R/translate-sql.r

In sctyner/dplyr050: A Grammar of Data Manipulation

#' Translate an expression to sql.
#'
#' @section Base translation:
#' The base translator, \code{base_sql},
#' provides custom mappings for \code{!} (to NOT), \code{&&} and \code{&} to
#' \code{AND}, \code{||} and \code{|} to \code{OR}, \code{^} to \code{POWER},
#' \code{\%>\%} to \code{\%}, \code{ceiling} to \code{CEIL}, \code{mean} to
#' \code{AVG}, \code{var} to \code{VARIANCE}, \code{tolower} to \code{LOWER},
#' \code{toupper} to \code{UPPER} and \code{nchar} to \code{length}.
#'
#' \code{c} and \code{:} keep their usual R behaviour so you can easily create
#' vectors that are passed to sql.
#'
#' All other functions will be preserved as is. R's infix functions
#' (e.g. \code{\%like\%}) will be converted to their sql equivalents
#' (e.g. \code{LIKE}). You can use this to access SQL string concatenation:
#' \code{||} is mapped to \code{OR}, but \code{\%||\%} is mapped to \code{||}.
#' To suppress this behaviour, and force errors immediately when dplyr doesn't
#' know how to translate a function it encounters, using set the
#' \code{dplyr.strict_sql} option to \code{TRUE}.
#'
#' You can also use \code{sql} to insert a raw sql string.
#'
#' @section SQLite translation:
#' The SQLite variant currently only adds one additional function: a mapping
#' from \code{sd} to the SQL aggregation function \code{stdev}.
#'
#' @param ...,dots Expressions to translate. \code{sql_translate}
#'   automatically quotes them for you.  \code{sql_translate_} expects
#'   a list of already quoted objects.
#' @param con An optional database connection to control the details of
#'   the translation. The default, \code{NULL}, generates ANSI SQL.
#' @param vars A character vector giving variable names in the remote
#'   data source. If this is supplied, \code{translate_sql} will call
#'   \code{\link{partial_eval}} to interpolate in the values from local
#'   variables.
#' @param vars_group,vars_order Grouping and ordering variables used for
#'   windowed functions.
#' @param window Use \code{FALSE} to suppress generation of the \code{OVER}
#'   statement used for window functions. This is necessary when generating
#'   SQL for a grouped summary.
#' @export
#' @examples
#' # Regular maths is translated in a very straightforward way
#' translate_sql(x + 1)
#' translate_sql(sin(x) + tan(y))
#'
#' # Note that all variable names are escaped
#' translate_sql(like == "x")
#' # In ANSI SQL: "" quotes variable _names_, '' quotes strings
#'
#' # Logical operators are converted to their sql equivalents
#' translate_sql(x < 5 & !(y >= 5))
#' # xor() doesn't have a direct SQL equivalent
#' translate_sql(xor(x, y))
#'
#' # If is translated into case when
#' translate_sql(if (x > 5) "big" else "small")
#'
#' # Infix functions are passed onto SQL with % removed
#' translate_sql(first %like% "Had*")
#' translate_sql(first %is% NULL)
#' translate_sql(first %in% c("John", "Roger", "Robert"))
#'
#'
#' # And be careful if you really want integers
#' translate_sql(x == 1)
#' translate_sql(x == 1L)
#'
#' # If you have an already quoted object, use translate_sql_:
#' x <- quote(y + 1 / sin(t))
#' translate_sql_(list(x))
#'
#' # Translation with known variables ------------------------------------------
#'
#' # If the variables in the dataset are known, translate_sql will interpolate
#' # in literal values from the current environment
#' x <- 10
#' translate_sql(mpg > x)
#' translate_sql(mpg > x, vars = names(mtcars))
#'
#' # By default all computations happens in sql
#' translate_sql(cyl == 2 + 2, vars = names(mtcars))
#' # Use local to force local evaluation
#' translate_sql(cyl == local(2 + 2), vars = names(mtcars))
#'
#' # This is also needed if you call a local function:
#' inc <- function(x) x + 1
#' translate_sql(mpg > inc(x), vars = names(mtcars))
#' translate_sql(mpg > local(inc(x)), vars = names(mtcars))
#'
#' # Windowed translation --------------------------------------------
#' # Known window functions automatically get OVER()
#' translate_sql(mpg > mean(mpg))
#'
#' # Suppress this with window = FALSE
#' translate_sql(mpg > mean(mpg), window = FALSE)
#'
#' # vars_group controls partition:
#' translate_sql(mpg > mean(mpg), vars_group = "cyl")
#'
#' # and vars_order controls ordering for those functions that need it
#' translate_sql(cumsum(mpg))
#' translate_sql(cumsum(mpg), vars_order = "mpg")
translate_sql <- function(...,
                          con = NULL,
                          vars = character(),
                          vars_group = NULL,
                          vars_order = NULL,
                          window = TRUE) {
  dots <- lazyeval::lazy_dots(...)

  translate_sql_(dots,
    con = con,
    vars = vars,
    vars_group = vars_group,
    vars_order = vars_order,
    window = window
  )
}

#' @export
#' @rdname translate_sql
translate_sql_ <- function(dots,
                           con = NULL,
                           vars = character(),
                           vars_group = NULL,
                           vars_order = NULL,
                           window = TRUE) {
  expr <- lazyeval::as.lazy_dots(dots, env = parent.frame())
  if (!any(has_names(expr))) {
    names(expr) <- NULL
  }

  if (length(vars) > 0) {
    # If variables are known, partially evaluate input
    expr <- partial_eval2(expr, vars)
  } else {
    # Otherwise just extract expressions, ignoring the environment
    # from which they came
    expr <- lapply(expr, "[[", "expr")
  }
  variant <- sql_translate_env(con)

  if (window) {
    old_con <- set_partition_con(con)
    on.exit(set_partition_con(old_con), add = TRUE)

    old_group <- set_partition_group(vars_group)
    on.exit(set_partition_group(old_group), add = TRUE)

    old_order <- set_partition_order(vars_order)
    on.exit(set_partition_order(old_order), add = TRUE)
  }

  pieces <- lapply(expr, function(x) {
    if (is.atomic(x)) return(escape(x, con = con))

    env <- sql_env(x, variant, con, window = window)
    escape(eval(x, envir = env))
  })

  sql(unlist(pieces))
}

sql_env <- function(expr, variant, con, window = FALSE,
                    strict = getOption("dplyr.strict_sql")) {
  stopifnot(is.sql_variant(variant))

  # Default for unknown functions
  if (!strict) {
    unknown <- setdiff(all_calls(expr), names(variant))
    default_env <- ceply(unknown, default_op, parent = emptyenv())
  } else {
    default_env <- new.env(parent = emptyenv())
  }


  # Known R -> SQL functions
  special_calls <- copy_env(variant$scalar, parent = default_env)
  if (!window) {
    special_calls2 <- copy_env(variant$aggregate, parent = special_calls)
  } else {
    special_calls2 <- copy_env(variant$window, parent = special_calls)
  }

  # Existing symbols in expression
  names <- all_names(expr)
  name_env <- ceply(names, function(x) escape(ident(x), con = con),
    parent = special_calls2)

  # Known sql expressions
  symbol_env <- copy_env(base_symbols, parent = name_env)
  symbol_env
}

default_op <- function(x) {
  assert_that(is.string(x))
  infix <- c("::", "$", "@", "^", "*", "/", "+", "-", ">", ">=", "<", "<=",
    "==", "!=", "!", "&", "&&", "|", "||", "~", "<-", "<<-")

  if (x %in% infix) {
    sql_infix(x)
  } else if (grepl("^%.*%$", x)) {
    x <- substr(x, 2, nchar(x) - 1)
    sql_infix(x)
  } else {
    sql_prefix(x)
  }
}


all_calls <- function(x) {
  if (!is.call(x)) return(NULL)

  fname <- as.character(x[[1]])
  unique(c(fname, unlist(lapply(x[-1], all_calls), use.names = FALSE)))
}

all_names <- function(x) {
  if (is.name(x)) return(as.character(x))
  if (!is.call(x)) return(NULL)

  unique(unlist(lapply(x[-1], all_names), use.names = FALSE))
}

# character vector -> environment
ceply <- function(x, f, ..., parent = parent.frame()) {
  if (length(x) == 0) return(new.env(parent = parent))
  l <- lapply(x, f, ...)
  names(l) <- x
  list2env(l, parent = parent)
}