# Implementer’s interface

    library(conflicted)
    library(dplyr)
    conflict_prefer("filter", "dplyr")
    #> [conflicted] Removing existing preference.
    #> [conflicted] Will prefer dplyr::filter over any other package.

duckplyr also defines a set of generics that provide a low-level implementer’s interface for dplyr’s high-level user interface. Other packages may then implement methods for those generics.

    library(conflicted)
    library(dplyr)
    conflict_prefer("filter", "dplyr")
    #> [conflicted] Removing existing preference.
    #> [conflicted] Will prefer dplyr::filter over any other package.
    library(duckplyr)

    #> ✔ Overwriting dplyr methods with duckplyr methods.
    #> ℹ Turn off with `duckplyr::methods_restore()`.

    # Create a relational to be used by examples below
    new_dfrel <- function(x) {
      stopifnot(is.data.frame(x))
      new_relational(list(x), class = "dfrel")
    }
    mtcars_rel <- new_dfrel(mtcars[1:5, 1:4])

    # Example 1: return a data.frame
    rel_to_df.dfrel <- function(rel, ...) {
      unclass(rel)[[1]]
    }
    rel_to_df(mtcars_rel)
    #>                    mpg cyl disp  hp
    #> Mazda RX4         21.0   6  160 110
    #> Mazda RX4 Wag     21.0   6  160 110
    #> Datsun 710        22.8   4  108  93
    #> Hornet 4 Drive    21.4   6  258 110
    #> Hornet Sportabout 18.7   8  360 175

    # Example 2: A (random) filter
    rel_filter.dfrel <- function(rel, exprs, ...) {
      df <- unclass(rel)[[1]]

      # A real implementation would evaluate the predicates defined
      # by the exprs argument
      new_dfrel(df[sample.int(nrow(df), 3, replace = TRUE), ])
    }

    rel_filter(
      mtcars_rel,
      list(
        relexpr_function(
          "gt",
          list(relexpr_reference("cyl"), relexpr_constant("6"))
        )
      )
    )
    #> [[1]]
    #>                    mpg cyl disp  hp
    #> Mazda RX4 Wag     21.0   6  160 110
    #> Mazda RX4 Wag.1   21.0   6  160 110
    #> Hornet Sportabout 18.7   8  360 175
    #>
    #> attr(,"class")
    #> [1] "dfrel"      "relational"

    # Example 3: A custom projection
    rel_project.dfrel <- function(rel, exprs, ...) {
      df <- unclass(rel)[[1]]

      # A real implementation would evaluate the expressions defined
      # by the exprs argument
      new_dfrel(df[seq_len(min(3, base::ncol(df)))])
    }

    rel_project(
      mtcars_rel,
      list(relexpr_reference("cyl"), relexpr_reference("disp"))
    )
    #> [[1]]
    #>                    mpg cyl disp
    #> Mazda RX4         21.0   6  160
    #> Mazda RX4 Wag     21.0   6  160
    #> Datsun 710        22.8   4  108
    #> Hornet 4 Drive    21.4   6  258
    #> Hornet Sportabout 18.7   8  360
    #>
    #> attr(,"class")
    #> [1] "dfrel"      "relational"

    # Example 4: A custom ordering (eg, ascending by mpg)
    rel_order.dfrel <- function(rel, exprs, ...) {
      df <- unclass(rel)[[1]]

      # A real implementation would evaluate the expressions defined
      # by the exprs argument
      new_dfrel(df[order(df[[1]]), ])
    }

    rel_order(
      mtcars_rel,
      list(relexpr_reference("mpg"))
    )
    #> [[1]]
    #>                    mpg cyl disp  hp
    #> Hornet Sportabout 18.7   8  360 175
    #> Mazda RX4         21.0   6  160 110
    #> Mazda RX4 Wag     21.0   6  160 110
    #> Hornet 4 Drive    21.4   6  258 110
    #> Datsun 710        22.8   4  108  93
    #>
    #> attr(,"class")
    #> [1] "dfrel"      "relational"

    # Example 5: A custom join
    rel_join.dfrel <- function(left, right, conds, join, ...) {
      left_df <- unclass(left)[[1]]
      right_df <- unclass(right)[[1]]

      # A real implementation would evaluate the expressions
      # defined by the conds argument,
      # use different join types based on the join argument,
      # and implement the join itself instead of relaying to left_join().
      new_dfrel(dplyr::left_join(left_df, right_df))
    }

    rel_join(new_dfrel(data.frame(mpg = 21)), mtcars_rel)
    #> Joining with `by = join_by(mpg)`
    #> Joining with `by = join_by(mpg)`
    #> [[1]]
    #>   mpg cyl disp  hp
    #> 1  21   6  160 110
    #> 2  21   6  160 110
    #>
    #> attr(,"class")
    #> [1] "dfrel"      "relational"

    # Example 6: Limit the maximum rows returned
    rel_limit.dfrel <- function(rel, n, ...) {
      df <- unclass(rel)[[1]]

      new_dfrel(df[seq_len(n), ])
    }

    rel_limit(mtcars_rel, 3)
    #> [[1]]
    #>                mpg cyl disp  hp
    #> Mazda RX4     21.0   6  160 110
    #> Mazda RX4 Wag 21.0   6  160 110
    #> Datsun 710    22.8   4  108  93
    #>
    #> attr(,"class")
    #> [1] "dfrel"      "relational"

    # Example 7: Suppress duplicate rows
    #  (ignoring row names)
    rel_distinct.dfrel <- function(rel, ...) {
      df <- unclass(rel)[[1]]

      new_dfrel(df[!duplicated(df), ])
    }

    rel_distinct(new_dfrel(mtcars[1:3, 1:4]))
    #> [[1]]
    #>             mpg cyl disp  hp
    #> Mazda RX4  21.0   6  160 110
    #> Datsun 710 22.8   4  108  93
    #>
    #> attr(,"class")
    #> [1] "dfrel"      "relational"

    # Example 8: Return column names
    rel_names.dfrel <- function(rel, ...) {
      df <- unclass(rel)[[1]]

      names(df)
    }

    rel_names(mtcars_rel)
    #> [1] "mpg"  "cyl"  "disp" "hp"