The goal of the duckplyr R package is to provide a drop-in replacement for dplyr that uses DuckDB as a backend for fast operation. DuckDB is an in-process OLAP database management system, dplyr is the grammar of data manipulation in the tidyverse.
duckplyr also defines a set of generics that provide a low-level implementer’s interface for dplyr’s high-level user interface.
Installation
Install duckplyr from CRAN with:
install.packages("duckplyr")You can also install the development version of duckplyr from R-universe:
install.packages("duckplyr", repos = c("https://tidyverse.r-universe.dev", "https://cloud.r-project.org"))Or from GitHub with:
# install.packages("pak", repos = sprintf("https://r-lib.github.io/p/pak/stable/%s/%s/%s", .Platform$pkgType, R.Version()$os, R.Version()$arch))
pak::pak("tidyverse/duckplyr")Examples
library(conflicted)
library(dplyr)
conflict_prefer("filter", "dplyr")
#> [conflicted] Will prefer dplyr::filter over any
#> other package.There are two ways to use duckplyr.
- To enable duckplyr for individual data frames, use
duckplyr::as_duckplyr_tibble()as the first step in your pipe, without attaching the package. - By calling
library(duckplyr), it overwrites dplyr methods and is automatically enabled for the entire session without having to callas_duckplyr_tibble(). To turn this off, callmethods_restore().
The examples below illustrate both methods. See also the companion demo repository for a use case with a large dataset.
Usage for individual data frames
This example illustrates usage of duckplyr for individual data frames.
Use duckplyr::as_duckplyr_tibble() to enable processing with duckdb:
out <-
palmerpenguins::penguins %>%
# CAVEAT: factor columns are not supported yet
mutate(across(where(is.factor), as.character)) %>%
duckplyr::as_duckplyr_tibble() %>%
mutate(bill_area = bill_length_mm * bill_depth_mm) %>%
summarize(.by = c(species, sex), mean_bill_area = mean(bill_area)) %>%
filter(species != "Gentoo")The result is a tibble, with its own class.
class(out)
#> [1] "duckplyr_df" "tbl_df" "tbl" "data.frame"
names(out)
#> [1] "species" "sex" "mean_bill_area"duckdb is responsible for eventually carrying out the operations. Despite the late filter, the summary is not computed for the Gentoo species.
out %>%
explain()
#> ┌---------------------------┐
#> │ ORDER_BY │
#> │ -------------------- │
#> │ dataframe_42_42 │
#> │ 42.___row_number ASC │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ FILTER │
#> │ -------------------- │
#> │ "r_base::!="(species, │
#> │ 'Gentoo') │
#> │ │
#> │ ~34 Rows │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ PROJECTION │
#> │ -------------------- │
#> │ #0 │
#> │ #1 │
#> │ #2 │
#> │ #3 │
#> │ │
#> │ ~172 Rows │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ STREAMING_WINDOW │
#> │ -------------------- │
#> │ Projections: │
#> │ ROW_NUMBER() OVER () │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ ORDER_BY │
#> │ -------------------- │
#> │ dataframe_42_42 │
#> │ 42.___row_number ASC │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ HASH_GROUP_BY │
#> │ -------------------- │
#> │ Groups: │
#> │ #0 │
#> │ #1 │
#> │ │
#> │ Aggregates: │
#> │ min(#2) │
#> │ mean(#3) │
#> │ │
#> │ ~172 Rows │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ PROJECTION │
#> │ -------------------- │
#> │ species │
#> │ sex │
#> │ ___row_number │
#> │ bill_area │
#> │ │
#> │ ~344 Rows │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ PROJECTION │
#> │ -------------------- │
#> │ #0 │
#> │ #1 │
#> │ #2 │
#> │ #3 │
#> │ │
#> │ ~344 Rows │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ STREAMING_WINDOW │
#> │ -------------------- │
#> │ Projections: │
#> │ ROW_NUMBER() OVER () │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ PROJECTION │
#> │ -------------------- │
#> │ species │
#> │ sex │
#> │ bill_area │
#> │ │
#> │ ~344 Rows │
#> └-------------┬-------------┘
#> ┌-------------┴-------------┐
#> │ R_DATAFRAME_SCAN │
#> │ -------------------- │
#> │ data.frame │
#> │ │
#> │ Projections: │
#> │ species │
#> │ bill_length_mm │
#> │ bill_depth_mm │
#> │ sex │
#> │ │
#> │ ~344 Rows │
#> └---------------------------┘All data frame operations are supported. Computation happens upon the first request.
out$mean_bill_area
#> materializing:
#> ---------------------
#> --- Relation Tree ---
#> ---------------------
#> Projection [species as species, sex as sex, mean_bill_area as mean_bill_area]
#> Order [___row_number ASC]
#> Filter ["!="(species, 'Gentoo')]
#> Projection [species as species, sex as sex, mean_bill_area as mean_bill_area, row_number() OVER () as ___row_number]
#> Projection [species as species, sex as sex, mean_bill_area as mean_bill_area]
#> Order [___row_number ASC]
#> Aggregate [species, sex, min(___row_number), mean(bill_area)]
#> Projection [species as species, island as island, bill_length_mm as bill_length_mm, bill_depth_mm as bill_depth_mm, flipper_length_mm as flipper_length_mm, body_mass_g as body_mass_g, sex as sex, "year" as year, bill_area as bill_area, row_number() OVER () as ___row_number]
#> Projection [species as species, island as island, bill_length_mm as bill_length_mm, bill_depth_mm as bill_depth_mm, flipper_length_mm as flipper_length_mm, body_mass_g as body_mass_g, sex as sex, "year" as year, "*"(bill_length_mm, bill_depth_mm) as bill_area]
#> r_dataframe_scan(0xdeadbeef)
#>
#> ---------------------
#> -- Result Columns --
#> ---------------------
#> - species (VARCHAR)
#> - sex (VARCHAR)
#> - mean_bill_area (DOUBLE)
#>
#> [1] 770.2627 656.8523 694.9360 819.7503 984.2279After the computation has been carried out, the results are available immediately:
out
#> # A tibble: 5 × 3
#> species sex mean_bill_area
#> <chr> <chr> <dbl>
#> 1 Adelie male 770.
#> 2 Adelie female 657.
#> 3 Adelie NA 695.
#> 4 Chinstrap female 820.
#> 5 Chinstrap male 984.Session-wide usage
This example illustrates usage of duckplyr for all data frames in the R session.
Use library(duckplyr) or duckplyr::methods_overwrite() to overwrite dplyr methods and enable processing with duckdb for all data frames:
duckplyr::methods_overwrite()
#> ✔ Overwriting dplyr methods with duckplyr methods.
#> ℹ Turn off with `duckplyr::methods_restore()`.This is the same query as above, without as_duckplyr_tibble():
out <-
palmerpenguins::penguins %>%
# CAVEAT: factor columns are not supported yet
mutate(across(where(is.factor), as.character)) %>%
mutate(bill_area = bill_length_mm * bill_depth_mm) %>%
summarize(.by = c(species, sex), mean_bill_area = mean(bill_area)) %>%
filter(species != "Gentoo")The result is a plain tibble now:
class(out)
#> [1] "tbl_df" "tbl" "data.frame"Querying the number of rows also starts the computation:
nrow(out)
#> materializing:
#> ---------------------
#> --- Relation Tree ---
#> ---------------------
#> Projection [species as species, sex as sex, mean_bill_area as mean_bill_area]
#> Order [___row_number ASC]
#> Filter ["!="(species, 'Gentoo')]
#> Projection [species as species, sex as sex, mean_bill_area as mean_bill_area, row_number() OVER () as ___row_number]
#> Projection [species as species, sex as sex, mean_bill_area as mean_bill_area]
#> Order [___row_number ASC]
#> Aggregate [species, sex, min(___row_number), mean(bill_area)]
#> Projection [species as species, island as island, bill_length_mm as bill_length_mm, bill_depth_mm as bill_depth_mm, flipper_length_mm as flipper_length_mm, body_mass_g as body_mass_g, sex as sex, "year" as year, bill_area as bill_area, row_number() OVER () as ___row_number]
#> Projection [species as species, island as island, bill_length_mm as bill_length_mm, bill_depth_mm as bill_depth_mm, flipper_length_mm as flipper_length_mm, body_mass_g as body_mass_g, sex as sex, "year" as year, "*"(bill_length_mm, bill_depth_mm) as bill_area]
#> r_dataframe_scan(0xdeadbeef)
#>
#> ---------------------
#> -- Result Columns --
#> ---------------------
#> - species (VARCHAR)
#> - sex (VARCHAR)
#> - mean_bill_area (DOUBLE)
#> [1] 5Restart R, or call duckplyr::methods_restore() to revert to the default dplyr implementation.
duckplyr::methods_restore()
#> ℹ Restoring dplyr methods.dplyr is active again:
palmerpenguins::penguins %>%
# CAVEAT: factor columns are not supported yet
mutate(across(where(is.factor), as.character)) %>%
mutate(bill_area = bill_length_mm * bill_depth_mm) %>%
summarize(.by = c(species, sex), mean_bill_area = mean(bill_area)) %>%
filter(species != "Gentoo")
#> # A tibble: 5 × 3
#> species sex mean_bill_area
#> <chr> <chr> <dbl>
#> 1 Adelie male 770.
#> 2 Adelie female 657.
#> 3 Adelie NA NA
#> 4 Chinstrap female 820.
#> 5 Chinstrap male 984.Telemetry
We would like to guide our efforts towards improving duckplyr, focusing on the features with the most impact. To this end, duckplyr collects and uploads telemetry data, but only if permitted by the user:
- No collection will happen unless the user explicitly opts in.
- Uploads are done upon request only.
- There is an option to automatically upload when the package is loaded, this is also opt-in.
The data collected contains:
- The package version
- The error message
- The operation being performed, and the arguments
- For the input data frames, only the structure is included (column types only), no column names or data
The first time the package encounters an unsupported function, data type, or operation, instructions are printed to the console.
palmerpenguins::penguins %>%
duckplyr::as_duckplyr_tibble() %>%
transmute(bill_area = bill_length_mm * bill_depth_mm) %>%
head(3)
#> The duckplyr package is configured to fall back to dplyr when it encounters an
#> incompatibility. Fallback events can be collected and uploaded for analysis to
#> guide future development. By default, no data will be collected or uploaded.
#> ℹ A fallback situation just occurred. The following information would have been
#> recorded:
#> {"version":"0.4.1","message":"Can't convert columns of class <factor> to
#> relational. Affected
#> column:\n`...1`.","name":"transmute","x":{"...1":"factor","...2":"factor","...3":"numeric","...4":"numeric","...5":"integer","...6":"integer","...7":"factor","...8":"integer"},"args":{"dots":{"...9":"...3
#> * ...4"}}}
#> → Run `duckplyr::fallback_sitrep()` to review the current settings.
#> → Run `Sys.setenv(DUCKPLYR_FALLBACK_COLLECT = 1)` to enable fallback logging,
#> and `Sys.setenv(DUCKPLYR_FALLBACK_VERBOSE = TRUE)` in addition to enable
#> printing of fallback situations to the console.
#> → Run `duckplyr::fallback_review()` to review the available reports, and
#> `duckplyr::fallback_upload()` to upload them.
#> ℹ See `?duckplyr::fallback()` for details.
#> ℹ This message will be displayed once every eight hours.
#> materializing:
#> ---------------------
#> --- Relation Tree ---
#> ---------------------
#> Limit 3
#> r_dataframe_scan(0xdeadbeef)
#>
#> ---------------------
#> -- Result Columns --
#> ---------------------
#> - bill_area (DOUBLE)
#>
#> # A tibble: 3 × 1
#> bill_area
#> <dbl>
#> 1 731.
#> 2 687.
#> 3 725.How is this different from dbplyr?
The duckplyr package is a dplyr backend that uses DuckDB, a high-performance, embeddable OLAP database. It is designed to be a fully compatible drop-in replacement for dplyr, with exactly the same syntax and semantics:
- Input and output are data frames or tibbles
- All dplyr verbs are supported, with fallback
- All R data types and functions are supported, with fallback
- No SQL is generated
The dbplyr package is a dplyr backend that connects to SQL databases, and is designed to work with various databases that support SQL, including DuckDB. Data must be copied into and collected from the database, and the syntax and semantics are similar but not identical to plain dplyr.
Extensibility
This package also provides generics, for which other packages may then implement methods.
#> ✔ 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
#> Datsun 710 22.8 4 108 93
#>
#> 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, 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"