| select | R Documentation | 
Wrapper to GMQL PROJECT operator
It creates, from an existing dataset, a new dataset with all the samples from input dataset, but keeping for each sample in the input dataset only those metadata and/or region attributes specified. Region coordinates and values of the remaining metadata and/or region attributes remain equal to those in the input dataset. It allows to:
Remove existing metadata and/or region attributes from a dataset
Update or set new metadata and/or region attributes in the result
## S4 method for signature 'GMQLDataset'
select(
  .data,
  metadata = NULL,
  metadata_update = NULL,
  all_but_meta = FALSE,
  regions = NULL,
  regions_update = NULL,
  all_but_reg = FALSE
)
| .data | GMQLDataset class object | 
| metadata | vector of strings made up by metadata attributes | 
| metadata_update | list of updating rules in the form of key = value generating new metadata attributes and/or attribute values. The following options are available: 
 | 
| all_but_meta | logical value indicating which metadata you want to exclude; If FALSE, only the metadata attributes specified in metadata argument are kept in the output of the operation; if TRUE, the metadata are all kept except those in metadata argument. If metadata input parameter is not defined all_but_meta is not considerd. | 
| regions | vector of strings made up by region attributes | 
| regions_update | list of updating rules in the form of key = value generating new genomic region attributes and/or values. The following options are available: 
 | 
| all_but_reg | logical value indicating which region attributes you want to exclude; if FALSE, only the regions attributes specified in regions argumentare kept in the output of the operation; if TRUE, the regions attributes are all kept except those in regions argument. If regions is not defined, all_but_reg is not considerd. | 
GMQLDataset object. It contains the value to use as input for the subsequent GMQLDataset method
## This statement initializes and runs the GMQL server for local execution 
## and creation of results on disk. Then, with system.file() it defines 
## the path to the folder "DATASET" in the subdirectory "example" 
## of the package "RGMQL" and opens such folder as a GMQL dataset 
## named "data"
init_gmql()
test_path <- system.file("example", "DATASET", package = "RGMQL")
data = read_gmql(test_path)
## This statement creates a new dataset called CTCF_NORM_SCORE by preserving 
## all region attributes apart from score, and creating a new region 
## attribute called new_score by dividing the existing score value of each 
## region by 1000.0 and incrementing it by 100.
## It also generates, for each sample of the new dataset, 
## a new metadata attribute called normalized with value 1, 
## which can be used in future selections.
CTCF_NORM_SCORE = select(data, metadata_update = list(normalized = 1), 
    regions_update = list(new_score = (score / 1000.0) + 100), 
    regions = c("score"), all_but_reg = TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.