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Mutating joins for an ir object

Source: R/tidyverse.R
mutate-joins.Rd

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")
)

Source

mutate-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.

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?

  • If NULL, the default, joins on equality retain only the keys from x, while joins on inequality retain the keys from both inputs.

  • If TRUE, all keys from both inputs are retained.

  • If FALSE, only keys from x are retained. For right and full joins, the data in key columns corresponding to rows that only exist in y are merged into the key columns from x. Can't be used when joining on inequality conditions.

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 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"
)
#> # A tibble: 5 × 8
#>   id_measurement id_sample sample_type sample_comment              klason_lignin
#> *          <int> <chr>     <chr>       <chr>                                 [1]
#> 1              1 GN 11-389 needles     Abies Firma Momi fir                0.360
#> 2              2 GN 11-400 needles     Cupressocyparis leylandii …         0.339
#> 3              3 GN 11-407 needles     Juniperus chinensis Chines…         0.268
#> 4              4 GN 11-411 needles     Metasequoia glyptostroboid…         0.350
#> 5              5 GN 11-416 needles     Pinus strobus Torulosa              0.331
#> # ℹ 3 more variables: holocellulose [1], spectra <named list>,
#> #   nitrogen_content <dbl>


## 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"
)
#> # A tibble: 58 × 8
#>    id_measurement id_sample sample_type sample_comment             klason_lignin
#>  *          <int> <chr>     <chr>       <chr>                                [1]
#>  1              1 GN 11-389 needles     Abies Firma Momi fir               0.360
#>  2              2 GN 11-400 needles     Cupressocyparis leylandii…         0.339
#>  3              3 GN 11-407 needles     Juniperus chinensis Chine…         0.268
#>  4              4 GN 11-411 needles     Metasequoia glyptostroboi…         0.350
#>  5              5 GN 11-416 needles     Pinus strobus Torulosa             0.331
#>  6              6 GN 11-419 needles     Pseudolarix amabili Golde…         0.279
#>  7              7 GN 11-422 needles     Sequoia sempervirens Cali…         0.330
#>  8              8 GN 11-423 needles     Taxodium distichum Cascad…         0.357
#>  9              9 GN 11-428 needles     Thuja occidentalis Easter…         0.369
#> 10             10 GN 11-434 needles     Tsuga caroliniana Carolin…         0.289
#> # ℹ 48 more rows
#> # ℹ 3 more variables: holocellulose [1], spectra <named list>,
#> #   nitrogen_content <dbl>


## 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"
)
#> # A tibble: 10 × 8
#>    id_measurement id_sample sample_type sample_comment             klason_lignin
#>  *          <int> <chr>     <chr>       <chr>                                [1]
#>  1              1 GN 11-389 needles     Abies Firma Momi fir               0.360
#>  2              2 GN 11-400 needles     Cupressocyparis leylandii…         0.339
#>  3              3 GN 11-407 needles     Juniperus chinensis Chine…         0.268
#>  4              4 GN 11-411 needles     Metasequoia glyptostroboi…         0.350
#>  5              5 GN 11-416 needles     Pinus strobus Torulosa             0.331
#>  6            101 NA        NA          NA                                NA    
#>  7            102 NA        NA          NA                                NA    
#>  8            103 NA        NA          NA                                NA    
#>  9            104 NA        NA          NA                                NA    
#> 10            105 NA        NA          NA                                NA    
#> # ℹ 3 more variables: holocellulose [1], spectra <named list>,
#> #   nitrogen_content <dbl>


## 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"
)
#> # A tibble: 63 × 8
#>    id_measurement id_sample sample_type sample_comment             klason_lignin
#>  *          <int> <chr>     <chr>       <chr>                                [1]
#>  1              1 GN 11-389 needles     Abies Firma Momi fir               0.360
#>  2              2 GN 11-400 needles     Cupressocyparis leylandii…         0.339
#>  3              3 GN 11-407 needles     Juniperus chinensis Chine…         0.268
#>  4              4 GN 11-411 needles     Metasequoia glyptostroboi…         0.350
#>  5              5 GN 11-416 needles     Pinus strobus Torulosa             0.331
#>  6              6 GN 11-419 needles     Pseudolarix amabili Golde…         0.279
#>  7              7 GN 11-422 needles     Sequoia sempervirens Cali…         0.330
#>  8              8 GN 11-423 needles     Taxodium distichum Cascad…         0.357
#>  9              9 GN 11-428 needles     Thuja occidentalis Easter…         0.369
#> 10             10 GN 11-434 needles     Tsuga caroliniana Carolin…         0.289
#> # ℹ 53 more rows
#> # ℹ 3 more variables: holocellulose [1], spectra <named list>,
#> #   nitrogen_content <dbl>