tier_match: Perform an iterative match by tier
In fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods

Description Usage Arguments Details Value See Also

Constructs a tier_match by running merge_plus with different parameters sequentially on the same data. Allows for sequential removal of observations after each tier.

tier_match(
  data1,
  data2,
  by = NULL,
  by.x = NULL,
  by.y = NULL,
  suffixes = c("_1", "_2"),
  check_merge = TRUE,
  unique_key_1,
  unique_key_2,
  tiers = list(),
  takeout = "both",
  match_type = "exact",
  clean = FALSE,
  clean_settings = build_clean_settings(),
  score_settings = NULL,
  filter = NULL,
  filter.args = list(),
  evaluate = match_evaluate,
  evaluate.args = list(),
  allow.cartesian = TRUE,
  fuzzy_settings = build_fuzzy_settings(),
  multivar_settings = build_multivar_settings(),
  verbose = FALSE
)

`data1`	data.frame. First to-merge dataset.
`data2`	data.frame. Second to-merge dataset.
`by`	character string. Variables to merge on (common across data 1 and data 2). See `merge`
`by.x`	character string. Variable to merge on in data1. See `merge`
`by.y`	character string. Variable to merge on in data2. See `merge`
`suffixes`	see `merge`
`check_merge`	logical. Checks that your unique_keys are indeed unique, and prevents merge from running if merge would result in data.frames larger than 5 million rows
`unique_key_1`	character vector. Primary key of data1 that uniquely identifies each row (can be multiple fields)
`unique_key_2`	character vector. Primary key of data2 that uniquely identifies each row (can be multiple fields)
`tiers`	list(). tier is a list of lists, where each list holds the parameters for creating that tier. All arguments to tier_match listed after this argument can either be supplied directly to tier_match, or indirectly via tiers.
`takeout`	character vector, either 'data1', 'data2', 'both', or 'neither'. Removes observations after each tier from the selected dataset.
`match_type`	string. If 'exact', match is exact, if 'fuzzy', match is fuzzy.
`clean`	Boolean, T/F, whether or not to clean strings prior to the match.
`clean_settings`	list. Settings for string cleaning. See `clean_strings` and `build_clean_settings`.
`score_settings`	list. Settings for post-hoc matchscoring. See `build_score_settings`.
`filter`	function or numeric. Filters a merged data1-data2 dataset. If a function, should take in a data.frame (data1 and data2 merged by name1 and name2) and spit out a trimmed verion of the data.frame (fewer rows). Think of this function as applying other conditions to matches, other than a match by name. The first argument of filter should be the data.frame. If numeric, will drop all observations with a matchscore lower than or equal to filter.
`filter.args`	list. Arguments passed to filter, if a function
`evaluate`	Function to evalute merge_plus output. see `evaluate_match`.
`evaluate.args`	list. Arguments passed to function specified by evaluate
`allow.cartesian`	whether or not to allow many-many matches, see data.table::merge()
`fuzzy_settings`	additional arguments for amatch, to be used if match_type = 'fuzzy'. Suggested defaults provided. (see amatch, method='jw')
`multivar_settings`	list of settings to go to the multivar match if match_type == 'multivar'. See `multivar-match`.
`verbose`	boolean, whether or not to print tier names and time to match each tier as the matching happens.