Separate a character column in an ir object into multiple columns with a regular expression or numeric locations

Separate a character column in an ir object into multiple columns with a regular expression or numeric locations

Usage

separate.ir(
  data,
  col,
  into,
  sep = "[^[:alnum:]]+",
  remove = TRUE,
  convert = FALSE,
  extra = "warn",
  fill = "warn",
  ...
)

Source

tidyr::separate()

Arguments

data

An object of class ir.

col

Column name or position. This is passed to tidyselect::vars_pull().

This argument is passed by expression and supports quasiquotation (you can unquote column names or column positions).

into

Names of new variables to create as character vector. Use NA to omit the variable in the output.

sep

Separator between columns.

If character, sep is interpreted as a regular expression. The default value is a regular expression that matches any sequence of non-alphanumeric values.

If numeric, sep is interpreted as character positions to split at. Positive values start at 1 at the far-left of the string; negative value start at -1 at the far-right of the string. The length of sep should be one less than into.

remove

If TRUE, remove input column from output data frame.

convert

If TRUE, will run type.convert() with as.is = TRUE on new columns. This is useful if the component columns are integer, numeric or logical.

NB: this will cause string "NA"s to be converted to NAs.

extra

If sep is a character vector, this controls what happens when there are too many pieces. There are three valid options:

• "warn" (the default): emit a warning and drop extra values.

• "drop": drop any extra values without a warning.

• "merge": only splits at most length(into) times

fill

If sep is a character vector, this controls what happens when there are not enough pieces. There are three valid options:

• "warn" (the default): emit a warning and fill from the right

• "right": fill with missing values on the right

• "left": fill with missing values on the left

...

Additional arguments passed on to methods.

Value

.data with separated columns. If the spectra column is dropped or invalidated (see ir_new_ir()), the ir class is dropped, else the object is of class ir.

See also

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

Examples

## separate
ir_sample_data %>%
  tidyr::separate(
    col = "id_sample",  c("a", "b", "c")
  )
#> # A tibble: 58 × 9
#>    id_measurement a     b     c     sample_type sample_comment     klason_lignin
#>  *          <int> <chr> <chr> <chr> <chr>       <chr>                        [1]
#>  1              1 GN    11    389   needles     Abies Firma Momi …         0.360
#>  2              2 GN    11    400   needles     Cupressocyparis l…         0.339
#>  3              3 GN    11    407   needles     Juniperus chinens…         0.268
#>  4              4 GN    11    411   needles     Metasequoia glypt…         0.350
#>  5              5 GN    11    416   needles     Pinus strobus Tor…         0.331
#>  6              6 GN    11    419   needles     Pseudolarix amabi…         0.279
#>  7              7 GN    11    422   needles     Sequoia sempervir…         0.330
#>  8              8 GN    11    423   needles     Taxodium distichu…         0.357
#>  9              9 GN    11    428   needles     Thuja occidentali…         0.369
#> 10             10 GN    11    434   needles     Tsuga caroliniana…         0.289
#> # … with 48 more rows, and 2 more variables: holocellulose [1],
#> #   spectra <named list>