As an example:
rquery operators allow us to write our earlier “treatment and control” example as follows.
dQ <- d %.>% extend_se(., if_else_block( testexpr = "rand()>=0.5", thenexprs = qae( a_1 := 'treatment', a_2 := 'control'), elseexprs = qae( a_1 := 'control', a_2 := 'treatment'))) %.>% select_columns(., c("rowNum", "a_1", "a_2"))
rquery pipelines are first-class objects; so we can extend them, save them, and even print them.
cat(format(dQ)) table('d') %.>% extend(., ifebtest_1 := rand() >= 0.5) %.>% extend(., a_1 := ifelse(ifebtest_1, "treatment", a_1), a_2 := ifelse(ifebtest_1, "control", a_2)) %.>% extend(., a_1 := ifelse(!( ifebtest_1 ), "control", a_1), a_2 := ifelse(!( ifebtest_1 ), "treatment", a_2)) %.>% select_columns(., rowNum, a_1, a_2)
rquery targets only databases, and right now primarilly
rquery is primarily a
SQL generator, allowing it to avoid some of the trade-offs required to directly support in-memory
data.frames. We demonstrate converting the above
rquery pipeline into
SQL and executing it here.
rquery itself is still in early development (and not yet ready for extensive use in production), but it is maturing fast, and we expect more
rquery announcements going forward. Our current intent is to bring in sponsors, partners, and
R community voices to help develop and steer
Categories: Pragmatic Data Science
Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.