Filtering joins for an ir object — filter-joins • ir

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"))

Source

filter-joins

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.

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"
)
#> # A tibble: 5 × 7
#>   id_measurement id_sample sample_type sample_comment              klason_lignin
#> *          <int> <chr>     <chr>       <chr>                       <units>      
#> 1              1 GN 11-389 needles     Abies Firma Momi fir        0.359944     
#> 2              2 GN 11-400 needles     Cupressocyparis leylandii … 0.339405     
#> 3              3 GN 11-407 needles     Juniperus chinensis Chines… 0.267552     
#> 4              4 GN 11-411 needles     Metasequoia glyptostroboid… 0.350016     
#> 5              5 GN 11-416 needles     Pinus strobus Torulosa      0.331100     
#> # ℹ 2 more variables: holocellulose <units>, spectra <named list>


## 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"
)
#> # A tibble: 53 × 7
#>    id_measurement id_sample sample_type sample_comment             klason_lignin
#>  *          <int> <chr>     <chr>       <chr>                      <units>      
#>  1              6 GN 11-419 needles     Pseudolarix amabili Golde… 0.279360     
#>  2              7 GN 11-422 needles     Sequoia sempervirens Cali… 0.329672     
#>  3              8 GN 11-423 needles     Taxodium distichum Cascad… 0.356950     
#>  4              9 GN 11-428 needles     Thuja occidentalis Easter… 0.369360     
#>  5             10 GN 11-434 needles     Tsuga caroliniana Carolin… 0.289050     
#>  6             11 GN 11-435 needles     Picea abies Norway Spruce  0.288000     
#>  7             12 GN 11-460 needles     Pinus taeda Loblolly pine  0.322300     
#>  8             13 HW 07-151 hardwood    Quercus sp. Red oak (from… 0.238095     
#>  9             14 HW 11-137 hardwood    Acer saccharum Sugar maple 0.242592     
#> 10             15 HW 11-144 hardwood    Fraxinus americana White … 0.259224     
#> # ℹ 43 more rows
#> # ℹ 2 more variables: holocellulose <units>, spectra <named list>

Filtering joins for an `ir` object

Usage

Source

Arguments

Value

See also

Examples