rows: Manipulate individual rows

In dplyr: A Grammar of Data Manipulation

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

x, y	A pair of data frames or data frame extensions (e.g. a tibble). y must have the same columns of x or a subset.
by	An unnamed character vector giving the key columns. The key columns must exist in both x and y. Keys typically uniquely identify each row, but this is only enforced for the key values of y when rows_update(), rows_patch(), or rows_upsert() are used. By default, we use the first column in y, since the first column is a reasonable place to put an identifier variable.
...	Other parameters passed onto methods.
conflict	For rows_insert(), how should keys in y that conflict with keys in x be handled? A conflict arises if there is a key in y that already exists in x. One of: "error", the default, will error if there are any keys in y that conflict with keys in x. "ignore" will ignore rows in y with keys that conflict with keys in x.
copy	If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
in_place	Should x be modified in place? This argument is only relevant for mutable backends (e.g. databases, data.tables). When TRUE, a modified version of x is returned invisibly; when FALSE, a new object representing the resulting changes is returned.
unmatched	For rows_update(), rows_patch(), and rows_delete(), how should keys in y that are unmatched by the keys in x be handled? One of: "error", the default, will error if there are any keys in y that are unmatched by the keys in x. "ignore" will ignore rows in y with keys that are unmatched by the keys in x.

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(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("rows_insert")}.

• rows_append(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("rows_append")}.

• rows_update(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("rows_update")}.

• rows_patch(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("rows_patch")}.

• rows_upsert(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("rows_upsert")}.

• rows_delete(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("rows_delete")}.

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")

dplyr documentation built on Nov. 17, 2023, 5:08 p.m.