A Visualization Tool for Mining Large Correlation Tables 101
TABLE B.1
Template for joining large numbers of SSC tables and creating an AN for them.
a.n <- a.nav.create(cbind(
"family.ID"=as.numeric(v.families),
v.sites, v.srs.bg, v.individual,
v.family, v.parent.race, v.parent.common,
v.proband.cdv, v.proband.ocuv, v.sibling.s1, v.sibling.s2,
v.ados.common,
v.ados.1, v.ados.1.raw, v.ados.2, v.ados.2.raw,
v.ados.3, v.ados.3.raw, v.ados.4, v.ados.4.raw,
v.adi.r.diagnostic, v.adi.r.pca, v.adi.r,
v.adi.r.dum, v.adi.r.loss,
v.ssc.diagnosis,
v.vineland.ii.p1, v.vineland.ii.s1,
v.cbcl.2.5.p1, v.cbcl.2.5.s1,
v.cbcl.6.18.p1, v.cbcl.6.18.s1,
v.abc, v.abc.raw, v.rbs.r, v.rbs.r.raw,
v.srs.parent.p1, v.srs.parent.recode.p1,
v.srs.teacher.p1, v.srs.teacher.recode.p1,
v.srs.parent.s1, v.srs.parent.recode.s1,
v.srs.teacher.s1, v.srs.teacher.recode.s1,
v.srs.adult.fa, v.srs.adult.recode.fa,
v.srs.adult.mo, v.srs.adult.recode.mo,
v.bapq.fa, v.bapq.recode.fa, v.bapq.mo, v.bapq.recode.mo,
v.fhi.interviewer.fa, v.fhi.interviewer.mo,
v.scq.current.p1, v.scq.life.p1,
v.scq.current.s1, v.scq.life.s1,
v.ctopp.nr, v.purdue.pegboard, v.dcdq, v.ppvt,
v.das.ii.early.years, v.das.ii.school.age,
v.ctrf.2.5, v.trf.6.18,
v.ssc.med.hx.v2.autoimmune.disorders, v.ssc.med.hx.v2.birth.defects,
v.ssc.med.hx.v2.chronic.illnesses, v.ssc.med.hx.v2.diet.medication.sleep,
v.ssc.med.hx.v2.genetic.disorders, v.ssc.med.hx.v2.labor.delivery.birth.feeding,
v.ssc.med.hx.v2.language.disorders,
v.ssc.med.hx.v2.medical.history.child.1, v.ssc.med.hx.v2.medical.history.child.2,
v.ssc.med.hx.v2.medical.history.child.3,
v.ssc.med.hx.v2.medications.drugs.mother,
v.ssc.med.hx.v2.neurological.conditions,
v.ssc.med.hx.v2.other.developmental.disorders, v.ssc.med.hx.v2.pdd,
v.ssc.med.hx.v2.pregnancy.history, v.ssc.med.hx.v2.pregnancy.illness.vaccinations,
v.ssc.psuh.fa, vv.ssc.psuh.mo,
v.temperature.form.raw
), remove=T )
Readers should make a selection from this template as the full collection creates a data
matrix with about 3000 variables.
R environments represent one of the few data types that disobeys the functional
programming paradigm that is otherwise fundamental to R. As a consequence, assignment
of an AN does not allocate a new copy but passes a reference instead. In particular, the
R statement
b.n <- a.n