Package 'tidySummarizedExperiment'

Description

as_tibble() turns an existing object, such as a data frame or matrix, into a so-called tibble, a data frame with class tbl_df. This is in contrast with tibble(), which builds a tibble from individual columns. as_tibble() is to tibble() as base::as.data.frame() is to base::data.frame().

as_tibble() is an S3 generic, with methods for:

• data.frame: Thin wrapper around the list method that implements tibble's treatment of rownames.

• matrix, poly, ts, table

• Default: Other inputs are first coerced with base::as.data.frame().

as_tibble_row() converts a vector to a tibble with one row. If the input is a list, all elements must have size one.

as_tibble_col() converts a vector to a tibble with one column.

Usage

## S3 method for class 'SummarizedExperiment'
as_tibble(
  x,
  ...,
  .name_repair = c("check_unique", "unique", "universal", "minimal"),
  rownames = pkgconfig::get_config("tibble::rownames", NULL)
)

Arguments

Value

tibble

Row names

The default behavior is to silently remove row names.

New code should explicitly convert row names to a new column using the rownames argument.

For existing code that relies on the retention of row names, call pkgconfig::set_config("tibble::rownames" = NA) in your script or in your package's .onLoad() function.

Life cycle

Using as_tibble() for vectors is superseded as of version 3.0.0, prefer the more expressive as_tibble_row() and as_tibble_col() variants for new code.

See Also

tibble() constructs a tibble from individual columns. enframe() converts a named vector to a tibble with a column of names and column of values. Name repair is implemented using vctrs::vec_as_names().

Examples

tidySummarizedExperiment::pasilla %>%
    as_tibble()
    
tidySummarizedExperiment::pasilla %>%
    as_tibble(.subset=-c(condition, type))

Description

This is an efficient implementation of the common pattern of 'do.call(rbind, dfs)' or 'do.call(cbind, dfs)' for binding many data frames into one.

This is an efficient implementation of the common pattern of 'do.call(rbind, dfs)' or 'do.call(cbind, dfs)' for binding many data frames into one.

Usage

## S3 method for class 'SummarizedExperiment'
bind_rows(..., .id = NULL, add.cell.ids = NULL)

## S3 method for class 'SummarizedExperiment'
bind_cols(..., .id = NULL)

## S3 method for class 'RangedSummarizedExperiment'
bind_cols(..., .id = NULL)

Arguments

Details

The output of 'bind_rows()' will contain a column if that column appears in any of the inputs.

The output of 'bind_rows()' will contain a column if that column appears in any of the inputs.

Value

'bind_rows()' and 'bind_cols()' return the same type as the first input, either a data frame, 'tbl_df', or 'grouped_df'.

'bind_rows()' and 'bind_cols()' return the same type as the first input, either a data frame, 'tbl_df', or 'grouped_df'.

Examples

data(se)
ttservice::bind_rows(se, se)

se_bind <- se |> select(dex,  albut)
se |> ttservice::bind_cols(se_bind)

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

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

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

data(se)
se |> count(dex)

Description

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

Usage

## S3 method for class 'SummarizedExperiment'
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

data(pasilla)
pasilla |> distinct(.sample)

Description

extract() has been superseded in favour of separate_wider_regex() because it has a more polished API and better handling of problems. Superseded functions will not go away, but will only receive critical bug fixes.

Given a regular expression with capturing groups, extract() turns each group into a new column. If the groups don't match, or the input is NA, the output will be NA.

Usage

## S3 method for class 'SummarizedExperiment'
extract(
  data,
  col,
  into,
  regex = "([[:alnum:]]+)",
  remove = TRUE,
  convert = FALSE,
  ...
)

Arguments

Value

tidySummarizedExperiment

See Also

separate() to split up by a separator.

Examples

tidySummarizedExperiment::pasilla |>
    extract(type, into="sequencing", regex="([a-z]*)_end", convert=TRUE)

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

## S3 method for class 'SummarizedExperiment'
filter(.data, ..., .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

data(pasilla)
pasilla |>  filter(.sample == "untrt1")

# Learn more in ?dplyr_tidy_eval

Description

One of the main features of the tbl_df class is the printing:

• Tibbles only print as many rows and columns as fit on one screen, supplemented by a summary of the remaining rows and columns.

• Tibble reveals the type of each column, which keeps the user informed about whether a variable is, e.g., ⁠<chr>⁠ or ⁠<fct>⁠ (character versus factor). See vignette("types") for an overview of common type abbreviations.

Printing can be tweaked for a one-off call by calling print() explicitly and setting arguments like n and width. More persistent control is available by setting the options described in pillar::pillar_options. See also vignette("digits") for a comparison to base options, and vignette("numbers") that showcases num() and char() for creating columns with custom formatting options.

As of tibble 3.1.0, printing is handled entirely by the pillar package. If you implement a package that extends tibble, the printed output can be customized in various ways. See vignette("extending", package = "pillar") for details, and pillar::pillar_options for options that control the display in the console.

Usage

## S3 method for class 'SummarizedExperiment'
print(x, ..., n = NULL, width = NULL, n_extra = NULL)

Arguments

Value

Prints a message to the console describing the contents of the tidySummarizedExperiment.

Examples

data(pasilla)
print(pasilla)

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

## S3 method for class 'SummarizedExperiment'
full_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

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

data(pasilla)

tt <- pasilla
tt |> full_join(tibble::tibble(condition="treated", dose=10))

Description

ggplot() initializes a ggplot object. It can be used to declare the input data frame for a graphic and to specify the set of plot aesthetics intended to be common throughout all subsequent layers unless specifically overridden.

Usage

## S3 method for class 'SummarizedExperiment'
ggplot(data = NULL, mapping = aes(), ..., environment = parent.frame())

Arguments

Details

ggplot() is used to construct the initial plot object, and is almost always followed by a plus sign (+) to add components to the plot.

There are three common patterns used to invoke ggplot():

• ⁠ggplot(data = df, mapping = aes(x, y, other aesthetics))⁠

• ggplot(data = df)

• ggplot()

The first pattern is recommended if all layers use the same data and the same set of aesthetics, although this method can also be used when adding a layer using data from another data frame.

The second pattern specifies the default data frame to use for the plot, but no aesthetics are defined up front. This is useful when one data frame is used predominantly for the plot, but the aesthetics vary from one layer to another.

The third pattern initializes a skeleton ggplot object, which is fleshed out as layers are added. This is useful when multiple data frames are used to produce different layers, as is often the case in complex graphics.

The ⁠data =⁠ and ⁠mapping =⁠ specifications in the arguments are optional (and are often omitted in practice), so long as the data and the mapping values are passed into the function in the right order. In the examples below, however, they are left in place for clarity.

Value

ggplot

See Also

The first steps chapter of the online ggplot2 book.

Examples

library(ggplot2)
data(pasilla)
pasilla %>%
    ggplot(aes(.sample, counts)) +
    geom_boxplot()

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

## S3 method for class 'SummarizedExperiment'
group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))

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

data(pasilla)
pasilla  |> group_by(.sample)

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

## S3 method for class 'SummarizedExperiment'
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

data(pasilla, package = "tidySummarizedExperiment")
pasilla |> group_split(condition)
pasilla |> group_split(counts > 0)
pasilla |> group_split(condition, counts > 0)

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

## S3 method for class 'SummarizedExperiment'
inner_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

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

data(pasilla)

tt <- pasilla 
tt |> inner_join(tt |>
    distinct(condition) |>
    mutate(new_column=1:2) |>
    slice(1))

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

## S3 method for class 'SummarizedExperiment'
left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

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

data(pasilla)

tt <- pasilla 
tt |> left_join(tt |>
    distinct(condition) |>
    mutate(new_column=1:2))

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

## S3 method for class 'SummarizedExperiment'
mutate(.data, ...)

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: rename(), slice(), summarise()

Examples

data(pasilla)
pasilla |> mutate(logcounts=log2(counts))

Description

Allows mutate call on features (rowData) of a SummarizedExperiment

Usage

mutate_features(.data, ...)

Arguments

Value

a SummarizedExperiment with modified rowData

Description

Allows mutate call on samples (colData) of a SummarizedExperiment

Usage

mutate_samples(.data, ...)

Arguments

Value

a SummarizedExperiment with modified colData

Description

Nesting creates a list-column of data frames; unnesting flattens it back out into regular columns. Nesting is implicitly a summarising operation: you get one row for each group defined by the non-nested columns. This is useful in conjunction with other summaries that work with whole datasets, most notably models.

Learn more in vignette("nest").

Usage

## S3 method for class 'SummarizedExperiment'
nest(.data, ..., .names_sep = NULL)

Arguments

Details

If neither ... nor .by are supplied, nest() will nest all variables, and will use the column name supplied through .key.

Value

tidySummarizedExperiment_nested

New syntax

tidyr 1.0.0 introduced a new syntax for nest() and unnest() that's designed to be more similar to other functions. Converting to the new syntax should be straightforward (guided by the message you'll receive) but if you just need to run an old analysis, you can easily revert to the previous behaviour using nest_legacy() and unnest_legacy() as follows:

library(tidyr)
nest <- nest_legacy
unnest <- unnest_legacy

Grouped data frames

df %>% nest(data = c(x, y)) specifies the columns to be nested; i.e. the columns that will appear in the inner data frame. df %>% nest(.by = c(x, y)) specifies the columns to nest by; i.e. the columns that will remain in the outer data frame. An alternative way to achieve the latter is to nest() a grouped data frame created by dplyr::group_by(). The grouping variables remain in the outer data frame and the others are nested. The result preserves the grouping of the input.

Variables supplied to nest() will override grouping variables so that df %>% group_by(x, y) %>% nest(data = !z) will be equivalent to df %>% nest(data = !z).

You can't supply .by with a grouped data frame, as the groups already represent what you are nesting by.

Examples

tidySummarizedExperiment::pasilla |>
    nest(data=-condition)

Description

A SummarizedExperiment dataset containing the transcriptome information for Drosophila Melanogaster.

Usage

data(pasilla)

Format

containing 14599 features and 7 biological replicates.

Source

https://bioconductor.org/packages/release/data/experiment/html/pasilla.html

Description

pivot_longer() "lengthens" data, increasing the number of rows and decreasing the number of columns. The inverse transformation is pivot_wider()

Learn more in vignette("pivot").

Usage

## S3 method for class 'SummarizedExperiment'
pivot_longer(
  data,
  cols,
  ...,
  cols_vary = "fastest",
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = NULL,
  names_transform = NULL,
  names_repair = "check_unique",
  values_to = "value",
  values_drop_na = FALSE,
  values_ptypes = NULL,
  values_transform = NULL
)

Arguments

Details

pivot_longer() is an updated approach to gather(), designed to be both simpler to use and to handle more use cases. We recommend you use pivot_longer() for new code; gather() isn't going away but is no longer under active development.

Value

tidySummarizedExperiment

Examples

# See vignette("pivot") for examples and explanation
library(dplyr)
tidySummarizedExperiment::pasilla %>%
    pivot_longer(c(condition, type),
        names_to="name", values_to="value")

Description

pivot_wider() "widens" data, increasing the number of columns and decreasing the number of rows. The inverse transformation is pivot_longer().

Learn more in vignette("pivot").

Usage

## S3 method for class 'SummarizedExperiment'
pivot_wider(
  data,
  ...,
  id_cols = NULL,
  id_expand = FALSE,
  names_from = name,
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE,
  names_vary = "fastest",
  names_expand = FALSE,
  names_repair = "check_unique",
  values_from = value,
  values_fill = NULL,
  values_fn = NULL,
  unused_fn = NULL
)

Arguments

Details

pivot_wider() is an updated approach to spread(), designed to be both simpler to use and to handle more use cases. We recommend you use pivot_wider() for new code; spread() isn't going away but is no longer under active development.

Value

tidySummarizedExperiment

See Also

pivot_wider_spec() to pivot "by hand" with a data frame that defines a pivoting specification.

Examples

# See vignette("pivot") for examples and explanation
library(dplyr)
tidySummarizedExperiment::pasilla %>%
    pivot_wider(names_from=feature, values_from=counts)

Description

This function maps R objects to plotly.js, an (MIT licensed) web-based interactive charting library. It provides abstractions for doing common things (e.g. mapping data values to fill colors (via color) or creating animations (via frame)) and sets some different defaults to make the interface feel more 'R-like' (i.e., closer to plot() and ggplot2::qplot()).

Usage

## S3 method for class 'tbl_df'
plot_ly(
  data = data.frame(),
  ...,
  type = NULL,
  name = NULL,
  color = NULL,
  colors = NULL,
  alpha = NULL,
  stroke = NULL,
  strokes = NULL,
  alpha_stroke = 1,
  size = NULL,
  sizes = c(10, 100),
  span = NULL,
  spans = c(1, 20),
  symbol = NULL,
  symbols = NULL,
  linetype = NULL,
  linetypes = NULL,
  split = NULL,
  frame = NULL,
  width = NULL,
  height = NULL,
  source = "A"
)

## S3 method for class 'SummarizedExperiment'
plot_ly(
  data = data.frame(),
  ...,
  type = NULL,
  name = NULL,
  color = NULL,
  colors = NULL,
  alpha = NULL,
  stroke = NULL,
  strokes = NULL,
  alpha_stroke = 1,
  size = NULL,
  sizes = c(10, 100),
  span = NULL,
  spans = c(1, 20),
  symbol = NULL,
  symbols = NULL,
  linetype = NULL,
  linetypes = NULL,
  split = NULL,
  frame = NULL,
  width = NULL,
  height = NULL,
  source = "A"
)

Arguments

Details

Unless type is specified, this function just initiates a plotly object with 'global' attributes that are passed onto downstream uses of add_trace() (or similar). A formula must always be used when referencing column name(s) in data (e.g. plot_ly(mtcars, x = ~wt)). Formulas are optional when supplying values directly, but they do help inform default axis/scale titles (e.g., plot_ly(x = mtcars$wt) vs plot_ly(x = ~mtcars$wt))

Value

plotly

plotly

Author(s)

Carson Sievert

References

https://plotly-r.com/overview.html

See Also

• For initializing a plotly-geo object: plot_geo()

• For initializing a plotly-mapbox object: plot_mapbox()

• For translating a ggplot2 object to a plotly object: ggplotly()

• For modifying any plotly object: layout(), add_trace(), style()

• For linked brushing: highlight()

• For arranging multiple plots: subplot(), crosstalk::bscols()

• For inspecting plotly objects: plotly_json()

• For quick, accurate, and searchable plotly.js reference: schema()

Examples

data(se)
se |>
    plot_ly(x = ~counts)

data(se)
se |>
    plot_ly(x = ~counts)

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

## S3 method for class 'SummarizedExperiment'
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

data(pasilla)
pasilla |> pull(feature)

Description

rename() changes the names of individual variables using new_name = old_name syntax; rename_with() renames columns using a function.

Usage

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

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: mutate(), slice(), summarise()

Examples

data(pasilla)
pasilla |> rename(cond=condition)

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

## S3 method for class 'SummarizedExperiment'
right_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

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

data(pasilla)

tt <- pasilla
tt |> right_join(tt |>
    distinct(condition) |>
    mutate(new_column=1:2) |>
    slice(1))

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

## S3 method for class 'SummarizedExperiment'
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

# TODO

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

## S3 method for class 'SummarizedExperiment'
sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...)

## S3 method for class 'SummarizedExperiment'
sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...)

Arguments

Value

tidySummarizedExperiment

Examples

data(pasilla)
pasilla |> sample_n(50)
pasilla |> sample_frac(0.1)

Description

A SummarizedExperiment dataset containing the transcriptome information for Drosophila Melanogaster.

Usage

data(se)

Format

containing 14599 features and 7 biological replicates.

Source

https://bioconductor.org/packages/release/data/experiment/html/pasilla.html

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

## S3 method for class 'SummarizedExperiment'
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()

Examples

data(pasilla)
pasilla |> select(.sample, .feature, counts)

Description

separate() has been superseded in favour of separate_wider_position() and separate_wider_delim() because the two functions make the two uses more obvious, the API is more polished, and the handling of problems is better. Superseded functions will not go away, but will only receive critical bug fixes.

Given either a regular expression or a vector of character positions, separate() turns a single character column into multiple columns.

Usage

## S3 method for class 'SummarizedExperiment'
separate(
  data,
  col,
  into,
  sep = "[^[:alnum:]]+",
  remove = TRUE,
  convert = FALSE,
  extra = "warn",
  fill = "warn",
  ...
)

Arguments

Value

tidySummarizedExperiment

See Also

unite(), the complement, extract() which uses regular expression capturing groups.

Examples

un <- tidySummarizedExperiment::pasilla |>
    unite("group", c(condition, type))
un |> separate(col=group, into=c("condition", "type"))

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

## S3 method for class 'SummarizedExperiment'
slice(.data, ..., .preserve = 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: mutate(), rename(), summarise()

Examples

data(pasilla)
pasilla |> slice(1)

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

## S3 method for class 'SummarizedExperiment'
summarise(.data, ...)

## S3 method for class 'SummarizedExperiment'
summarize(.data, ...)

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: mutate(), rename(), slice()

Examples

data(pasilla)
pasilla |> summarise(mean(counts))

Description

For easier customization, the formatting of a tibble is split into three components: header, body, and footer. The tbl_format_header() method is responsible for formatting the header of a tibble.

Override this method if you need to change the appearance of the entire header. If you only need to change or extend the components shown in the header, override or extend tbl_sum() for your class which is called by the default method.

Usage

## S3 method for class 'tidySummarizedExperiment'
tbl_format_header(x, setup, ...)

Arguments

Value

A character vector.

Examples

# TODO

Description

tidy for Seurat

Usage

tidy(object)

## S3 method for class 'SummarizedExperiment'
tidy(object)

## S3 method for class 'RangedSummarizedExperiment'
tidy(object)

Arguments

Value

A tidyseurat object.

Examples

data(pasilla)
pasilla %>% tidy()

Description

Convenience function to paste together multiple columns into one.

Usage

## S3 method for class 'SummarizedExperiment'
unite(data, col, ..., sep = "_", remove = TRUE, na.rm = FALSE)

Arguments

Value

tidySummarizedExperiment

See Also

separate(), the complement.

Examples

tidySummarizedExperiment::pasilla |>
    unite("group", c(condition, type))

Description

Unnest expands a list-column containing data frames into rows and columns.

Usage

## S3 method for class 'tidySummarizedExperiment_nested'
unnest(
  data,
  cols,
  ...,
  keep_empty = FALSE,
  ptype = NULL,
  names_sep = NULL,
  names_repair = "check_unique",
  .drop,
  .id,
  .sep,
  .preserve
)

unnest_summarized_experiment(
  data,
  cols,
  ...,
  keep_empty = FALSE,
  ptype = NULL,
  names_sep = NULL,
  names_repair = "check_unique",
  .drop,
  .id,
  .sep,
  .preserve
)

Arguments

Value

tidySummarizedExperiment

New syntax

tidyr 1.0.0 introduced a new syntax for nest() and unnest() that's designed to be more similar to other functions. Converting to the new syntax should be straightforward (guided by the message you'll receive) but if you just need to run an old analysis, you can easily revert to the previous behaviour using nest_legacy() and unnest_legacy() as follows:

library(tidyr)
nest <- nest_legacy
unnest <- unnest_legacy

See Also

Other rectangling: hoist(), unnest_longer(), unnest_wider()

Examples

tidySummarizedExperiment::pasilla |>
    nest(data=-condition) |>
    unnest(data)

tidySummarizedExperiment::pasilla |>
    nest(data=-condition) |>
    unnest_summarized_experiment(data)