This package is used to study complex survey data. It aims to provide an efficient computing framework for large survey data. All types of survey design are supported by this package.
NOTE: For multistage sampling a single stage approximation is used.1
] add SurveyThe SurveyDesign constructor can take data corresponding to any type of design.
Depending on the keyword arguments passed, the data is processed in order to obtain
correct results for the given design.
The following examples show how to create and manipulate different survey designs using the Academic Performance Index dataset for Californian schools.
A survey design can be created by calling the constructor with some keywords, depending on the survey type. Let's create a simple random sample, a stratified sample, a single-stage and a two-stage cluster sample.
julia> apisrs = load_data("apisrs");
julia> srs = SurveyDesign(apisrs; weights=:pw)
SurveyDesign:
data: 200×47 DataFrame
strata: none
cluster: none
popsize: [6190.0, 6190.0, 6190.0 … 6190.0]
sampsize: [200, 200, 200 … 200]
weights: [31.0, 31.0, 31.0 … 31.0]
probs: [0.0323, 0.0323, 0.0323 … 0.0323]
julia> apistrat = load_data("apistrat");
julia> dstrat = SurveyDesign(apistrat; strata=:stype, weights=:pw)
SurveyDesign:
data: 200×46 DataFrame
strata: stype
[E, E, E … H]
cluster: none
popsize: [6190.0, 6190.0, 6190.0 … 6190.0]
sampsize: [200, 200, 200 … 200]
weights: [44.2, 44.2, 44.2 … 15.1]
probs: [0.0226, 0.0226, 0.0226 … 0.0662]
julia> apiclus1 = load_data("apiclus1");
julia> dclus1 = SurveyDesign(apiclus1; clusters=:dnum, weights=:pw)
SurveyDesign:
data: 183×46 DataFrame
strata: none
cluster: dnum
[637, 637, 637 … 448]
popsize: [6190.0, 6190.0, 6190.0 … 6190.0]
sampsize: [15, 15, 15 … 15]
weights: [33.8, 33.8, 33.8 … 33.8]
probs: [0.0295, 0.0295, 0.0295 … 0.0295]
julia> apiclus2 = load_data("apiclus2");
julia> dclus2 = SurveyDesign(apiclus2; clusters=[:dnum, :snum], weights=:pw)
SurveyDesign:
data: 126×47 DataFrame
strata: none
cluster: dnum
[15, 63, 83 … 795]
popsize: [5130.0, 5130.0, 5130.0 … 5130.0]
sampsize: [40, 40, 40 … 40]
weights: [18.9, 18.9, 18.9 … 18.9]
probs: [0.0528, 0.0528, 0.0528 … 0.0528]Using these designs we can compute estimates of statistics such as mean and population total. The designs must first be resampled using bootstrapping in order to compute the standard errors.
julia> bootsrs = bootweights(srs; replicates=1000)
ReplicateDesign{BootstrapReplicates}:
data: 200×1047 DataFrame
strata: none
cluster: none
popsize: [6190.0, 6190.0, 6190.0 … 6190.0]
sampsize: [200, 200, 200 … 200]
weights: [31.0, 31.0, 31.0 … 31.0]
allprobs: [0.0323, 0.0323, 0.0323 … 0.0323]
type: bootstrap
replicates: 1000
julia> mean(:api00, bootsrs)
1×2 DataFrame
Row │ mean SE
│ Float64 Float64
─────┼──────────────────
1 │ 656.585 9.5409
julia> total(:enroll, bootsrs)
1×2 DataFrame
Row │ total SE
│ Float64 Float64
─────┼──────────────────────
1 │ 3.62107e6 1.72846e5Now we know the mean academic performance index from the year 2000 and the total number of students enrolled in the sampled Californian schools. We can also calculate the statistic of multiple variables in one go...
julia> mean([:api99, :api00], bootsrs)
2×3 DataFrame
Row │ names mean SE
│ String Float64 Float64
─────┼──────────────────────────
1 │ api99 624.685 9.84669
2 │ api00 656.585 9.5409... and we can also calculate domain estimates:
julia> total(:enroll, :cname, bootsrs)
38×3 DataFrame
Row │ cname total SE
│ String15 Float64 Any
─────┼────────────────────────────────────────────
1 │ Kern 1.95823e5 74731.2
2 │ Los Angeles 867129.0 1.36622e5
3 │ Orange 1.68786e5 63858.0
4 │ San Luis Obispo 6720.49 6790.49
⋮ │ ⋮ ⋮ ⋮
35 │ Calaveras 12976.4 13241.6
36 │ Napa 39239.0 30181.9
37 │ Lake 6410.79 6986.29
38 │ Merced 15392.1 15202.2
30 rows omittedThis gives us the total number of enrolled students in each county.
All functionalities are supported by each design type.
We want to implement all the features provided by the Survey package in R in a Julia-native way. The main goal is to have a complete package that provides a large range of functionality and takes efficiency into consideration for large surveys to be analysed fast.
The milestones section of the repository contains a list of features that contributors can implement in the short-term. Please see contributing guidelines on how to contribute to the project.
We gratefully acknowledge the JuliaLab at MIT for financial support for this project.