Package 'duckplyr'

Anti join

Description

This is a method for the dplyr::anti_join() generic. anti_join() returns all rows from x without a match in y.

Usage

## S3 method for class 'duckplyr_df'
anti_join(x, y, by = NULL, copy = FALSE, ..., na_matches = c("na", "never"))

Arguments

x, y	A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
by	A join specification created with join_by(), or a character vector of variables to join by. If NULL, the default, ⁠*_join()⁠ will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly. To join on different variables between x and y, use a join_by() specification. For example, join_by(a == b) will match x$a to y$b. To join by multiple variables, use a join_by() specification with multiple expressions. For example, join_by(a == b, c == d) will match x$a to y$b and x$c to y$d. If the column names are the same between x and y, you can shorten this by listing only the variable names, like join_by(a, c). join_by() can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins. For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example, by = c("a", "b") joins x$a to y$a and x$b to y$b. If variable names differ between x and y, use a named character vector like by = c("x_a" = "y_a", "x_b" = "y_b"). To perform a cross-join, generating all combinations of x and y, see cross_join().
copy	If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
...	Other parameters passed onto methods.
na_matches	Should two NA or two NaN values match? "na", the default, treats two NA or two NaN values as equal, like %in%, match(), and merge(). "never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).

See Also

dplyr::anti_join()

Examples

library("dplyr") # for the data
library("duckplyr")
band_members %>% anti_join(band_instruments)

---

Order rows using column values

Description

This is a method for the dplyr::arrange() generic. See "Fallbacks" section for differences in implementation. arrange() orders the rows of a data frame by the values of selected columns.

Unlike other dplyr verbs, arrange() largely ignores grouping; you need to explicitly mention grouping variables (or use .by_group = TRUE) in order to group by them, and functions of variables are evaluated once per data frame, not once per group.

Usage

## S3 method for class 'duckplyr_df'
arrange(.data, ..., .by_group = FALSE, .locale = NULL)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	<data-masking> Variables, or functions of variables. Use desc() to sort a variable in descending order.
.by_group	If TRUE, will sort first by grouping variable. Applies to grouped data frames only.
.locale	The locale to sort character vectors in. If NULL, the default, uses the "C" locale unless the dplyr.legacy_locale global option escape hatch is active. See the dplyr-locale help page for more details. If a single string from stringi::stri_locale_list() is supplied, then this will be used as the locale to sort with. For example, "en" will sort with the American English locale. This requires the stringi package. If "C" is supplied, then character vectors will always be sorted in the C locale. This does not require stringi and is often much faster than supplying a locale identifier. The C locale is not the same as English locales, such as "en", particularly when it comes to data containing a mix of upper and lower case letters. This is explained in more detail on the locale help page under the ⁠Default locale⁠ section.

Fallbacks

You cannot use arrange.duckplyr_df when:

• .by_group = TRUE,

• providing a value for the .locale argument,

• providing a value for the dplyr.legacy_locale option.

If you do the code will fall back to dplyr::arrange() without any error.

See Also

dplyr::arrange()

Examples

library("duckplyr")
arrange(mtcars, cyl, disp)
arrange(mtcars, desc(disp))

---

Configuration options

Description

The behavior of duckplyr can be fine-tuned with several environment variables, and one option.

Environment variables

DUCKPLYR_OUTPUT_ORDER: If TRUE, row output order is preserved. The default may change the row order where dplyr would keep it stable. Preserving the order leads to more complicated execution plans with less potential for optimization, and thus may be slower.

DUCKPLYR_FORCE: If TRUE, fail if duckdb cannot handle a request.

DUCKPLYR_FALLBACK_INFO: If TRUE, print a message when a fallback to dplyr occurs because duckdb cannot handle a request.

DUCKPLYR_CHECK_ROUNDTRIP: If TRUE, check if all columns are roundtripped perfectly when creating a relational object from a data frame, This is slow, and mostly useful for debugging. The default is to check roundtrip of attributes.

DUCKPLYR_EXPERIMENTAL: If TRUE, pass experimental = TRUE to certain duckdb functions. Currently unused.

DUCKPLYR_METHODS_OVERWRITE: If TRUE, call methods_overwrite() when the package is loaded.

See fallback for more options related to logging and uploading of fallback events.

Examples

# Sys.setenv(DUCKPLYR_OUTPUT_ORDER = TRUE)
data.frame(a = 3:1) %>%
  as_duckplyr_df() %>%
  inner_join(data.frame(a = 1:4), by = "a")

withr::with_envvar(c(DUCKPLYR_OUTPUT_ORDER = "TRUE"), {
  data.frame(a = 3:1) %>%
    as_duckplyr_df() %>%
    inner_join(data.frame(a = 1:4), by = "a")
})

# Sys.setenv(DUCKPLYR_FORCE = TRUE)
add_one <- function(x) {
  x + 1
}

data.frame(a = 3:1) %>%
  as_duckplyr_df() %>%
  mutate(b = add_one(a))

try(withr::with_envvar(c(DUCKPLYR_FORCE = "TRUE"), {
  data.frame(a = 3:1) %>%
    as_duckplyr_df() %>%
    mutate(b = add_one(a))
}))

# Sys.setenv(DUCKPLYR_FALLBACK_INFO = TRUE)
withr::with_envvar(c(DUCKPLYR_FALLBACK_INFO = "TRUE"), {
  data.frame(a = 3:1) %>%
    as_duckplyr_df() %>%
    mutate(b = add_one(a))
})

---

Count the observations in each group

Description

This is a method for the dplyr::count() generic. See "Fallbacks" section for differences in implementation. count() lets you quickly count the unique values of one or more variables: df %>% count(a, b) is roughly equivalent to df %>% group_by(a, b) %>% summarise(n = n()). count() is paired with tally(), a lower-level helper that is equivalent to df %>% summarise(n = n()). Supply wt to perform weighted counts, switching the summary from n = n() to n = sum(wt).

Usage

## S3 method for class 'duckplyr_df'
count(
  x,
  ...,
  wt = NULL,
  sort = FALSE,
  name = NULL,
  .drop = group_by_drop_default(x)
)

Arguments

x	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
...	<data-masking> Variables to group by.
wt	<data-masking> Frequency weights. Can be NULL or a variable: If NULL (the default), counts the number of rows in each group. If a variable, computes sum(wt) for each group.
sort	If TRUE, will show the largest groups at the top.
name	The name of the new column in the output. If omitted, it will default to n. If there's already a column called n, it will use nn. If there's a column called n and nn, it'll use nnn, and so on, adding ns until it gets a new name.
.drop	Handling of factor levels that don't appear in the data, passed on to group_by(). For count(): if FALSE will include counts for empty groups (i.e. for levels of factors that don't exist in the data). For add_count(): deprecated since it can't actually affect the output.

Fallbacks

You cannot use count.duckplyr_df

• with complex expressions in ...,

• with .drop = FALSE,

• with sort = TRUE.

If you do the code will fall back to dplyr::count() without any error.

See Also

dplyr::count()

Examples

library("duckplyr")
count(mtcars, am)

---

Keep distinct/unique rows

Description

This is a method for the dplyr::distinct() generic. Keep only unique/distinct rows from a data frame. This is similar to unique.data.frame() but considerably faster.

Usage

## S3 method for class 'duckplyr_df'
distinct(.data, ..., .keep_all = FALSE)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	<data-masking> Optional variables to use when determining uniqueness. If there are multiple rows for a given combination of inputs, only the first row will be preserved. If omitted, will use all variables in the data frame.
.keep_all	If TRUE, keep all variables in .data. If a combination of ... is not distinct, this keeps the first row of values.

See Also

dplyr::distinct()

Examples

df <- tibble(
  x = sample(10, 100, rep = TRUE),
  y = sample(10, 100, rep = TRUE)
)
nrow(df)
nrow(distinct(df))

---

Execute a statement for the default connection

Description

The duckplyr package relies on a DBI connection to an in-memory database. The duck_exec() function allows running SQL statements with this connection to, e.g., set up credentials or attach other databases. See https://duckdb.org/docs/configuration/overview.html for more information on the configuration options.

Usage

duck_exec(sql, ..., con = NULL)

Arguments

sql	The statement to run.
...	These dots are for future extensions and must be empty.
con	The connection, defaults to the default connection.

Value

The return value of the DBI::dbExecute() call, invisibly.

See Also

duck_sql()

Examples

duck_exec("SET threads TO 2")

---

Read Parquet, CSV, and other files using DuckDB

Description

These functions ingest data from a file. In many cases, these functions return immediately because they only read the metadata. The actual data is only read when it is actually processed.

duck_parquet() reads a CSV file using DuckDB's read_parquet() table function.

duck_csv() reads a CSV file using DuckDB's read_csv_auto() table function.

duck_json() reads a JSON file using DuckDB's read_json() table function.

duck_file() uses arbitrary readers to read data. See https://duckdb.org/docs/data/overview for a documentation of the available functions and their options. To read multiple files with the same schema, pass a wildcard or a character vector to the path argument,

Usage

duck_parquet(path, ..., lazy = TRUE, options = list())

duck_csv(path, ..., lazy = TRUE, options = list())

duck_json(path, ..., lazy = TRUE, options = list())

duck_file(path, table_function, ..., lazy = TRUE, options = list())

Arguments

path	Path to files, glob patterns * and ⁠?⁠ are supported.
...	These dots are for future extensions and must be empty.
lazy	Logical, whether to create a lazy duckplyr frame. If TRUE (the default), dplyr::collect() must be called before the data can be accessed.
options	Arguments to the DuckDB function indicated by table_function.
table_function	The name of a table-valued DuckDB function such as "read_parquet", "read_csv", "read_csv_auto" or "read_json".

Details

By default, a lazy duckplyr frame is created. This means that all the data can be shown and all dplyr verbs can be used, but attempting to access the columns of the data frame or using an unsupported verb, data type, or function will result in an error. Pass lazy = FALSE to transparently switch to local processing as needed, or use dplyr::collect() to explicitly materialize and continue local processing.

Value

A duckplyr frame, see as_duck_tbl() for details.

Examples

# Create simple CSV file
path <- tempfile("duckplyr_test_", fileext = ".csv")
write.csv(data.frame(a = 1:3, b = letters[4:6]), path, row.names = FALSE)

# Reading is immediate
df <- duck_csv(path)

# Names are always available
names(df)

# Materialization upon access is turned off by default
try(print(df$a))

# Materialize explicitly
collect(df)$a

# Automatic materialization with lazy = FALSE
df <- duck_csv(path, lazy = FALSE)
df$a

# Specify column types
duck_csv(
  path,
  options = list(delim = ",", types = list(c("DOUBLE", "VARCHAR")))
)

# Create and read a simple JSON file
path <- tempfile("duckplyr_test_", fileext = ".json")
writeLines('[{"a": 1, "b": "x"}, {"a": 2, "b": "y"}]', path)

# Reading needs the json extension
duck_exec("INSTALL json")
duck_exec("LOAD json")
duck_json(path)

---

Return SQL query as duck_tbl

Description

Runs a query and returns it as a duckplyr frame.

Usage

duck_sql(sql, ..., lazy = TRUE, con = NULL)

Arguments

sql	The SQL to run.
...	These dots are for future extensions and must be empty.
lazy	Logical, whether to create a lazy duckplyr frame. If TRUE (the default), dplyr::collect() must be called before the data can be accessed.
con	The connection, defaults to the default connection.

Details

Using data frames from the calling environment is not supported yet, see https://github.com/duckdb/duckdb-r/issues/645 for details.

See Also

duck_exec()

Examples

duck_sql("FROM duckdb_settings()")

---

duckplyr data frames

Description

Data frames backed by duckplyr have a special class, "duckplyr_df", in addition to the default classes. This ensures that dplyr methods are dispatched correctly. For such objects, dplyr verbs such as dplyr::mutate(), dplyr::select() or dplyr::filter() will attempt to use DuckDB. If this is not possible, the original dplyr implementation is used.

duck_tbl() works like tibble::tibble(). In contrast to dbplyr, duckplyr data frames are "eager" by default. To avoid unwanted expensive computation, they can be converted to "lazy" duckplyr frames on which dplyr::collect() needs to be called explicitly.

as_duck_tbl() converts a data frame or a dplyr lazy table to a duckplyr data frame. This is a generic function that can be overridden for custom classes.

is_duck_tbl() returns TRUE if x is a duckplyr data frame.

Usage

duck_tbl(..., .lazy = FALSE)

as_duck_tbl(x, ..., .lazy = FALSE)

is_duck_tbl(x)

Arguments

...	For duck_tbl(), passed on to tibble::tibble(). For as_duck_tbl(), passed on to methods.
.lazy	Logical, whether to create a lazy duckplyr frame. If TRUE, dplyr::collect() must be called before the data can be accessed.
x	The object to convert or to test.

Details

Set the DUCKPLYR_FALLBACK_INFO and DUCKPLYR_FORCE environment variables for more control over the behavior, see config for more details.

Value

For duck_tbl() and as_duck_tbl(), an object with the following classes:

• "lazy_duckplyr_df" if .lazy is TRUE

• "duckplyr_df"

• Classes of a tibble::tibble

For is_duck_tbl(), a scalar logical.

Examples

x <- duck_tbl(a = 1)
x

library(dplyr)
x %>%
  mutate(b = 2)

x$a

y <- duck_tbl(a = 1, .lazy = TRUE)
y
try(length(y$a))
length(collect(y)$a)

---

Explain details of a tbl

Description

This is a method for the dplyr::explain() generic. This is a generic function which gives more details about an object than print(), and is more focused on human readable output than str().

Usage

## S3 method for class 'duckplyr_df'
explain(x, ...)

Arguments

x	An object to explain
...	Other parameters possibly used by generic

See Also

dplyr::explain()

Examples

lahman_s <- dbplyr::lahman_sqlite()
batting <- tbl(lahman_s, "Batting")
explain(batting)

---

Fallback to dplyr

Description

The duckplyr package aims at providing a fully compatible drop-in replacement for dplyr. To achieve this, only a carefully selected subset of dplyr's operations, R functions, and R data types are implemented. Whenever duckplyr encounters an incompatibility, it falls back to dplyr.

To assist future development, the fallback situations can be logged to the console or to a local file and uploaded for analysis. By default, duckplyr will not log or upload anything. The functions and environment variables on this page control the process.

fallback_sitrep() prints the current settings for fallback logging and uploading, the number of reports ready for upload, and the location of the logs.

fallback_review() prints the available reports for review to the console.

fallback_upload() uploads the available reports to a central server for analysis. The server is hosted on AWS and the reports are stored in a private S3 bucket. Only authorized personnel have access to the reports.

fallback_purge() deletes some or all available reports.

Usage

fallback_sitrep()

fallback_review(oldest = NULL, newest = NULL, detail = TRUE)

fallback_upload(oldest = NULL, newest = NULL, strict = TRUE)

fallback_purge(oldest = NULL, newest = NULL)

Arguments

oldest, newest	The number of oldest or newest reports to review. If not specified, all reports are dispayed.
detail	Print the full content of the reports. Set to FALSE to only print the file names.
strict	If TRUE, the function aborts if any of the reports fail to upload. With FALSE, only a message is printed.

Details

Logging and uploading are both opt-in. By default, for logging, a message is printed to the console for the first time in a session and then once every 8 hours.

The following environment variables control the logging and uploading:

• DUCKPLYR_FALLBACK_COLLECT controls logging, set it to 1 or greater to enable logging. If the value is 0, logging is disabled. Future versions of duckplyr may start logging additional data and thus require a higher value to enable logging. Set to 99 to enable logging for all future versions. Use usethis::edit_r_environ() to edit the environment file.

• DUCKPLYR_FALLBACK_VERBOSE controls printing, set it to TRUE or FALSE to enable or disable printing. If the value is TRUE, a message is printed to the console for each fallback situation. This setting is only relevant if logging is enabled.

• DUCKPLYR_FALLBACK_AUTOUPLOAD controls uploading, set it to 1 or greater to enable uploading. If the value is 0, uploading is disabled. Currently, uploading is active if the value is 1 or greater. Future versions of duckplyr may start logging additional data and thus require a higher value to enable uploading. Set to 99 to enable uploading for all future versions. Use usethis::edit_r_environ() to edit the environment file.

• DUCKPLYR_FALLBACK_LOG_DIR controls the location of the logs. It must point to a directory (existing or not) where the logs will be written. By default, logs are written to a directory in the user's cache directory as returned by tools::R_user_dir("duckplyr", "cache").

All code related to fallback logging and uploading is in the fallback.R and telemetry.R files.

Examples

fallback_sitrep()

---

Keep rows that match a condition

Description

This is a method for the dplyr::select() generic. See "Fallbacks" section for differences in implementation. The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with [.

Usage

## S3 method for class 'duckplyr_df'
filter(.data, ..., .by = NULL, .preserve = FALSE)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	<data-masking> Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.
.by	<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.
.preserve	Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

Fallbacks

You cannot use filter.duckplyr_df

• with no filter conditions,

• nor for a grouped operation (if .by is set).

If you do the code will fall back to dplyr::filter() without any error.

See Also

dplyr::filter()

Examples

filter(mtcars, mpg > 30)

---

Flight data

Description

Provides a copy of nycflights13::flights that is compatible with duckplyr, as a tibble. Call as_duck_tbl() to enable duckplyr operations.

Usage

flights_df()

Examples

flights_df()

---

Full join

Description

This is a method for the dplyr::full_join() generic. See "Fallbacks" section for differences in implementation. A full_join() keeps all observations in x and y.

Usage

## S3 method for class 'duckplyr_df'
full_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL,
  na_matches = c("na", "never"),
  multiple = "all",
  relationship = NULL
)

Arguments

x, y	A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
by	A join specification created with join_by(), or a character vector of variables to join by. If NULL, the default, ⁠*_join()⁠ will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly. To join on different variables between x and y, use a join_by() specification. For example, join_by(a == b) will match x$a to y$b. To join by multiple variables, use a join_by() specification with multiple expressions. For example, join_by(a == b, c == d) will match x$a to y$b and x$c to y$d. If the column names are the same between x and y, you can shorten this by listing only the variable names, like join_by(a, c). join_by() can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins. For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example, by = c("a", "b") joins x$a to y$a and x$b to y$b. If variable names differ between x and y, use a named character vector like by = c("x_a" = "y_a", "x_b" = "y_b"). To perform a cross-join, generating all combinations of x and y, see cross_join().
copy	If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
suffix	If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
...	Other parameters passed onto methods.
keep	Should the join keys from both x and y be preserved in the output? If NULL, the default, joins on equality retain only the keys from x, while joins on inequality retain the keys from both inputs. If TRUE, all keys from both inputs are retained. If FALSE, only keys from x are retained. For right and full joins, the data in key columns corresponding to rows that only exist in y are merged into the key columns from x. Can't be used when joining on inequality conditions.
na_matches	Should two NA or two NaN values match? "na", the default, treats two NA or two NaN values as equal, like %in%, match(), and merge(). "never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).
multiple	Handling of rows in x with multiple matches in y. For each row of x: "all", the default, returns every match detected in y. This is the same behavior as SQL. "any" returns one match detected in y, with no guarantees on which match will be returned. It is often faster than "first" and "last" if you just need to detect if there is at least one match. "first" returns the first match detected in y. "last" returns the last match detected in y.
relationship	Handling of the expected relationship between the keys of x and y. If the expectations chosen from the list below are invalidated, an error is thrown. NULL, the default, doesn't expect there to be any relationship between x and y. However, for equality joins it will check for a many-to-many relationship (which is typically unexpected) and will warn if one occurs, encouraging you to either take a closer look at your inputs or make this relationship explicit by specifying "many-to-many". See the Many-to-many relationships section for more details. "one-to-one" expects: Each row in x matches at most 1 row in y. Each row in y matches at most 1 row in x. "one-to-many" expects: Each row in y matches at most 1 row in x. "many-to-one" expects: Each row in x matches at most 1 row in y. "many-to-many" doesn't perform any relationship checks, but is provided to allow you to be explicit about this relationship if you know it exists. relationship doesn't handle cases where there are zero matches. For that, see unmatched.

Fallbacks

You cannot use full_join.duckplyr_df

• for an implicit cross join,

• for a value of the multiple argument that isn't the default "all".

If you do the code will fall back to dplyr::full_join() without any error.

See Also

dplyr::full_join()

Examples

library("dplyr")
full_join(band_members, band_instruments)

---

Return the First Parts of an Object

Description

This is a method for the head() generic. See "Fallbacks" section for differences in implementation. Return the first rows of a data.frame

Usage

## S3 method for class 'duckplyr_df'
head(x, n = 6L, ...)

Arguments

x	A data.frame
n	A positive integer, how many rows to return.
...	Not used yet.

Fallbacks

You cannot use head.duckplyr_df

• with a negative n.

If you do the code will fall back to head() without any error.

See Also

head()

Examples

head(mtcars, 2)

---

Inner join

Description

This is a method for the dplyr::inner_join() generic. See "Fallbacks" section for differences in implementation. An inner_join() only keeps observations from x that have a matching key in y.

Usage

## S3 method for class 'duckplyr_df'
inner_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL,
  na_matches = c("na", "never"),
  multiple = "all",
  unmatched = "drop",
  relationship = NULL
)

Arguments

x, y	A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
by	A join specification created with join_by(), or a character vector of variables to join by. If NULL, the default, ⁠*_join()⁠ will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly. To join on different variables between x and y, use a join_by() specification. For example, join_by(a == b) will match x$a to y$b. To join by multiple variables, use a join_by() specification with multiple expressions. For example, join_by(a == b, c == d) will match x$a to y$b and x$c to y$d. If the column names are the same between x and y, you can shorten this by listing only the variable names, like join_by(a, c). join_by() can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins. For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example, by = c("a", "b") joins x$a to y$a and x$b to y$b. If variable names differ between x and y, use a named character vector like by = c("x_a" = "y_a", "x_b" = "y_b"). To perform a cross-join, generating all combinations of x and y, see cross_join().
copy	If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
suffix	If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
...	Other parameters passed onto methods.
keep	Should the join keys from both x and y be preserved in the output? If NULL, the default, joins on equality retain only the keys from x, while joins on inequality retain the keys from both inputs. If TRUE, all keys from both inputs are retained. If FALSE, only keys from x are retained. For right and full joins, the data in key columns corresponding to rows that only exist in y are merged into the key columns from x. Can't be used when joining on inequality conditions.
na_matches	Should two NA or two NaN values match? "na", the default, treats two NA or two NaN values as equal, like %in%, match(), and merge(). "never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).
multiple	Handling of rows in x with multiple matches in y. For each row of x: "all", the default, returns every match detected in y. This is the same behavior as SQL. "any" returns one match detected in y, with no guarantees on which match will be returned. It is often faster than "first" and "last" if you just need to detect if there is at least one match. "first" returns the first match detected in y. "last" returns the last match detected in y.
unmatched	How should unmatched keys that would result in dropped rows be handled? "drop" drops unmatched keys from the result. "error" throws an error if unmatched keys are detected. unmatched is intended to protect you from accidentally dropping rows during a join. It only checks for unmatched keys in the input that could potentially drop rows. For left joins, it checks y. For right joins, it checks x. For inner joins, it checks both x and y. In this case, unmatched is also allowed to be a character vector of length 2 to specify the behavior for x and y independently.
relationship	Handling of the expected relationship between the keys of x and y. If the expectations chosen from the list below are invalidated, an error is thrown. NULL, the default, doesn't expect there to be any relationship between x and y. However, for equality joins it will check for a many-to-many relationship (which is typically unexpected) and will warn if one occurs, encouraging you to either take a closer look at your inputs or make this relationship explicit by specifying "many-to-many". See the Many-to-many relationships section for more details. "one-to-one" expects: Each row in x matches at most 1 row in y. Each row in y matches at most 1 row in x. "one-to-many" expects: Each row in y matches at most 1 row in x. "many-to-one" expects: Each row in x matches at most 1 row in y. "many-to-many" doesn't perform any relationship checks, but is provided to allow you to be explicit about this relationship if you know it exists. relationship doesn't handle cases where there are zero matches. For that, see unmatched.

Fallbacks

You cannot use inner_join.duckplyr_df

• for an implicit crossjoin,

• for a value of the multiple argument that isn't the default "all".

• for a value of the unmatched argument that isn't the default "drop".

If you do the code will fall back to dplyr::inner_join() without any error.

See Also

dplyr::inner_join()

Examples

library("dplyr")
inner_join(band_members, band_instruments)

---

Intersect

Description

This is a method for the dplyr::intersect() generic. See "Fallbacks" section for differences in implementation. intersect(x, y) finds all rows in both x and y.

Usage

## S3 method for class 'duckplyr_df'
intersect(x, y, ...)

Arguments

x, y	Pair of compatible data frames. A pair of data frames is compatible if they have the same column names (possibly in different orders) and compatible types.
...	These dots are for future extensions and must be empty.

Fallbacks

You cannot use intersect.duckplyr_df

• if column names are duplicated in one of the tables

• if column names are different in both tables.

If you do the code will fall back to dplyr::intersect() without any error.

See Also

dplyr::intersect()

Examples

df1 <- tibble(x = 1:3)
df2 <- tibble(x = 3:5)
intersect(df1, df2)

---

Retrieve details about the most recent computation

Description

Before a result is computed, it is specified as a "relation" object. This function retrieves this object for the last computation that led to the materialization of a data frame.

Usage

last_rel()

Value

A duckdb "relation" object, or NULL if no computation has been performed yet.

---

Left join

Description

This is a method for the dplyr::left_join() generic. See "Fallbacks" section for differences in implementation. A left_join() keeps all observations in x.

Usage

## S3 method for class 'duckplyr_df'
left_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL,
  na_matches = c("na", "never"),
  multiple = "all",
  unmatched = "drop",
  relationship = NULL
)

Arguments

x, y	A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
by	A join specification created with join_by(), or a character vector of variables to join by. If NULL, the default, ⁠*_join()⁠ will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly. To join on different variables between x and y, use a join_by() specification. For example, join_by(a == b) will match x$a to y$b. To join by multiple variables, use a join_by() specification with multiple expressions. For example, join_by(a == b, c == d) will match x$a to y$b and x$c to y$d. If the column names are the same between x and y, you can shorten this by listing only the variable names, like join_by(a, c). join_by() can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins. For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example, by = c("a", "b") joins x$a to y$a and x$b to y$b. If variable names differ between x and y, use a named character vector like by = c("x_a" = "y_a", "x_b" = "y_b"). To perform a cross-join, generating all combinations of x and y, see cross_join().
copy	If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
suffix	If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
...	Other parameters passed onto methods.
keep	Should the join keys from both x and y be preserved in the output? If NULL, the default, joins on equality retain only the keys from x, while joins on inequality retain the keys from both inputs. If TRUE, all keys from both inputs are retained. If FALSE, only keys from x are retained. For right and full joins, the data in key columns corresponding to rows that only exist in y are merged into the key columns from x. Can't be used when joining on inequality conditions.
na_matches	Should two NA or two NaN values match? "na", the default, treats two NA or two NaN values as equal, like %in%, match(), and merge(). "never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).
multiple	Handling of rows in x with multiple matches in y. For each row of x: "all", the default, returns every match detected in y. This is the same behavior as SQL. "any" returns one match detected in y, with no guarantees on which match will be returned. It is often faster than "first" and "last" if you just need to detect if there is at least one match. "first" returns the first match detected in y. "last" returns the last match detected in y.
unmatched	How should unmatched keys that would result in dropped rows be handled? "drop" drops unmatched keys from the result. "error" throws an error if unmatched keys are detected. unmatched is intended to protect you from accidentally dropping rows during a join. It only checks for unmatched keys in the input that could potentially drop rows. For left joins, it checks y. For right joins, it checks x. For inner joins, it checks both x and y. In this case, unmatched is also allowed to be a character vector of length 2 to specify the behavior for x and y independently.
relationship	Handling of the expected relationship between the keys of x and y. If the expectations chosen from the list below are invalidated, an error is thrown. NULL, the default, doesn't expect there to be any relationship between x and y. However, for equality joins it will check for a many-to-many relationship (which is typically unexpected) and will warn if one occurs, encouraging you to either take a closer look at your inputs or make this relationship explicit by specifying "many-to-many". See the Many-to-many relationships section for more details. "one-to-one" expects: Each row in x matches at most 1 row in y. Each row in y matches at most 1 row in x. "one-to-many" expects: Each row in y matches at most 1 row in x. "many-to-one" expects: Each row in x matches at most 1 row in y. "many-to-many" doesn't perform any relationship checks, but is provided to allow you to be explicit about this relationship if you know it exists. relationship doesn't handle cases where there are zero matches. For that, see unmatched.

Fallbacks

You cannot use left_join.duckplyr_df

• for an implicit cross join,

• for a value of the multiple argument that isn't the default "all".

• for a value of the unmatched argument that isn't the default "drop".

If you do the code will fall back to dplyr::left_join() without any error.

See Also

dplyr::left_join()

Examples

library("dplyr")
left_join(band_members, band_instruments)

---

Forward all dplyr methods to duckplyr

Description

After calling methods_overwrite(), all dplyr methods are redirected to duckplyr for the duraton of the session, or until a call to methods_restore(). The methods_overwrite() function is called automatically when the package is loaded if the environment variable DUCKPLYR_METHODS_OVERWRITE is set to TRUE.

Usage

methods_overwrite()

methods_restore()

Value

Called for their side effects.

Examples

tibble(a = 1:3) %>%
  mutate(b = a + 1)

methods_overwrite()

tibble(a = 1:3) %>%
  mutate(b = a + 1)

methods_restore()

tibble(a = 1:3) %>%
  mutate(b = a + 1)

---

Create, modify, and delete columns (duckplyr)

Description

This is a method for the dplyr::mutate() generic. mutate() creates new columns that are functions of existing variables. It can also modify (if the name is the same as an existing column) and delete columns (by setting their value to NULL).

Usage

## S3 method for class 'duckplyr_df'
mutate(
  .data,
  ...,
  .by = NULL,
  .keep = c("all", "used", "unused", "none"),
  .before = NULL,
  .after = NULL
)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	<data-masking> Name-value pairs. The name gives the name of the column in the output. The value can be: A vector of length 1, which will be recycled to the correct length. A vector the same length as the current group (or the whole data frame if ungrouped). NULL, to remove the column. A data frame or tibble, to create multiple columns in the output.
.by	<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.
.keep	Control which columns from .data are retained in the output. Grouping columns and columns created by ... are always kept. "all" retains all columns from .data. This is the default. "used" retains only the columns used in ... to create new columns. This is useful for checking your work, as it displays inputs and outputs side-by-side. "unused" retains only the columns not used in ... to create new columns. This is useful if you generate new columns, but no longer need the columns used to generate them. "none" doesn't retain any extra columns from .data. Only the grouping variables and columns created by ... are kept.
.before, .after	<tidy-select> Optionally, control where new columns should appear (the default is to add to the right hand side). See relocate() for more details.

See Also

dplyr::mutate()

Examples

library("duckplyr")
df <- data.frame(x = c(1, 2), row.names = c("a", "b"))
df <- mutate(df, y = 2)
df

---

Relational implementer's interface

Description

The constructor and generics described here define a class that helps separating dplyr's user interface from the actual underlying operations. In the longer term, this will help packages that implement the dplyr interface (such as dbplyr, dtplyr, arrow and similar) to focus on the core details of their functionality, rather than on the intricacies of dplyr's user interface.

new_relational() constructs an object of class "relational". Users are encouraged to provide the class argument. The typical use case will be to create a wrapper function.

rel_to_df() extracts a data frame representation from a relational object, to be used by dplyr::collect().

rel_filter() keeps rows that match a predicate, to be used by dplyr::filter().

rel_project() selects columns or creates new columns, to be used by dplyr::select(), dplyr::rename(), dplyr::mutate(), dplyr::relocate(), and others.

rel_aggregate() combines several rows into one, to be used by dplyr::summarize().

rel_order() reorders rows by columns or expressions, to be used by dplyr::arrange().

rel_join() joins or merges two tables, to be used by dplyr::left_join(), dplyr::right_join(), dplyr::inner_join(), dplyr::full_join(), dplyr::cross_join(), dplyr::semi_join(), and dplyr::anti_join().

rel_limit() limits the number of rows in a table, to be used by utils::head().

rel_distinct() only keeps the distinct rows in a table, to be used by dplyr::distinct().

rel_set_intersect() returns rows present in both tables, to be used by generics::intersect().

rel_set_diff() returns rows present in any of both tables, to be used by generics::setdiff().

rel_set_symdiff() returns rows present in any of both tables, to be used by dplyr::symdiff().

rel_union_all() returns rows present in any of both tables, to be used by dplyr::union_all().

rel_explain() prints an explanation of the plan executed by the relational object.

rel_alias() returns the alias name for a relational object.

rel_set_alias() sets the alias name for a relational object.

rel_names() returns the column names as character vector, to be used by colnames().

Usage

new_relational(..., class = NULL)

rel_to_df(rel, ...)

rel_filter(rel, exprs, ...)

rel_project(rel, exprs, ...)

rel_aggregate(rel, groups, aggregates, ...)

rel_order(rel, orders, ascending, ...)

rel_join(
  left,
  right,
  conds,
  join = c("inner", "left", "right", "outer", "cross", "semi", "anti"),
  join_ref_type = c("regular", "natural", "cross", "positional", "asof"),
  ...
)

rel_limit(rel, n, ...)

rel_distinct(rel, ...)

rel_set_intersect(rel_a, rel_b, ...)

rel_set_diff(rel_a, rel_b, ...)

rel_set_symdiff(rel_a, rel_b, ...)

rel_union_all(rel_a, rel_b, ...)

rel_explain(rel, ...)

rel_alias(rel, ...)

rel_set_alias(rel, alias, ...)

rel_names(rel, ...)

Arguments

...	Reserved for future extensions, must be empty.
class	Classes added in front of the "relational" base class.
rel, rel_a, rel_b, left, right	A relational object.
exprs	A list of "relational_relexpr" objects to filter by, created by new_relexpr().
groups	A list of expressions to group by.
aggregates	A list of expressions with aggregates to compute.
orders	A list of expressions to order by.
ascending	A logical vector describing the sort order.
conds	A list of expressions to use for the join.
join	The type of join.
join_ref_type	The ref type of join.
n	The number of rows.
alias	the new alias

Value

• new_relational() returns a new relational object.

• rel_to_df() returns a data frame.

• rel_names() returns a character vector.

• All other generics return a modified relational object.

Examples

new_dfrel <- function(x) {
  stopifnot(is.data.frame(x))
  new_relational(list(x), class = "dfrel")
}
mtcars_rel <- new_dfrel(mtcars[1:5, 1:4])

rel_to_df.dfrel <- function(rel, ...) {
  unclass(rel)[[1]]
}
rel_to_df(mtcars_rel)

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[seq_len(min(3, nrow(df))), ])
}

rel_filter(
  mtcars_rel,
  list(
    relexpr_function(
      "gt",
      list(relexpr_reference("cyl"), relexpr_constant("6"))
    )
  )
)

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"))
)

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"))
)

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)


rel_limit.dfrel <- function(rel, n, ...) {
  df <- unclass(rel)[[1]]

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

rel_limit(mtcars_rel, 3)

rel_distinct.dfrel <- function(rel, ...) {
  df <- unclass(rel)[[1]]

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

rel_distinct(new_dfrel(mtcars[1:3, 1:4]))

rel_names.dfrel <- function(rel, ...) {
  df <- unclass(rel)[[1]]

  names(df)
}

rel_names(mtcars_rel)

---

Relational expressions

Description

These functions provide a backend-agnostic way to construct expression trees built of column references, constants, and functions. All subexpressions in an expression tree can have an alias.

new_relexpr() constructs an object of class "relational_relexpr". It is used by the higher-level constructors, users should rarely need to call it directly.

relexpr_reference() constructs a reference to a column.

relexpr_constant() wraps a constant value.

relexpr_function() applies a function. The arguments to this function are a list of other expression objects.

relexpr_comparison() wraps a comparison expression.

relexpr_window() applies a function over a window, similarly to the SQL OVER clause.

relexpr_set_alias() assigns an alias to an expression.

Usage

new_relexpr(x, class = NULL)

relexpr_reference(name, rel = NULL, alias = NULL)

relexpr_constant(val, alias = NULL)

relexpr_function(name, args, alias = NULL)

relexpr_comparison(cmp_op, exprs)

relexpr_window(
  expr,
  partitions,
  order_bys = list(),
  offset_expr = NULL,
  default_expr = NULL,
  alias = NULL
)

relexpr_set_alias(expr, alias = NULL)

Arguments

x	An object.
class	Classes added in front of the "relational_relexpr" base class.
name	The name of the column or function to reference.
rel	The name of the relation to reference.
alias	An alias for the new expression.
val	The value to use in the constant expression.
args	Function arguments, a list of expr objects.
cmp_op	Comparison operator, e.g., "<" or "==".
exprs	Expressions to compare, a list of expr objects.
expr	An expr object.
partitions	Partitions, a list of expr objects.
order_bys	which variables to order results by (list).
offset_expr	offset relational expression.
default_expr	default relational expression.

Value

an object of class "relational_relexpr"

an object of class "relational_relexpr"

an object of class "relational_relexpr"

an object of class "relational_relexpr"

an object of class "relational_relexpr"

an object of class "relational_relexpr"

Examples

relexpr_set_alias(
  alias = "my_predicate",
  relexpr_function(
    "<",
    list(
      relexpr_reference("my_number"),
      relexpr_constant(42)
    )
  )
)

---

Verbs not implemented in duckplyr

Description

The following dplyr generics have no counterpart method in duckplyr. If you want to help add a new verb, please refer to our contributing guide https://duckplyr.tidyverse.org/CONTRIBUTING.html#support-new-verbs

Unsupported verbs

For these verbs, duckplyr will fall back to dplyr.

• dplyr::add_count()

• dplyr::compute()

• dplyr::cross_join()

• dplyr::do()

• dplyr::group_by()

• dplyr::group_indices()

• dplyr::group_keys()

• dplyr::group_map()

• dplyr::group_modify()

• dplyr::group_nest()

• dplyr::group_size()

• dplyr::group_split()

• dplyr::group_trim()

• dplyr::groups()

• dplyr::n_groups()

• dplyr::nest_by()

• dplyr::nest_join()

• dplyr::reframe()

• dplyr::rename_with()

• dplyr::rows_append()

• dplyr::rows_delete()

• dplyr::rows_insert()

• dplyr::rows_patch()

• dplyr::rows_update()

• dplyr::rows_upsert()

• dplyr::rowwise()

• generics::setequal()

• dplyr::slice_head()

• dplyr::slice_sample()

• dplyr::slice_tail()

• dplyr::slice()

• dplyr::ungroup()

---

Extract a single column

Description

This is a method for the dplyr::pull() generic. See "Fallbacks" section for differences in implementation. pull() is similar to $. It's mostly useful because it looks a little nicer in pipes, it also works with remote data frames, and it can optionally name the output.

Usage

## S3 method for class 'duckplyr_df'
pull(.data, var = -1, name = NULL, ...)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
var	A variable specified as: a literal variable name a positive integer, giving the position counting from the left a negative integer, giving the position counting from the right. The default returns the last column (on the assumption that's the column you've created most recently). This argument is taken by expression and supports quasiquotation (you can unquote column names and column locations).
name	An optional parameter that specifies the column to be used as names for a named vector. Specified in a similar manner as var.
...	For use by methods.

Fallbacks

You cannot use pull.duckplyr_df

• with a selection that returns no columns:

If you do the code will fall back to dplyr::pull() without any error.

See Also

dplyr::pull()

Examples

pull(mtcars, cyl)
pull(mtcars, 1)

---

Change column order

Description

This is a method for the dplyr::relocate() generic. See "Fallbacks" section for differences in implementation. Use relocate() to change column positions, using the same syntax as select() to make it easy to move blocks of columns at once.

Usage

## S3 method for class 'duckplyr_df'
relocate(.data, ..., .before = NULL, .after = NULL)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	<tidy-select> Columns to move.
.before, .after	<tidy-select> Destination of columns selected by .... Supplying neither will move columns to the left-hand side; specifying both is an error.

Fallbacks

You cannot use relocate.duckplyr_df

• with a selection that returns no columns:

If you do the code will fall back to dplyr::relocate() without any error.

See Also

dplyr::relocate()

Examples

df <- tibble(a = 1, b = 1, c = 1, d = "a", e = "a", f = "a")
relocate(df, f)

---

Rename columns

Description

This is a method for the dplyr::rename() generic. See "Fallbacks" section for differences in implementation. rename() changes the names of individual variables using new_name = old_name syntax.

Usage

## S3 method for class 'duckplyr_df'
rename(.data, ...)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	For rename(): <tidy-select> Use new_name = old_name to rename selected variables. For rename_with(): additional arguments passed onto .fn.

Fallbacks

You cannot use rename.duckplyr_df

• with a selection that returns no columns:

If you do the code will fall back to dplyr::rename() without any error.

See Also

dplyr::rename()

Examples

rename(mtcars, thing = mpg)

---

Right join

Description

This is a method for the dplyr::right_join() generic. See "Fallbacks" section for differences in implementation. A right_join() keeps all observations in y.

Usage

## S3 method for class 'duckplyr_df'
right_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL,
  na_matches = c("na", "never"),
  multiple = "all",
  unmatched = "drop",
  relationship = NULL
)

Arguments

x, y	A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
by	A join specification created with join_by(), or a character vector of variables to join by. If NULL, the default, ⁠*_join()⁠ will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly. To join on different variables between x and y, use a join_by() specification. For example, join_by(a == b) will match x$a to y$b. To join by multiple variables, use a join_by() specification with multiple expressions. For example, join_by(a == b, c == d) will match x$a to y$b and x$c to y$d. If the column names are the same between x and y, you can shorten this by listing only the variable names, like join_by(a, c). join_by() can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins. For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example, by = c("a", "b") joins x$a to y$a and x$b to y$b. If variable names differ between x and y, use a named character vector like by = c("x_a" = "y_a", "x_b" = "y_b"). To perform a cross-join, generating all combinations of x and y, see cross_join().
copy	If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
suffix	If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
...	Other parameters passed onto methods.
keep	Should the join keys from both x and y be preserved in the output? If NULL, the default, joins on equality retain only the keys from x, while joins on inequality retain the keys from both inputs. If TRUE, all keys from both inputs are retained. If FALSE, only keys from x are retained. For right and full joins, the data in key columns corresponding to rows that only exist in y are merged into the key columns from x. Can't be used when joining on inequality conditions.
na_matches	Should two NA or two NaN values match? "na", the default, treats two NA or two NaN values as equal, like %in%, match(), and merge(). "never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).
multiple	Handling of rows in x with multiple matches in y. For each row of x: "all", the default, returns every match detected in y. This is the same behavior as SQL. "any" returns one match detected in y, with no guarantees on which match will be returned. It is often faster than "first" and "last" if you just need to detect if there is at least one match. "first" returns the first match detected in y. "last" returns the last match detected in y.
unmatched	How should unmatched keys that would result in dropped rows be handled? "drop" drops unmatched keys from the result. "error" throws an error if unmatched keys are detected. unmatched is intended to protect you from accidentally dropping rows during a join. It only checks for unmatched keys in the input that could potentially drop rows. For left joins, it checks y. For right joins, it checks x. For inner joins, it checks both x and y. In this case, unmatched is also allowed to be a character vector of length 2 to specify the behavior for x and y independently.
relationship	Handling of the expected relationship between the keys of x and y. If the expectations chosen from the list below are invalidated, an error is thrown. NULL, the default, doesn't expect there to be any relationship between x and y. However, for equality joins it will check for a many-to-many relationship (which is typically unexpected) and will warn if one occurs, encouraging you to either take a closer look at your inputs or make this relationship explicit by specifying "many-to-many". See the Many-to-many relationships section for more details. "one-to-one" expects: Each row in x matches at most 1 row in y. Each row in y matches at most 1 row in x. "one-to-many" expects: Each row in y matches at most 1 row in x. "many-to-one" expects: Each row in x matches at most 1 row in y. "many-to-many" doesn't perform any relationship checks, but is provided to allow you to be explicit about this relationship if you know it exists. relationship doesn't handle cases where there are zero matches. For that, see unmatched.

Fallbacks

You cannot use right_join.duckplyr_df

• for an implicit cross join,

• for a value of the multiple argument that isn't the default "all".

• for a value of the unmatched argument that isn't the default "drop".

If you do the code will fall back to dplyr::right_join() without any error.

See Also

dplyr::right_join()

Examples

library("dplyr")
right_join(band_members, band_instruments)

---

Keep or drop columns using their names and types

Description

This is a method for the dplyr::select() generic. See "Fallbacks" section for differences in implementation. Select (and optionally rename) variables in a data frame, using a concise mini-language that makes it easy to refer to variables based on their name (e.g. a:f selects all columns from a on the left to f on the right) or type (e.g. where(is.numeric) selects all numeric columns).

Usage

## S3 method for class 'duckplyr_df'
select(.data, ...)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	<tidy-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

Fallbacks

You cannot use select.duckplyr_df

• with no expression,

• nor with a selection that returns no columns:

If you do the code will fall back to dplyr::select() without any error.

See Also

dplyr::select()

Examples

library("duckplyr")
select(mtcars, mpg)

---

Semi join

Description

This is a method for the dplyr::semi_join() generic. semi_join() returns all rows from x with a match in y.

Usage

## S3 method for class 'duckplyr_df'
semi_join(x, y, by = NULL, copy = FALSE, ..., na_matches = c("na", "never"))

Arguments

x, y	A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
by	A join specification created with join_by(), or a character vector of variables to join by. If NULL, the default, ⁠*_join()⁠ will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly. To join on different variables between x and y, use a join_by() specification. For example, join_by(a == b) will match x$a to y$b. To join by multiple variables, use a join_by() specification with multiple expressions. For example, join_by(a == b, c == d) will match x$a to y$b and x$c to y$d. If the column names are the same between x and y, you can shorten this by listing only the variable names, like join_by(a, c). join_by() can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins. For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example, by = c("a", "b") joins x$a to y$a and x$b to y$b. If variable names differ between x and y, use a named character vector like by = c("x_a" = "y_a", "x_b" = "y_b"). To perform a cross-join, generating all combinations of x and y, see cross_join().
copy	If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
...	Other parameters passed onto methods.
na_matches	Should two NA or two NaN values match? "na", the default, treats two NA or two NaN values as equal, like %in%, match(), and merge(). "never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).

See Also

dplyr::semi_join()

Examples

library(duckplyr)
library(dplyr)
band_members %>% semi_join(band_instruments)

---

Set diff

Description

This is a method for the dplyr::setdiff() generic. See "Fallbacks" section for differences in implementation. setdiff(x, y) finds all rows in x that aren't in y.

Usage

## S3 method for class 'duckplyr_df'
setdiff(x, y, ...)

Arguments

x, y	Pair of compatible data frames. A pair of data frames is compatible if they have the same column names (possibly in different orders) and compatible types.
...	These dots are for future extensions and must be empty.

Fallbacks

You cannot use setdiff.duckplyr_df

• if column names are duplicated in one of the tables

• if column names are different in both tables.

If you do the code will fall back to dplyr::setdiff() without any error.

See Also

dplyr::setdiff()

Examples

df1 <- tibble(x = 1:3)
df2 <- tibble(x = 3:5)
setdiff(df1, df2)
setdiff(df2, df1)

---

Show stats

Description

Prints statistics on how many calls were handled by DuckDB. The output shows the total number of requests in the current session, split by fallbacks to dplyr and requests handled by duckdb.

Usage

stats_show()

Value

Called for its side effect.

Examples

stats_show()

tibble(a = 1:3) %>%
  as_duckplyr_df() %>%
  mutate(b = a + 1)

stats_show()

---

Summarise each group down to one row

Description

This is a method for the dplyr::summarise() generic. See "Fallbacks" section for differences in implementation. summarise() creates a new data frame. It returns one row for each combination of grouping variables; if there are no grouping variables, the output will have a single row summarising all observations in the input. It will contain one column for each grouping variable and one column for each of the summary statistics that you have specified.

Usage

## S3 method for class 'duckplyr_df'
summarise(.data, ..., .by = NULL, .groups = NULL)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	<data-masking> Name-value pairs of summary functions. The name will be the name of the variable in the result. The value can be: A vector of length 1, e.g. min(x), n(), or sum(is.na(y)). A data frame, to add multiple columns from a single expression. Returning values with size 0 or >1 was deprecated as of 1.1.0. Please use reframe() for this instead.
.by	<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.
.groups	Grouping structure of the result. "drop_last": dropping the last level of grouping. This was the only supported option before version 1.0.0. "drop": All levels of grouping are dropped. "keep": Same grouping structure as .data. "rowwise": Each row is its own group. When .groups is not specified, it is chosen based on the number of rows of the results: If all the results have 1 row, you get "drop_last". If the number of rows varies, you get "keep" (note that returning a variable number of rows was deprecated in favor of reframe(), which also unconditionally drops all levels of grouping). In addition, a message informs you of that choice, unless the result is ungrouped, the option "dplyr.summarise.inform" is set to FALSE, or when summarise() is called from a function in a package.

Fallbacks

You cannot use summarise.duckplyr_df

• with .groups = "rowwise".

If you do the code will fall back to dplyr::summarise() without any error.

See Also

dplyr::summarise()

Examples

summarise(mtcars, mean = mean(disp), n = n())

---

Symmetric difference

Description

This is a method for the dplyr::symdiff() generic. See "Fallbacks" section for differences in implementation. symdiff(x, y) computes the symmetric difference, i.e. all rows in x that aren't in y and all rows in y that aren't in x.

Usage

## S3 method for class 'duckplyr_df'
symdiff(x, y, ...)

Arguments

x, y	Pair of compatible data frames. A pair of data frames is compatible if they have the same column names (possibly in different orders) and compatible types.
...	These dots are for future extensions and must be empty.

Fallbacks

You cannot use symdiff.duckplyr_df

• if column names are duplicated in one of the tables

• if column names are different in both tables.

If you do the code will fall back to dplyr::symdiff() without any error.

See Also

dplyr::symdiff()

Examples

df1 <- tibble(x = 1:3)
df2 <- tibble(x = 3:5)
symdiff(df1, df2)

---

Create, modify, and delete columns

Description

This is a method for the dplyr::transmute() generic. See "Fallbacks" section for differences in implementation. transmute() creates a new data frame containing only the specified computations. It's superseded because you can perform the same job with mutate(.keep = "none").

Usage

## S3 method for class 'duckplyr_df'
transmute(.data, ...)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	<data-masking> Name-value pairs. The name gives the name of the column in the output. The value can be: A vector of length 1, which will be recycled to the correct length. A vector the same length as the current group (or the whole data frame if ungrouped). NULL, to remove the column. A data frame or tibble, to create multiple columns in the output.

Fallbacks

You cannot use transmute.duckplyr_df

• with a selection that returns no columns:

If you do the code will fall back to dplyr::transmute() without any error.

See Also

dplyr::transmute()

Examples

library("duckplyr")
transmute(mtcars, mpg2 = mpg*2)

---

Union of all

Description

This is a method for the dplyr::union_all() generic. See "Fallbacks" section for differences in implementation. union_all(x, y) finds all rows in either x or y, including duplicates.

Usage

## S3 method for class 'duckplyr_df'
union_all(x, y, ...)

Arguments

x, y	Pair of compatible data frames. A pair of data frames is compatible if they have the same column names (possibly in different orders) and compatible types.
...	These dots are for future extensions and must be empty.

Fallbacks

You cannot use union_all.duckplyr_df

• if column names are duplicated in one of the tables

• if column names are different in both tables.

If you do the code will fall back to dplyr::union_all() without any error.

See Also

dplyr::union_all()

Examples

df1 <- tibble(x = 1:3)
df2 <- tibble(x = 3:5)
union_all(df1, df2)

---