calling
R_CheckDeviceAvailable() ensures there is a free slot or throws
an error.
[23] in device coordinates
[24] It is technically possible to use alpha-blending on metafile devices such as printers, but it seems few drivers have support for this.
[25] It is available on Windows 2000 or later, and so had to be optional in R 2.6.0.
[26] Linux distributions tend to unbundle texinfo.tex from ‘texinfo’.
[27] but LENGTH is a macro under some internal
uses.