Type:	Package
Title:	A Grammar of Data Manipulation
Version:	1.1.4
Description:	A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
License:	MIT + file LICENSE
URL:	https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr
BugReports:	https://github.com/tidyverse/dplyr/issues
Depends:	R (≥ 3.5.0)
Imports:	cli (≥ 3.4.0), generics, glue (≥ 1.3.2), lifecycle (≥ 1.0.3), magrittr (≥ 1.5), methods, pillar (≥ 1.9.0), R6, rlang (≥ 1.1.0), tibble (≥ 3.2.0), tidyselect (≥ 1.2.0), utils, vctrs (≥ 0.6.4)
Suggests:	bench, broom, callr, covr, DBI, dbplyr (≥ 2.2.1), ggplot2, knitr, Lahman, lobstr, microbenchmark, nycflights13, purrr, rmarkdown, RMySQL, RPostgreSQL, RSQLite, stringi (≥ 1.7.6), testthat (≥ 3.1.5), tidyr (≥ 1.3.0), withr
VignetteBuilder:	knitr
Config/Needs/website:	tidyverse, shiny, pkgdown, tidyverse/tidytemplate
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
NeedsCompilation:	yes
Packaged:	2023-11-16 21:48:56 UTC; hadleywickham
Author:	Hadley Wickham [aut, cre], Romain François [aut], Lionel Henry [aut], Kirill Müller [aut], Davis Vaughan [aut], Posit Software, PBC [cph, fnd]
Maintainer:	Hadley Wickham <hadley@posit.co>
Repository:	CRAN
Date/Publication:	2023-11-17 16:50:02 UTC

dplyr: A Grammar of Data Manipulation

Description

To learn more about dplyr, start with the vignettes: browseVignettes(package = "dplyr")

Author(s)

Maintainer: Hadley Wickham hadley@posit.co (ORCID)

Authors:

• Romain François (ORCID)

• Lionel Henry

• Kirill Müller (ORCID)

• Davis Vaughan davis@posit.co (ORCID)

Other contributors:

• Posit Software, PBC [copyright holder, funder]

See Also

Useful links:

• https://dplyr.tidyverse.org

• https://github.com/tidyverse/dplyr

• Report bugs at https://github.com/tidyverse/dplyr/issues

Apply a function (or functions) across multiple columns

Description

across() makes it easy to apply the same transformation to multiple columns, allowing you to use select() semantics inside in "data-masking" functions like summarise() and mutate(). See vignette("colwise") for more details.

if_any() and if_all() apply the same predicate function to a selection of columns and combine the results into a single logical vector: if_any() is TRUE when the predicate is TRUE for any of the selected columns, if_all() is TRUE when the predicate is TRUE for all selected columns.

If you just need to select columns without applying a transformation to each of them, then you probably want to use pick() instead.

across() supersedes the family of "scoped variants" like summarise_at(), summarise_if(), and summarise_all().

Usage

across(.cols, .fns, ..., .names = NULL, .unpack = FALSE)

if_any(.cols, .fns, ..., .names = NULL)

if_all(.cols, .fns, ..., .names = NULL)

Arguments

Value

across() typically returns a tibble with one column for each column in .cols and each function in .fns. If .unpack is used, more columns may be returned depending on how the results of .fns are unpacked.

if_any() and if_all() return a logical vector.

Timing of evaluation

R code in dplyr verbs is generally evaluated once per group. Inside across() however, code is evaluated once for each combination of columns and groups. If the evaluation timing is important, for example if you're generating random variables, think about when it should happen and place your code in consequence.

gdf <-
  tibble(g = c(1, 1, 2, 3), v1 = 10:13, v2 = 20:23) %>%
  group_by(g)

set.seed(1)

# Outside: 1 normal variate
n <- rnorm(1)
gdf %>% mutate(across(v1:v2, ~ .x + n))
#> # A tibble: 4 x 3
#> # Groups:   g [3]
#>       g    v1    v2
#>   <dbl> <dbl> <dbl>
#> 1     1  9.37  19.4
#> 2     1 10.4   20.4
#> 3     2 11.4   21.4
#> 4     3 12.4   22.4

# Inside a verb: 3 normal variates (ngroup)
gdf %>% mutate(n = rnorm(1), across(v1:v2, ~ .x + n))
#> # A tibble: 4 x 4
#> # Groups:   g [3]
#>       g    v1    v2      n
#>   <dbl> <dbl> <dbl>  <dbl>
#> 1     1  10.2  20.2  0.184
#> 2     1  11.2  21.2  0.184
#> 3     2  11.2  21.2 -0.836
#> 4     3  14.6  24.6  1.60

# Inside `across()`: 6 normal variates (ncol * ngroup)
gdf %>% mutate(across(v1:v2, ~ .x + rnorm(1)))
#> # A tibble: 4 x 3
#> # Groups:   g [3]
#>       g    v1    v2
#>   <dbl> <dbl> <dbl>
#> 1     1  10.3  20.7
#> 2     1  11.3  21.7
#> 3     2  11.2  22.6
#> 4     3  13.5  22.7

See Also

c_across() for a function that returns a vector

Examples

# For better printing
iris <- as_tibble(iris)

# across() -----------------------------------------------------------------
# Different ways to select the same set of columns
# See <https://tidyselect.r-lib.org/articles/syntax.html> for details
iris %>%
  mutate(across(c(Sepal.Length, Sepal.Width), round))
iris %>%
  mutate(across(c(1, 2), round))
iris %>%
  mutate(across(1:Sepal.Width, round))
iris %>%
  mutate(across(where(is.double) & !c(Petal.Length, Petal.Width), round))

# Using an external vector of names
cols <- c("Sepal.Length", "Petal.Width")
iris %>%
  mutate(across(all_of(cols), round))

# If the external vector is named, the output columns will be named according
# to those names
names(cols) <- tolower(cols)
iris %>%
  mutate(across(all_of(cols), round))

# A purrr-style formula
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), ~ mean(.x, na.rm = TRUE)))

# A named list of functions
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd)))

# Use the .names argument to control the output names
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), mean, .names = "mean_{.col}"))
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd), .names = "{.col}.{.fn}"))

# If a named external vector is used for column selection, .names will use
# those names when constructing the output names
iris %>%
  group_by(Species) %>%
  summarise(across(all_of(cols), mean, .names = "mean_{.col}"))

# When the list is not named, .fn is replaced by the function's position
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean, sd), .names = "{.col}.fn{.fn}"))

# When the functions in .fns return a data frame, you typically get a
# "packed" data frame back
quantile_df <- function(x, probs = c(0.25, 0.5, 0.75)) {
  tibble(quantile = probs, value = quantile(x, probs))
}

iris %>%
  reframe(across(starts_with("Sepal"), quantile_df))

# Use .unpack to automatically expand these packed data frames into their
# individual columns
iris %>%
  reframe(across(starts_with("Sepal"), quantile_df, .unpack = TRUE))

# .unpack can utilize a glue specification if you don't like the defaults
iris %>%
  reframe(across(starts_with("Sepal"), quantile_df, .unpack = "{outer}.{inner}"))

# This is also useful inside mutate(), for example, with a multi-lag helper
multilag <- function(x, lags = 1:3) {
  names(lags) <- as.character(lags)
  purrr::map_dfr(lags, lag, x = x)
}

iris %>%
  group_by(Species) %>%
  mutate(across(starts_with("Sepal"), multilag, .unpack = TRUE)) %>%
  select(Species, starts_with("Sepal"))

# if_any() and if_all() ----------------------------------------------------
iris %>%
  filter(if_any(ends_with("Width"), ~ . > 4))
iris %>%
  filter(if_all(ends_with("Width"), ~ . > 2))

Convert row names to an explicit variable.

Description

Please use tibble::rownames_to_column() instead.

Usage

add_rownames(df, var = "rowname")

Arguments

Flexible equality comparison for data frames

Description

all_equal() allows you to compare data frames, optionally ignoring row and column names. It is deprecated as of dplyr 1.1.0, because it makes it too easy to ignore important differences.

Usage

all_equal(
  target,
  current,
  ignore_col_order = TRUE,
  ignore_row_order = TRUE,
  convert = FALSE,
  ...
)

Arguments

Value

TRUE if equal, otherwise a character vector describing the reasons why they're not equal. Use isTRUE() if using the result in an if expression.

Examples

scramble <- function(x) x[sample(nrow(x)), sample(ncol(x))]

# `all_equal()` ignored row and column ordering by default,
# but we now feel that that makes it too easy to make mistakes
mtcars2 <- scramble(mtcars)
all_equal(mtcars, mtcars2)

# Instead, be explicit about the row and column ordering
all.equal(
  mtcars,
  mtcars2[rownames(mtcars), names(mtcars)]
)

Apply predicate to all variables

Description

all_vars() and any_vars() were only needed for the scoped verbs, which have been superseded by the use of across() in an existing verb. See vignette("colwise") for details.

These quoting functions signal to scoped filtering verbs (e.g. filter_if() or filter_all()) that a predicate expression should be applied to all relevant variables. The all_vars() variant takes the intersection of the predicate expressions with & while the any_vars() variant takes the union with |.

Usage

all_vars(expr)

any_vars(expr)

Arguments

See Also

vars() for other quoting functions that you can use with scoped verbs.

Helper for consistent documentation of .by

Description

Use ⁠@inheritParams args_by⁠ to consistently document .by.

Arguments

Order rows using column values

Description

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

arrange(.data, ..., .by_group = FALSE)

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

Arguments

Details

Missing values

Unlike base sorting with sort(), NA are:

• always sorted to the end for local data, even when wrapped with desc().

• treated differently for remote data, depending on the backend.

Value

An object of the same type as .data. The output has the following properties:

• All rows appear in the output, but (usually) in a different place.

• Columns are not modified.

• Groups are not modified.

• Data frame attributes are preserved.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

Other single table verbs: filter(), mutate(), reframe(), rename(), select(), slice(), summarise()

Examples

arrange(mtcars, cyl, disp)
arrange(mtcars, desc(disp))

# grouped arrange ignores groups
by_cyl <- mtcars %>% group_by(cyl)
by_cyl %>% arrange(desc(wt))
# Unless you specifically ask:
by_cyl %>% arrange(desc(wt), .by_group = TRUE)

# use embracing when wrapping in a function;
# see ?rlang::args_data_masking for more details
tidy_eval_arrange <- function(.data, var) {
  .data %>%
    arrange({{ var }})
}
tidy_eval_arrange(mtcars, mpg)

# Use `across()` or `pick()` to select columns with tidy-select
iris %>% arrange(pick(starts_with("Sepal")))
iris %>% arrange(across(starts_with("Sepal"), desc))

Arrange rows by a selection of variables

Description

Scoped verbs (⁠_if⁠, ⁠_at⁠, ⁠_all⁠) have been superseded by the use of pick() or across() in an existing verb. See vignette("colwise") for details.

These scoped variants of arrange() sort a data frame by a selection of variables. Like arrange(), you can modify the variables before ordering with the .funs argument.

Usage

arrange_all(.tbl, .funs = list(), ..., .by_group = FALSE, .locale = NULL)

arrange_at(.tbl, .vars, .funs = list(), ..., .by_group = FALSE, .locale = NULL)

arrange_if(
  .tbl,
  .predicate,
  .funs = list(),
  ...,
  .by_group = FALSE,
  .locale = NULL
)

Arguments

Grouping variables

The grouping variables that are part of the selection participate in the sorting of the data frame.

Examples

df <- as_tibble(mtcars)
arrange_all(df)
# ->
arrange(df, pick(everything()))

arrange_all(df, desc)
# ->
arrange(df, across(everything(), desc))

Copy tables to same source, if necessary

Description

Copy tables to same source, if necessary

Usage

auto_copy(x, y, copy = FALSE, ...)

Arguments

Database and SQL generics.

Description

The sql_ generics are used to build the different types of SQL queries. The default implementations in dbplyr generates ANSI 92 compliant SQL. The db_ generics execute actions on the database. The default implementations in dbplyr typically just call the standard DBI S4 method.

Usage

db_desc(x)

sql_translate_env(con)

db_list_tables(con)

db_has_table(con, table)

db_data_type(con, fields)

db_save_query(con, sql, name, temporary = TRUE, ...)

db_begin(con, ...)

db_commit(con, ...)

db_rollback(con, ...)

db_write_table(con, table, types, values, temporary = FALSE, ...)

db_create_table(con, table, types, temporary = FALSE, ...)

db_insert_into(con, table, values, ...)

db_create_indexes(con, table, indexes = NULL, unique = FALSE, ...)

db_create_index(con, table, columns, name = NULL, unique = FALSE, ...)

db_drop_table(con, table, force = FALSE, ...)

db_analyze(con, table, ...)

db_explain(con, sql, ...)

db_query_fields(con, sql, ...)

db_query_rows(con, sql, ...)

sql_select(
  con,
  select,
  from,
  where = NULL,
  group_by = NULL,
  having = NULL,
  order_by = NULL,
  limit = NULL,
  distinct = FALSE,
  ...
)

sql_subquery(con, from, name = random_table_name(), ...)

sql_join(con, x, y, vars, type = "inner", by = NULL, ...)

sql_semi_join(con, x, y, anti = FALSE, by = NULL, ...)

sql_set_op(con, x, y, method)

sql_escape_string(con, x)

sql_escape_ident(con, x)

Arguments

Details

A few backend methods do not call the standard DBI S4 methods including

• db_data_type(): Calls DBI::dbDataType() for every field (e.g. data frame column) and returns a vector of corresponding SQL data types

• db_save_query(): Builds and executes a ⁠CREATE [TEMPORARY] TABLE <table> ...⁠ SQL command.

• db_create_index(): Builds and executes a ⁠CREATE INDEX <name> ON <table>⁠ SQL command.

• db_drop_table(): Builds and executes a ⁠DROP TABLE [IF EXISTS] <table>⁠ SQL command.

• db_analyze(): Builds and executes an ⁠ANALYZE <table>⁠ SQL command.

Currently, copy_to() is the only user of db_begin(), db_commit(), db_rollback(), db_write_table(), db_create_indexes(), db_drop_table() and db_analyze(). If you find yourself overriding many of these functions it may suggest that you should just override copy_to() instead.

db_create_table() and db_insert_into() have been deprecated in favour of db_write_table().

Value

Usually a logical value indicating success. Most failures should generate an error. However, db_has_table() should return NA if temporary tables cannot be listed with DBI::dbListTables() (due to backend API limitations for example). As a result, you methods will rely on the backend to throw an error if a table exists when it shouldn't.

Band membership

Description

These data sets describe band members of the Beatles and Rolling Stones. They are toy data sets that can be displayed in their entirety on a slide (e.g. to demonstrate a join).

Usage

band_members

band_instruments

band_instruments2

Format

Each is a tibble with two variables and three observations

Details

band_instruments and band_instruments2 contain the same data but use different column names for the first column of the data set. band_instruments uses name, which matches the name of the key column of band_members; band_instruments2 uses artist, which does not.

Examples

band_members
band_instruments
band_instruments2

Detect where values fall in a specified range

Description

This is a shortcut for x >= left & x <= right, implemented for local vectors and translated to the appropriate SQL for remote tables.

Usage

between(x, left, right)

Arguments

Details

x, left, and right are all cast to their common type before the comparison is made.

Value

A logical vector the same size as x.

See Also

join_by() if you are looking for documentation for the between() overlap join helper.

Examples

between(1:12, 7, 9)

x <- rnorm(1e2)
x[between(x, -1, 1)]

# On a tibble using `filter()`
filter(starwars, between(height, 100, 150))

Bind multiple data frames by column

Description

Bind any number of data frames by column, making a wider result. This is similar to do.call(cbind, dfs).

Where possible prefer using a join to combine multiple data frames. bind_cols() binds the rows in order in which they appear so it is easy to create meaningless results without realising it.

Usage

bind_cols(
  ...,
  .name_repair = c("unique", "universal", "check_unique", "minimal")
)

Arguments

Value

A data frame the same type as the first element of ....

Examples

df1 <- tibble(x = 1:3)
df2 <- tibble(y = 3:1)
bind_cols(df1, df2)

# Row sizes must be compatible when column-binding
try(bind_cols(tibble(x = 1:3), tibble(y = 1:2)))

Bind multiple data frames by row

Description

Bind any number of data frames by row, making a longer result. This is similar to do.call(rbind, dfs), but the output will contain all columns that appear in any of the inputs.

Usage

bind_rows(..., .id = NULL)

Arguments

Value

A data frame the same type as the first element of ....

Examples

df1 <- tibble(x = 1:2, y = letters[1:2])
df2 <- tibble(x = 4:5, z = 1:2)

# You can supply individual data frames as arguments:
bind_rows(df1, df2)

# Or a list of data frames:
bind_rows(list(df1, df2))

# When you supply a column name with the `.id` argument, a new
# column is created to link each row to its original data frame
bind_rows(list(df1, df2), .id = "id")
bind_rows(list(a = df1, b = df2), .id = "id")

Combine values from multiple columns

Description

c_across() is designed to work with rowwise() to make it easy to perform row-wise aggregations. It has two differences from c():

• It uses tidy select semantics so you can easily select multiple variables. See vignette("rowwise") for more details.

• It uses vctrs::vec_c() in order to give safer outputs.

Usage

c_across(cols)

Arguments

See Also

across() for a function that returns a tibble.

Examples

df <- tibble(id = 1:4, w = runif(4), x = runif(4), y = runif(4), z = runif(4))
df %>%
  rowwise() %>%
  mutate(
    sum = sum(c_across(w:z)),
    sd = sd(c_across(w:z))
  )

A general vectorised switch()

Description

This function allows you to vectorise multiple switch() statements. Each case is evaluated sequentially and the first match for each element determines the corresponding value in the output vector. If no cases match, the .default is used.

case_match() is an R equivalent of the SQL "simple" ⁠CASE WHEN⁠ statement.

Connection to case_when()

While case_when() uses logical expressions on the left-hand side of the formula, case_match() uses values to match against .x with. The following two statements are roughly equivalent:

case_when(
  x %in% c("a", "b") ~ 1,
  x %in% "c" ~ 2,
  x %in% c("d", "e") ~ 3
)

case_match(
  x,
  c("a", "b") ~ 1,
  "c" ~ 2,
  c("d", "e") ~ 3
)

Usage

case_match(.x, ..., .default = NULL, .ptype = NULL)

Arguments

Value

A vector with the same size as .x and the same type as the common type of the RHS inputs and .default (if not overridden by .ptype).

See Also

case_when()

Examples

x <- c("a", "b", "a", "d", "b", NA, "c", "e")

# `case_match()` acts like a vectorized `switch()`.
# Unmatched values "fall through" as a missing value.
case_match(
  x,
  "a" ~ 1,
  "b" ~ 2,
  "c" ~ 3,
  "d" ~ 4
)

# Missing values can be matched exactly, and `.default` can be used to
# control the value used for unmatched values of `.x`
case_match(
  x,
  "a" ~ 1,
  "b" ~ 2,
  "c" ~ 3,
  "d" ~ 4,
  NA ~ 0,
  .default = 100
)

# Input values can be grouped into the same expression to map them to the
# same output value
case_match(
  x,
  c("a", "b") ~ "low",
  c("c", "d", "e") ~ "high"
)

# `case_match()` isn't limited to character input:
y <- c(1, 2, 1, 3, 1, NA, 2, 4)

case_match(
  y,
  c(1, 3) ~ "odd",
  c(2, 4) ~ "even",
  .default = "missing"
)

# Setting `.default` to the original vector is a useful way to replace
# selected values, leaving everything else as is
case_match(y, NA ~ 0, .default = y)

starwars %>%
  mutate(
    # Replace missings, but leave everything else alone
    hair_color = case_match(hair_color, NA ~ "unknown", .default = hair_color),
    # Replace some, but not all, of the species
    species = case_match(
      species,
      "Human" ~ "Humanoid",
      "Droid" ~ "Robot",
      c("Wookiee", "Ewok") ~ "Hairy",
      .default = species
    ),
    .keep = "used"
  )

A general vectorised if-else

Description

This function allows you to vectorise multiple if_else() statements. Each case is evaluated sequentially and the first match for each element determines the corresponding value in the output vector. If no cases match, the .default is used as a final "else" statment.

case_when() is an R equivalent of the SQL "searched" ⁠CASE WHEN⁠ statement.

Usage

case_when(..., .default = NULL, .ptype = NULL, .size = NULL)

Arguments

Value

A vector with the same size as the common size computed from the inputs in ... and the same type as the common type of the RHS inputs in ....

See Also

case_match()

Examples

x <- 1:70
case_when(
  x %% 35 == 0 ~ "fizz buzz",
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
  .default = as.character(x)
)

# Like an if statement, the arguments are evaluated in order, so you must
# proceed from the most specific to the most general. This won't work:
case_when(
  x %%  5 == 0 ~ "fizz",
  x %%  7 == 0 ~ "buzz",
  x %% 35 == 0 ~ "fizz buzz",
  .default = as.character(x)
)

# If none of the cases match and no `.default` is supplied, NA is used:
case_when(
  x %% 35 == 0 ~ "fizz buzz",
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
)

# Note that `NA` values on the LHS are treated like `FALSE` and will be
# assigned the `.default` value. You must handle them explicitly if you
# want to use a different value. The exact way to handle missing values is
# dependent on the set of LHS conditions you use.
x[2:4] <- NA_real_
case_when(
  x %% 35 == 0 ~ "fizz buzz",
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
  is.na(x) ~ "nope",
  .default = as.character(x)
)

# `case_when()` evaluates all RHS expressions, and then constructs its
# result by extracting the selected (via the LHS expressions) parts.
# In particular `NaN`s are produced in this case:
y <- seq(-2, 2, by = .5)
case_when(
  y >= 0 ~ sqrt(y),
  .default = y
)

# `case_when()` is particularly useful inside `mutate()` when you want to
# create a new variable that relies on a complex combination of existing
# variables
starwars %>%
  select(name:mass, gender, species) %>%
  mutate(
    type = case_when(
      height > 200 | mass > 200 ~ "large",
      species == "Droid" ~ "robot",
      .default = "other"
    )
  )


# `case_when()` is not a tidy eval function. If you'd like to reuse
# the same patterns, extract the `case_when()` call in a normal
# function:
case_character_type <- function(height, mass, species) {
  case_when(
    height > 200 | mass > 200 ~ "large",
    species == "Droid" ~ "robot",
    .default = "other"
  )
}

case_character_type(150, 250, "Droid")
case_character_type(150, 150, "Droid")

# Such functions can be used inside `mutate()` as well:
starwars %>%
  mutate(type = case_character_type(height, mass, species)) %>%
  pull(type)

# `case_when()` ignores `NULL` inputs. This is useful when you'd
# like to use a pattern only under certain conditions. Here we'll
# take advantage of the fact that `if` returns `NULL` when there is
# no `else` clause:
case_character_type <- function(height, mass, species, robots = TRUE) {
  case_when(
    height > 200 | mass > 200 ~ "large",
    if (robots) species == "Droid" ~ "robot",
    .default = "other"
  )
}

starwars %>%
  mutate(type = case_character_type(height, mass, species, robots = FALSE)) %>%
  pull(type)

dbplyr compatibility functions

Description

In dplyr 0.7.0, a number of database and SQL functions moved from dplyr to dbplyr. The generic functions stayed in dplyr (since there is no easy way to conditionally import a generic from different packages), but many other SQL and database helper functions moved. If you have written a backend, these functions generate the code you need to work with both dplyr 0.5.0 dplyr 0.7.0.

Usage

check_dbplyr()

wrap_dbplyr_obj(obj_name)

Examples

wrap_dbplyr_obj("build_sql")
wrap_dbplyr_obj("base_agg")

Find the first non-missing element

Description

Given a set of vectors, coalesce() finds the first non-missing value at each position. It's inspired by the SQL COALESCE function which does the same thing for SQL NULLs.

Usage

coalesce(..., .ptype = NULL, .size = NULL)

Arguments

Value

A vector with the same type and size as the common type and common size of the vectors in ....

See Also

na_if() to replace specified values with an NA. tidyr::replace_na() to replace NA with a value.

Examples

# Use a single value to replace all missing values
x <- sample(c(1:5, NA, NA, NA))
coalesce(x, 0L)

# The equivalent to a missing value in a list is `NULL`
coalesce(list(1, 2, NULL), list(NA))

# Or generate a complete vector from partially missing pieces
y <- c(1, 2, NA, NA, 5)
z <- c(NA, NA, 3, 4, 5)
coalesce(y, z)

# Supply lists by splicing them into dots:
vecs <- list(
  c(1, 2, NA, NA, 5),
  c(NA, NA, 3, 4, 5)
)
coalesce(!!!vecs)

Combine vectors

Description

combine() is deprecated in favour of vctrs::vec_c(). combine() attempted to automatically guess whether you wanted c() or unlist(), but could fail in surprising ways. We now believe it's better to be explicit.

Usage

combine(...)

Arguments

Examples

f1 <- factor("a")
f2 <- factor("b")

combine(f1, f2)
# ->
vctrs::vec_c(f1, f1)

combine(list(f1, f2))
# ->
vctrs::vec_c(!!!list(f1, f2))

Extract out common by variables

Description

Extract out common by variables

Usage

common_by(by = NULL, x, y)

Force computation of a database query

Description

compute() stores results in a remote temporary table. collect() retrieves data into a local tibble. collapse() is slightly different: it doesn't force computation, but instead forces generation of the SQL query. This is sometimes needed to work around bugs in dplyr's SQL generation.

All functions preserve grouping and ordering.

Usage

compute(x, ...)

collect(x, ...)

collapse(x, ...)

Arguments

Methods

These functions are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

• compute(): no methods found

• collect(): no methods found

• collapse(): no methods found

See Also

copy_to(), the opposite of collect(): it takes a local data frame and uploads it to the remote source.

Examples

mtcars2 <- dbplyr::src_memdb() %>%
  copy_to(mtcars, name = "mtcars2-cc", overwrite = TRUE)

remote <- mtcars2 %>%
  filter(cyl == 8) %>%
  select(mpg:drat)

# Compute query and save in remote table
compute(remote)

# Compute query bring back to this session
collect(remote)

# Creates a fresh query based on the generated SQL
collapse(remote)

Generate a unique identifier for consecutive combinations

Description

consecutive_id() generates a unique identifier that increments every time a variable (or combination of variables) changes. Inspired by data.table::rleid().

Usage

consecutive_id(...)

Arguments

Value

A numeric vector the same length as the longest element of ....

Examples

consecutive_id(c(TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, NA, NA))
consecutive_id(c(1, 1, 1, 2, 1, 1, 2, 2))

df <- data.frame(x = c(0, 0, 1, 0), y = c(2, 2, 2, 2))
df %>% group_by(x, y) %>% summarise(n = n())
df %>% group_by(id = consecutive_id(x, y), x, y) %>% summarise(n = n())

Information about the "current" group or variable

Description

These functions return information about the "current" group or "current" variable, so only work inside specific contexts like summarise() and mutate().

• n() gives the current group size.

• cur_group() gives the group keys, a tibble with one row and one column for each grouping variable.

• cur_group_id() gives a unique numeric identifier for the current group.

• cur_group_rows() gives the row indices for the current group.

• cur_column() gives the name of the current column (in across() only).

See group_data() for equivalent functions that return values for all groups.

See pick() for a way to select a subset of columns using tidyselect syntax while inside summarise() or mutate().

Usage

n()

cur_group()

cur_group_id()

cur_group_rows()

cur_column()

data.table

If you're familiar with data.table:

• cur_group_id() <-> .GRP

• cur_group() <-> .BY

• cur_group_rows() <-> .I

See pick() for an equivalent to .SD.

Examples

df <- tibble(
  g = sample(rep(letters[1:3], 1:3)),
  x = runif(6),
  y = runif(6)
)
gf <- df %>% group_by(g)

gf %>% summarise(n = n())

gf %>% mutate(id = cur_group_id())
gf %>% reframe(row = cur_group_rows())
gf %>% summarise(data = list(cur_group()))

gf %>% mutate(across(everything(), ~ paste(cur_column(), round(.x, 2))))

Copy a local data frame to a remote src

Description

This function uploads a local data frame into a remote data source, creating the table definition as needed. Wherever possible, the new object will be temporary, limited to the current connection to the source.

Usage

copy_to(dest, df, name = deparse(substitute(df)), overwrite = FALSE, ...)

Arguments

Value

a tbl object in the remote source

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

collect() for the opposite action; downloading remote data into a local dbl.

Examples

## Not run: 
iris2 <- dbplyr::src_memdb() %>% copy_to(iris, overwrite = TRUE)
iris2

## End(Not run)

Count the observations in each group

Description

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).

add_count() and add_tally() are equivalents to count() and tally() but use mutate() instead of summarise() so that they add a new column with group-wise counts.

Usage

count(x, ..., wt = NULL, sort = FALSE, name = NULL)

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

tally(x, wt = NULL, sort = FALSE, name = NULL)

add_count(x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = deprecated())

add_tally(x, wt = NULL, sort = FALSE, name = NULL)

Arguments

Value

An object of the same type as .data. count() and add_count() group transiently, so the output has the same groups as the input.

Examples

# count() is a convenient way to get a sense of the distribution of
# values in a dataset
starwars %>% count(species)
starwars %>% count(species, sort = TRUE)
starwars %>% count(sex, gender, sort = TRUE)
starwars %>% count(birth_decade = round(birth_year, -1))

# use the `wt` argument to perform a weighted count. This is useful
# when the data has already been aggregated once
df <- tribble(
  ~name,    ~gender,   ~runs,
  "Max",    "male",       10,
  "Sandra", "female",      1,
  "Susan",  "female",      4
)
# counts rows:
df %>% count(gender)
# counts runs:
df %>% count(gender, wt = runs)

# When factors are involved, `.drop = FALSE` can be used to retain factor
# levels that don't appear in the data
df2 <- tibble(
  id = 1:5,
  type = factor(c("a", "c", "a", NA, "a"), levels = c("a", "b", "c"))
)
df2 %>% count(type)
df2 %>% count(type, .drop = FALSE)

# Or, using `group_by()`:
df2 %>% group_by(type, .drop = FALSE) %>% count()

# tally() is a lower-level function that assumes you've done the grouping
starwars %>% tally()
starwars %>% group_by(species) %>% tally()

# both count() and tally() have add_ variants that work like
# mutate() instead of summarise
df %>% add_count(gender, wt = runs)
df %>% add_tally(wt = runs)

Cross join

Description

Cross joins match each row in x to every row in y, resulting in a data frame with nrow(x) * nrow(y) rows.

Since cross joins result in all possible matches between x and y, they technically serve as the basis for all mutating joins, which can generally be thought of as cross joins followed by a filter. In practice, a more specialized procedure is used for better performance.

Usage

cross_join(x, y, ..., copy = FALSE, suffix = c(".x", ".y"))

Arguments

Value

An object of the same type as x (including the same groups). The output has the following properties:

• There are nrow(x) * nrow(y) rows returned.

• Output columns include all columns from both x and y. Column name collisions are resolved using suffix.

• The order of the rows and columns of x is preserved as much as possible.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

Other joins: filter-joins, mutate-joins, nest_join()

Examples

# Cross joins match each row in `x` to every row in `y`.
# Data within the columns is not used in the matching process.
cross_join(band_instruments, band_members)

# Control the suffix added to variables duplicated in
# `x` and `y` with `suffix`.
cross_join(band_instruments, band_members, suffix = c("", "_y"))

Cumulativate versions of any, all, and mean

Description

dplyr provides cumall(), cumany(), and cummean() to complete R's set of cumulative functions.

Usage

cumall(x)

cumany(x)

cummean(x)

Arguments

Value

A vector the same length as x.

Cumulative logical functions

These are particularly useful in conjunction with filter():

• cumall(x): all cases until the first FALSE.

• cumall(!x): all cases until the first TRUE.

• cumany(x): all cases after the first TRUE.

• cumany(!x): all cases after the first FALSE.

Examples

# `cummean()` returns a numeric/integer vector of the same length
# as the input vector.
x <- c(1, 3, 5, 2, 2)
cummean(x)
cumsum(x) / seq_along(x)

# `cumall()` and `cumany()` return logicals
cumall(x < 5)
cumany(x == 3)

# `cumall()` vs. `cumany()`
df <- data.frame(
  date = as.Date("2020-01-01") + 0:6,
  balance = c(100, 50, 25, -25, -50, 30, 120)
)
# all rows after first overdraft
df %>% filter(cumany(balance < 0))
# all rows until first overdraft
df %>% filter(cumall(!(balance < 0)))

Defunct functions

Description

These functions were deprecated for at least two years before being made defunct. If there's a known replacement, calling the function will tell you about it.

Usage

# Deprecated in 0.5.0 -------------------------------------

id(.variables, drop = FALSE)

# Deprecated in 0.7.0 -------------------------------------

failwith(default = NULL, f, quiet = FALSE)

# Deprecated in 0.8.* -------------------------------------

select_vars(vars = chr(), ..., include = chr(), exclude = chr())

rename_vars(vars = chr(), ..., strict = TRUE)

select_var(vars, var = -1)

current_vars(...)

# Deprecated in 1.0.0 -------------------------------------

bench_tbls(tbls, op, ..., times = 10)

compare_tbls(tbls, op, ref = NULL, compare = equal_data_frame, ...)

compare_tbls2(tbls_x, tbls_y, op, ref = NULL, compare = equal_data_frame, ...)

eval_tbls(tbls, op)

eval_tbls2(tbls_x, tbls_y, op)

location(df)

changes(x, y)

Information about the "current" group or variable

Description

These functions were deprecated in dplyr 1.1.0.

• cur_data() is deprecated in favor of pick().

• cur_data_all() is deprecated but does not have a direct replacement as selecting the grouping variables is not well-defined and is unlikely to ever be useful.

Usage

cur_data()

cur_data_all()

Descending order

Description

Transform a vector into a format that will be sorted in descending order. This is useful within arrange().

Usage

desc(x)

Arguments

Examples

desc(1:10)
desc(factor(letters))

first_day <- seq(as.Date("1910/1/1"), as.Date("1920/1/1"), "years")
desc(first_day)

starwars %>% arrange(desc(mass))

Describing dimensions

Description

Prints the dimensions of an array-like object in a user-friendly manner, substituting NA with ?? (for SQL queries).

Usage

dim_desc(x)

Arguments

Examples

dim_desc(mtcars)

Keep distinct/unique rows

Description

Keep only unique/distinct rows from a data frame. This is similar to unique.data.frame() but considerably faster.

Usage

distinct(.data, ..., .keep_all = FALSE)

Arguments

Value

An object of the same type as .data. The output has the following properties:

• Rows are a subset of the input but appear in the same order.

• Columns are not modified if ... is empty or .keep_all is TRUE. Otherwise, distinct() first calls mutate() to create new columns.

• Groups are not modified.

• Data frame attributes are preserved.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

Examples

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

distinct(df, x)
distinct(df, y)

# You can choose to keep all other variables as well
distinct(df, x, .keep_all = TRUE)
distinct(df, y, .keep_all = TRUE)

# You can also use distinct on computed variables
distinct(df, diff = abs(x - y))

# Use `pick()` to select columns with tidy-select
distinct(starwars, pick(contains("color")))

# Grouping -------------------------------------------------

df <- tibble(
  g = c(1, 1, 2, 2, 2),
  x = c(1, 1, 2, 1, 2),
  y = c(3, 2, 1, 3, 1)
)
df <- df %>% group_by(g)

# With grouped data frames, distinctness is computed within each group
df %>% distinct(x)

# When `...` are omitted, `distinct()` still computes distinctness using
# all variables in the data frame
df %>% distinct()

Select distinct rows by a selection of variables

Description

Scoped verbs (⁠_if⁠, ⁠_at⁠, ⁠_all⁠) have been superseded by the use of pick() or across() in an existing verb. See vignette("colwise") for details.

These scoped variants of distinct() extract distinct rows by a selection of variables. Like distinct(), you can modify the variables before ordering with the .funs argument.

Usage

distinct_all(.tbl, .funs = list(), ..., .keep_all = FALSE)

distinct_at(.tbl, .vars, .funs = list(), ..., .keep_all = FALSE)

distinct_if(.tbl, .predicate, .funs = list(), ..., .keep_all = FALSE)

Arguments

Grouping variables

The grouping variables that are part of the selection are taken into account to determine distinct rows.

Examples

df <- tibble(x = rep(2:5, each = 2) / 2, y = rep(2:3, each = 4) / 2)

distinct_all(df)
# ->
distinct(df, pick(everything()))

distinct_at(df, vars(x,y))
# ->
distinct(df, pick(x, y))

distinct_if(df, is.numeric)
# ->
distinct(df, pick(where(is.numeric)))

# You can supply a function that will be applied before extracting the distinct values
# The variables of the sorted tibble keep their original values.
distinct_all(df, round)
# ->
distinct(df, across(everything(), round))

Same basic philosophy as group_by_prepare(): lazy_dots comes in, list of data and vars (character vector) comes out.

Description

⁠*_prepare()⁠ performs standard manipulation that is needed prior to actual data processing. They are only be needed by packages that implement dplyr backends.

Usage

distinct_prepare(
  .data,
  vars,
  group_vars = character(),
  .keep_all = FALSE,
  caller_env = caller_env(2),
  error_call = caller_env()
)

group_by_prepare(
  .data,
  ...,
  .add = FALSE,
  .dots = deprecated(),
  add = deprecated(),
  error_call = caller_env()
)

Value

A list

Do anything

Description

do() is superseded as of dplyr 1.0.0, because its syntax never really felt like it belonged with the rest of dplyr. It's replaced by a combination of reframe() (which can produce multiple rows and multiple columns), nest_by() (which creates a rowwise tibble of nested data), and pick() (which allows you to access the data for the "current" group).

Usage

do(.data, ...)

Arguments

Examples

# do() with unnamed arguments becomes reframe() or summarise()
# . becomes pick()
by_cyl <- mtcars %>% group_by(cyl)
by_cyl %>% do(head(., 2))
# ->
by_cyl %>% reframe(head(pick(everything()), 2))
by_cyl %>% slice_head(n = 2)

# Can refer to variables directly
by_cyl %>% do(mean = mean(.$vs))
# ->
by_cyl %>% summarise(mean = mean(vs))

# do() with named arguments becomes nest_by() + mutate() & list()
models <- by_cyl %>% do(mod = lm(mpg ~ disp, data = .))
# ->
models <- mtcars %>%
  nest_by(cyl) %>%
  mutate(mod = list(lm(mpg ~ disp, data = data)))
models %>% summarise(rsq = summary(mod)$r.squared)

# use broom to turn models into data
models %>% do(data.frame(
  var = names(coef(.$mod)),
  coef(summary(.$mod)))
)

# ->
models %>% reframe(broom::tidy(mod))

Per-operation grouping with .by/by

Description

There are two ways to group in dplyr:

• Persistent grouping with group_by()

• Per-operation grouping with .by/by

This help page is dedicated to explaining where and why you might want to use the latter.

Depending on the dplyr verb, the per-operation grouping argument may be named .by or by. The Supported verbs section below outlines this on a case-by-case basis. The remainder of this page will refer to .by for simplicity.

Grouping radically affects the computation of the dplyr verb you use it with, and one of the goals of .by is to allow you to place that grouping specification alongside the code that actually uses it. As an added benefit, with .by you no longer need to remember to ungroup() after summarise(), and summarise() won't ever message you about how it's handling the groups!

This idea comes from data.table, which allows you to specify by alongside modifications in j, like: dt[, .(x = mean(x)), by = g].

Supported verbs

• mutate(.by = )

• summarise(.by = )

• reframe(.by = )

• filter(.by = )

• slice(.by = )

• slice_head(by = ) and slice_tail(by = )

• slice_min(by = ) and slice_max(by = )

• slice_sample(by = )

Note that some dplyr verbs use by while others use .by. This is a purely technical difference.

Differences between .by and group_by()

Using .by

Let's take a look at the two grouping approaches using this expenses data set, which tracks costs accumulated across various ids and regions:

expenses <- tibble(
  id = c(1, 2, 1, 3, 1, 2, 3),
  region = c("A", "A", "A", "B", "B", "A", "A"),
  cost = c(25, 20, 19, 12, 9, 6, 6)
)
expenses
#> # A tibble: 7 x 3
#>      id region  cost
#>   <dbl> <chr>  <dbl>
#> 1     1 A         25
#> 2     2 A         20
#> 3     1 A         19
#> 4     3 B         12
#> 5     1 B          9
#> 6     2 A          6
#> 7     3 A          6

Imagine that you wanted to compute the average cost per region. You'd probably write something like this:

expenses %>%
  group_by(region) %>%
  summarise(cost = mean(cost))
#> # A tibble: 2 x 2
#>   region  cost
#>   <chr>  <dbl>
#> 1 A       15.2
#> 2 B       10.5

Instead, you can now specify the grouping inline within the verb:

expenses %>%
  summarise(cost = mean(cost), .by = region)
#> # A tibble: 2 x 2
#>   region  cost
#>   <chr>  <dbl>
#> 1 A       15.2
#> 2 B       10.5

.by applies to a single operation, meaning that since expenses was an ungrouped data frame, the result after applying .by will also always be an ungrouped data frame, regardless of the number of grouping columns.

expenses %>%
  summarise(cost = mean(cost), .by = c(id, region))
#> # A tibble: 5 x 3
#>      id region  cost
#>   <dbl> <chr>  <dbl>
#> 1     1 A         22
#> 2     2 A         13
#> 3     3 B         12
#> 4     1 B          9
#> 5     3 A          6

Compare that with group_by() %>% summarise(), where summarise() generally peels off 1 layer of grouping by default, typically with a message that it is doing so:

expenses %>%
  group_by(id, region) %>%
  summarise(cost = mean(cost))
#> `summarise()` has grouped output by 'id'. You can override using the `.groups`
#> argument.
#> # A tibble: 5 x 3
#> # Groups:   id [3]
#>      id region  cost
#>   <dbl> <chr>  <dbl>
#> 1     1 A         22
#> 2     1 B          9
#> 3     2 A         13
#> 4     3 A          6
#> 5     3 B         12

Because .by grouping applies to a single operation, you don't need to worry about ungrouping, and it never needs to emit a message to remind you what it is doing with the groups.

Note that with .by we specified multiple columns to group by using the tidy-select syntax c(id, region). If you have a character vector of column names you'd like to group by, you can do so with .by = all_of(my_cols). It will group by the columns in the order they were provided.

To prevent surprising results, you can't use .by on an existing grouped data frame:

expenses %>% 
  group_by(id) %>%
  summarise(cost = mean(cost), .by = c(id, region))
#> Error in `summarise()`:
#> ! Can't supply `.by` when `.data` is a grouped data frame.

So far we've focused on the usage of .by with summarise(), but .by works with a number of other dplyr verbs. For example, you could append the mean cost per region onto the original data frame as a new column rather than computing a summary:

expenses %>%
  mutate(cost_by_region = mean(cost), .by = region)
#> # A tibble: 7 x 4
#>      id region  cost cost_by_region
#>   <dbl> <chr>  <dbl>          <dbl>
#> 1     1 A         25           15.2
#> 2     2 A         20           15.2
#> 3     1 A         19           15.2
#> 4     3 B         12           10.5
#> 5     1 B          9           10.5
#> 6     2 A          6           15.2
#> 7     3 A          6           15.2

Or you could slice out the maximum cost per combination of id and region:

# Note that the argument is named `by` in `slice_max()`
expenses %>%
  slice_max(cost, n = 1, by = c(id, region))
#> # A tibble: 5 x 3
#>      id region  cost
#>   <dbl> <chr>  <dbl>
#> 1     1 A         25
#> 2     2 A         20
#> 3     3 B         12
#> 4     1 B          9
#> 5     3 A          6

Result ordering

When used with .by, summarise(), reframe(), and slice() all maintain the ordering of the existing data. This is different from group_by(), which has always sorted the group keys in ascending order.

df <- tibble(
  month = c("jan", "jan", "feb", "feb", "mar"),
  temp = c(20, 25, 18, 20, 40)
)

# Uses ordering by "first appearance" in the original data
df %>%
  summarise(average_temp = mean(temp), .by = month)
#> # A tibble: 3 x 2
#>   month average_temp
#>   <chr>        <dbl>
#> 1 jan           22.5
#> 2 feb           19  
#> 3 mar           40

# Sorts in ascending order
df %>%
  group_by(month) %>%
  summarise(average_temp = mean(temp))
#> # A tibble: 3 x 2
#>   month average_temp
#>   <chr>        <dbl>
#> 1 feb           19  
#> 2 jan           22.5
#> 3 mar           40

If you need sorted group keys, we recommend that you explicitly use arrange() either before or after the call to summarise(), reframe(), or slice(). This also gives you full access to all of arrange()'s features, such as desc() and the .locale argument.

Verbs without .by support

If a dplyr verb doesn't support .by, then that typically means that the verb isn't inherently affected by grouping. For example, pull() and rename() don't support .by, because specifying columns to group by would not affect their implementations.

That said, there are a few exceptions to this where sometimes a dplyr verb doesn't support .by, but does have special support for grouped data frames created by group_by(). This is typically because the verbs are required to retain the grouping columns, for example:

• select() always retains grouping columns, with a message if any aren't specified in the select() call.

• distinct() and count() place unspecified grouping columns at the front of the data frame before computing their results.

• arrange() has a .by_group argument to optionally order by grouping columns first.

If group_by() didn't exist, then these verbs would not have special support for grouped data frames.

Data-masking

Description

This page is now located at ?rlang::args_data_masking.

Extending dplyr with new data frame subclasses

Description

These three functions, along with ⁠names<-⁠ and 1d numeric [ (i.e. x[loc]) methods, provide a minimal interface for extending dplyr to work with new data frame subclasses. This means that for simple cases you should only need to provide a couple of methods, rather than a method for every dplyr verb.

These functions are a stop-gap measure until we figure out how to solve the problem more generally, but it's likely that any code you write to implement them will find a home in what comes next.

Usage

dplyr_row_slice(data, i, ...)

dplyr_col_modify(data, cols)

dplyr_reconstruct(data, template)

Arguments

Basic advice

This section gives you basic advice if you want to extend dplyr to work with your custom data frame subclass, and you want the dplyr methods to behave in basically the same way.

• If you have data frame attributes that don't depend on the rows or columns (and should unconditionally be preserved), you don't need to do anything. The one exception to this is if your subclass extends a data.frame directly rather than extending a tibble. The ⁠[.data.frame⁠ method does not preserve attributes, so you'll need to write a [ method for your subclass that preserves attributes important for your class.

• If you have scalar attributes that depend on rows, implement a dplyr_reconstruct() method. Your method should recompute the attribute depending on rows now present.

• If you have scalar attributes that depend on columns, implement a dplyr_reconstruct() method and a 1d [ method. For example, if your class requires that certain columns be present, your method should return a data.frame or tibble when those columns are removed.

• If your attributes are vectorised over rows, implement a dplyr_row_slice() method. This gives you access to i so you can modify the row attribute accordingly. You'll also need to think carefully about how to recompute the attribute in dplyr_reconstruct(), and you will need to carefully verify the behaviour of each verb, and provide additional methods as needed.

• If your attributes that are vectorised over columns, implement dplyr_col_modify(), 1d [, and ⁠names<-⁠ methods. All of these methods know which columns are being modified, so you can update the column attribute according. You'll also need to think carefully about how to recompute the attribute in dplyr_reconstruct(), and you will need to carefully verify the behaviour of each verb, and provide additional methods as needed.

Current usage

• arrange(), filter(), slice() (and the rest of the ⁠slice_*()⁠ family), semi_join(), and anti_join() work by generating a vector of row indices, and then subsetting with dplyr_row_slice().

• mutate() generates a list of new column value (using NULL to indicate when columns should be deleted), then passes that to dplyr_col_modify(). It also uses 1d [ to implement .keep, and will call relocate() if either .before or .after are supplied.

• summarise() and reframe() work similarly to mutate() but the data modified by dplyr_col_modify() comes from group_data() or is built from .by.

• select() uses 1d [ to select columns, then ⁠names<-⁠ to rename them. rename() just uses ⁠names<-⁠. relocate() just uses 1d [.

• inner_join(), left_join(), right_join(), and full_join() coerce x to a tibble, modify the rows, then use dplyr_reconstruct() to convert back to the same type as x.

• nest_join() converts both x and y to tibbles, modifies the rows, and uses dplyr_col_modify() to handle modified key variables and the list-column that y becomes. It also uses dplyr_reconstruct() to convert the outer result back to the type of x, and to convert the nested tibbles back to the type of y.

• distinct() does a mutate() if any expressions are present, then uses 1d [ to select variables to keep, then dplyr_row_slice() to select distinct rows.

Note that group_by() and ungroup() don't use any of these generics and you'll need to provide methods for them directly, or rely on .by for per-operation grouping.

Argument type: tidy-select

Description

This page describes the ⁠<tidy-select>⁠ argument modifier which indicates the argument supports tidy selections. Tidy selection provides a concise dialect of R for selecting variables based on their names or properties.

Tidy selection is a variant of tidy evaluation. This means that inside functions, tidy-select arguments require special attention, as described in the Indirection section below. If you've never heard of tidy evaluation before, start with vignette("programming").

Overview of selection features

Tidyverse selections implement a dialect of R where operators make it easy to select variables:

• : for selecting a range of consecutive variables.

• ! for taking the complement of a set of variables.

• & and | for selecting the intersection or the union of two sets of variables.

• c() for combining selections.

In addition, you can use selection helpers. Some helpers select specific columns:

• everything(): Matches all variables.

• last_col(): Select last variable, possibly with an offset.

• group_cols(): Select all grouping columns.

Other helpers select variables by matching patterns in their names:

• starts_with(): Starts with a prefix.

• ends_with(): Ends with a suffix.

• contains(): Contains a literal string.

• matches(): Matches a regular expression.

• num_range(): Matches a numerical range like x01, x02, x03.

Or from variables stored in a character vector:

• all_of(): Matches variable names in a character vector. All names must be present, otherwise an out-of-bounds error is thrown.

• any_of(): Same as all_of(), except that no error is thrown for names that don't exist.

Or using a predicate function:

• where(): Applies a function to all variables and selects those for which the function returns TRUE.

Indirection

There are two main cases:

• If you have a character vector of column names, use all_of() or any_of(), depending on whether or not you want unknown variable names to cause an error, e.g. select(df, all_of(vars)), select(df, !any_of(vars)).

• If you want the user to be able to supply a tidyselect specification in a function argument, embrace the function argument, e.g. select(df, {{ vars }}).

Locale used by arrange()

Description

This page documents details about the locale used by arrange() when ordering character vectors.

Default locale

The default locale used by arrange() is the C locale. This is used when .locale = NULL unless the dplyr.legacy_locale global option is set to TRUE. You can also force the C locale to be used unconditionally with .locale = "C".

The C locale is not exactly the same as English locales, such as "en". The main difference is that the C locale groups the English alphabet by case, while most English locales group the alphabet by letter. For example, c("a", "b", "C", "B", "c") will sort as c("B", "C", "a", "b", "c") in the C locale, with all uppercase letters coming before lowercase letters, but will sort as c("a", "b", "B", "c", "C") in an English locale. This often makes little practical difference during data analysis, because both return identical results when case is consistent between observations.

Reproducibility

The C locale has the benefit of being completely reproducible across all supported R versions and operating systems with no extra effort.

If you set .locale to an option from stringi::stri_locale_list(), then stringi must be installed by anyone who wants to run your code. If you utilize this in a package, then stringi should be placed in Imports.

Legacy behavior

Prior to dplyr 1.1.0, character columns were ordered in the system locale. If you need to temporarily revert to this behavior, you can set the global option dplyr.legacy_locale to TRUE, but this should be used sparingly and you should expect this option to be removed in a future version of dplyr. It is better to update existing code to explicitly use .locale instead. Note that setting dplyr.legacy_locale will also force calls to group_by() to use the system locale when internally ordering the groups.

Setting .locale will override any usage of dplyr.legacy_locale.

Examples

df <- tibble(x = c("a", "b", "C", "B", "c"))
df

# Default locale is C, which groups the English alphabet by case, placing
# uppercase letters before lowercase letters.
arrange(df, x)

# The American English locale groups the alphabet by letter.
# Explicitly override `.locale` with `"en"` for this ordering.
arrange(df, x, .locale = "en")

# This Danish letter is expected to sort after `z`
df <- tibble(x = c("o", "p", "\u00F8", "z"))
df

# The American English locale sorts it right after `o`
arrange(df, x, .locale = "en")

# Using `"da"` for Danish ordering gives the expected result
arrange(df, x, .locale = "da")

# If you need the legacy behavior of `arrange()`, which respected the
# system locale, then you can set the global option `dplyr.legacy_locale`,
# but expect this to be removed in the future. We recommend that you use
# the `.locale` argument instead.
rlang::with_options(dplyr.legacy_locale = TRUE, {
  arrange(df, x)
})

Explain details of a tbl

Description

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

explain(x, ...)

show_query(x, ...)

Arguments

Value

The first argument, invisibly.

Databases

Explaining a tbl_sql will run the SQL EXPLAIN command which will describe the query plan. This requires a little bit of knowledge about how EXPLAIN works for your database, but is very useful for diagnosing performance problems.

Examples

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

# The batting database has indices on all ID variables:
# SQLite automatically picks the most restrictive index
batting %>% filter(lgID == "NL" & yearID == 2000L) %>% explain()

# OR's will use multiple indexes
batting %>% filter(lgID == "NL" | yearID == 2000) %>% explain()

# Joins will use indexes in both tables
teams <- tbl(lahman_s, "Teams")
batting %>% left_join(teams, c("yearID", "teamID")) %>% explain()

Keep rows that match a condition

Description

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

filter(.data, ..., .by = NULL, .preserve = FALSE)

Arguments

Details

The filter() function is used to subset the rows of .data, applying the expressions in ... to the column values to determine which rows should be retained. It can be applied to both grouped and ungrouped data (see group_by() and ungroup()). However, dplyr is not yet smart enough to optimise the filtering operation on grouped datasets that do not need grouped calculations. For this reason, filtering is often considerably faster on ungrouped data.

Value

An object of the same type as .data. The output has the following properties:

• Rows are a subset of the input, but appear in the same order.

• Columns are not modified.

• The number of groups may be reduced (if .preserve is not TRUE).

• Data frame attributes are preserved.

Useful filter functions

There are many functions and operators that are useful when constructing the expressions used to filter the data:

• ==, >, >= etc

• &, |, !, xor()

• is.na()

• between(), near()

Grouped tibbles

Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering:

starwars %>% filter(mass > mean(mass, na.rm = TRUE))

With the grouped equivalent:

starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))

In the ungrouped version, filter() compares the value of mass in each row to the global average (taken over the whole data set), keeping only the rows with mass greater than this global average. In contrast, the grouped version calculates the average mass separately for each gender group, and keeps rows with mass greater than the relevant within-gender average.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

Other single table verbs: arrange(), mutate(), reframe(), rename(), select(), slice(), summarise()

Examples

# Filtering by one criterion
filter(starwars, species == "Human")
filter(starwars, mass > 1000)

# Filtering by multiple criteria within a single logical expression
filter(starwars, hair_color == "none" & eye_color == "black")
filter(starwars, hair_color == "none" | eye_color == "black")

# When multiple expressions are used, they are combined using &
filter(starwars, hair_color == "none", eye_color == "black")


# The filtering operation may yield different results on grouped
# tibbles because the expressions are computed within groups.
#
# The following filters rows where `mass` is greater than the
# global average:
starwars %>% filter(mass > mean(mass, na.rm = TRUE))

# Whereas this keeps rows with `mass` greater than the gender
# average:
starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))


# To refer to column names that are stored as strings, use the `.data` pronoun:
vars <- c("mass", "height")
cond <- c(80, 150)
starwars %>%
  filter(
    .data[[vars[[1]]]] > cond[[1]],
    .data[[vars[[2]]]] > cond[[2]]
  )
# Learn more in ?rlang::args_data_masking

Filter within a selection of variables

Description

Scoped verbs (⁠_if⁠, ⁠_at⁠, ⁠_all⁠) have been superseded by the use of if_all() or if_any() in an existing verb. See vignette("colwise") for details.

These scoped filtering verbs apply a predicate expression to a selection of variables. The predicate expression should be quoted with all_vars() or any_vars() and should mention the pronoun . to refer to variables.

Usage

filter_all(.tbl, .vars_predicate, .preserve = FALSE)

filter_if(.tbl, .predicate, .vars_predicate, .preserve = FALSE)

filter_at(.tbl, .vars, .vars_predicate, .preserve = FALSE)

Arguments

Grouping variables

The grouping variables that are part of the selection are taken into account to determine filtered rows.

Examples

# While filter() accepts expressions with specific variables, the
# scoped filter verbs take an expression with the pronoun `.` and
# replicate it over all variables. This expression should be quoted
# with all_vars() or any_vars():
all_vars(is.na(.))
any_vars(is.na(.))


# You can take the intersection of the replicated expressions:
filter_all(mtcars, all_vars(. > 150))
# ->
filter(mtcars, if_all(everything(), ~ .x > 150))

# Or the union:
filter_all(mtcars, any_vars(. > 150))
# ->
filter(mtcars, if_any(everything(), ~ . > 150))


# You can vary the selection of columns on which to apply the
# predicate. filter_at() takes a vars() specification:
filter_at(mtcars, vars(starts_with("d")), any_vars((. %% 2) == 0))
# ->
filter(mtcars, if_any(starts_with("d"), ~ (.x %% 2) == 0))

# And filter_if() selects variables with a predicate function:
filter_if(mtcars, ~ all(floor(.) == .), all_vars(. != 0))
# ->
is_int <- function(x) all(floor(x) == x)
filter(mtcars, if_all(where(is_int), ~ .x != 0))

Filtering joins

Description

Filtering joins filter rows from x based on the presence or absence of matches in y:

• semi_join() return all rows from x with a match in y.

• anti_join() return all rows from x without a match in y.

Usage

semi_join(x, y, by = NULL, copy = FALSE, ...)

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

anti_join(x, y, by = NULL, copy = FALSE, ...)

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

Arguments

Value

An object of the same type as x. The output has the following properties:

• Rows are a subset of the input, but appear in the same order.

• Columns are not modified.

• Data frame attributes are preserved.

• Groups are taken from x. The number of groups may be reduced.

Methods

These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

• semi_join(): no methods found.

• anti_join(): no methods found.

See Also

Other joins: cross_join(), mutate-joins, nest_join()

Examples

# "Filtering" joins keep cases from the LHS
band_members %>% semi_join(band_instruments)
band_members %>% anti_join(band_instruments)

# To suppress the message about joining variables, supply `by`
band_members %>% semi_join(band_instruments, by = join_by(name))
# This is good practice in production code

Create a list of function calls

Description

funs() is deprecated; please use list() instead. We deprecated this function because it provided a unique way of specifying anonymous functions, rather than adopting the conventions used by purrr and other packages in the tidyverse.

Usage

funs(..., .args = list())

Arguments

Examples

funs("mean", mean(., na.rm = TRUE))
# ->
list(mean = mean, mean = ~ mean(.x, na.rm = TRUE))

funs(m1 = mean, m2 = "mean", m3 = mean(., na.rm = TRUE))
# ->
list(m1 = mean, m2 = "mean", m3 = ~ mean(.x, na.rm = TRUE))

Get a glimpse of your data

Description

glimpse() is like a transposed version of print(): columns run down the page, and data runs across. This makes it possible to see every column in a data frame. It's a little like str() applied to a data frame but it tries to show you as much data as possible. (And it always shows the underlying data, even when applied to a remote data source.)

glimpse() is provided by the pillar package, and re-exported by dplyr. See pillar::glimpse() for more details.

Value

x original x is (invisibly) returned, allowing glimpse() to be used within a data pipeline.

Examples

glimpse(mtcars)

# Note that original x is (invisibly) returned, allowing `glimpse()` to be
# used within a pipeline.
mtcars %>%
  glimpse() %>%
  select(1:3)

glimpse(starwars)

Group by one or more variables

Description

Most data operations are done on groups defined by variables. group_by() takes an existing tbl and converts it into a grouped tbl where operations are performed "by group". ungroup() removes grouping.

Usage

group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))

ungroup(x, ...)

Arguments

Value

A grouped data frame with class grouped_df, unless the combination of ... and add yields a empty set of grouping columns, in which case a tibble will be returned.

Methods

These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

• group_by(): no methods found.

• ungroup(): no methods found.

Ordering

Currently, group_by() internally orders the groups in ascending order. This results in ordered output from functions that aggregate groups, such as summarise().

When used as grouping columns, character vectors are ordered in the C locale for performance and reproducibility across R sessions. If the resulting ordering of your grouped operation matters and is dependent on the locale, you should follow up the grouped operation with an explicit call to arrange() and set the .locale argument. For example:

data %>%
  group_by(chr) %>%
  summarise(avg = mean(x)) %>%
  arrange(chr, .locale = "en")

This is often useful as a preliminary step before generating content intended for humans, such as an HTML table.

Legacy behavior

Prior to dplyr 1.1.0, character vector grouping columns were ordered in the system locale. If you need to temporarily revert to this behavior, you can set the global option dplyr.legacy_locale to TRUE, but this should be used sparingly and you should expect this option to be removed in a future version of dplyr. It is better to update existing code to explicitly call arrange(.locale = ) instead. Note that setting dplyr.legacy_locale will also force calls to arrange() to use the system locale.

See Also

Other grouping functions: group_map(), group_nest(), group_split(), group_trim()

Examples

by_cyl <- mtcars %>% group_by(cyl)

# grouping doesn't change how the data looks (apart from listing
# how it's grouped):
by_cyl

# It changes how it acts with the other dplyr verbs:
by_cyl %>% summarise(
  disp = mean(disp),
  hp = mean(hp)
)
by_cyl %>% filter(disp == max(disp))

# Each call to summarise() removes a layer of grouping
by_vs_am <- mtcars %>% group_by(vs, am)
by_vs <- by_vs_am %>% summarise(n = n())
by_vs
by_vs %>% summarise(n = sum(n))

# To removing grouping, use ungroup
by_vs %>%
  ungroup() %>%
  summarise(n = sum(n))

# By default, group_by() overrides existing grouping
by_cyl %>%
  group_by(vs, am) %>%
  group_vars()

# Use add = TRUE to instead append
by_cyl %>%
  group_by(vs, am, .add = TRUE) %>%
  group_vars()

# You can group by expressions: this is a short-hand
# for a mutate() followed by a group_by()
mtcars %>%
  group_by(vsam = vs + am)

# The implicit mutate() step is always performed on the
# ungrouped data. Here we get 3 groups:
mtcars %>%
  group_by(vs) %>%
  group_by(hp_cut = cut(hp, 3))

# If you want it to be performed by groups,
# you have to use an explicit mutate() call.
# Here we get 3 groups per value of vs
mtcars %>%
  group_by(vs) %>%
  mutate(hp_cut = cut(hp, 3)) %>%
  group_by(hp_cut)

# when factors are involved and .drop = FALSE, groups can be empty
tbl <- tibble(
  x = 1:10,
  y = factor(rep(c("a", "c"), each  = 5), levels = c("a", "b", "c"))
)
tbl %>%
  group_by(y, .drop = FALSE) %>%
  group_rows()

Group by a selection of variables

Description

Scoped verbs (⁠_if⁠, ⁠_at⁠, ⁠_all⁠) have been superseded by the use of pick() or across() in an existing verb. See vignette("colwise") for details.

These scoped variants of group_by() group a data frame by a selection of variables. Like group_by(), they have optional mutate semantics.

Usage

group_by_all(
  .tbl,
  .funs = list(),
  ...,
  .add = FALSE,
  .drop = group_by_drop_default(.tbl)
)

group_by_at(
  .tbl,
  .vars,
  .funs = list(),
  ...,
  .add = FALSE,
  .drop = group_by_drop_default(.tbl)
)

group_by_if(
  .tbl,
  .predicate,
  .funs = list(),
  ...,
  .add = FALSE,
  .drop = group_by_drop_default(.tbl)
)

Arguments

Grouping variables

Existing grouping variables are maintained, even if not included in the selection.

Examples

# Group a data frame by all variables:
group_by_all(mtcars)
# ->
mtcars %>% group_by(pick(everything()))

# Group by variables selected with a predicate:
group_by_if(iris, is.factor)
# ->
iris %>% group_by(pick(where(is.factor)))

# Group by variables selected by name:
group_by_at(mtcars, vars(vs, am))
# ->
mtcars %>% group_by(pick(vs, am))

# Like group_by(), the scoped variants have optional mutate
# semantics. This provide a shortcut for group_by() + mutate():
d <- tibble(x=c(1,1,2,2), y=c(1,2,1,2))
group_by_all(d, as.factor)
# ->
d %>% group_by(across(everything(), as.factor))

group_by_if(iris, is.factor, as.character)
# ->
iris %>% group_by(across(where(is.factor), as.character))

Default value for .drop argument of group_by

Description

Default value for .drop argument of group_by

Usage

group_by_drop_default(.tbl)

Arguments

Value

TRUE unless .tbl is a grouped data frame that was previously obtained by group_by(.drop = FALSE)

Examples

group_by_drop_default(iris)

iris %>%
  group_by(Species) %>%
  group_by_drop_default()

iris %>%
  group_by(Species, .drop = FALSE) %>%
  group_by_drop_default()

Select grouping variables

Description

This selection helpers matches grouping variables. It can be used in select() or vars() selections.

Usage

group_cols(vars = NULL, data = NULL)

Arguments

See Also

groups() and group_vars() for retrieving the grouping variables outside selection contexts.

Examples

gdf <- iris %>% group_by(Species)
gdf %>% select(group_cols())

# Remove the grouping variables from mutate selections:
gdf %>% mutate_at(vars(-group_cols()), `/`, 100)
# -> No longer necessary with across()
gdf %>% mutate(across(everything(), ~ . / 100))

Grouping metadata

Description

This collection of functions accesses data about grouped data frames in various ways:

• group_data() returns a data frame that defines the grouping structure. The columns give the values of the grouping variables. The last column, always called .rows, is a list of integer vectors that gives the location of the rows in each group.

• group_keys() returns a data frame describing the groups.

• group_rows() returns a list of integer vectors giving the rows that each group contains.

• group_indices() returns an integer vector the same length as .data that gives the group that each row belongs to.

• group_vars() gives names of grouping variables as character vector.

• groups() gives the names of the grouping variables as a list of symbols.

• group_size() gives the size of each group.

• n_groups() gives the total number of groups.

See context for equivalent functions that return values for the current group.

Usage

group_data(.data)

group_keys(.tbl, ...)

group_rows(.data)

group_indices(.data, ...)

group_vars(x)

groups(x)

group_size(x)

n_groups(x)

Arguments

Examples

df <- tibble(x = c(1,1,2,2))
group_vars(df)
group_rows(df)
group_data(df)
group_indices(df)

gf <- group_by(df, x)
group_vars(gf)
group_rows(gf)
group_data(gf)
group_indices(gf)

Apply a function to each group

Description

group_map(), group_modify() and group_walk() are purrr-style functions that can be used to iterate on grouped tibbles.

Usage

group_map(.data, .f, ..., .keep = FALSE)

group_modify(.data, .f, ..., .keep = FALSE)

group_walk(.data, .f, ..., .keep = FALSE)

Arguments

Details

Use group_modify() when summarize() is too limited, in terms of what you need to do and return for each group. group_modify() is good for "data frame in, data frame out". If that is too limited, you need to use a nested or split workflow. group_modify() is an evolution of do(), if you have used that before.

Each conceptual group of the data frame is exposed to the function .f with two pieces of information:

• The subset of the data for the group, exposed as .x.

• The key, a tibble with exactly one row and columns for each grouping variable, exposed as .y.

For completeness, group_modify(), group_map and group_walk() also work on ungrouped data frames, in that case the function is applied to the entire data frame (exposed as .x), and .y is a one row tibble with no column, consistently with group_keys().

Value

• group_modify() returns a grouped tibble. In that case .f must return a data frame.

• group_map() returns a list of results from calling .f on each group.

• group_walk() calls .f for side effects and returns the input .tbl, invisibly.

See Also

Other grouping functions: group_by(), group_nest(), group_split(), group_trim()

Examples

# return a list
mtcars %>%
  group_by(cyl) %>%
  group_map(~ head(.x, 2L))

# return a tibble grouped by `cyl` with 2 rows per group
# the grouping data is recalculated
mtcars %>%
  group_by(cyl) %>%
  group_modify(~ head(.x, 2L))


# a list of tibbles
iris %>%
  group_by(Species) %>%
  group_map(~ broom::tidy(lm(Petal.Length ~ Sepal.Length, data = .x)))

# a restructured grouped tibble
iris %>%
  group_by(Species) %>%
  group_modify(~ broom::tidy(lm(Petal.Length ~ Sepal.Length, data = .x)))


# a list of vectors
iris %>%
  group_by(Species) %>%
  group_map(~ quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)))

# to use group_modify() the lambda must return a data frame
iris %>%
  group_by(Species) %>%
  group_modify(~ {
     quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)) %>%
     tibble::enframe(name = "prob", value = "quantile")
  })

iris %>%
  group_by(Species) %>%
  group_modify(~ {
    .x %>%
      purrr::map_dfc(fivenum) %>%
      mutate(nms = c("min", "Q1", "median", "Q3", "max"))
  })

# group_walk() is for side effects
dir.create(temp <- tempfile())
iris %>%
  group_by(Species) %>%
  group_walk(~ write.csv(.x, file = file.path(temp, paste0(.y$Species, ".csv"))))
list.files(temp, pattern = "csv$")
unlink(temp, recursive = TRUE)

# group_modify() and ungrouped data frames
mtcars %>%
  group_modify(~ head(.x, 2L))

Nest a tibble using a grouping specification

Description

Nest a tibble using a grouping specification

Usage

group_nest(.tbl, ..., .key = "data", keep = FALSE)

Arguments

Value

A tbl with one row per unique combination of the grouping variables. The first columns are the grouping variables, followed by a list column of tibbles with matching rows of the remaining columns.

Lifecycle

group_nest() is not stable because tidyr::nest(.by =) provides very similar behavior. It may be deprecated in the future.

Grouped data frames

The primary use case for group_nest() is with already grouped data frames, typically a result of group_by(). In this case group_nest() only uses the first argument, the grouped tibble, and warns when ... is used.

Ungrouped data frames

When used on ungrouped data frames, group_nest() forwards the ... to group_by() before nesting, therefore the ... are subject to the data mask.

See Also

Other grouping functions: group_by(), group_map(), group_split(), group_trim()

Examples

#----- use case 1: a grouped data frame
iris %>%
  group_by(Species) %>%
  group_nest()

# this can be useful if the grouped data has been altered before nesting
iris %>%
  group_by(Species) %>%
  filter(Sepal.Length > mean(Sepal.Length)) %>%
  group_nest()

#----- use case 2: using group_nest() on a ungrouped data frame with
#                  a grouping specification that uses the data mask
starwars %>%
  group_nest(species, homeworld)

Split data frame by groups

Description

group_split() works like base::split() but:

• It uses the grouping structure from group_by() and therefore is subject to the data mask

• It does not name the elements of the list based on the grouping as this only works well for a single character grouping variable. Instead, use group_keys() to access a data frame that defines the groups.

group_split() is primarily designed to work with grouped data frames. You can pass ... to group and split an ungrouped data frame, but this is generally not very useful as you want have easy access to the group metadata.

Usage

group_split(.tbl, ..., .keep = TRUE)

Arguments

Value

A list of tibbles. Each tibble contains the rows of .tbl for the associated group and all the columns, including the grouping variables. Note that this returns a list_of which is slightly stricter than a simple list but is useful for representing lists where every element has the same type.

Lifecycle

group_split() is not stable because you can achieve very similar results by manipulating the nested column returned from tidyr::nest(.by =). That also retains the group keys all within a single data structure. group_split() may be deprecated in the future.

See Also

Other grouping functions: group_by(), group_map(), group_nest(), group_trim()

Examples

ir <- iris %>% group_by(Species)

group_split(ir)
group_keys(ir)

Trim grouping structure

Description

Drop unused levels of all factors that are used as grouping variables, then recalculates the grouping structure.

group_trim() is particularly useful after a filter() that is intended to select a subset of groups.

Usage

group_trim(.tbl, .drop = group_by_drop_default(.tbl))

Arguments

Value

A grouped data frame

See Also

Other grouping functions: group_by(), group_map(), group_nest(), group_split()

Examples

iris %>%
  group_by(Species) %>%
  filter(Species == "setosa", .preserve = TRUE) %>%
  group_trim()

A grouped data frame.

Description

The easiest way to create a grouped data frame is to call the group_by() method on a data frame or tbl: this will take care of capturing the unevaluated expressions for you.

These functions are designed for programmatic use. For data analysis purposes see group_data() for the accessor functions that retrieve various metadata from a grouped data frames.

Usage

grouped_df(data, vars, drop = group_by_drop_default(data))

is.grouped_df(x)

is_grouped_df(x)

Arguments

Flag a character vector as SQL identifiers

Description

ident() takes unquoted strings and flags them as identifiers. ident_q() assumes its input has already been quoted, and ensures it does not get quoted again. This is currently used only for schema.table.

Usage

ident(...)

Arguments

Examples

# Identifiers are escaped with "

ident("x")

Vectorised if-else

Description

if_else() is a vectorized if-else. Compared to the base R equivalent, ifelse(), this function allows you to handle missing values in the condition with missing and always takes true, false, and missing into account when determining what the output type should be.

Usage

if_else(condition, true, false, missing = NULL, ..., ptype = NULL, size = NULL)

Arguments

Value

A vector with the same size as condition and the same type as the common type of true, false, and missing.

Where condition is TRUE, the matching values from true, where it is FALSE, the matching values from false, and where it is NA, the matching values from missing, if provided, otherwise a missing value will be used.

Examples

x <- c(-5:5, NA)
if_else(x < 0, NA, x)

# Explicitly handle `NA` values in the `condition` with `missing`
if_else(x < 0, "negative", "positive", missing = "missing")

# Unlike `ifelse()`, `if_else()` preserves types
x <- factor(sample(letters[1:5], 10, replace = TRUE))
ifelse(x %in% c("a", "b", "c"), x, NA)
if_else(x %in% c("a", "b", "c"), x, NA)

# `if_else()` is often useful for creating new columns inside of `mutate()`
starwars %>%
  mutate(category = if_else(height < 100, "short", "tall"), .keep = "used")

Join specifications

Description

join_by() constructs a specification that describes how to join two tables using a small domain specific language. The result can be supplied as the by argument to any of the join functions (such as left_join()).

Usage

join_by(...)

Arguments

Join types

The following types of joins are supported by dplyr:

• Equality joins

• Inequality joins

• Rolling joins

• Overlap joins

• Cross joins

Equality, inequality, rolling, and overlap joins are discussed in more detail below. Cross joins are implemented through cross_join().

Equality joins

Equality joins require keys to be equal between one or more pairs of columns, and are the most common type of join. To construct an equality join using join_by(), supply two column names to join with separated by ==. Alternatively, supplying a single name will be interpreted as an equality join between two columns of the same name. For example, join_by(x) is equivalent to join_by(x == x).

Inequality joins

Inequality joins match on an inequality, such as >, >=, <, or <=, and are common in time series analysis and genomics. To construct an inequality join using join_by(), supply two column names separated by one of the above mentioned inequalities.

Note that inequality joins will match a single row in x to a potentially large number of rows in y. Be extra careful when constructing inequality join specifications!

Rolling joins

Rolling joins are a variant of inequality joins that limit the results returned from an inequality join condition. They are useful for "rolling" the closest match forward/backwards when there isn't an exact match. To construct a rolling join, wrap an inequality with closest().

• closest(expr)

  expr must be an inequality involving one of: >, >=, <, or <=.

  For example, closest(x >= y) is interpreted as: For each value in x, find the closest value in y that is less than or equal to that x value.

closest() will always use the left-hand table (x) as the primary table, and the right-hand table (y) as the one to find the closest match in, regardless of how the inequality is specified. For example, closest(y$a >= x$b) will always be interpreted as closest(x$b <= y$a).

Overlap joins

Overlap joins are a special case of inequality joins involving one or two columns from the left-hand table overlapping a range defined by two columns from the right-hand table. There are three helpers that join_by() recognizes to assist with constructing overlap joins, all of which can be constructed from simpler inequalities.

• between(x, y_lower, y_upper, ..., bounds = "[]")

  For each value in x, this finds everywhere that value falls between ⁠[y_lower, y_upper]⁠. Equivalent to ⁠x >= y_lower, x <= y_upper⁠ by default.

  bounds can be one of "[]", "[)", "(]", or "()" to alter the inclusiveness of the lower and upper bounds. This changes whether >= or > and <= or < are used to build the inequalities shown above.

  Dots are for future extensions and must be empty.

• within(x_lower, x_upper, y_lower, y_upper)

  For each range in ⁠[x_lower, x_upper]⁠, this finds everywhere that range falls completely within ⁠[y_lower, y_upper]⁠. Equivalent to ⁠x_lower >= y_lower, x_upper <= y_upper⁠.

  The inequalities used to build within() are the same regardless of the inclusiveness of the supplied ranges.

• overlaps(x_lower, x_upper, y_lower, y_upper, ..., bounds = "[]")

  For each range in ⁠[x_lower, x_upper]⁠, this finds everywhere that range overlaps ⁠[y_lower, y_upper]⁠ in any capacity. Equivalent to ⁠x_lower <= y_upper, x_upper >= y_lower⁠ by default.

  bounds can be one of "[]", "[)", "(]", or "()" to alter the inclusiveness of the lower and upper bounds. "[]" uses <= and >=, but the 3 other options use < and > and generate the exact same inequalities.

  Dots are for future extensions and must be empty.

These conditions assume that the ranges are well-formed and non-empty, i.e. x_lower <= x_upper when bounds are treated as "[]", and x_lower < x_upper otherwise.

Column referencing

When specifying join conditions, join_by() assumes that column names on the left-hand side of the condition refer to the left-hand table (x), and names on the right-hand side of the condition refer to the right-hand table (y). Occasionally, it is clearer to be able to specify a right-hand table name on the left-hand side of the condition, and vice versa. To support this, column names can be prefixed by ⁠x$⁠ or ⁠y$⁠ to explicitly specify which table they come from.

Examples

sales <- tibble(
  id = c(1L, 1L, 1L, 2L, 2L),
  sale_date = as.Date(c("2018-12-31", "2019-01-02", "2019-01-05", "2019-01-04", "2019-01-01"))
)
sales

promos <- tibble(
  id = c(1L, 1L, 2L),
  promo_date = as.Date(c("2019-01-01", "2019-01-05", "2019-01-02"))
)
promos

# Match `id` to `id`, and `sale_date` to `promo_date`
by <- join_by(id, sale_date == promo_date)
left_join(sales, promos, by)

# For each `sale_date` within a particular `id`,
# find all `promo_date`s that occurred before that particular sale
by <- join_by(id, sale_date >= promo_date)
left_join(sales, promos, by)

# For each `sale_date` within a particular `id`,
# find only the closest `promo_date` that occurred before that sale
by <- join_by(id, closest(sale_date >= promo_date))
left_join(sales, promos, by)

# If you want to disallow exact matching in rolling joins, use `>` rather
# than `>=`. Note that the promo on `2019-01-05` is no longer considered the
# closest match for the sale on the same date.
by <- join_by(id, closest(sale_date > promo_date))
left_join(sales, promos, by)

# Same as before, but also require that the promo had to occur at most 1
# day before the sale was made. We'll use a full join to see that id 2's
# promo on `2019-01-02` is no longer matched to the sale on `2019-01-04`.
sales <- mutate(sales, sale_date_lower = sale_date - 1)
by <- join_by(id, closest(sale_date >= promo_date), sale_date_lower <= promo_date)
full_join(sales, promos, by)

# ---------------------------------------------------------------------------

segments <- tibble(
  segment_id = 1:4,
  chromosome = c("chr1", "chr2", "chr2", "chr1"),
  start = c(140, 210, 380, 230),
  end = c(150, 240, 415, 280)
)
segments

reference <- tibble(
  reference_id = 1:4,
  chromosome = c("chr1", "chr1", "chr2", "chr2"),
  start = c(100, 200, 300, 415),
  end = c(150, 250, 399, 450)
)
reference

# Find every time a segment `start` falls between the reference
# `[start, end]` range.
by <- join_by(chromosome, between(start, start, end))
full_join(segments, reference, by)

# If you wanted the reference columns first, supply `reference` as `x`
# and `segments` as `y`, then explicitly refer to their columns using `x$`
# and `y$`.
by <- join_by(chromosome, between(y$start, x$start, x$end))
full_join(reference, segments, by)

# Find every time a segment falls completely within a reference.
# Sometimes using `x$` and `y$` makes your intentions clearer, even if they
# match the default behavior.
by <- join_by(chromosome, within(x$start, x$end, y$start, y$end))
inner_join(segments, reference, by)

# Find every time a segment overlaps a reference in any way.
by <- join_by(chromosome, overlaps(x$start, x$end, y$start, y$end))
full_join(segments, reference, by)

# It is common to have right-open ranges with bounds like `[)`, which would
# mean an end value of `415` would no longer overlap a start value of `415`.
# Setting `bounds` allows you to compute overlaps with those kinds of ranges.
by <- join_by(chromosome, overlaps(x$start, x$end, y$start, y$end, bounds = "[)"))
full_join(segments, reference, by)

Show warnings from the last command

Description

Warnings that occur inside a dplyr verb like mutate() are caught and stashed away instead of being emitted to the console. This prevents rowwise and grouped data frames from flooding the console with warnings. To see the original warnings, use last_dplyr_warnings().

Usage

last_dplyr_warnings(n = 5)

Arguments

Compute lagged or leading values

Description

Find the "previous" (lag()) or "next" (lead()) values in a vector. Useful for comparing values behind of or ahead of the current values.

Usage

lag(x, n = 1L, default = NULL, order_by = NULL, ...)

lead(x, n = 1L, default = NULL, order_by = NULL, ...)

Arguments

Value

A vector with the same type and size as x.

Examples

lag(1:5)
lead(1:5)

x <- 1:5
tibble(behind = lag(x), x, ahead = lead(x))

# If you want to look more rows behind or ahead, use `n`
lag(1:5, n = 1)
lag(1:5, n = 2)

lead(1:5, n = 1)
lead(1:5, n = 2)

# If you want to define a value to pad with, use `default`
lag(1:5)
lag(1:5, default = 0)

lead(1:5)
lead(1:5, default = 6)

# If the data are not already ordered, use `order_by`
scrambled <- slice_sample(
  tibble(year = 2000:2005, value = (0:5) ^ 2),
  prop = 1
)

wrong <- mutate(scrambled, previous_year_value = lag(value))
arrange(wrong, year)

right <- mutate(scrambled, previous_year_value = lag(value, order_by = year))
arrange(right, year)

Create a "tbl" object

Description

tbl() is the standard constructor for tbls. as.tbl() coerces, and is.tbl() tests.

Usage

make_tbl(subclass, ...)

Arguments

Create, modify, and delete columns

Description

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

mutate(.data, ...)

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

Arguments

Value

An object of the same type as .data. The output has the following properties:

• Columns from .data will be preserved according to the .keep argument.

• Existing columns that are modified by ... will always be returned in their original location.

• New columns created through ... will be placed according to the .before and .after arguments.

• The number of rows is not affected.

• Columns given the value NULL will be removed.

• Groups will be recomputed if a grouping variable is mutated.

• Data frame attributes are preserved.

Useful mutate functions

• +, -, log(), etc., for their usual mathematical meanings

• lead(), lag()

• dense_rank(), min_rank(), percent_rank(), row_number(), cume_dist(), ntile()

• cumsum(), cummean(), cummin(), cummax(), cumany(), cumall()

• na_if(), coalesce()

• if_else(), recode(), case_when()

Grouped tibbles

Because mutating expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped mutate:

starwars %>%
  select(name, mass, species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

With the grouped equivalent:

starwars %>%
  select(name, mass, species) %>%
  group_by(species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

The former normalises mass by the global average whereas the latter normalises by the averages within species levels.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages: no methods found.

See Also

Other single table verbs: arrange(), filter(), reframe(), rename(), select(), slice(), summarise()

Examples

# Newly created variables are available immediately
starwars %>%
  select(name, mass) %>%
  mutate(
    mass2 = mass * 2,
    mass2_squared = mass2 * mass2
  )

# As well as adding new variables, you can use mutate() to
# remove variables and modify existing variables.
starwars %>%
  select(name, height, mass, homeworld) %>%
  mutate(
    mass = NULL,
    height = height * 0.0328084 # convert to feet
  )

# Use across() with mutate() to apply a transformation
# to multiple columns in a tibble.
starwars %>%
  select(name, homeworld, species) %>%
  mutate(across(!name, as.factor))
# see more in ?across

# Window functions are useful for grouped mutates:
starwars %>%
  select(name, mass, homeworld) %>%
  group_by(homeworld) %>%
  mutate(rank = min_rank(desc(mass)))
# see `vignette("window-functions")` for more details

# By default, new columns are placed on the far right.
df <- tibble(x = 1, y = 2)
df %>% mutate(z = x + y)
df %>% mutate(z = x + y, .before = 1)
df %>% mutate(z = x + y, .after = x)

# By default, mutate() keeps all columns from the input data.
df <- tibble(x = 1, y = 2, a = "a", b = "b")
df %>% mutate(z = x + y, .keep = "all") # the default
df %>% mutate(z = x + y, .keep = "used")
df %>% mutate(z = x + y, .keep = "unused")
df %>% mutate(z = x + y, .keep = "none")

# Grouping ----------------------------------------
# The mutate operation may yield different results on grouped
# tibbles because the expressions are computed within groups.
# The following normalises `mass` by the global average:
starwars %>%
  select(name, mass, species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

# Whereas this normalises `mass` by the averages within species
# levels:
starwars %>%
  select(name, mass, species) %>%
  group_by(species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

# Indirection ----------------------------------------
# Refer to column names stored as strings with the `.data` pronoun:
vars <- c("mass", "height")
mutate(starwars, prod = .data[[vars[[1]]]] * .data[[vars[[2]]]])
# Learn more in ?rlang::args_data_masking

Mutate multiple columns

Description

Scoped verbs (⁠_if⁠, ⁠_at⁠, ⁠_all⁠) have been superseded by the use of pick() or across() in an existing verb. See vignette("colwise") for details.

The scoped variants of mutate() and transmute() make it easy to apply the same transformation to multiple variables. There are three variants:

• _all affects every variable

• _at affects variables selected with a character vector or vars()

• _if affects variables selected with a predicate function:

Usage

mutate_all(.tbl, .funs, ...)

mutate_if(.tbl, .predicate, .funs, ...)

mutate_at(.tbl, .vars, .funs, ..., .cols = NULL)

transmute_all(.tbl, .funs, ...)

transmute_if(.tbl, .predicate, .funs, ...)

transmute_at(.tbl, .vars, .funs, ..., .cols = NULL)

Arguments

Value

A data frame. By default, the newly created columns have the shortest names needed to uniquely identify the output. To force inclusion of a name, even when not needed, name the input (see examples for details).

Grouping variables

If applied on a grouped tibble, these operations are not applied to the grouping variables. The behaviour depends on whether the selection is implicit (all and if selections) or explicit (at selections).

• Grouping variables covered by explicit selections in mutate_at() and transmute_at() are always an error. Add -group_cols() to the vars() selection to avoid this:

  data %>% mutate_at(vars(-group_cols(), ...), myoperation)
  

  Or remove group_vars() from the character vector of column names:

  nms <- setdiff(nms, group_vars(data))
  data %>% mutate_at(vars, myoperation)
  

• Grouping variables covered by implicit selections are ignored by mutate_all(), transmute_all(), mutate_if(), and transmute_if().

Naming

The names of the new columns are derived from the names of the input variables and the names of the functions.

• if there is only one unnamed function (i.e. if .funs is an unnamed list of length one), the names of the input variables are used to name the new columns;

• for ⁠_at⁠ functions, if there is only one unnamed variable (i.e., if .vars is of the form vars(a_single_column)) and .funs has length greater than one, the names of the functions are used to name the new columns;

• otherwise, the new names are created by concatenating the names of the input variables and the names of the functions, separated with an underscore "_".

The .funs argument can be a named or unnamed list. If a function is unnamed and the name cannot be derived automatically, a name of the form "fn#" is used. Similarly, vars() accepts named and unnamed arguments. If a variable in .vars is named, a new column by that name will be created.

Name collisions in the new columns are disambiguated using a unique suffix.

See Also

The other scoped verbs, vars()

Examples

iris <- as_tibble(iris)

# All variants can be passed functions and additional arguments,
# purrr-style. The _at() variants directly support strings. Here
# we'll scale the variables `height` and `mass`:
scale2 <- function(x, na.rm = FALSE) (x - mean(x, na.rm = na.rm)) / sd(x, na.rm)
starwars %>% mutate_at(c("height", "mass"), scale2)
# ->
starwars %>% mutate(across(c("height", "mass"), scale2))

# You can pass additional arguments to the function:
starwars %>% mutate_at(c("height", "mass"), scale2, na.rm = TRUE)
starwars %>% mutate_at(c("height", "mass"), ~scale2(., na.rm = TRUE))
# ->
starwars %>% mutate(across(c("height", "mass"), ~ scale2(.x, na.rm = TRUE)))

# You can also supply selection helpers to _at() functions but you have
# to quote them with vars():
iris %>% mutate_at(vars(matches("Sepal")), log)
iris %>% mutate(across(matches("Sepal"), log))

# The _if() variants apply a predicate function (a function that
# returns TRUE or FALSE) to determine the relevant subset of
# columns. Here we divide all the numeric columns by 100:
starwars %>% mutate_if(is.numeric, scale2, na.rm = TRUE)
starwars %>% mutate(across(where(is.numeric), ~ scale2(.x, na.rm = TRUE)))

# mutate_if() is particularly useful for transforming variables from
# one type to another
iris %>% mutate_if(is.factor, as.character)
iris %>% mutate_if(is.double, as.integer)
# ->
iris %>% mutate(across(where(is.factor), as.character))
iris %>% mutate(across(where(is.double), as.integer))

# Multiple transformations ----------------------------------------

# If you want to apply multiple transformations, pass a list of
# functions. When there are multiple functions, they create new
# variables instead of modifying the variables in place:
iris %>% mutate_if(is.numeric, list(scale2, log))
iris %>% mutate_if(is.numeric, list(~scale2(.), ~log(.)))
iris %>% mutate_if(is.numeric, list(scale = scale2, log = log))
# ->
iris %>%
  as_tibble() %>%
  mutate(across(where(is.numeric), list(scale = scale2, log = log)))

# When there's only one function in the list, it modifies existing
# variables in place. Give it a name to instead create new variables:
iris %>% mutate_if(is.numeric, list(scale2))
iris %>% mutate_if(is.numeric, list(scale = scale2))

Mutating joins

Description

Mutating joins add columns from y to x, matching observations based on the keys. There are four mutating joins: the inner join, and the three outer joins.

Inner join

An inner_join() only keeps observations from x that have a matching key in y.

The most important property of an inner join is that unmatched rows in either input are not included in the result. This means that generally inner joins are not appropriate in most analyses, because it is too easy to lose observations.

Outer joins

The three outer joins keep observations that appear in at least one of the data frames:

• A left_join() keeps all observations in x.

• A right_join() keeps all observations in y.

• A full_join() keeps all observations in x and y.

Usage

inner_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL
)

## S3 method for class 'data.frame'
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
)

left_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL
)

## S3 method for class 'data.frame'
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
)

right_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL
)

## S3 method for class 'data.frame'
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
)

full_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL
)

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

Arguments

Value

An object of the same type as x (including the same groups). The order of the rows and columns of x is preserved as much as possible. The output has the following properties:

• The rows are affect by the join type.

  • inner_join() returns matched x rows.

  • left_join() returns all x rows.

  • right_join() returns matched of x rows, followed by unmatched y rows.

  • full_join() returns all x rows, followed by unmatched y rows.

• Output columns include all columns from x and all non-key columns from y. If keep = TRUE, the key columns from y are included as well.

• If non-key columns in x and y have the same name, suffixes are added to disambiguate. If keep = TRUE and key columns in x and y have the same name, suffixes are added to disambiguate these as well.

• If keep = FALSE, output columns included in by are coerced to their common type between x and y.

Many-to-many relationships

By default, dplyr guards against many-to-many relationships in equality joins by throwing a warning. These occur when both of the following are true:

• A row in x matches multiple rows in y.

• A row in y matches multiple rows in x.

This is typically surprising, as most joins involve a relationship of one-to-one, one-to-many, or many-to-one, and is often the result of an improperly specified join. Many-to-many relationships are particularly problematic because they can result in a Cartesian explosion of the number of rows returned from the join.

If a many-to-many relationship is expected, silence this warning by explicitly setting relationship = "many-to-many".

In production code, it is best to preemptively set relationship to whatever relationship you expect to exist between the keys of x and y, as this forces an error to occur immediately if the data doesn't align with your expectations.

Inequality joins typically result in many-to-many relationships by nature, so they don't warn on them by default, but you should still take extra care when specifying an inequality join, because they also have the capability to return a large number of rows.

Rolling joins don't warn on many-to-many relationships either, but many rolling joins follow a many-to-one relationship, so it is often useful to set relationship = "many-to-one" to enforce this.

Note that in SQL, most database providers won't let you specify a many-to-many relationship between two tables, instead requiring that you create a third junction table that results in two one-to-many relationships instead.

Methods

These functions are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

• inner_join(): no methods found.

• left_join(): no methods found.

• right_join(): no methods found.

• full_join(): no methods found.

See Also

Other joins: cross_join(), filter-joins, nest_join()

Examples

band_members %>% inner_join(band_instruments)
band_members %>% left_join(band_instruments)
band_members %>% right_join(band_instruments)
band_members %>% full_join(band_instruments)

# To suppress the message about joining variables, supply `by`
band_members %>% inner_join(band_instruments, by = join_by(name))
# This is good practice in production code

# Use an equality expression if the join variables have different names
band_members %>% full_join(band_instruments2, by = join_by(name == artist))
# By default, the join keys from `x` and `y` are coalesced in the output; use
# `keep = TRUE` to keep the join keys from both `x` and `y`
band_members %>%
  full_join(band_instruments2, by = join_by(name == artist), keep = TRUE)

# If a row in `x` matches multiple rows in `y`, all the rows in `y` will be
# returned once for each matching row in `x`.
df1 <- tibble(x = 1:3)
df2 <- tibble(x = c(1, 1, 2), y = c("first", "second", "third"))
df1 %>% left_join(df2)

# If a row in `y` also matches multiple rows in `x`, this is known as a
# many-to-many relationship, which is typically a result of an improperly
# specified join or some kind of messy data. In this case, a warning is
# thrown by default:
df3 <- tibble(x = c(1, 1, 1, 3))
df3 %>% left_join(df2)

# In the rare case where a many-to-many relationship is expected, set
# `relationship = "many-to-many"` to silence this warning
df3 %>% left_join(df2, relationship = "many-to-many")

# Use `join_by()` with a condition other than `==` to perform an inequality
# join. Here we match on every instance where `df1$x > df2$x`.
df1 %>% left_join(df2, join_by(x > x))

# By default, NAs match other NAs so that there are two
# rows in the output of this join:
df1 <- data.frame(x = c(1, NA), y = 2)
df2 <- data.frame(x = c(1, NA), z = 3)
left_join(df1, df2)

# You can optionally request that NAs don't match, giving a
# a result that more closely resembles SQL joins
left_join(df1, df2, na_matches = "never")

Count unique combinations

Description

n_distinct() counts the number of unique/distinct combinations in a set of one or more vectors. It's a faster and more concise equivalent to nrow(unique(data.frame(...))).

Usage

n_distinct(..., na.rm = FALSE)

Arguments

Value

A single number.

Examples

x <- c(1, 1, 2, 2, 2)
n_distinct(x)

y <- c(3, 3, NA, 3, 3)
n_distinct(y)
n_distinct(y, na.rm = TRUE)

# Pairs (1, 3), (2, 3), and (2, NA) are distinct
n_distinct(x, y)

# (2, NA) is dropped, leaving 2 distinct combinations
n_distinct(x, y, na.rm = TRUE)

# Also works with data frames
n_distinct(data.frame(x, y))

Convert values to NA

Description

This is a translation of the SQL command NULLIF. It is useful if you want to convert an annoying value to NA.

Usage

na_if(x, y)

Arguments

Value

A modified version of x that replaces any values that are equal to y with NA.

See Also

coalesce() to replace missing values with a specified value.

tidyr::replace_na() to replace NA with a value.

Examples

na_if(1:5, 5:1)

x <- c(1, -1, 0, 10)
100 / x
100 / na_if(x, 0)

y <- c("abc", "def", "", "ghi")
na_if(y, "")

# `na_if()` allows you to replace `NaN` with `NA`,
# even though `NaN == NaN` returns `NA`
z <- c(1, NaN, NA, 2, NaN)
na_if(z, NaN)

# `na_if()` is particularly useful inside `mutate()`,
# and is meant for use with vectors rather than entire data frames
starwars %>%
  select(name, eye_color) %>%
  mutate(eye_color = na_if(eye_color, "unknown"))

# `na_if()` can also be used with `mutate()` and `across()`
# to alter multiple columns
starwars %>%
   mutate(across(where(is.character), ~na_if(., "unknown")))

Compare two numeric vectors

Description

This is a safe way of comparing if two vectors of floating point numbers are (pairwise) equal. This is safer than using ==, because it has a built in tolerance

Usage

near(x, y, tol = .Machine$double.eps^0.5)

Arguments

Examples

sqrt(2) ^ 2 == 2
near(sqrt(2) ^ 2, 2)

Nest by one or more variables

Description

nest_by() is closely related to group_by(). However, instead of storing the group structure in the metadata, it is made explicit in the data, giving each group key a single row along with a list-column of data frames that contain all the other data.

nest_by() returns a rowwise data frame, which makes operations on the grouped data particularly elegant. See vignette("rowwise") for more details.

Usage

nest_by(.data, ..., .key = "data", .keep = FALSE)

Arguments

Details

Note that df %>% nest_by(x, y) is roughly equivalent to

df %>%
  group_by(x, y) %>%
  summarise(data = list(pick(everything()))) %>%
  rowwise()

If you want to unnest a nested data frame, you can either use tidyr::unnest() or take advantage of reframe()s multi-row behaviour:

nested %>%
  reframe(data)

Value

A rowwise data frame. The output has the following properties:

• The rows come from the underlying group_keys().

• The columns are the grouping keys plus one list-column of data frames.

• Data frame attributes are not preserved, because nest_by() fundamentally creates a new data frame.

A tbl with one row per unique combination of the grouping variables. The first columns are the grouping variables, followed by a list column of tibbles with matching rows of the remaining columns.

Lifecycle

nest_by() is not stable because tidyr::nest(.by =) provides very similar behavior. It may be deprecated in the future.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

Examples

# After nesting, you get one row per group
iris %>% nest_by(Species)
starwars %>% nest_by(species)

# The output is grouped by row, which makes modelling particularly easy
models <- mtcars %>%
  nest_by(cyl) %>%
  mutate(model = list(lm(mpg ~ wt, data = data)))
models

models %>% summarise(rsq = summary(model)$r.squared)


# This is particularly elegant with the broom functions
models %>% summarise(broom::glance(model))
models %>% reframe(broom::tidy(model))


# Note that you can also `reframe()` to unnest the data
models %>% reframe(data)

Nest join

Description

A nest join leaves x almost unchanged, except that it adds a new list-column, where each element contains the rows from y that match the corresponding row in x.

Usage

nest_join(x, y, by = NULL, copy = FALSE, keep = NULL, name = NULL, ...)

## S3 method for class 'data.frame'
nest_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  keep = NULL,
  name = NULL,
  ...,
  na_matches = c("na", "never"),
  unmatched = "drop"
)

Arguments

Value

The output:

• Is same type as x (including having the same groups).

• Has exactly the same number of rows as x.

• Contains all the columns of x in the same order with the same values. They are only modified (slightly) if keep = FALSE, when columns listed in by will be coerced to their common type across x and y.

• Gains one new column called {name} on the far right, a list column containing data frames the same type as y.

Relationship to other joins

You can recreate many other joins from the result of a nest join:

• inner_join() is a nest_join() plus tidyr::unnest().

• left_join() is a nest_join() plus tidyr::unnest(keep_empty = TRUE).

• semi_join() is a nest_join() plus a filter() where you check that every element of data has at least one row.

• anti_join() is a nest_join() plus a filter() where you check that every element has zero rows.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

Other joins: cross_join(), filter-joins, mutate-joins

Examples

df1 <- tibble(x = 1:3)
df2 <- tibble(x = c(2, 3, 3), y = c("a", "b", "c"))

out <- nest_join(df1, df2)
out
out$df2

Low-level construction and validation for the grouped_df and rowwise_df classes

Description

new_grouped_df() and new_rowwise_df() are constructors designed to be high-performance so only check types, not values. This means it is the caller's responsibility to create valid values, and hence this is for expert use only.

validate_grouped_df() and validate_rowwise_df() validate the attributes of a grouped_df or a rowwise_df.

Usage

new_grouped_df(x, groups, ..., class = character())

validate_grouped_df(x, check_bounds = FALSE)

new_rowwise_df(data, group_data = NULL, ..., class = character())

validate_rowwise_df(x)

Arguments

Examples

# 5 bootstrap samples
tbl <- new_grouped_df(
  tibble(x = rnorm(10)),
  groups = tibble(".rows" := replicate(5, sample(1:10, replace = TRUE), simplify = FALSE))
)
# mean of each bootstrap sample
summarise(tbl, x = mean(x))

Extract the first, last, or nth value from a vector

Description

These are useful helpers for extracting a single value from a vector. They are guaranteed to return a meaningful value, even when the input is shorter than expected. You can also provide an optional secondary vector that defines the ordering.

Usage

nth(x, n, order_by = NULL, default = NULL, na_rm = FALSE)

first(x, order_by = NULL, default = NULL, na_rm = FALSE)

last(x, order_by = NULL, default = NULL, na_rm = FALSE)

Arguments

Details

For most vector types, first(x), last(x), and nth(x, n) work like x[[1]], ⁠x[[length(x)]⁠, and x[[n]], respectively. The primary exception is data frames, where they instead retrieve rows, i.e. x[1, ], x[nrow(x), ], and x[n, ]. This is consistent with the tidyverse/vctrs principle which treats data frames as a vector of rows, rather than a vector of columns.

Value

If x is a list, a single element from that list. Otherwise, a vector the same type as x with size 1.

Examples

x <- 1:10
y <- 10:1

first(x)
last(y)

nth(x, 1)
nth(x, 5)
nth(x, -2)

# `first()` and `last()` are often useful in `summarise()`
df <- tibble(x = x, y = y)
df %>%
  summarise(
    across(x:y, first, .names = "{col}_first"),
    y_last = last(y)
  )

# Selecting a position that is out of bounds returns a default value
nth(x, 11)
nth(x, 0)

# This out of bounds behavior also applies to empty vectors
first(integer())

# You can customize the default value with `default`
nth(x, 11, default = -1L)
first(integer(), default = 0L)

# `order_by` provides optional ordering
last(x)
last(x, order_by = y)

# `na_rm` removes missing values before extracting the value
z <- c(NA, NA, 1, 3, NA, 5, NA)
first(z)
first(z, na_rm = TRUE)
last(z, na_rm = TRUE)
nth(z, 3, na_rm = TRUE)

# For data frames, these select entire rows
df <- tibble(a = 1:5, b = 6:10)
first(df)
nth(df, 4)

Bucket a numeric vector into n groups

Description

ntile() is a sort of very rough rank, which breaks the input vector into n buckets. If length(x) is not an integer multiple of n, the size of the buckets will differ by up to one, with larger buckets coming first.

Unlike other ranking functions, ntile() ignores ties: it will create evenly sized buckets even if the same value of x ends up in different buckets.

Usage

ntile(x = row_number(), n)

Arguments

See Also

Other ranking functions: percent_rank(), row_number()

Examples

x <- c(5, 1, 3, 2, 2, NA)
ntile(x, 2)
ntile(x, 4)

# If the bucket sizes are uneven, the larger buckets come first
ntile(1:8, 3)

# Ties are ignored
ntile(rep(1, 8), 3)

A helper function for ordering window function output

Description

This function makes it possible to control the ordering of window functions in R that don't have a specific ordering parameter. When translated to SQL it will modify the order clause of the OVER function.

Usage

order_by(order_by, call)

Arguments

Details

This function works by changing the call to instead call with_order() with the appropriate arguments.

Examples

order_by(10:1, cumsum(1:10))
x <- 10:1
y <- 1:10
order_by(x, cumsum(y))

df <- data.frame(year = 2000:2005, value = (0:5) ^ 2)
scrambled <- df[sample(nrow(df)), ]

wrong <- mutate(scrambled, running = cumsum(value))
arrange(wrong, year)

right <- mutate(scrambled, running = order_by(year, cumsum(value)))
arrange(right, year)

Proportional ranking functions

Description

These two ranking functions implement two slightly different ways to compute a percentile. For each x_i in x:

• cume_dist(x) counts the total number of values less than or equal to x_i, and divides it by the number of observations.

• percent_rank(x) counts the total number of values less than x_i, and divides it by the number of observations minus 1.

In both cases, missing values are ignored when counting the number of observations.

Usage

percent_rank(x)

cume_dist(x)

Arguments

Value

A numeric vector containing a proportion.

See Also

Other ranking functions: ntile(), row_number()

Examples

x <- c(5, 1, 3, 2, 2)

cume_dist(x)
percent_rank(x)

# You can understand what's going on by computing it by hand
sapply(x, function(xi) sum(x <= xi) / length(x))
sapply(x, function(xi) sum(x < xi)  / (length(x) - 1))
# The real computations are a little more complex in order to
# correctly deal with missing values

Select a subset of columns

Description

pick() provides a way to easily select a subset of columns from your data using select() semantics while inside a "data-masking" function like mutate() or summarise(). pick() returns a data frame containing the selected columns for the current group.

pick() is complementary to across():

• With pick(), you typically apply a function to the full data frame.

• With across(), you typically apply a function to each column.

Usage

pick(...)

Arguments

Details

Theoretically, pick() is intended to be replaceable with an equivalent call to tibble(). For example, pick(a, c) could be replaced with tibble(a = a, c = c), and pick(everything()) on a data frame with cols a, b, and c could be replaced with tibble(a = a, b = b, c = c). pick() specially handles the case of an empty selection by returning a 1 row, 0 column tibble, so an exact replacement is more like:

size <- vctrs::vec_size_common(..., .absent = 1L)
out <- vctrs::vec_recycle_common(..., .size = size)
tibble::new_tibble(out, nrow = size)

Value

A tibble containing the selected columns for the current group.

See Also

across()

Examples

df <- tibble(
  x = c(3, 2, 2, 2, 1),
  y = c(0, 2, 1, 1, 4),
  z1 = c("a", "a", "a", "b", "a"),
  z2 = c("c", "d", "d", "a", "c")
)
df

# `pick()` provides a way to select a subset of your columns using
# tidyselect. It returns a data frame.
df %>% mutate(cols = pick(x, y))

# This is useful for functions that take data frames as inputs.
# For example, you can compute a joint rank between `x` and `y`.
df %>% mutate(rank = dense_rank(pick(x, y)))

# `pick()` is also useful as a bridge between data-masking functions (like
# `mutate()` or `group_by()`) and functions with tidy-select behavior (like
# `select()`). For example, you can use `pick()` to create a wrapper around
# `group_by()` that takes a tidy-selection of columns to group on. For more
# bridge patterns, see
# https://rlang.r-lib.org/reference/topic-data-mask-programming.html#bridge-patterns.
my_group_by <- function(data, cols) {
  group_by(data, pick({{ cols }}))
}

df %>% my_group_by(c(x, starts_with("z")))

# Or you can use it to dynamically select columns to `count()` by
df %>% count(pick(starts_with("z")))

Progress bar with estimated time.

Description

This progress bar has been deprecated since providing progress bars is not the responsibility of dplyr. Instead, you might try the more powerful progress package.

This reference class represents a text progress bar displayed estimated time remaining. When finished, it displays the total duration. The automatic progress bar can be disabled by setting option dplyr.show_progress to FALSE.

Usage

progress_estimated(n, min_time = 0)

Arguments

Value

A ref class with methods tick(), print(), pause(), and stop().

Examples

p <- progress_estimated(3)
p$tick()
p$tick()
p$tick()

p <- progress_estimated(3)
for (i in 1:3) p$pause(0.1)$tick()$print()

p <- progress_estimated(3)
p$tick()$print()$
 pause(1)$stop()

# If min_time is set, progress bar not shown until that many
# seconds have elapsed
p <- progress_estimated(3, min_time = 3)
for (i in 1:3) p$pause(0.1)$tick()$print()

## Not run: 
p <- progress_estimated(10, min_time = 3)
for (i in 1:10) p$pause(0.5)$tick()$print()

## End(Not run)

Extract a single column

Description

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

pull(.data, var = -1, name = NULL, ...)

Arguments

Value

A vector the same size as .data.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

Examples

mtcars %>% pull(-1)
mtcars %>% pull(1)
mtcars %>% pull(cyl)


# Also works for remote sources
df <- dbplyr::memdb_frame(x = 1:10, y = 10:1, .name = "pull-ex")
df %>%
  mutate(z = x * y) %>%
  pull()


# Pull a named vector
starwars %>% pull(height, name)

Recode values

Description

recode() is superseded in favor of case_match(), which handles the most important cases of recode() with a more elegant interface. recode_factor() is also superseded, however, its direct replacement is not currently available but will eventually live in forcats. For creating new variables based on logical vectors, use if_else(). For even more complicated criteria, use case_when().

recode() is a vectorised version of switch(): you can replace numeric values based on their position or their name, and character or factor values only by their name. This is an S3 generic: dplyr provides methods for numeric, character, and factors. You can use recode() directly with factors; it will preserve the existing order of levels while changing the values. Alternatively, you can use recode_factor(), which will change the order of levels to match the order of replacements.

Usage

recode(.x, ..., .default = NULL, .missing = NULL)

recode_factor(.x, ..., .default = NULL, .missing = NULL, .ordered = FALSE)

Arguments

Value

A vector the same length as .x, and the same type as the first of ..., .default, or .missing. recode_factor() returns a factor whose levels are in the same order as in .... The levels in .default and .missing come last.

See Also

na_if() to replace specified values with a NA.

coalesce() to replace missing values with a specified value.

tidyr::replace_na() to replace NA with a value.

Examples

char_vec <- sample(c("a", "b", "c"), 10, replace = TRUE)

# `recode()` is superseded by `case_match()`
recode(char_vec, a = "Apple", b = "Banana")
case_match(char_vec, "a" ~ "Apple", "b" ~ "Banana", .default = char_vec)

# With `case_match()`, you don't need typed missings like `NA_character_`
recode(char_vec, a = "Apple", b = "Banana", .default = NA_character_)
case_match(char_vec, "a" ~ "Apple", "b" ~ "Banana", .default = NA)

# Throws an error as `NA` is logical, not character.
try(recode(char_vec, a = "Apple", b = "Banana", .default = NA))

# `case_match()` is easier to use with numeric vectors, because you don't
# need to turn the numeric values into names
num_vec <- c(1:4, NA)
recode(num_vec, `2` = 20L, `4` = 40L)
case_match(num_vec, 2 ~ 20, 4 ~ 40, .default = num_vec)

# `case_match()` doesn't have the ability to match by position like
# `recode()` does with numeric vectors
recode(num_vec, "a", "b", "c", "d")
recode(c(1,5,3), "a", "b", "c", "d", .default = "nothing")

# For `case_match()`, incompatible types are an error rather than a warning
recode(num_vec, `2` = "b", `4` = "d")
try(case_match(num_vec, 2 ~ "b", 4 ~ "d", .default = num_vec))

# The factor method of `recode()` can generally be replaced with
# `forcats::fct_recode()`
factor_vec <- factor(c("a", "b", "c"))
recode(factor_vec, a = "Apple")

# `recode_factor()` does not currently have a direct replacement, but we
# plan to add one to forcats. In the meantime, you can use the `.ptype`
# argument to `case_match()`.
recode_factor(
  num_vec,
  `1` = "z",
  `2` = "y",
  `3` = "x",
  .default = "D",
  .missing = "M"
)
case_match(
  num_vec,
  1 ~ "z",
  2 ~ "y",
  3 ~ "x",
  NA ~ "M",
  .default = "D",
  .ptype = factor(levels = c("z", "y", "x", "D", "M"))
)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

magrittr

%>%

pillar

type_sum

tibble

add_row, as_data_frame, as_tibble, data_frame, lst, tibble, tribble, view

tidyselect

all_of, any_of, contains, ends_with, everything, last_col, matches, num_range, one_of, starts_with, where

Transform each group to an arbitrary number of rows

Description

While summarise() requires that each argument returns a single value, and mutate() requires that each argument returns the same number of rows as the input, reframe() is a more general workhorse with no requirements on the number of rows returned per group.

reframe() creates a new data frame by applying functions to columns of an existing data frame. It is most similar to summarise(), with two big differences:

• reframe() can return an arbitrary number of rows per group, while summarise() reduces each group down to a single row.

• reframe() always returns an ungrouped data frame, while summarise() might return a grouped or rowwise data frame, depending on the scenario.

We expect that you'll use summarise() much more often than reframe(), but reframe() can be particularly helpful when you need to apply a complex function that doesn't return a single summary value.

Usage

reframe(.data, ..., .by = NULL)

Arguments

Value

If .data is a tibble, a tibble. Otherwise, a data.frame.

• The rows originate from the underlying grouping keys.

• The columns are a combination of the grouping keys and the expressions that you provide.

• The output is always ungrouped.

• Data frame attributes are not preserved, because reframe() fundamentally creates a new data frame.

Connection to tibble

reframe() is theoretically connected to two functions in tibble, tibble::enframe() and tibble::deframe():

• enframe(): vector -> data frame

• deframe(): data frame -> vector

• reframe(): data frame -> data frame

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

Other single table verbs: arrange(), filter(), mutate(), rename(), select(), slice(), summarise()

Examples

table <- c("a", "b", "d", "f")

df <- tibble(
  g = c(1, 1, 1, 2, 2, 2, 2),
  x = c("e", "a", "b", "c", "f", "d", "a")
)

# `reframe()` allows you to apply functions that return
# an arbitrary number of rows
df %>%
  reframe(x = intersect(x, table))

# Functions are applied per group, and each group can return a
# different number of rows.
df %>%
  reframe(x = intersect(x, table), .by = g)

# The output is always ungrouped, even when using `group_by()`
df %>%
  group_by(g) %>%
  reframe(x = intersect(x, table))

# You can add multiple columns at once using a single expression by returning
# a data frame.
quantile_df <- function(x, probs = c(0.25, 0.5, 0.75)) {
  tibble(
    val = quantile(x, probs, na.rm = TRUE),
    quant = probs
  )
}

x <- c(10, 15, 18, 12)
quantile_df(x)

starwars %>%
  reframe(quantile_df(height))

starwars %>%
  reframe(quantile_df(height), .by = homeworld)

starwars %>%
  reframe(
    across(c(height, mass), quantile_df, .unpack = TRUE),
    .by = homeworld
  )

Change column order

Description

Use relocate() to change column positions, using the same syntax as select() to make it easy to move blocks of columns at once.

Usage

relocate(.data, ..., .before = NULL, .after = NULL)

Arguments

Value

An object of the same type as .data. The output has the following properties:

• Rows are not affected.

• The same columns appear in the output, but (usually) in a different place and possibly renamed.

• Data frame attributes are preserved.

• Groups are not affected.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

Examples

df <- tibble(a = 1, b = 1, c = 1, d = "a", e = "a", f = "a")
df %>% relocate(f)
df %>% relocate(a, .after = c)
df %>% relocate(f, .before = b)
df %>% relocate(a, .after = last_col())

# relocated columns can change name
df %>% relocate(ff = f)

# Can also select variables based on their type
df %>% relocate(where(is.character))
df %>% relocate(where(is.numeric), .after = last_col())
# Or with any other select helper
df %>% relocate(any_of(c("a", "e", "i", "o", "u")))

# When .before or .after refers to multiple variables they will be
# moved to be immediately before/after the selected variables.
df2 <- tibble(a = 1, b = "a", c = 1, d = "a")
df2 %>% relocate(where(is.numeric), .after = where(is.character))
df2 %>% relocate(where(is.numeric), .before = where(is.character))

Rename columns

Description

rename() changes the names of individual variables using new_name = old_name syntax; rename_with() renames columns using a function.

Usage

rename(.data, ...)

rename_with(.data, .fn, .cols = everything(), ...)

Arguments

Value

An object of the same type as .data. The output has the following properties:

• Rows are not affected.

• Column names are changed; column order is preserved.

• Data frame attributes are preserved.

• Groups are updated to reflect new names.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

Other single table verbs: arrange(), filter(), mutate(), reframe(), select(), slice(), summarise()

Examples

iris <- as_tibble(iris) # so it prints a little nicer
rename(iris, petal_length = Petal.Length)

# Rename using a named vector and `all_of()`
lookup <- c(pl = "Petal.Length", sl = "Sepal.Length")
rename(iris, all_of(lookup))

# If your named vector might contain names that don't exist in the data,
# use `any_of()` instead
lookup <- c(lookup, new = "unknown")
try(rename(iris, all_of(lookup)))
rename(iris, any_of(lookup))

rename_with(iris, toupper)
rename_with(iris, toupper, starts_with("Petal"))
rename_with(iris, ~ tolower(gsub(".", "_", .x, fixed = TRUE)))


# If your renaming function uses `paste0()`, make sure to set
# `recycle0 = TRUE` to ensure that empty selections are recycled correctly
try(rename_with(
  iris,
  ~ paste0("prefix_", .x),
  starts_with("nonexistent")
))

rename_with(
  iris,
  ~ paste0("prefix_", .x, recycle0 = TRUE),
  starts_with("nonexistent")
)

Integer ranking functions

Description

Three ranking functions inspired by SQL2003. They differ primarily in how they handle ties:

• row_number() gives every input a unique rank, so that c(10, 20, 20, 30) would get ranks c(1, 2, 3, 4). It's equivalent to rank(ties.method = "first").

• min_rank() gives every tie the same (smallest) value so that c(10, 20, 20, 30) gets ranks c(1, 2, 2, 4). It's the way that ranks are usually computed in sports and is equivalent to rank(ties.method = "min").

• dense_rank() works like min_rank(), but doesn't leave any gaps, so that c(10, 20, 20, 30) gets ranks c(1, 2, 2, 3).

Usage

row_number(x)

min_rank(x)

dense_rank(x)

Arguments

Value

An integer vector.

See Also

Other ranking functions: ntile(), percent_rank()

Examples

x <- c(5, 1, 3, 2, 2, NA)
row_number(x)
min_rank(x)
dense_rank(x)

# Ranking functions can be used in `filter()` to select top/bottom rows
df <- data.frame(
  grp = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
  x = c(3, 2, 1, 1, 2, 2, 1, 1, 1),
  y = c(1, 3, 2, 3, 2, 2, 4, 1, 2),
  id = 1:9
)
# Always gives exactly 1 row per group
df %>% group_by(grp) %>% filter(row_number(x) == 1)
# May give more than 1 row if ties
df %>% group_by(grp) %>% filter(min_rank(x) == 1)
# Rank by multiple columns (to break ties) by selecting them with `pick()`
df %>% group_by(grp) %>% filter(min_rank(pick(x, y)) == 1)
# See slice_min() and slice_max() for another way to tackle the same problem

# You can use row_number() without an argument to refer to the "current"
# row number.
df %>% group_by(grp) %>% filter(row_number() == 1)

# It's easiest to see what this does with mutate():
df %>% group_by(grp) %>% mutate(grp_id = row_number())

Manipulate individual rows

Description

These functions provide a framework for modifying rows in a table using a second table of data. The two tables are matched by a set of key variables whose values typically uniquely identify each row. The functions are inspired by SQL's INSERT, UPDATE, and DELETE, and can optionally modify in_place for selected backends.

• rows_insert() adds new rows (like INSERT). By default, key values in y must not exist in x.

• rows_append() works like rows_insert() but ignores keys.

• rows_update() modifies existing rows (like UPDATE). Key values in y must be unique, and, by default, key values in y must exist in x.

• rows_patch() works like rows_update() but only overwrites NA values.

• rows_upsert() inserts or updates depending on whether or not the key value in y already exists in x. Key values in y must be unique.

• rows_delete() deletes rows (like DELETE). By default, key values in y must exist in x.

Usage

rows_insert(
  x,
  y,
  by = NULL,
  ...,
  conflict = c("error", "ignore"),
  copy = FALSE,
  in_place = FALSE
)

rows_append(x, y, ..., copy = FALSE, in_place = FALSE)

rows_update(
  x,
  y,
  by = NULL,
  ...,
  unmatched = c("error", "ignore"),
  copy = FALSE,
  in_place = FALSE
)

rows_patch(
  x,
  y,
  by = NULL,
  ...,
  unmatched = c("error", "ignore"),
  copy = FALSE,
  in_place = FALSE
)

rows_upsert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE)

rows_delete(
  x,
  y,
  by = NULL,
  ...,
  unmatched = c("error", "ignore"),
  copy = FALSE,
  in_place = FALSE
)

Arguments

Value

An object of the same type as x. The order of the rows and columns of x is preserved as much as possible. The output has the following properties:

• rows_update() and rows_patch() preserve the number of rows; rows_insert(), rows_append(), and rows_upsert() return all existing rows and potentially new rows; rows_delete() returns a subset of the rows.

• Columns are not added, removed, or relocated, though the data may be updated.

• Groups are taken from x.

• Data frame attributes are taken from x.

If in_place = TRUE, the result will be returned invisibly.

Methods

These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

• rows_insert(): no methods found.

• rows_append(): no methods found.

• rows_update(): no methods found.

• rows_patch(): no methods found.

• rows_upsert(): no methods found.

• rows_delete(): no methods found.

Examples

data <- tibble(a = 1:3, b = letters[c(1:2, NA)], c = 0.5 + 0:2)
data

# Insert
rows_insert(data, tibble(a = 4, b = "z"))

# By default, if a key in `y` matches a key in `x`, then it can't be inserted
# and will throw an error. Alternatively, you can ignore rows in `y`
# containing keys that conflict with keys in `x` with `conflict = "ignore"`,
# or you can use `rows_append()` to ignore keys entirely.
try(rows_insert(data, tibble(a = 3, b = "z")))
rows_insert(data, tibble(a = 3, b = "z"), conflict = "ignore")
rows_append(data, tibble(a = 3, b = "z"))

# Update
rows_update(data, tibble(a = 2:3, b = "z"))
rows_update(data, tibble(b = "z", a = 2:3), by = "a")

# Variants: patch and upsert
rows_patch(data, tibble(a = 2:3, b = "z"))
rows_upsert(data, tibble(a = 2:4, b = "z"))

# Delete and truncate
rows_delete(data, tibble(a = 2:3))
rows_delete(data, tibble(a = 2:3, b = "b"))

# By default, for update, patch, and delete it is an error if a key in `y`
# doesn't exist in `x`. You can ignore rows in `y` that have unmatched keys
# with `unmatched = "ignore"`.
y <- tibble(a = 3:4, b = "z")
try(rows_update(data, y, by = "a"))
rows_update(data, y, by = "a", unmatched = "ignore")
rows_patch(data, y, by = "a", unmatched = "ignore")
rows_delete(data, y, by = "a", unmatched = "ignore")

Group input by rows

Description

rowwise() allows you to compute on a data frame a row-at-a-time. This is most useful when a vectorised function doesn't exist.

Most dplyr verbs preserve row-wise grouping. The exception is summarise(), which return a grouped_df. You can explicitly ungroup with ungroup() or as_tibble(), or convert to a grouped_df with group_by().

Usage

rowwise(data, ...)

Arguments

Value

A row-wise data frame with class rowwise_df. Note that a rowwise_df is implicitly grouped by row, but is not a grouped_df.

List-columns

Because a rowwise has exactly one row per group it offers a small convenience for working with list-columns. Normally, summarise() and mutate() extract a groups worth of data with [. But when you index a list in this way, you get back another list. When you're working with a rowwise tibble, then dplyr will use [[ instead of [ to make your life a little easier.

See Also

nest_by() for a convenient way of creating rowwise data frames with nested data.

Examples

df <- tibble(x = runif(6), y = runif(6), z = runif(6))
# Compute the mean of x, y, z in each row
df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))
# use c_across() to more easily select many variables
df %>% rowwise() %>% mutate(m = mean(c_across(x:z)))

# Compute the minimum of x and y in each row
df %>% rowwise() %>% mutate(m = min(c(x, y, z)))
# In this case you can use an existing vectorised function:
df %>% mutate(m = pmin(x, y, z))
# Where these functions exist they'll be much faster than rowwise
# so be on the lookout for them.

# rowwise() is also useful when doing simulations
params <- tribble(
 ~sim, ~n, ~mean, ~sd,
    1,  1,     1,   1,
    2,  2,     2,   4,
    3,  3,    -1,   2
)
# Here I supply variables to preserve after the computation
params %>%
  rowwise(sim) %>%
  reframe(z = rnorm(n, mean, sd))

# If you want one row per simulation, put the results in a list()
params %>%
  rowwise(sim) %>%
  summarise(z = list(rnorm(n, mean, sd)), .groups = "keep")

Figure out if two sources are the same (or two tbl have the same source)

Description

Figure out if two sources are the same (or two tbl have the same source)

Usage

same_src(x, y)

Arguments

Value

a logical flag

Sample n rows from a table

Description

sample_n() and sample_frac() have been superseded in favour of slice_sample(). While they will not be deprecated in the near future, retirement means that we will only perform critical bug fixes, so we recommend moving to the newer alternative.

These functions were superseded because we realised it was more convenient to have two mutually exclusive arguments to one function, rather than two separate functions. This also made it to clean up a few other smaller design issues with sample_n()/sample_frac:

• The connection to slice() was not obvious.

• The name of the first argument, tbl, is inconsistent with other single table verbs which use .data.

• The size argument uses tidy evaluation, which is surprising and undocumented.

• It was easier to remove the deprecated .env argument.

• ... was in a suboptimal position.

Usage

sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...)

sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...)

Arguments

Examples

df <- tibble(x = 1:5, w = c(0.1, 0.1, 0.1, 2, 2))

# sample_n() -> slice_sample() ----------------------------------------------
# Was:
sample_n(df, 3)
sample_n(df, 10, replace = TRUE)
sample_n(df, 3, weight = w)

# Now:
slice_sample(df, n = 3)
slice_sample(df, n = 10, replace = TRUE)
slice_sample(df, n = 3, weight_by = w)

# Note that sample_n() would error if n was bigger than the group size
# slice_sample() will just use the available rows for consistency with
# the other slice helpers like slice_head()
try(sample_n(df, 10))
slice_sample(df, n = 10)

# sample_frac() -> slice_sample() -------------------------------------------
# Was:
sample_frac(df, 0.25)
sample_frac(df, 2, replace = TRUE)

# Now:
slice_sample(df, prop = 0.25)
slice_sample(df, prop = 2, replace = TRUE)

Operate on a selection of variables

Description

Scoped verbs (⁠_if⁠, ⁠_at⁠, ⁠_all⁠) have been superseded by the use of pick() or across() in an existing verb. See vignette("colwise") for details.

The variants suffixed with ⁠_if⁠, ⁠_at⁠ or ⁠_all⁠ apply an expression (sometimes several) to all variables within a specified subset. This subset can contain all variables (⁠_all⁠ variants), a vars() selection (⁠_at⁠ variants), or variables selected with a predicate (⁠_if⁠ variants).

The verbs with scoped variants are:

• mutate(), transmute() and summarise(). See summarise_all().

• filter(). See filter_all().

• group_by(). See group_by_all().

• rename() and select(). See select_all().

• arrange(). See arrange_all()

There are three kinds of scoped variants. They differ in the scope of the variable selection on which operations are applied:

• Verbs suffixed with ⁠_all()⁠ apply an operation on all variables.

• Verbs suffixed with ⁠_at()⁠ apply an operation on a subset of variables specified with the quoting function vars(). This quoting function accepts tidyselect::vars_select() helpers like starts_with(). Instead of a vars() selection, you can also supply an integerish vector of column positions or a character vector of column names.

• Verbs suffixed with ⁠_if()⁠ apply an operation on the subset of variables for which a predicate function returns TRUE. Instead of a predicate function, you can also supply a logical vector.

Arguments

Grouping variables

Most of these operations also apply on the grouping variables when they are part of the selection. This includes:

• arrange_all(), arrange_at(), and arrange_if()

• distinct_all(), distinct_at(), and distinct_if()

• filter_all(), filter_at(), and filter_if()

• group_by_all(), group_by_at(), and group_by_if()

• select_all(), select_at(), and select_if()

This is not the case for summarising and mutating variants where operations are not applied on grouping variables. The behaviour depends on whether the selection is implicit (all and if selections) or explicit (at selections). Grouping variables covered by explicit selections (with summarise_at(), mutate_at(), and transmute_at()) are always an error. For implicit selections, the grouping variables are always ignored. In this case, the level of verbosity depends on the kind of operation:

• Summarising operations (summarise_all() and summarise_if()) ignore grouping variables silently because it is obvious that operations are not applied on grouping variables.

• On the other hand it isn't as obvious in the case of mutating operations (mutate_all(), mutate_if(), transmute_all(), and transmute_if()). For this reason, they issue a message indicating which grouping variables are ignored.

Deprecated SE versions of main verbs.

Description

dplyr used to offer twin versions of each verb suffixed with an underscore. These versions had standard evaluation (SE) semantics: rather than taking arguments by code, like NSE verbs, they took arguments by value. Their purpose was to make it possible to program with dplyr. However, dplyr now uses tidy evaluation semantics. NSE verbs still capture their arguments, but you can now unquote parts of these arguments. This offers full programmability with NSE verbs. Thus, the underscored versions are now superfluous.

Unquoting triggers immediate evaluation of its operand and inlines the result within the captured expression. This result can be a value or an expression to be evaluated later with the rest of the argument. See vignette("programming") for more information.

Usage

add_count_(x, vars, wt = NULL, sort = FALSE)

add_tally_(x, wt, sort = FALSE)

arrange_(.data, ..., .dots = list())

count_(x, vars, wt = NULL, sort = FALSE, .drop = group_by_drop_default(x))

distinct_(.data, ..., .dots, .keep_all = FALSE)

do_(.data, ..., .dots = list())

filter_(.data, ..., .dots = list())

funs_(dots, args = list(), env = base_env())

group_by_(.data, ..., .dots = list(), add = FALSE)

group_indices_(.data, ..., .dots = list())

mutate_(.data, ..., .dots = list())

tally_(x, wt, sort = FALSE)

transmute_(.data, ..., .dots = list())

rename_(.data, ..., .dots = list())

rename_vars_(vars, args)

select_(.data, ..., .dots = list())

select_vars_(vars, args, include = chr(), exclude = chr())

slice_(.data, ..., .dots = list())

summarise_(.data, ..., .dots = list())

summarize_(.data, ..., .dots = list())

Arguments

Keep or drop columns using their names and types

Description

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).

Overview of selection features

Tidyverse selections implement a dialect of R where operators make it easy to select variables:

• : for selecting a range of consecutive variables.

• ! for taking the complement of a set of variables.

• & and | for selecting the intersection or the union of two sets of variables.

• c() for combining selections.

In addition, you can use selection helpers. Some helpers select specific columns:

• everything(): Matches all variables.

• last_col(): Select last variable, possibly with an offset.

• group_cols(): Select all grouping columns.

Other helpers select variables by matching patterns in their names:

• starts_with(): Starts with a prefix.

• ends_with(): Ends with a suffix.

• contains(): Contains a literal string.

• matches(): Matches a regular expression.

• num_range(): Matches a numerical range like x01, x02, x03.

Or from variables stored in a character vector:

• all_of(): Matches variable names in a character vector. All names must be present, otherwise an out-of-bounds error is thrown.

• any_of(): Same as all_of(), except that no error is thrown for names that don't exist.

Or using a predicate function:

• where(): Applies a function to all variables and selects those for which the function returns TRUE.

Usage

select(.data, ...)

Arguments

Value

An object of the same type as .data. The output has the following properties:

• Rows are not affected.

• Output columns are a subset of input columns, potentially with a different order. Columns will be renamed if new_name = old_name form is used.

• Data frame attributes are preserved.

• Groups are maintained; you can't select off grouping variables.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

Examples

Here we show the usage for the basic selection operators. See the specific help pages to learn about helpers like starts_with().

The selection language can be used in functions like dplyr::select() or tidyr::pivot_longer(). Let's first attach the tidyverse:

library(tidyverse)

# For better printing
iris <- as_tibble(iris)

Select variables by name:

starwars %>% select(height)
#> # A tibble: 87 x 1
#>   height
#>    <int>
#> 1    172
#> 2    167
#> 3     96
#> 4    202
#> # i 83 more rows

iris %>% pivot_longer(Sepal.Length)
#> # A tibble: 150 x 6
#>   Sepal.Width Petal.Length Petal.Width Species name         value
#>         <dbl>        <dbl>       <dbl> <fct>   <chr>        <dbl>
#> 1         3.5          1.4         0.2 setosa  Sepal.Length   5.1
#> 2         3            1.4         0.2 setosa  Sepal.Length   4.9
#> 3         3.2          1.3         0.2 setosa  Sepal.Length   4.7
#> 4         3.1          1.5         0.2 setosa  Sepal.Length   4.6
#> # i 146 more rows

Select multiple variables by separating them with commas. Note how the order of columns is determined by the order of inputs:

starwars %>% select(homeworld, height, mass)
#> # A tibble: 87 x 3
#>   homeworld height  mass
#>   <chr>      <int> <dbl>
#> 1 Tatooine     172    77
#> 2 Tatooine     167    75
#> 3 Naboo         96    32
#> 4 Tatooine     202   136
#> # i 83 more rows

Functions like tidyr::pivot_longer() don't take variables with dots. In this case use c() to select multiple variables:

iris %>% pivot_longer(c(Sepal.Length, Petal.Length))
#> # A tibble: 300 x 5
#>   Sepal.Width Petal.Width Species name         value
#>         <dbl>       <dbl> <fct>   <chr>        <dbl>
#> 1         3.5         0.2 setosa  Sepal.Length   5.1
#> 2         3.5         0.2 setosa  Petal.Length   1.4
#> 3         3           0.2 setosa  Sepal.Length   4.9
#> 4         3           0.2 setosa  Petal.Length   1.4
#> # i 296 more rows

Operators:

The : operator selects a range of consecutive variables:

starwars %>% select(name:mass)
#> # A tibble: 87 x 3
#>   name           height  mass
#>   <chr>           <int> <dbl>
#> 1 Luke Skywalker    172    77
#> 2 C-3PO             167    75
#> 3 R2-D2              96    32
#> 4 Darth Vader       202   136
#> # i 83 more rows

The ! operator negates a selection:

starwars %>% select(!(name:mass))
#> # A tibble: 87 x 11
#>   hair_color skin_color  eye_color birth_year sex   gender    homeworld species
#>   <chr>      <chr>       <chr>          <dbl> <chr> <chr>     <chr>     <chr>  
#> 1 blond      fair        blue            19   male  masculine Tatooine  Human  
#> 2 <NA>       gold        yellow         112   none  masculine Tatooine  Droid  
#> 3 <NA>       white, blue red             33   none  masculine Naboo     Droid  
#> 4 none       white       yellow          41.9 male  masculine Tatooine  Human  
#> # i 83 more rows
#> # i 3 more variables: films <list>, vehicles <list>, starships <list>

iris %>% select(!c(Sepal.Length, Petal.Length))
#> # A tibble: 150 x 3
#>   Sepal.Width Petal.Width Species
#>         <dbl>       <dbl> <fct>  
#> 1         3.5         0.2 setosa 
#> 2         3           0.2 setosa 
#> 3         3.2         0.2 setosa 
#> 4         3.1         0.2 setosa 
#> # i 146 more rows

iris %>% select(!ends_with("Width"))
#> # A tibble: 150 x 3
#>   Sepal.Length Petal.Length Species
#>          <dbl>        <dbl> <fct>  
#> 1          5.1          1.4 setosa 
#> 2          4.9          1.4 setosa 
#> 3          4.7          1.3 setosa 
#> 4          4.6          1.5 setosa 
#> # i 146 more rows

& and | take the intersection or the union of two selections:

iris %>% select(starts_with("Petal") & ends_with("Width"))
#> # A tibble: 150 x 1
#>   Petal.Width
#>         <dbl>
#> 1         0.2
#> 2         0.2
#> 3         0.2
#> 4         0.2
#> # i 146 more rows

iris %>% select(starts_with("Petal") | ends_with("Width"))
#> # A tibble: 150 x 3
#>   Petal.Length Petal.Width Sepal.Width
#>          <dbl>       <dbl>       <dbl>
#> 1          1.4         0.2         3.5
#> 2          1.4         0.2         3  
#> 3          1.3         0.2         3.2
#> 4          1.5         0.2         3.1
#> # i 146 more rows

To take the difference between two selections, combine the & and ! operators:

iris %>% select(starts_with("Petal") & !ends_with("Width"))
#> # A tibble: 150 x 1
#>   Petal.Length
#>          <dbl>
#> 1          1.4
#> 2          1.4
#> 3          1.3
#> 4          1.5
#> # i 146 more rows

See Also

Other single table verbs: arrange(), filter(), mutate(), reframe(), rename(), slice(), summarise()

Select and rename a selection of variables

Description

rename_if(), rename_at(), and rename_all() have been superseded by rename_with(). The matching select statements have been superseded by the combination of a select() + rename_with(). Any predicate functions passed as arguments to select() or rename_with() must be wrapped in where().

These functions were superseded because mutate_if() and friends were superseded by across(). select_if() and rename_if() already use tidy selection so they can't be replaced by across() and instead we need a new function.

Usage

select_all(.tbl, .funs = list(), ...)

rename_all(.tbl, .funs = list(), ...)

select_if(.tbl, .predicate, .funs = list(), ...)

rename_if(.tbl, .predicate, .funs = list(), ...)

select_at(.tbl, .vars, .funs = list(), ...)

rename_at(.tbl, .vars, .funs = list(), ...)

Arguments

Examples

mtcars <- as_tibble(mtcars) # for nicer printing

mtcars %>% rename_all(toupper)
# ->
mtcars %>% rename_with(toupper)

# NB: the transformation comes first in rename_with
is_whole <- function(x) all(floor(x) == x)
mtcars %>% rename_if(is_whole, toupper)
# ->
mtcars %>% rename_with(toupper, where(is_whole))

mtcars %>% rename_at(vars(mpg:hp), toupper)
# ->
mtcars %>% rename_with(toupper, mpg:hp)

# You now must select() and then rename

mtcars %>% select_all(toupper)
# ->
mtcars %>% rename_with(toupper)

# Selection drops unselected variables:
mtcars %>% select_if(is_whole, toupper)
# ->
mtcars %>% select(where(is_whole)) %>% rename_with(toupper)

mtcars %>% select_at(vars(-contains("ar"), starts_with("c")), toupper)
# ->
mtcars %>%
  select(!contains("ar") | starts_with("c")) %>%
  rename_with(toupper)

Set operations

Description

Perform set operations using the rows of a data frame.

• intersect(x, y) finds all rows in both x and y.

• union(x, y) finds all rows in either x or y, excluding duplicates.

• union_all(x, y) finds all rows in either x or y, including duplicates.

• setdiff(x, y) finds all rows in x that aren't in y.

• 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.

• setequal(x, y) returns TRUE if x and y contain the same rows (ignoring order).

Note that intersect(), union(), setdiff(), and symdiff() remove duplicates in x and y.

Usage

intersect(x, y, ...)

union(x, y, ...)

union_all(x, y, ...)

setdiff(x, y, ...)

setequal(x, y, ...)

symdiff(x, y, ...)

Arguments

Base functions

intersect(), union(), setdiff(), and setequal() override the base functions of the same name in order to make them generic. The existing behaviour for vectors is preserved by providing default methods that call the base functions.

Examples

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

intersect(df1, df2)
union(df1, df2)
union_all(df1, df2)
setdiff(df1, df2)
setdiff(df2, df1)
symdiff(df1, df2)

setequal(df1, df2)
setequal(df1, df1[3:1, ])

# Note that the following functions remove pre-existing duplicates:
df1 <- tibble(x = c(1:3, 3, 3))
df2 <- tibble(x = c(3:5, 5))

intersect(df1, df2)
union(df1, df2)
setdiff(df1, df2)
symdiff(df1, df2)

Subset rows using their positions

Description

slice() lets you index rows by their (integer) locations. It allows you to select, remove, and duplicate rows. It is accompanied by a number of helpers for common use cases:

• slice_head() and slice_tail() select the first or last rows.

• slice_sample() randomly selects rows.

• slice_min() and slice_max() select rows with the smallest or largest values of a variable.

If .data is a grouped_df, the operation will be performed on each group, so that (e.g.) slice_head(df, n = 5) will select the first five rows in each group.

Usage

slice(.data, ..., .by = NULL, .preserve = FALSE)

slice_head(.data, ..., n, prop, by = NULL)

slice_tail(.data, ..., n, prop, by = NULL)

slice_min(
  .data,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = FALSE
)

slice_max(
  .data,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = FALSE
)

slice_sample(.data, ..., n, prop, by = NULL, weight_by = NULL, replace = FALSE)

Arguments

Details

Slice does not work with relational databases because they have no intrinsic notion of row order. If you want to perform the equivalent operation, use filter() and row_number().

Value

An object of the same type as .data. The output has the following properties:

• Each row may appear 0, 1, or many times in the output.

• Columns are not modified.

• Groups are not modified.

• Data frame attributes are preserved.

Methods

These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

• slice(): no methods found.

• slice_head(): no methods found.

• slice_tail(): no methods found.

• slice_min(): no methods found.

• slice_max(): no methods found.

• slice_sample(): no methods found.

See Also

Other single table verbs: arrange(), filter(), mutate(), reframe(), rename(), select(), summarise()

Examples

# Similar to head(mtcars, 1):
mtcars %>% slice(1L)
# Similar to tail(mtcars, 1):
mtcars %>% slice(n())
mtcars %>% slice(5:n())
# Rows can be dropped with negative indices:
slice(mtcars, -(1:4))

# First and last rows based on existing order
mtcars %>% slice_head(n = 5)
mtcars %>% slice_tail(n = 5)

# Rows with minimum and maximum values of a variable
mtcars %>% slice_min(mpg, n = 5)
mtcars %>% slice_max(mpg, n = 5)

# slice_min() and slice_max() may return more rows than requested
# in the presence of ties.
mtcars %>% slice_min(cyl, n = 1)
# Use with_ties = FALSE to return exactly n matches
mtcars %>% slice_min(cyl, n = 1, with_ties = FALSE)
# Or use additional variables to break the tie:
mtcars %>% slice_min(tibble(cyl, mpg), n = 1)

# slice_sample() allows you to random select with or without replacement
mtcars %>% slice_sample(n = 5)
mtcars %>% slice_sample(n = 5, replace = TRUE)

# you can optionally weight by a variable - this code weights by the
# physical weight of the cars, so heavy cars are more likely to get
# selected
mtcars %>% slice_sample(weight_by = wt, n = 5)

# Group wise operation ----------------------------------------
df <- tibble(
  group = rep(c("a", "b", "c"), c(1, 2, 4)),
  x = runif(7)
)

# All slice helpers operate per group, silently truncating to the group
# size, so the following code works without error
df %>% group_by(group) %>% slice_head(n = 2)

# When specifying the proportion of rows to include non-integer sizes
# are rounded down, so group a gets 0 rows
df %>% group_by(group) %>% slice_head(prop = 0.5)

# Filter equivalents --------------------------------------------
# slice() expressions can often be written to use `filter()` and
# `row_number()`, which can also be translated to SQL. For many databases,
# you'll need to supply an explicit variable to use to compute the row number.
filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

SQL escaping.

Description

These functions are critical when writing functions that translate R functions to sql functions. Typically a conversion function should escape all its inputs and return an sql object.

Usage

sql(...)

Arguments

Create a "src" object

Description

src() is the standard constructor for srcs and is.src() tests.

Usage

src(subclass, ...)

is.src(x)

Arguments

Source for database backends

Description

These functions have been deprecated; instead please use tbl() directly on an DBIConnection. See https://dbplyr.tidyverse.org/ for more details.

Usage

src_mysql(
  dbname,
  host = NULL,
  port = 0L,
  username = "root",
  password = "",
  ...
)

src_postgres(
  dbname = NULL,
  host = NULL,
  port = NULL,
  user = NULL,
  password = NULL,
  ...
)

src_sqlite(path, create = FALSE)

Arguments

Value

An S3 object with class src_dbi, src_sql, src.

Examples

con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
copy_to(con, mtcars)

# To retrieve a single table from a source, use `tbl()`
mtcars <- con %>% tbl("mtcars")
mtcars

# You can also use pass raw SQL if you want a more sophisticated query
con %>% tbl(sql("SELECT * FROM mtcars WHERE cyl == 8"))

A local source

Description

This function was deprecated since it existed to support a style of testing dplyr backends that turned out not to be useful.

Usage

src_local(tbl, pkg = NULL, env = NULL)

src_df(pkg = NULL, env = NULL)

Arguments

List all tbls provided by a source.

Description

This is a generic method which individual src's will provide methods for. Most methods will not be documented because it's usually pretty obvious what possible results will be.

Usage

src_tbls(x, ...)

Arguments

Starwars characters

Description

The original data, from SWAPI, the Star Wars API, https://swapi.py4e.com/, has been revised to reflect additional research into gender and sex determinations of characters.

Usage

starwars

Format

A tibble with 87 rows and 14 variables:

name

Name of the character

height

Height (cm)

mass

Weight (kg)

hair_color,skin_color,eye_color

Hair, skin, and eye colors

birth_year

Year born (BBY = Before Battle of Yavin)

sex

The biological sex of the character, namely male, female, hermaphroditic, or none (as in the case for Droids).

gender

The gender role or gender identity of the character as determined by their personality or the way they were programmed (as in the case for Droids).

homeworld

Name of homeworld

species

Name of species

films

List of films the character appeared in

vehicles

List of vehicles the character has piloted

starships

List of starships the character has piloted

Examples

starwars

Storm tracks data

Description

This dataset is the NOAA Atlantic hurricane database best track data, https://www.nhc.noaa.gov/data/#hurdat. The data includes the positions and attributes of storms from 1975-2022. Storms from 1979 onward are measured every six hours during the lifetime of the storm. Storms in earlier years have some missing data.

Usage

storms

Format

A tibble with 19,537 observations and 13 variables:

name

Storm Name

year,month,day

Date of report

hour

Hour of report (in UTC)

lat,long

Location of storm center

status

Storm classification (Tropical Depression, Tropical Storm, or Hurricane)

category

Saffir-Simpson hurricane category calculated from wind speed.

• NA: Not a hurricane

• 1: 64+ knots

• 2: 83+ knots

• 3: 96+ knots

• 4: 113+ knots

• 5: 137+ knots

wind

storm's maximum sustained wind speed (in knots)

pressure

Air pressure at the storm's center (in millibars)

tropicalstorm_force_diameter

Diameter (in nautical miles) of the area experiencing tropical storm strength winds (34 knots or above). Only available starting in 2004.

hurricane_force_diameter

Diameter (in nautical miles) of the area experiencing hurricane strength winds (64 knots or above). Only available starting in 2004.

See Also

The script to create the storms data set: https://github.com/tidyverse/dplyr/blob/main/data-raw/storms.R

Examples

storms

# Show a few recent storm paths
if (requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggplot2)
  storms %>%
    filter(year >= 2000) %>%
    ggplot(aes(long, lat, color = paste(year, name))) +
    geom_path(show.legend = FALSE) +
    facet_wrap(~year)
}

storms

Summarise each group down to one row

Description

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.

summarise() and summarize() are synonyms.

Usage

summarise(.data, ..., .by = NULL, .groups = NULL)

summarize(.data, ..., .by = NULL, .groups = NULL)

Arguments

Value

An object usually of the same type as .data.

• The rows come from the underlying group_keys().

• The columns are a combination of the grouping keys and the summary expressions that you provide.

• The grouping structure is controlled by the ⁠.groups=⁠ argument, the output may be another grouped_df, a tibble or a rowwise data frame.

• Data frame attributes are not preserved, because summarise() fundamentally creates a new data frame.

Useful functions

• Center: mean(), median()

• Spread: sd(), IQR(), mad()

• Range: min(), max(),

• Position: first(), last(), nth(),

• Count: n(), n_distinct()

• Logical: any(), all()

Backend variations

The data frame backend supports creating a variable and using it in the same summary. This means that previously created summary variables can be further transformed or combined within the summary, as in mutate(). However, it also means that summary variables with the same names as previous variables overwrite them, making those variables unavailable to later summary variables.

This behaviour may not be supported in other backends. To avoid unexpected results, consider using new names for your summary variables, especially when creating multiple summaries.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

Other single table verbs: arrange(), filter(), mutate(), reframe(), rename(), select(), slice()

Examples

# A summary applied to ungrouped tbl returns a single row
mtcars %>%
  summarise(mean = mean(disp), n = n())

# Usually, you'll want to group first
mtcars %>%
  group_by(cyl) %>%
  summarise(mean = mean(disp), n = n())

# Each summary call removes one grouping level (since that group
# is now just a single row)
mtcars %>%
  group_by(cyl, vs) %>%
  summarise(cyl_n = n()) %>%
  group_vars()

# BEWARE: reusing variables may lead to unexpected results
mtcars %>%
  group_by(cyl) %>%
  summarise(disp = mean(disp), sd = sd(disp))

# Refer to column names stored as strings with the `.data` pronoun:
var <- "mass"
summarise(starwars, avg = mean(.data[[var]], na.rm = TRUE))
# Learn more in ?rlang::args_data_masking

# In dplyr 1.1.0, returning multiple rows per group was deprecated in favor
# of `reframe()`, which never messages and always returns an ungrouped
# result:
mtcars %>%
   group_by(cyl) %>%
   summarise(qs = quantile(disp, c(0.25, 0.75)), prob = c(0.25, 0.75))
# ->
mtcars %>%
   group_by(cyl) %>%
   reframe(qs = quantile(disp, c(0.25, 0.75)), prob = c(0.25, 0.75))

Summarise multiple columns

Description

Scoped verbs (⁠_if⁠, ⁠_at⁠, ⁠_all⁠) have been superseded by the use of pick() or across() in an existing verb. See vignette("colwise") for details.

The scoped variants of summarise() make it easy to apply the same transformation to multiple variables. There are three variants.

• summarise_all() affects every variable

• summarise_at() affects variables selected with a character vector or vars()

• summarise_if() affects variables selected with a predicate function

Usage

summarise_all(.tbl, .funs, ...)

summarise_if(.tbl, .predicate, .funs, ...)

summarise_at(.tbl, .vars, .funs, ..., .cols = NULL)

summarize_all(.tbl, .funs, ...)

summarize_if(.tbl, .predicate, .funs, ...)

summarize_at(.tbl, .vars, .funs, ..., .cols = NULL)

Arguments

Value

A data frame. By default, the newly created columns have the shortest names needed to uniquely identify the output. To force inclusion of a name, even when not needed, name the input (see examples for details).

Grouping variables

If applied on a grouped tibble, these operations are not applied to the grouping variables. The behaviour depends on whether the selection is implicit (all and if selections) or explicit (at selections).

• Grouping variables covered by explicit selections in summarise_at() are always an error. Add -group_cols() to the vars() selection to avoid this:

  data %>%
    summarise_at(vars(-group_cols(), ...), myoperation)
  

  Or remove group_vars() from the character vector of column names:

  nms <- setdiff(nms, group_vars(data))
  data %>% summarise_at(nms, myoperation)
  

• Grouping variables covered by implicit selections are silently ignored by summarise_all() and summarise_if().

Naming

The names of the new columns are derived from the names of the input variables and the names of the functions.

• if there is only one unnamed function (i.e. if .funs is an unnamed list of length one), the names of the input variables are used to name the new columns;

• for ⁠_at⁠ functions, if there is only one unnamed variable (i.e., if .vars is of the form vars(a_single_column)) and .funs has length greater than one, the names of the functions are used to name the new columns;

• otherwise, the new names are created by concatenating the names of the input variables and the names of the functions, separated with an underscore "_".

The .funs argument can be a named or unnamed list. If a function is unnamed and the name cannot be derived automatically, a name of the form "fn#" is used. Similarly, vars() accepts named and unnamed arguments. If a variable in .vars is named, a new column by that name will be created.

Name collisions in the new columns are disambiguated using a unique suffix.

See Also

The other scoped verbs, vars()

Examples

# The _at() variants directly support strings:
starwars %>%
  summarise_at(c("height", "mass"), mean, na.rm = TRUE)
# ->
starwars %>% summarise(across(c("height", "mass"), ~ mean(.x, na.rm = TRUE)))

# You can also supply selection helpers to _at() functions but you have
# to quote them with vars():
starwars %>%
  summarise_at(vars(height:mass), mean, na.rm = TRUE)
# ->
starwars %>%
  summarise(across(height:mass, ~ mean(.x, na.rm = TRUE)))

# The _if() variants apply a predicate function (a function that
# returns TRUE or FALSE) to determine the relevant subset of
# columns. Here we apply mean() to the numeric columns:
starwars %>%
  summarise_if(is.numeric, mean, na.rm = TRUE)
starwars %>%
  summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))

by_species <- iris %>%
  group_by(Species)

# If you want to apply multiple transformations, pass a list of
# functions. When there are multiple functions, they create new
# variables instead of modifying the variables in place:
by_species %>%
  summarise_all(list(min, max))
# ->
by_species %>%
  summarise(across(everything(), list(min = min, max = max)))

Summarise and mutate multiple columns.

Description

mutate_each() and summarise_each() are deprecated in favour of the new across() function that works within summarise() and mutate().

Usage

summarise_each(tbl, funs, ...)

summarise_each_(tbl, funs, vars)

mutate_each(tbl, funs, ...)

mutate_each_(tbl, funs, vars)

summarize_each(tbl, funs, ...)

summarize_each_(tbl, funs, vars)

Create a table from a data source

Description

This is a generic method that dispatches based on the first argument.

Usage

tbl(src, ...)

is.tbl(x)

Arguments

Coerce to a tibble

Description

Please use tibble::as_tibble() instead.

Usage

tbl_df(data)

as.tbl(x, ...)

Arguments

Return a prototype of a tbl

Description

Used in ⁠_if⁠ functions to enable type-based selection even when the data is lazily generated. Should either return the complete tibble, or if that can not be computed quickly, a 0-row tibble where the columns are of the correct type.

Usage

tbl_ptype(.data)

List variables provided by a tbl.

Description

tbl_vars() returns all variables while tbl_nongroup_vars() returns only non-grouping variables. The groups attribute of the object returned by tbl_vars() is a character vector of the grouping columns.

Usage

tbl_vars(x)

tbl_nongroup_vars(x)

Arguments

See Also

group_vars() for a function that returns grouping variables.

Other tidy eval tools

Description

These tidy eval functions are no longer for normal usage, but are still exported from dplyr for backward compatibility. See ?rlang::args_data_masking and vignette("programming") for the latest recommendations.

• expr()

• enquo()

• enquos()

• sym()

• syms()

• as_label()

• quo()

• quos()

• quo_name()

• ensym()

• ensyms()

• enexpr()

• enexprs()

Select top (or bottom) n rows (by value)

Description

top_n() has been superseded in favour of slice_min()/slice_max(). While it will not be deprecated in the near future, retirement means that we will only perform critical bug fixes, so we recommend moving to the newer alternatives.

top_n() was superseded because the name was fundamentally confusing as it returned what you might reasonably consider to be the bottom rows. Additionally, the wt variable had a confusing name, and strange default (the last column in the data frame). Unfortunately we could not see an easy way to fix the existing top_n() function without breaking existing code, so we created a new alternative.

Usage

top_n(x, n, wt)

top_frac(x, n, wt)

Arguments

Examples

df <- data.frame(x = c(6, 4, 1, 10, 3, 1, 1))

df %>% top_n(2)  # highest values
df %>% top_n(-2) # lowest values
# now use
df %>% slice_max(x, n = 2)
df %>% slice_min(x, n = 2)

# top_frac() -> prop argument of slice_min()/slice_max()
df %>% top_frac(.5)
# ->
df %>% slice_max(x, prop = 0.5)

Create, modify, and delete columns

Description

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

transmute(.data, ...)

Arguments

Value

An object of the same type as .data. The output has the following properties:

• Columns created or modified through ... will be returned in the order specified by ....

• Unmodified grouping columns will be placed at the front.

• The number of rows is not affected.

• Columns given the value NULL will be removed.

• Groups will be recomputed if a grouping variable is mutated.

• Data frame attributes are preserved.

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages: no methods found.

Select variables

Description

vars() is superseded because it is only needed for the scoped verbs (i.e. mutate_at(), summarise_at(), and friends), which have been been superseded in favour of across(). See vignette("colwise") for details.

This helper is intended to provide tidy-select semantics for scoped verbs like mutate_at() and summarise_at(). Note that anywhere you can supply vars() specification, you can also supply a numeric vector of column positions or a character vector of column names.

Usage

vars(...)

Arguments