ProbDup: Identify probable duplicates of accessions

Description

ProbDup identifies probable duplicates of germplasm accessions in KWIC indexes created from PGR passport databases using fuzzy, phonetic and semantic matching strategies.

Usage

ProbDup(
  kwic1,
  kwic2 = NULL,
  method = c("a", "b", "c"),
  excep = NULL,
  chunksize = 1000,
  useBytes = TRUE,
  fuzzy = TRUE,
  max.dist = 3,
  force.exact = TRUE,
  max.alpha = 4,
  max.digit = Inf,
  phonetic = TRUE,
  encoding = c("primary", "alternate"),
  phon.min.alpha = 5,
  min.enc = 3,
  semantic = FALSE,
  syn = NULL
)

Arguments

kwic1

An object of class KWIC.

kwic2

An object of class KWIC. Required for method "b" and "c" only (see Details).

method

The method to be followed for identification of probable duplicates. Either "a", "b" or "c". (see Details).

excep

A vector of the keywords in KWIC not to be used for probable duplicate search (see Details).

chunksize

A value indicating the size of KWIC index keyword block to be used for searching for matches at a time in case of large number of keywords(see Note).

useBytes

logical. If TRUE, performs byte-wise comparison instead of character-wise comparison (see Note).

fuzzy

logical. If TRUE identifies probable duplicates based on fuzzy matching.

max.dist

The maximum levenshtein distance between keyword strings allowed for a match. Default is 3 (see Details).

force.exact

logical. If TRUE, enforces exact matching instead of fuzzy matching for keyword strings which match the criteria specified in arguments max.alpha and max.digit (see Details).

max.alpha

Maximum number of alphabet characters present in a keyword string up to which exact matching is enforced rather than fuzzy matching. Default is 4 (see Details).

max.digit

Maximum number of numeric characters present in a keyword string up to which exact matching is enforced rather than fuzzy matching. Default is Inf (see Details).

phonetic

logical. If TRUE identifies probable duplicates based on phonetic matching.

encoding

Double metaphone encoding for phonetic matching. The default is "primary" (see Details).

phon.min.alpha

Minimum number of alphabet characters to be present in a keyword string for phonetic matching (see Details).

min.enc

Minimum number of characters to be be present in double metaphone encoding of a keyword string for phonetic matching (see Details).

semantic

logical. If TRUE identifies probable duplicates based on semantic matching.

syn

A list with character vectors of synsets (see Details).

Value

A list of class ProbDup containing the following data frames of probable duplicate sets identified along with the corresponding keywords and set counts:

FuzzyDuplicates
PhoneticDuplicates
SemanticDuplicates

Each data frame has the following columns:

`SET_NO`	The set number.
`TYPE`	The type of probable duplicate set. 'F' for fuzzy, 'P' for phonetic and 'S' for semantic matching sets.
`ID`	The primary IDs of records of accessions comprising a set.
`ID:KW`	The 'matching' keywords along with the IDs.
`COUNT`	The number of elements in a set.

The prefix [K*] indicates the KWIC index of origin of the KEYWORD or PRIM_ID.

Details

This function performs fuzzy, phonetic and semantic matching of keywords in KWIC indexes of PGR passport databases (created using KWIC function) to identify probable duplicates of germplasm accessions. The function can execute matching according to either of the following three methods as specified by the method argument.

Method a:: Perform string matching of keywords in a single KWIC index to identify probable duplicates of accessions in a single PGR passport database.
Method b:: Perform string matching of keywords in the first KWIC index (query) with that of the keywords in the second index (source) to identify probable duplicates of accessions of the first PGR passport database among the accessions in the second database.
Method c:: Perform string matching of keywords in two different KWIC indexes jointly to identify probable duplicates of accessions from among two PGR passport databases.

Fuzzy matching or approximate string matching of keywords is carried out by computing the generalized levenshtein (edit) distance between them. This distance measure counts the number of deletions, insertions and substitutions necessary to turn one string to the another. A distance of up to max.dist are considered for a match.

Exact matching will be enforced when the argument force.exact is TRUE. It can be used to avoid fuzzy matching when the number of alphabet characters in keywords is lesser than a critical value (max.alpha). Similarly, the value of max.digit can also be set according to the requirements. The default value of Inf avoids fuzzy matching and enforces exact matching for all keywords having any numerical characters. If max.digit and max.alpha are both set to Inf, exact matching will be enforced for all the keywords.

When exact matching is enforced, for keywords having both alphabet and numeric characters and with the number of alphabet characters greater than max.digit, matching will be carried out separately for alphabet and numeric characters present.

Phonetic matching of keywords is carried out using the Double Metaphone phonetic algorithm (DoubleMetaphone) to identify keywords that have the similar pronunciation. Either the primary or alternate encodings can be used by specifying the encoding argument. The argument phon.min.alpha sets the limits for the number of alphabet characters to be present in a string for executing phonetic matching. Similarly min.enc sets the limits for the number of characters to be present in the encoding of a keyword for phonetic matching.

Semantic matching matches keywords based on a list of accession name synonyms supplied as list with character vectors of synonym sets (synsets) to the syn argument. Synonyms in this context refers to interchangeable identifiers or names by which an accession is recognized. Multiple keywords specified as members of the same synset in syn are merged together. To facilitate accurate identification of synonyms from the KWIC index, identical data standardization operations using the MergeKW and DataClean functions for both the original database fields and the synset list are recommended.

The probable duplicate sets identified initially here may be intersecting with other sets. To get the disjoint sets after the union of all the intersecting sets use the DisProbDup function.

The function AddProbDup can be used to add the information associated with the identified sets in an object of class ProbDup as fields(columns) to the original PGR passport database.

All of the string matching operations here are executed through the stringdist-package functions.

References

van der Loo, M. P. J. 2014. "The Stringdist Package for Approximate String Matching." R Journal 6 (1):111-22. https://journal.r-project.org/archive/2014/RJ-2014-011/index.html.

Examples

Run this code

# NOT RUN {
# Method "a"
#===========

# Load PGR passport database
GN <- GN1000

# Specify as a vector the database fields to be used
GNfields <- c("NationalID", "CollNo", "DonorID", "OtherID1", "OtherID2")

# Clean the data
GN[GNfields] <- lapply(GN[GNfields], function(x) DataClean(x))
y1 <- list(c("Gujarat", "Dwarf"), c("Castle", "Cary"), c("Small", "Japan"),
c("Big", "Japan"), c("Mani", "Blanco"), c("Uganda", "Erect"),
c("Mota", "Company"))
y2 <- c("Dark", "Light", "Small", "Improved", "Punjab", "SAM")
y3 <- c("Local", "Bold", "Cary", "Mutant", "Runner", "Giant", "No.",
        "Bunch", "Peanut")
GN[GNfields] <- lapply(GN[GNfields], function(x) MergeKW(x, y1, delim = c("space", "dash")))
GN[GNfields] <- lapply(GN[GNfields], function(x) MergePrefix(x, y2, delim = c("space", "dash")))
GN[GNfields] <- lapply(GN[GNfields], function(x) MergeSuffix(x, y3, delim = c("space", "dash")))

# Generate KWIC index
GNKWIC <- KWIC(GN, GNfields)

# Specify the exceptions as a vector
exep <- c("A", "B", "BIG", "BOLD", "BUNCH", "C", "COMPANY", "CULTURE",
         "DARK", "E", "EARLY", "EC", "ERECT", "EXOTIC", "FLESH", "GROUNDNUT",
         "GUTHUKAI", "IMPROVED", "K", "KUTHUKADAL", "KUTHUKAI", "LARGE",
         "LIGHT", "LOCAL", "OF", "OVERO", "P", "PEANUT", "PURPLE", "R",
         "RED", "RUNNER", "S1", "SAM", "SMALL", "SPANISH", "TAN", "TYPE",
         "U", "VALENCIA", "VIRGINIA", "WHITE")

# Specify the synsets as a list
syn <- list(c("CHANDRA", "AH114"), c("TG1", "VIKRAM"))

# Fetch probable duplicate sets
GNdup <- ProbDup(kwic1 = GNKWIC, method = "a", excep = exep, fuzzy = TRUE,
                 phonetic = TRUE, encoding = "primary",
                 semantic = TRUE, syn = syn)
GNdup

# Method "b and c"
#=================

# Load PGR passport databases
GN1 <- GN1000[!grepl("^ICG", GN1000$DonorID), ]
GN1$DonorID <- NULL
GN2 <- GN1000[grepl("^ICG", GN1000$DonorID), ]
GN2 <- GN2[!grepl("S", GN2$DonorID), ]
GN2$NationalID <- NULL

# Specify as a vector the database fields to be used
GN1fields <- c("NationalID", "CollNo", "OtherID1", "OtherID2")
GN2fields <- c("DonorID", "CollNo", "OtherID1", "OtherID2")

# Clean the data
GN1[GN1fields] <- lapply(GN1[GN1fields], function(x) DataClean(x))
GN2[GN2fields] <- lapply(GN2[GN2fields], function(x) DataClean(x))
y1 <- list(c("Gujarat", "Dwarf"), c("Castle", "Cary"), c("Small", "Japan"),
c("Big", "Japan"), c("Mani", "Blanco"), c("Uganda", "Erect"),
c("Mota", "Company"))
y2 <- c("Dark", "Light", "Small", "Improved", "Punjab", "SAM")
y3 <- c("Local", "Bold", "Cary", "Mutant", "Runner", "Giant", "No.",
        "Bunch", "Peanut")
GN1[GN1fields] <- lapply(GN1[GN1fields], function(x) MergeKW(x, y1, delim = c("space", "dash")))
GN1[GN1fields] <- lapply(GN1[GN1fields], function(x) MergePrefix(x, y2, delim = c("space", "dash")))
GN1[GN1fields] <- lapply(GN1[GN1fields], function(x) MergeSuffix(x, y3, delim = c("space", "dash")))
GN2[GN2fields] <- lapply(GN2[GN2fields], function(x) MergeKW(x, y1, delim = c("space", "dash")))
GN2[GN2fields] <- lapply(GN2[GN2fields], function(x) MergePrefix(x, y2, delim = c("space", "dash")))
GN2[GN2fields] <- lapply(GN2[GN2fields], function(x) MergeSuffix(x, y3, delim = c("space", "dash")))

# Remove duplicated DonorID records in GN2
GN2 <- GN2[!duplicated(GN2$DonorID), ]

# Generate KWIC index
GN1KWIC <- KWIC(GN1, GN1fields)
GN2KWIC <- KWIC(GN2, GN2fields)

# Specify the exceptions as a vector
exep <- c("A", "B", "BIG", "BOLD", "BUNCH", "C", "COMPANY", "CULTURE",
         "DARK", "E", "EARLY", "EC", "ERECT", "EXOTIC", "FLESH", "GROUNDNUT",
         "GUTHUKAI", "IMPROVED", "K", "KUTHUKADAL", "KUTHUKAI", "LARGE",
         "LIGHT", "LOCAL", "OF", "OVERO", "P", "PEANUT", "PURPLE", "R",
         "RED", "RUNNER", "S1", "SAM", "SMALL", "SPANISH", "TAN", "TYPE",
         "U", "VALENCIA", "VIRGINIA", "WHITE")

# Specify the synsets as a list
syn <- list(c("CHANDRA", "AH114"), c("TG1", "VIKRAM"))

# Fetch probable duplicate sets
GNdupb <- ProbDup(kwic1 = GN1KWIC, kwic2 = GN2KWIC, method = "b",
                  excep = exep, fuzzy = TRUE, phonetic = TRUE,
                  encoding = "primary", semantic = TRUE, syn = syn)
GNdupb

GNdupc <- ProbDup(kwic1 = GN1KWIC, kwic2 = GN2KWIC, method = "c",
                  excep = exep, fuzzy = TRUE, phonetic = TRUE,
                  encoding = "primary", semantic = TRUE, syn = syn)
GNdupc

# }

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