Morning StackOverflow,

I am creating a function that searches through a single column ColumnOfDatasetToSearch of a matrix Dataset for a number of search terms SearchFeatures. It works well for matrices that have 10^4 rows but really slows down when the row number gets above 10^6 or when SearchFeatures has more than a 100 terms. I thought that vectorizing the ColumnOfDatasetToSearch would improve my speed but only had modest performance improvement.

ListSearcher <- function(SearchFeatures, Dataset, ColumnOfDatasetToSearch){
  RowNumber <- NA
  ColumnOfInterest <- pull(Dataset, ColumnOfDatasetToSearch)
  LengthOfSearchTerms <- length(SearchFeatures)
  for (j in 1:LengthOfSearchTerms){
    if(length(i <- grep(SearchFeatures[j], ColumnOfInterest)))
      RowNumber <- append(RowNumber, i)
  }
  IdentifiersWithThoseSerchTerms <- unique(na.omit((Dataset$Identifiers[RowNumber])))
  return(IdentifiersWithThoseSerchTerms)
}

Thanks in advance for your suggestions.

NewToCoding

CodePudding user response：

Imagine you are using the dataset iris and want to return the column Petal.Length instead of Identifiers.

Does this work? It should be considerably faster

ListSearcher <- function(SearchFeatures, Dataset, ColumnOfDatasetToSearch){
  searchstring <- paste0(SearchFeatures, collapse =  "|")
  selection <- grepl(searchstring, Dataset[[ColumnOfDatasetToSearch]])
  Dataset[selection, ]$Petal.Length
}
# try with a subset of iris
iris[c(1,2,51,52), ]
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
#> 1           5.1         3.5          1.4         0.2     setosa
#> 2           4.9         3.0          1.4         0.2     setosa
#> 51          7.0         3.2          4.7         1.4 versicolor
#> 52          6.4         3.2          4.5         1.5 versicolor
ListSearcher(c("ver", "se") , iris[c(1,2,51,52), ], "Species")
#> [1] 1.4 1.4 4.7 4.5