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Apply Crossmap Transformation to Conformable Data

Source: R/apply_xmap.r
apply_xmap.Rd

This function applies crossmap transformation to a dataset, transforming data based on specified mapping rules.

Usage

apply_xmap(.data, .xmap, values_from, keys_from = names(.xmap$.from), ...)

diagnose_apply_xmap(
  .data,
  .xmap,
  values_from,
  keys_from = names(.xmap$.from),
  ...
)

Arguments

.data

The dataset to transform.

.xmap

An xmap_tbl object.

values_from

A tidyselect expression of columns in .data with values to transform

keys_from

A tidyselect expression specifies the column in .data to match with .xmap$from

...

(reserved)

Value

A tibble with transformed data.

diagnose_apply_xmap() returns an xmap_diagnosis object: a list with valid (a scalar logical) and details (a named list of tibbles of offending rows, one per check, NULL where that check passed). Printing the result shows a readable pass/fail report; see new_xmap_diagnosis().

Details

diagnose_apply_xmap() checks whether .data is conformable with .xmap – the same two conditions apply_xmap() checks – and returns detail on any offending rows, to help resolve the specific issue rather than just knowing something's wrong. The returned xmap_diagnosis's details has one entry per condition (NULL where that check passed):

  • not_covered: rows of .data whose keys_from key has no matching link in .xmap$.from

  • missing_values: rows of .data with a missing value in one or more values_from columns

Functions

  • diagnose_apply_xmap(): Returns an xmap_diagnosis object diagnosing why .data fails apply_xmap()'s conformability checks.

Examples

abc_xmap <- demo$abc_links |>
  as_xmap_tbl(from = "lower", to = "upper", weight_by = "share")
abc_data <- tibble::tibble(
  lower = unique(demo$abc_links$lower),
  count = runif(length(unique(demo$abc_links$lower)), min = 100, max = 500)
)
apply_xmap(
  .data = abc_data,
  .xmap = abc_xmap,
  values_from = count
)
#> Matching keys in `.data$lower` with `.xmap$.from$lower`
#> ℹ To silence, set `keys_from = lower`
#> # A tibble: 5 × 2
#>   upper count
#>   <chr> <dbl>
#> 1 AA    132. 
#> 2 BB    774. 
#> 3 CC     48.9
#> 4 DD     97.7
#> 5 EE     16.3