README.Rmd

---
output:
  github_document:
    html_preview: false
---

<!-- README.md and index.md are generated from README.Rmd. Please edit that file. -->

```{r setup, include = FALSE}
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)
})
```

# duckplyr <a href="https://duckplyr.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" /></a>

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> A **drop-in replacement** for dplyr, powered by DuckDB for **speed**.

[dplyr](https://dplyr.tidyverse.org/) 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](https://duckdb.org/) 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](https://r4ds.hadley.nz/data-transform) in R for Data Science.


## Installation

Install duckplyr from CRAN with:

``` r
install.packages("duckplyr")
```

You can also install the development version of duckplyr from [R-universe](https://tidyverse.r-universe.dev/builds):

``` r
install.packages("duckplyr", repos = c("https://tidyverse.r-universe.dev", "https://cloud.r-project.org"))
```

Or from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("tidyverse/duckplyr")
```

## Drop-in replacement for dplyr

Calling `library(duckplyr)` overwrites dplyr methods, enabling duckplyr for the entire session.

```{r dropin-attach}
library(conflicted)
library(duckplyr)
```

```{r load-all, include = FALSE}
# Done after library(duckplyr) to leave the original output
pkgload::load_all()
```

```{r dropin-simulate-library, echo = FALSE}
Sys.setenv(DUCKPLYR_FALLBACK_COLLECT = 0)
```

```{r dropin-dplyr}
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")`.

```{r dropin-pipeline}
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:

```{r dropin-class}
class(out)
```

Nothing has been computed yet.
Querying the number of rows, or a column, starts the computation:

```{r dropin-touch, cache = TRUE}
out$month
```

Note that, unlike dplyr, the results are not ordered, see `?config` for details.
However, once materialized, the results are stable:

```{r dropin-stable, cache = TRUE}
out
```

If a computation is not supported by DuckDB, duckplyr will automatically fall back to dplyr.

```{r dropin-fallback, cache = TRUE}
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.

```{r dropin-restore}
duckplyr::methods_restore()
```

## Analyzing larger-than-memory data

An extended variant of the `nycflights13::flights` dataset is also available for download as Parquet files.

```{r remote-url}
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](https://duckdb.org/docs/extensions/httpfs/overview.html), we can query these files directly from R, without even downloading them first.

```{r remote-https}
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.

```{r remote-thrifty, error = TRUE}
nrow(flights)
```

For printing, only the first few rows of the result are fetched.

```{r remote-print, cache = TRUE}
flights
```

```{r remote-count, cache = TRUE}
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.

```{r remote-exec, cache = TRUE}
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:

```{r remote-na-rm, cache = TRUE}
flights |>
  summarize(mean(ArrDelay - DepDelay))
```


## Further reading

- `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


## Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on [GitHub](https://github.com/tidyverse/duckplyr/issues). For questions and other discussion, please use [forum.posit.co](https://forum.posit.co/).


## Code of conduct

Please note that this project is released with a [Contributor Code of Conduct](https://duckplyr.tidyverse.org/CODE_OF_CONDUCT).
By participating in this project you agree to abide by its terms.