PGRdup (version 0.2.2.1)

ReviewProbDup: Retrieve probable duplicate set information from PGR passport database for review

Description

ReviewProbDup retrieves information associated with the probable duplicate sets from the original PGR passport database(s) from which they were identified in order to facilitate manual clerical review.

Usage

ReviewProbDup(pdup, db1, db2 = NULL, extra.db1 = NULL, extra.db2 = NULL, max.count = 30, insert.blanks = TRUE)

Arguments

pdup

An object of class ProbDup.

db1

A data frame of the PGR passport database.

db2

A data frame of the PGR passport database. Required when pdup was created using more than one KWIC Index.

extra.db1

A character vector of extra db1 column names to be retrieved.

extra.db2

A character vector of extra db2 column names to be retrieved.

max.count

The maximum count of probable duplicate sets whose information is to be retrieved.

insert.blanks

logical. If TRUE, inserts a row of /codeNAs after each set.

Value

A data frame of the long/narrow form of the probable duplicate sets data along with associated fields from the original database(s). The core columns in the resulting data frame are as follows:

SET_NO

The set number.

TYPE

The type of probable duplicate set. 'F' for fuzzy, 'P' for phonetic and 'S' for semantic matching sets.

K[*]

The KWIC index or database of origin of the record. The method is specified within the square brackets in the column name.

PRIM_ID

The primary ID of the accession record from which the set could be identified.

IDKW

The 'matching' keywords along with the IDs.

DEL

Column to indicate whether record has to be deleted or not.

SPLIT

Column to indicate whether record has to be branched and assembled into new set.

COUNT

The number of elements in a set.

For the retrieved columns(fields) the prefix K* indicates the KWIC index of origin.

Details

This function helps to retrieve PGR passport information associated with fuzzy, phonetic or semantic probable duplicate sets in an object of class ProbDup from the original databases(s) from which they were identified. The original information of accessions comprising a set, which have not been subjected to data standardization can be compared under manual clerical review for the validation of the set.

By default only the fields(columns) which were used initially for creation of the KWIC indexes using the KWIC function are retrieved. Additional fields(columns) if necessary can be specified using the extra.db1 and extra.db2 arguments.

The output data frame can be subjected to clerical review either after exporting into an external spreadsheet using write.csv function or by using the edit function.

The column DEL can be used to indicate whether a record has to be deleted from a set or not. Y indicates "Yes", and the default N indicates "No".

The column SPLIT similarly can be used to indicate whether a record in a set has to be branched into a new set. A set of identical integers in this column other than the default 0 can be used to indicate that they are to be removed and assembled into a new set.

See Also

DataClean, KWIC, ProbDup

Examples

## Not run: 
# 
# # 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)
# 
# # Get disjoint probable duplicate sets of each kind
# disGNdup <- DisProbDup(GNdup, combine = NULL)
# 
# # Get the data frame for reviewing the duplicate sets identified
# RevGNdup <- ReviewProbDup(pdup = disGNdup, db1 = GN1000,
#                           extra.db1 = c("SourceCountry", "TransferYear"), 
#                           max.count = 30, insert.blanks = TRUE)
# # Examine and review the duplicate sets using edit function
# RevGNdup <- edit(RevGNdup)
# 
# # OR examine and review the duplicate sets after exporting them as a csv file
# write.csv(file="Duplicate sets for review.csv", x=RevGNdup)
#                                                                                                                                           
# ## End(Not run)