Parsing R code: Freedom of expression is not always a good idea

February 28, 2012
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(This article was first published on The Shape of Code » R, and kindly contributed to R-bloggers)

With my growing interest in R it was inevitable that I would end up writing a parser for it. The fact that the language is relatively small (the add-on packages do the serious work) hastened the event because it did not look like much work; famous last words. I knew about R’s design and implementation being strongly influenced by the world view of functional programming and this should have set alarm bells ringing; this community have a history of willfully ignoring some of the undesirable consequences of their penchant of taking simple ideas and over generalizing them (i.e., I should have expected hidden complications).

While the official R language definition only contains a tiny fraction of the information needed to create a full implementation I decided to use it rather than ‘cheat’ and look at the R project implementation sources. I took as my oracle of correctness the source code of the substantial amount of R in its 3,000+ package library. This approach would help me uncover some of the incorrect preconceived ideas I have about how R source fits together.

I started with a C lexer and chopped and changed (it is difficult to do decent error recovery in automatically generated lexers and I prefer to avoid them). A few surprises cropped up ** is supported as an undocumented form of ^ and by default ]] must be treated as two tokens (e.g., two ] in a[b[c]] but one ]] in d[[e]], an exception to the very commonly used maximal munch rule).

The R grammar is all about expressions with some statement bits and pieces thrown in. R operator precedence follows that of Fortran, except the precedence of unary plus/minus has been increased to be above multiply/divide (instead of below). Easy peasy, cut and paste an existing expression grammar and done by tea time :-) . Several tea times later I have a grammar that parses all of the R packages (apart from 80+ real syntax errors contained therein and a hand full of kinky operator combinations I’m not willing to contort the grammar to support). So what happened?

Two factors accounts for most of the difference between my estimate of the work required and the actual work required:

  • my belief that a well written grammar has no ambiguities (while zero is a common goal for many projects a handful might be acceptable if the building is on fire and I have to leave). A major advantage of automatic generation of parser tables from a grammar specification is being warned about ambiguities in the grammar (either shift/reduce or reduce/reduce conflicts). At an early stage I was pulling my hair out over having 59 conflicts and decided to relent and look at the R project source and was amazed to find their grammar has 81 ambiguities!

    I have managed to get the number of ambiguities down to the mid-30s, not good at all but it will have to do for the time being.

  • some of my preconceptions about of how R syntax worked were seriously wrong. In some cases I spotted my mistake quickly because I recognized the behavior from other languages I know, other misconceptions took a lot longer to understand and handle because I did not believe anybody would design expression evaluation to work that way.

The root cause of the difference can more concretely be traced to the approach to specifying language syntax. The R project grammar is written using the form commonly seen in functional language implementations and introductory compiler books. This form has the advantage of being very short and apparently simple; the following is a cut down example in a form of BNF used by many parser generators:

expr :   expr  op  expr  |
         IDENTIFIER      ;
 
op :  &'  |  '=='  |  '>'  |  '+'  |  '-'  |  '*'  |  '/'  | '^' ;

This specifies a sequence of IDENTIFIERs separated by binary operators and is ambiguous when the expression contains more than two operators, e.g., a + b * c can be parsed in more than one way. Parser generators such as Yacc will complain and flag any ambiguity and pick one of the possibilities as the default behavior for handling a given ambiguity; developers can specify additional grammar information in the file read by Yacc to guide its behavior when deciding how to resolve specific ambiguities. For instance, the relative precedence of operators can be specified and this information would be used by Yacc to decide that the ambiguous expression a + b * c should be parsed as-if it had been written like a + (b * c) rather than like (a + b) * c. The R project grammar is short, highly ambiguous and relies on the information contained in the explicitly specified relative operator precedence and associativity directives to resolve the ambiguities.

An alternative method of specifying the grammar is to have a separate list of grammar rules for each level of precedence (I always use this approach). In this approach there is no ambiguity, the precedence and associativity are implicitly specified by how the grammar is written. Obviously this approach creates much longer grammars, there will be at least two rules for every precedence level (19 in R, many with multiple operators). The following is a cut down example containing just multiple, divide, add and subtract:

...
multiplicative_expression:
             cast_expression                               |
             multiplicative_expression '*' cast_expression |
             multiplicative_expression '/' cast_expression ;
 
additive_expression:
             multiplicative_expression                         |
             additive_expression '+' multiplicative_expression |
             additive_expression '-' multiplicative_expression ;
...

The advantages of this approach are that, because there are no ambiguities, the developer can see exactly how the grammar behaves and if an ambiguity is accidentally introduced when editing the source it should be noticed when the parser generator reports a problem where previously there were none (rather than the new ambiguity being hidden in the barrage of existing ones that are ignored because they are so numerous).

My first big misconception about R syntax was to think that R had statements, it doesn’t. What other languages would treat as statements R always treats as expressions. The if, for and while constructs have values (e.g., 2*(if (x == y) 2 else 4)). No problem, I used Algol 68 as an undergraduate, which supports this kind of usage. I assumed that when an if appeared as an operand in an expression it would have to be bracketed using () or {} to avoid creating a substantial number of parsing ambiguities; WRONG. No brackets need be specified, the R expression if (x == y) 2 else 4+1 is ambiguous (it could be treated as-if it had been written if (x == y) 2 else (4+1) or (if (x == y) 2 else 4)+1) and the R project grammar relies on its precedence specification to resolve the conflict (in favor of the first possibility listed above).

My next big surprise came from the handling of unary operators. Most modern languages give all unary operators the same precedence, generally higher than any binary operator. Following Fortran the R unary operators have a variety of different precedence levels; however R did not adopt the restrictions Fortran places on where unary operators can occur.

I assumed that R had adopted the restrictions used by other languages containing unary operators at different precedence levels, e.g., not allowing a unary operator token to follow a binary operator token (i.e., there has to be an intervening opening parenthesis); WRONG. R allows me to write 1 == !1 < 0, while Fortran (and Ada, etc) require that a parenthesis be inserted between the operands == ! (hopefully resulting in the intent being clearer).

I had to think a bit to come up with an explicit set of grammar rules to handle R unary operator's freedom of expression (without creating any ambiguities).

Stepping back from the details. My view is that programming language syntax should be designed to reduce the number of mistakes developers make. Requiring that brackets appear in certain contexts helps prevent mistakes by the original author and subsequent readers of the code.

Claims that R (or any other language) syntax is 'natural' is clearly spurious and really no more than a statement of preference by the speaker. Our DNA has not yet been found to equip us to handle one programming language better than another.

Over the coming months I hope to have the time to analyse R source looking for faults that might not have occurred had brackets been used. Also how much code might be broken if R started to require brackets in certain contexts?

An example of the difference that brackets can make is provided by the handling of the unary ! operator in R and C/C++/Java/etc. Take the expression !x > y, which R parses as-if written !(x > y) and C/C++/Java/etc as if written (!x) > y. I would not claim that either is better than the other from the point of view of developers getting the behavior right, I know that some C programmers get it wrong and I suspect that some R programmers do too.

By increasing the precedence of unary plus/minus the R designers ensured that 8/-2/2 was parsed like (8/-2)/2 rather than 8/(-2/2).

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