major update bold search public

removed filters as whole data was being retrieved by bold.fetch making the filters here redundant

R/external.bold.public.search.R

@@ -5,20 +5,7 @@
 #' @param taxonomy A single or multiple character vector specifying the taxonomic names at any hierarchical level. Default value is NULL.
 #' @param geography A single or multiple character vector specifying any of the country/province/state/region/sector/site names/codes. Default value is NULL.
 #' @param bins A single or multiple character vector specifying the BIN ids. Default value is NULL.
-#' @param datasets A single or multiple character vector specifying dataset codes. Default value is NULL.
-#' @param filt_latitude A single or a vector of two numbers specifying the latitudinal range in decimal degrees. Values should be separated by a comma. Default value is NULL.
-#' @param filt_longitude A single or a vector of two numbers specifying the longitudinal range in decimal degrees. Values should be separated by a comma. Default value is NULL.
-#' @param filt_shapefile A file path pointing to a shapefile or name of the shapefile (.shp) imported in the R session. Default value is NULL.
-#' @param filt_institutes A single or multiple character vector specifying names of institutes. Default value is NULL.
-#' @param filt_identified.by A single or multiple character vector specifying names of people responsible for identifying the organism. Default value is NULL.
-#' @param filt_seq.source A single or multiple character vector specifying the data portals from where the (sequence) data was mined. Default value is NULL.
-#' @param filt_marker A single or multiple character vector specifying of gene names. Default value is NULL.
-#' @param filt_collection.period A single or a vector of two date values specifying the collection period range (start, end). Values should be separated by a comma. Default value is NULL.
-#' @param filt_basecount A single or a vector of two numbers specifying range of basepairs number. 	Values should be separated by a comma. Default value is NULL.
-#' @param filt_altitude A single or a vector of two numbers specifying the altitude range in meters. Values should be separated by a comma. Default value is NULL.
-#' @param filt_depth A single or a vector of two numbers specifying the depth range. Values should be separated by a comma. Default value is NULL.
-#'
-#' @details `bold.public.search` searches publicly available data on BOLD, retrieving associated proccessids and sampleids, which can then be accessed using `bold.fetch`. Search parameters can include one or a combination of taxonomy, geography, bin uri or dataset codes. While there is no limit on the amount of ID data that can be downloaded, complex combinations of the search parameters may exceed the predetermined weburl character length (2048 characters). Searches using a single parameter are not subject to this limit. For multiparameter searches (e.g. taxonomy + geography + bins/datasets; see the example: Taxonomy + Geography + BIN id), it’s crucial that the parameters are logically combined to ensure accurate and non-empty results. Downloaded IDs can be filtered further on one or a combination of arguments (such as institutes, identifiers, altitude, depth etc.). It is essential to explicitly name the filter arguments (eg. `filt_institutes` = ’CBG’ instead of just ’CBG’) to avoid any errors.
+#' @details `bold.public.search` searches publicly available data on BOLD, retrieving associated proccessids and sampleids, which can then be accessed using `bold.fetch`. Search parameters can include one or a combination of taxonomy, geography, bin uri or dataset codes. While there is no limit on the amount of ID data that can be downloaded, complex combinations of the search parameters may exceed the predetermined weburl character length (2048 characters). Searches using a single parameter are not subject to this limit. For multiparameter searches (e.g. taxonomy + geography + bins/datasets; see the example: Taxonomy + Geography + BIN id), it’s crucial that the parameters are logically combined to ensure accurate and non-empty results.
 #'
 #' @returns A data frame containing all the processids and sampleids related to the query search.
 #'
@@ -52,34 +39,21 @@
 #'
 bold.public.search <- function(taxonomy = NULL,
                                geography = NULL,
-                               bins = NULL,
-                               datasets = NULL,
-                               filt_latitude=NULL,
-                               filt_longitude=NULL,
-                               filt_shapefile=NULL,
-                               filt_institutes=NULL,
-                               filt_identified.by=NULL,
-                               filt_seq.source=NULL,
-                               filt_marker=NULL,
-                               filt_collection.period=NULL,
-                               filt_basecount=NULL,
-                               filt_altitude=NULL,
-                               filt_depth=NULL)
+                               bins = NULL)
 
 {
 
   # Arguments list (list used since combination could entail long vectors of any of the 5 arguments in any numbers and combinations)
 
   args <- list(taxonomy = taxonomy,
                geography = geography,
-               bins = bins,
-               datasets = datasets)
+               bins = bins)
+
+  if (length(args)>1)
 
-  # if (length(args)>1)
-  #
-  # {
-  #   warning("The combination of any of the taxonomy, geography, bins, ids and datasets inputs should be logical otherwise output obtained might either be empty or not correct")
-  # }
+  {
+    warning("The combination of any of the taxonomy, geography, bins, ids and datasets inputs should be logical otherwise output obtained might either be empty or not correct")
+  }
 
   # Filter out NULL values and get their values
 
@@ -163,30 +137,21 @@ bold.public.search <- function(taxonomy = NULL,
 
       result.rem.col=lapply(result.post.filter,
                             function (df)
-                              {
+                            {
                               res=check_and_return_preset_df(df,
-                                        category = "check_return",
-                                        preset = public.data.fields)
+                                                             category = "check_return",
+                                                             preset = public.data.fields)
                               return(res)
-                              }
-                            )
-
-      # Finding out the common columns from all the results
-
-      # common.cols=Reduce(intersect,
-      #                    lapply(result.rem.col,
-      #                           colnames))
-
-      # Using the above column names vector to select common columns from all dataframes
-
-      # res.comm.col <- lapply(result.rem.col,
-      #                        function(df) df[, common.cols, drop = FALSE])
+                            }
+      )
 
 
       # Binding the list of dataframes
 
       result=result.rem.col%>%
-        bind_rows(.)
+        bind_rows(.)%>%
+        dplyr::select(processid,
+                      sampleid)
 
     }
 
@@ -199,33 +164,6 @@ bold.public.search <- function(taxonomy = NULL,
 
   }
 
-result = bold.connectr.filters(bold.df = result,
-                                   latitude=filt_latitude,
-                                   longitude=filt_longitude,
-                                   shapefile=filt_shapefile,
-                                   institutes=filt_institutes,
-                                   identified.by=filt_identified.by,
-                                   seq.source=filt_seq.source,
-                                   marker=filt_marker,
-                                   collection.period=filt_collection.period,
-                                   basecount=filt_basecount,
-                                   altitude=filt_altitude,
-                                   depth=filt_depth)
-
-  # Generate only processid and sampleid output
-
-  final.res = result%>%
-    dplyr::select(processid,
-                  sampleid)
-
-  # Condition to state if the result is empty
-
-  if(nrow(final.res)==0)
-
-  {
-    stop("Result is an empty dataframe. Please recheck the search queries")
-  }
-
-  return(final.res)
+  return(result)
 
 }