mutate-joins {ir}

Mutating joins for an ir object

Description

Mutating joins for an ir object

Usage

inner_join.ir(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = FALSE,
  na_matches = c("na", "never")
)

left_join.ir(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = FALSE,
  na_matches = c("na", "never")
)

right_join.ir(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = FALSE,
  na_matches = c("na", "never")
)

full_join.ir(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = FALSE,
  na_matches = c("na", "never")
)

Arguments

x

An object of class ir.

y

A data frame.

by

A character vector of variables to join by.

If NULL, the default, ⁠*_join()⁠ will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly.

To join by different variables on x and y, use a named vector. For example, by = c("a" = "b") will match x$a to y$b.

To join by multiple variables, use a vector with length > 1. For example, by = c("a", "b") will match x$a to y$a and x$b to y$b. Use a named vector to match different variables in x and y. For example, by = c("a" = "b", "c" = "d") will match x$a to y$b and x$c to y$d.

To perform a cross-join, generating all combinations of x and y, use by = character().

copy

If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.

suffix

If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.

...

Other parameters passed onto methods.

keep

Should the join keys from both x and y be preserved in the output?

na_matches

Should NA and NaN values match one another?

The default, "na", treats two NA or NaN values as equal, like %in%, match(), merge().

Use "never" to always treat two NA or NaN values as different, like joins for database sources, similarly to merge(incomparables = FALSE).

Value

x and y joined. If the spectra column is renamed, the ir class is dropped. See mutate-joins.

Source

mutate-joins

See Also

Other tidyverse: arrange.ir(), distinct.ir(), extract.ir(), filter-joins, filter.ir(), group_by, mutate, nest, pivot_longer.ir(), pivot_wider.ir(), rename, rowwise.ir(), select.ir(), separate.ir(), separate_rows.ir(), slice, summarize, unite.ir()

Examples

## inner_join
set.seed(234)
dplyr::inner_join(
  ir_sample_data,
  tibble::tibble(
    id_measurement = c(1:5, 101:105),
    nitrogen_content = rbeta(n = 10, 0.2, 0.1)
  ),
  by = "id_measurement"
)


## left_join
set.seed(234)
dplyr::left_join(
  ir_sample_data,
  tibble::tibble(
    id_measurement = c(1:5, 101:105),
    nitrogen_content = rbeta(n = 10, 0.2, 0.1)
  ),
  by = "id_measurement"
)


## right_join
set.seed(234)
dplyr::right_join(
  ir_sample_data,
  tibble::tibble(
    id_measurement = c(1:5, 101:105),
    nitrogen_content = rbeta(n = 10, 0.2, 0.1)
  ),
  by = "id_measurement"
)


## full_join
set.seed(234)
dplyr::full_join(
  ir_sample_data,
  tibble::tibble(
    id_measurement = c(1:5, 101:105),
    nitrogen_content = rbeta(n = 10, 0.2, 0.1)
  ),
  by = "id_measurement"
)