knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/dropin-", out.width = "100%" ) Sys.setenv(DUCKPLYR_META_ENABLE = FALSE) set.seed(20230702) clean_output <- function(x, options) { x <- gsub("0x[0-9a-f]+", "0xdeadbeef", x) x <- gsub("dataframe_[0-9]*_[0-9]*", " dataframe_42_42 ", x) x <- gsub("[0-9]*\\.___row_number ASC", "42.___row_number ASC", x) index <- x index <- gsub("─", "-", index) index <- strsplit(paste(index, collapse = "\n"), "\n---\n")[[1]][[2]] writeLines(index, "index.md") # FIXME: Change to the main site after release x <- gsub('(`vignette[(]"([^"]+)"[)]`)', "[\\1](https://duckplyr.tidyverse.org/articles/\\2.html)", x) x <- fansi::strip_sgr(x) x } options( cli.num_colors = 256, cli.width = 71, width = 71, pillar.bold = TRUE, pillar.max_title_chars = 5, pillar.min_title_chars = 5, pillar.max_footer_lines = 12, conflicts.policy = list(warn = FALSE) ) local({ hook_source <- knitr::knit_hooks$get("document") knitr::knit_hooks$set(document = clean_output) })
A drop-in replacement for dplyr, powered by DuckDB for speed.
dplyr is the grammar of data manipulation in the tidyverse. The duckplyr package will run all of your existing dplyr code with identical results, using DuckDB where possible to compute the results faster. In addition, you can analyze larger-than-memory datasets straight from files on your disk or from the web.
If you are new to dplyr, the best place to start is the data transformation chapter in R for Data Science.
Install duckplyr from CRAN with:
install.packages("duckplyr")
You can also install the development version of duckplyr from R-universe:
install.packages("duckplyr", repos = c("https://tidyverse.r-universe.dev", "https://cloud.r-project.org"))
Or from GitHub with:
# install.packages("pak") pak::pak("tidyverse/duckplyr")
Calling library(duckplyr)
overwrites dplyr methods, enabling duckplyr for the entire session.
library(conflicted) library(duckplyr)
# Done after library(duckplyr) to leave the original output pkgload::load_all()
Sys.setenv(DUCKPLYR_FALLBACK_COLLECT = 0)
conflict_prefer("filter", "dplyr", quiet = TRUE)
The following code aggregates the inflight delay by year and month for the first half of the year.
We use a variant of the nycflights13::flights
dataset, where the timezone has been set to UTC to work around a current limitation of duckplyr, see vignette("limits")
.
flights_df() out <- flights_df() |> filter(!is.na(arr_delay), !is.na(dep_delay)) |> mutate(inflight_delay = arr_delay - dep_delay) |> summarize( .by = c(year, month), mean_inflight_delay = mean(inflight_delay), median_inflight_delay = median(inflight_delay), ) |> filter(month <= 6)
The result is a plain tibble:
class(out)
Nothing has been computed yet. Querying the number of rows, or a column, starts the computation:
out$month
Note that, unlike dplyr, the results are not ordered, see ?config
for details.
However, once materialized, the results are stable:
out
If a computation is not supported by DuckDB, duckplyr will automatically fall back to dplyr.
flights_df() |> summarize( .by = origin, dest = paste(sort(unique(dest)), collapse = " ") )
Restart R, or call duckplyr::methods_restore()
to revert to the default dplyr implementation.
duckplyr::methods_restore()
An extended variant of the nycflights13::flights
dataset is also available for download as Parquet files.
year <- 2022:2024 base_url <- "https://blobs.duckdb.org/flight-data-partitioned/" files <- paste0("Year=", year, "/data_0.parquet") urls <- paste0(base_url, files) tibble(urls)
Using the httpfs DuckDB extension, we can query these files directly from R, without even downloading them first.
db_exec("INSTALL httpfs") db_exec("LOAD httpfs") flights <- read_parquet_duckdb(urls)
Like with local data frames, queries on the remote data are executed lazily.
Unlike with local data frames, the default is to disallow automatic materialization if the result is too large in order to protect memory: the results are not materialized until explicitly requested, with a collect()
call for instance.
nrow(flights)
For printing, only the first few rows of the result are fetched.
flights
flights |> count(Year)
Complex queries can be executed on the remote data. Note how only the relevant columns are fetched and the 2024 data isn't even touched, as it's not needed for the result.
out <- flights |> mutate(InFlightDelay = ArrDelay - DepDelay) |> summarize( .by = c(Year, Month), MeanInFlightDelay = mean(InFlightDelay, na.rm = TRUE), MedianInFlightDelay = median(InFlightDelay, na.rm = TRUE), ) |> filter(Year < 2024) out |> explain() out |> print() |> system.time()
Over 10M rows analyzed in about 10 seconds over the internet, that's not bad. Of course, working with Parquet, CSV, or JSON files downloaded locally is possible as well.
For full compatibility, na.rm = FALSE
by default in the aggregation functions:
flights |> summarize(mean(ArrDelay - DepDelay))
vignette("large")
: Tools for working with large data
vignette("prudence")
: How duckplyr can help protect memory when working with large data
vignette("fallback")
: How the fallback to dplyr works internally
vignette("limits")
: Translation of dplyr employed by duckplyr, and current limitations
vignette("developers")
: Using duckplyr for individual data frames and in other packages
vignette("telemetry")
: Telemetry in duckplyr
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub. For questions and other discussion, please use forum.posit.co.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
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