A tbl_svy wraps a locally stored svydesign and adds methods for dplyr single-table verbs like mutate, group_by and summarise. Create a tbl_svy using as_survey_design.

Methods

tbl_df implements these methods from dplyr.

select or rename

Select or rename variables in a survey's dataset.

mutate or transmute

Modify and create variables in a survey's dataset.

group_by and summarise

Get descriptive statistics from survey.

Examples

library(survey) library(dplyr) data(api) svy <- as_survey_design(apistrat, strata = stype, weights = pw) svy
#> Stratified Independent Sampling design (with replacement) #> Called via srvyr #> Sampling variables: #> - ids: `1` #> - strata: stype #> - weights: pw #> Data variables: cds (chr), stype (fct), name (chr), sname (chr), snum (dbl), #> dname (chr), dnum (int), cname (chr), cnum (int), flag (int), pcttest (int), #> api00 (int), api99 (int), target (int), growth (int), sch.wide (fct), #> comp.imp (fct), both (fct), awards (fct), meals (int), ell (int), yr.rnd #> (fct), mobility (int), acs.k3 (int), acs.46 (int), acs.core (int), pct.resp #> (int), not.hsg (int), hsg (int), some.col (int), col.grad (int), grad.sch #> (int), avg.ed (dbl), full (int), emer (int), enroll (int), api.stu (int), pw #> (dbl), fpc (dbl)
# Data manipulation verbs --------------------------------------------------- filter(svy, pcttest > 95)
#> Stratified Independent Sampling design (with replacement) #> Called via srvyr #> Sampling variables: #> - ids: `1` #> - strata: stype #> - weights: pw #> Data variables: cds (chr), stype (fct), name (chr), sname (chr), snum (dbl), #> dname (chr), dnum (int), cname (chr), cnum (int), flag (int), pcttest (int), #> api00 (int), api99 (int), target (int), growth (int), sch.wide (fct), #> comp.imp (fct), both (fct), awards (fct), meals (int), ell (int), yr.rnd #> (fct), mobility (int), acs.k3 (int), acs.46 (int), acs.core (int), pct.resp #> (int), not.hsg (int), hsg (int), some.col (int), col.grad (int), grad.sch #> (int), avg.ed (dbl), full (int), emer (int), enroll (int), api.stu (int), pw #> (dbl), fpc (dbl)
select(svy, starts_with("acs")) # variables used in survey design are automatically kept
#> Stratified Independent Sampling design (with replacement) #> Called via srvyr #> Sampling variables: #> - ids: `1` #> - strata: stype #> - weights: pw #> Data variables: acs.k3 (int), acs.46 (int), acs.core (int)
summarise(svy, col.grad = survey_mean(col.grad))
#> col.grad col.grad_se #> 1 19.86719 1.055931
mutate(svy, api_diff = api00 - api99)
#> Stratified Independent Sampling design (with replacement) #> Called via srvyr #> Sampling variables: #> - ids: `1` #> - strata: stype #> - weights: pw #> Data variables: cds (chr), stype (fct), name (chr), sname (chr), snum (dbl), #> dname (chr), dnum (int), cname (chr), cnum (int), flag (int), pcttest (int), #> api00 (int), api99 (int), target (int), growth (int), sch.wide (fct), #> comp.imp (fct), both (fct), awards (fct), meals (int), ell (int), yr.rnd #> (fct), mobility (int), acs.k3 (int), acs.46 (int), acs.core (int), pct.resp #> (int), not.hsg (int), hsg (int), some.col (int), col.grad (int), grad.sch #> (int), avg.ed (dbl), full (int), emer (int), enroll (int), api.stu (int), pw #> (dbl), fpc (dbl), api_diff (int)
# Group by operations ------------------------------------------------------- # To calculate survey svy_group <- group_by(svy, dname) summarise(svy, col.grad = survey_mean(col.grad), api00 = survey_mean(api00, vartype = "ci"))
#> col.grad col.grad_se api00 api00_low api00_upp #> 1 19.86719 1.055931 662.2874 643.4814 681.0934