5.4 Deleting data

The delete() generic function can be used to manually delete workflow data. This function is used internally within patRoon to implement filtering and subsetting operations, but may also be useful for advanced data processing.

Like the subset operator this function accepts a i and j parameter to specify which data should be operated on:

Class	Argument i	Argument j
features	analysis	feature index
featureGroups	analysis	feature group
formulas, compounds	feature group	candidate index
components	component	feature group

If i or j is not specified (NULL) then data is removed for the complete selection. Some examples are shown below:

# delete 2nd feature in analysis-1
fList <- delete(fList, i = "analysis-1", j = 2)
# delete first ten features in all analyses
fList <- delete(fList, i = NULL, j = 1:10)

# completely remove third/fourth analyses from feature groups
fGroups <- delete(fGroups, i = 3:4)
# delete specific feature group
fGroups <- delete(fGroups, j = "M120_R268_30")
# delete range of feature groups
fGroups <- delete(fGroups, j = 500:750)

# remove all results for a feature group
formulas <- delete(formulas, i = "M120_R268_30")

# remove top candidate for all feature groups
compounds <- delete(compounds, j = 1)

# remove a component
components <- delete(components, i = "CMP1")
# remove specific feature group from a component
components <- delete(components, i = "CMP1", j = "M120_R268_30")
# remove specific feature group from all components
components <- delete(components, j = "M120_R268_30")

The j parameter can also be a function: in this case it is called repeatedly on parts of the data to select what should be deleted. How the function is called and what it should return depends on the workflow data class:

Class	Called on every	First argument	Second argument	Return value
features	analysis	data.table with features	analysis name	Features indices (as integer or logical)
featureGroups	feature group	vector with group intensities	feature group name	The analyses of the features to remove (as character, integer, logical)
formulas, compounds	feature group	data.table with annotations	feature group name	Candidate indices (rows)
components	component	data.table with the component	component name	The feature groups (as character, integer)

Some examples for this:

# remove features with intensities below 5000
fList <- delete(fList, j = function(f, ...) f$intensity <= 5E3)

# same, but for features in all feature groups from specific analyses
fGroups <- delete(i = 1:3, j = function(g, ...) g <= 5E3)

# remove formula candidates with high relative mass deviation
formulas <- delete(formulas, j = function(ft, ...) ft$error > 5)