fuzzy_match: Use string distances to match on names
In fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods

Description Usage Arguments Details Value

Use the stringdist package to perform a fuzzy match on two datasets.

fuzzy_match(
  data1,
  data2,
  by = NULL,
  by.x = NULL,
  by.y = NULL,
  suffixes,
  unique_key_1,
  unique_key_2,
  fuzzy_settings = list(method = "jw", p = 0.1, maxDist = 0.05, matchNA = FALSE,
    nthread = getOption("sd_num_thread"))
)

`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`	character vector with length==2. Suffix to add to like named variables after the merge. See `merge`
`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)
`fuzzy_settings`	list of arguments to pass to to the fuzzy matching function. See `amatch`.

stringdist amatch computes string distances between every pair of strings in two vectors, then picks the closest string pair for each observation in the dataset. This is used by fuzzy_match to perform a string distance-based match between two datasets. This process can take quite a long time, for quicker matches try adjusting the nthread argument in fuzzy_settings. The default fuzzy_settings are sensible starting points for company name matching, but adjusting these can greatly change how the match performs.

a data.table, the resultant merged data set, including all columns from both data sets.

fedmatch documentation built on Nov. 23, 2021, 1:07 a.m.