In duckdblabs/duckplyr: A 'DuckDB'-Backed Version of 'dplyr'

clean_output <- function(x, options) {
  x <- gsub("0x[0-9a-f]+", "0xdeadbeef", x)
  x <- gsub("dataframe_[0-9]*_[0-9]*", "      dataframe_42_42      ", x)
  x <- gsub("[0-9]*\\.___row_number ASC", "42.___row_number ASC", x)
  x <- gsub("─", "-", x)
  x
}

local({
  hook_source <- knitr::knit_hooks$get("document")
  knitr::knit_hooks$set(document = clean_output)
})

knitr::opts_chunk$set(
  collapse = TRUE,
  eval = identical(Sys.getenv("IN_PKGDOWN"), "true") || (getRversion() >= "4.1" && rlang::is_installed(c("conflicted", "nycflights13"))),
  comment = "#>"
)

Sys.setenv(DUCKPLYR_FALLBACK_COLLECT = 0)

library(conflicted)
library(dplyr)
conflict_prefer("filter", "dplyr")

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")
library(duckplyr)

methods_overwrite()

# 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)

# 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"))
    )
  )
)

# 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"))
)

# 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"))
)

# 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)

# 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)

# 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]))

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

  names(df)
}

rel_names(mtcars_rel)