ReviewProbDup
Retrieve probable duplicate set information from PGR passport database for review
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
pdupwas created using more than one KWIC Index. - extra.db1
- A character vector of extra
db1column names to be retrieved. - extra.db2
- A character vector of extra
db2column 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.
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.
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. |
K* indicates the KWIC index of
origin.
Note
When any primary ID/key records in the fuzzy, phonetic or semantic
duplicate sets are found to be missing from the original databases
db1 and db2, then they are ignored and only the matching
records are considered for retrieving the information with a warning.
This may be due to data standardization of the primary ID/key field using
the function DataClean before creation of the KWIC
index and subsequent identification of probable duplicate sets. In such a
case, it is recommended to use an identical data standardization operation
on the databases db1 and db2 before running this function.
With R <= v3.0.2,="" due="" to="" copying="" of="" named="" objects="" by="" Invalid .internal.selfref detected and fixed... warning can appear,
which may be safely ignored.
See Also
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)