Comparison with other survey analysis tools

There are multiple alternatives that offer survey analysis tools, most notably SAS, Stata and R.

R comparison

The inspiration for Survey.jl comes from R. Hence the syntax is in most cases very similar to the syntax in the survey package from R. To showcase this we will use the api datasets found in both R's survey and Survey.jl. See the Tutorial section for more details about the api datesets.

All examples show the R code first, followed by the Julia code.

Loading data

> data(api)
# all `api` datasets are loaded globally

julia> srs = load_data("apisrs")
# only one dataset is loaded and stored in a variable

Creating a design

> srs = svydesign(id=~1, data=apisrs, weights=~pw) # simple random sample
> dstrat = svydesign(id=~1, data=apistrat, strata=~stype, weights=~pw) # stratified
> clus1 = svydesign(id=~dnum, data=apiclus1, weights=~pw) # clustered (one stage)

julia> srs = SurveyDesign(apisrs; weights=:pw) # simple random sample
julia> dstrat = SurveyDesign(apistrat; strata=:stype, weights=:pw) # stratified
julia> clus1 = SurveyDesign(apiclus1; clusters=:dnum, weights=:pw) # clustered (one stage)

Creating a replicate design

> bsrs = as.svrepdesign(srs, type="subbootstrap")

julia> bsrs = bootweights(srs)

Computing the estimated mean

> svymean(~api00, bsrs)
> svymean(~api99+~api00, bsrs)

julia> mean(:api00, bsrs)
julia> mean([:api99, :api00], bsrs)

Computing the estimated total

> svytotal(~api00, bsrs)
> svytotal(~api99+~api00, bsrs)

julia> total(:api00, bsrs)
julia> total([:api99, :api00], bsrs)

Computing quantiles

> svyquantile(~api00, bsrs, 0.5)
> svyquantile(~api00, bsrs, c(0.25, 0.5, 0.75))

julia> quantile(:api00, bsrs, 0.5)
julia> quantile(:api00, bsrs, [0.25, 0.5, 0.75])

Domain estimation

> svyby(~api00, ~cname, bsrs, svymean)

julia> mean(:api00, :cname, bsrs)