filter-joins: Filtering joins for an 'ir' object

In henningte/ir: Functions to Handle and Preprocess Infrared Spectra

Filtering joins for an ir object

Description

Filtering joins for an ir object

Usage

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

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

Arguments

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

Value

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

Source

filter-joins

See Also

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

Examples

## semi_join
set.seed(234)
dplyr::semi_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"
)


## anti_join
set.seed(234)
dplyr::anti_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"
)

henningte/ir documentation built on July 4, 2025, 5:12 p.m.