rqdatatable is an implementation of the
rquery piped Codd-style relational algebra hosted on
rquery allow the expression of complex transformations as a series of relational operators and
rqdatatable implements the operators using
For example scoring a logistic regression model (which requires grouping, ordering, and ranking) is organized as follows. For more on this example please see “Let’s Have Some Sympathy For The Part-time R User”.
## Loading required package: rquery
scale <- 0.237 # example rquery pipeline rquery_pipeline <- local_td(dL) %.>% extend_nse(., probability := exp(assessmentTotal * scale)) %.>% normalize_cols(., "probability", partitionby = 'subjectID') %.>% pick_top_k(., k = 1, partitionby = 'subjectID', orderby = c('probability', 'surveyCategory'), reverse = c('probability', 'surveyCategory')) %.>% rename_columns(., c('diagnosis' = 'surveyCategory')) %.>% select_columns(., c('subjectID', 'diagnosis', 'probability')) %.>% orderby(., cols = 'subjectID')
We can show the expanded form of query tree.
table(dL; subjectID, surveyCategory, assessmentTotal) %.>% extend(., probability := exp(assessmentTotal * 0.237)) %.>% extend(., probability := probability / sum(probability), p= subjectID) %.>% extend(., row_number := row_number(), p= subjectID, o= "probability" DESC, "surveyCategory" DESC) %.>% select_rows(., row_number <= 1) %.>% rename(., c('diagnosis' = 'surveyCategory')) %.>% select_columns(., subjectID, diagnosis, probability) %.>% orderby(., subjectID)
And execute it using
## subjectID diagnosis probability ## 1: 1 withdrawal behavior 0.6706221 ## 2: 2 positive re-framing 0.5589742
One can also apply the pipeline to new tables.
## subjectID diagnosis probability ## 1: 7 positive re-framing 0.9722128
Initial bench-marking of
rqdatatable is very favorable (notes here).
rqdatatable has an “immediate mode” which allows direct application of pipelines stages without pre-assembling the pipeline. “Immediate mode” is a convenience for ad-hoc analyses, and has some negative performance impact, so we encourage users to build pipelines for most work. Some notes on the issue can be found here.
rqdatatable is a fairly complete implementation of
rquery. The main differences are the
rqdatatable implementations of
theta_join() are implemented by round-tripping through a database handle specified by the
rquery.rquery_db_executor option (so it is not they are not very desirable implementation).
rqdatatable please use