Rearrange sections and other changes.

docs/make.jl

@@ -15,9 +15,8 @@ makedocs(;
     ),
     pages=[
         "Home" => "index.md",
-        "Examples" => "examples.md",
-        "Comparison with R" => "R_comparison.md",
-        "Performance" => "performance.md",
+        "Moving from R" => "R_comparison.md",
+        "API reference" => "api.md"
     ],
     checkdocs=:exports,
 )

docs/src/R_comparison.md

@@ -1,98 +1,121 @@
-# Comparison with R
+# Moving from R to Julia
+This sections presents examples to help move from R to Julia. Examples show R and Julia code for common operations in survey analysis. <br>
+For the same operation, first the R and then the Julia code is presented. 
 
-In the following examples, we'll compare Julia's performance to R's on the same set of operations.
+## Simple random sample
 
-## Installing and loading the package
-**R**
+The `apisrs` data, which is provided in both `survey` and `Survey`, is used as an example. It's a simple random sample of the Academic Performance Index of Californian schools.
 
-```r
-install.package("survey")
+### 1. Creating a design object
+The following example shows how to construct a design object for a simple random sample. 
+
+```R
 library(survey)
+data(api)
+dsrs = svydesign(id = ~1, data = apisrs, weights = ~pw, fpc = ~fpc)
 ```
 
-**Julia**
 ```julia
-using Pkg
-Pkg.add(url = "https://github.com/xKDR/Survey.jl.git")
 using Survey
+srs = load_data("apisrs")
+dsrs = SimpleRandomSample(srs; popsize = :fpc)
 ```
 
-The following command in the Pkg REPL may also be used to install the package.
+### 2. Mean
+In the following example the mean of the variable `api00` is calculated. 
+
+```R
+svymean(~api00, dsrs)
 ```
-add "https://github.com/xKDR/Survey.jl.git"
+```julia
+mean(:api00, dsrs)
 ```
 
-## API data
+### 3. Total
+In the following example the sum of the variable `api00` is calculated. 
 
-The Academic Performance Index is computed for all California schools based on standardised testing of students. The [data sets](https://cran.r-project.org/web/packages/survey/survey.pdf) contain information for all schools with at least 100 students and for various probability samples of the data. apiclus1 is a cluster sample of school districts, apistrat is a sample stratified by stype.
+```R
+svytotal(~api00, dsrs)
+```
+```julia
+total(:api00, dsrs)
+```
 
-In the following examples, we'll use the apiclus1 data from the api dataset.
+### 4. Quantile
+In the following example the median of the variable `api00` is calculated.
+```R
+svyquantile(~api00, dsrs, 0.5)
+```
+```julia
+quantile(:api00, dsrs, 0.5)
+```
 
-The api dataset can be loaded using the following command:
+### 5. Domain estimation
+In the following example the mean of the variable `api00` is calculated grouped by the variable `cname`. 
 
-**R**
-```r
-data(api)
+```R
+svyby(~api00, ~cname, dsrs, svymean)
 ```
 
-**Julia**
 ```julia
-apiclus1 = load_data("apiclus1")
+by(:api00, :cname, dsrs, mean)
 ```
 
-## svydesign
-[The ```svydesign``` object combines a data frame and all the survey design information needed to analyse it.](https://www.rdocumentation.org/packages/survey/versions/4.1-1/topics/svydesign)
+## Stratified sample
 
-A ```design``` object can be constructed with the following command:
+The `apistrat` data, which is provided in both `survey` and `Survey`, is used as an example. It's a stratified sample of the Academic Performance Index of Californian schools.
 
-**R**
-```r
-dclus1 <-svydesign(id = ~1, weights = ~pw, data = apiclus1, fpc = ~fpc)
+### 1. Creating a design object
+The following example shows how to construct a design object for a stratified sample. 
+
+```R
+library(survey)
+data(api)
+dstrat = svydesign(id = ~1, data = apistrat, strata = ~stype, weights = ~pw, fpc = ~fpc)
 ```
 
-**Julia**
 ```julia
-dclus1 = design(id = :1, weights = :pw, data = apiclus1, fpc = :fpc)
+using Survey
+strat = load_data("apistrat")
+dstrat = StratifiedSample(strat, :stype; popsize  = :fpc)
 ```
 
-## by
-The `by` function can be used to generate stratified estimates.
-
-### Mean
-Weighted mean of a variable by strata can be computed using the following command:
+### 2. Mean
+In the following example the mean of the variable `api00` is calculated. 
 
-**R**
-```r
-svyby(~api00, by = ~cname, design = dclus1, svymean)
+```R
+svymean(~api00, dstrat)
 ```
-
-**Julia**
 ```julia
-by(:api00, :cname, dclus1, mean)
+mean(:api00, dstrat)
 ```
 
-### Sum
-Weighted sum of a variable by strata can be computed using the following command:
+### 3. Total
+In the following example the sum of the variable `api00` is calculated. 
 
-**R**
-```r
-svyby(~api00, by = ~cname, design = dclus1, svytotal)
+```R
+svytotal(~api00, dstrat)
+```
+```julia
+total(:api00, dstrat)
 ```
 
-**Julia**
+### 4. Quantile
+In the following example the median of the variable `api00` is calculated.
+```R
+svyquantile(~api00, dstrat, 0.5)
+```
 ```julia
-by(:api00, :cname, dclus1, total)
+quantile(:api00, dstrat, 0.5)
 ```
 
-### Quantile
-Weighted quantile of a variable by strata can be computed using the following command:
+### 5. Domain estimation
+In the following example the mean of the variable `api00` is calculated grouped by the variable `cname`. 
 
-**R**
-```r
-svyby(~api00, by = ~cname, design = dclus1, svyquantile, quantile = 0.63)
+```R
+svyby(~api00, ~cname, dstrat, svymean)
 ```
 
-**Julia**
 ```julia
-by(:api00, :cname, dclus1, quantile, 0.63)
-```
+by(:api00, :cname, dstrat, mean)
+```

docs/src/api.md

@@ -0,0 +1,32 @@
+# API
+
+## Index
+
+```@index
+Module = [Survey]
+Order = [:type, :function]
+Private = false
+```
+
+```@docs
+AbstractSurveyDesign
+SimpleRandomSample
+StratifiedSample
+load_data
+mean(x::Symbol, design::SimpleRandomSample)
+total(x::Symbol, design::SimpleRandomSample)
+quantile
+by
+colnames(design::AbstractSurveyDesign)
+dim(design::AbstractSurveyDesign)
+dimnames(design::AbstractSurveyDesign)
+plot(design::AbstractSurveyDesign, x::Symbol, y::Symbol; kwargs...)
+boxplot(design::AbstractSurveyDesign, x::Symbol, y::Symbol; kwargs...)
+hist(design::AbstractSurveyDesign, var::Symbol,
+				 bins::Union{Integer, AbstractVector} = freedman_diaconis(design, var);
+				 normalization = :density,
+				 kwargs...
+    			)
+freedman_diaconis
+sturges
+```

docs/src/index.md

@@ -4,42 +4,59 @@ CurrentModule = Survey
 
 # Survey
 
-This package is the Julia implementation of the [Survey package in R](https://cran.r-project.org/web/packages/survey/index.html) developed by [Professor Thomas Lumley](https://www.stat.auckland.ac.nz/people/tlum005).
+This package is used to study complex survey data. It aims to be a fast alternative to the [Survey package in R](https://cran.r-project.org/web/packages/survey/index.html) developed by [Professor Thomas Lumley](https://www.stat.auckland.ac.nz/people/tlum005).
 
-## The need for moving the code to Julia.
+This package currently supports simple random sample and stratified sample. In future releases, it will support multistage sampling as well. 
 
-At [xKDR](https://xkdr.org/) we processed millions of records from household surveys using the survey package in R. This process took hours of computing time. By implementing the code in Julia, we are able to do the processing in seconds. In this package we have implemented the functions `mean`, `quantile` and `sum`. We have kept the syntax between the two packages similar so that we can easily move our existing code to the new language.
+## Basic demo
 
-## Index
+The following demo uses the
+[Academic Performance Index](https://r-survey.r-forge.r-project.org/survey/html/api.html)
+(API) dataset for Californian schools. The data sets contain information for all schools
+with at least 100 students and for various probability samples of the data.
 
-```@index
-Module = [Survey]
-Order = [:type, :function]
-Private = false
+The API program has been discontinued at the end of 2018. Information is archived at
+[https://www.cde.ca.gov/re/pr/api.asp](https://www.cde.ca.gov/re/pr/api.asp)
+
+Firstly, a survey design needs a dataset from which to gather information. 
+
+
+The sample datasets provided with the package can be loaded as `DataFrames` using the `load_data` function:
+
+```julia
+julia> apisrs = load_data("apisrs");
 ```
+`apisrs` is a simple random sample of the Academic Performance Index of Californian schools.
+
+Next, we can build a design. The design corresponding to a simple random sample is [`SimpleRandomSample`](@ref), which can be instantiated by calling the constructor:
+
+```julia
+julia> srs = SimpleRandomSample(apisrs; weights = :pw)
+SimpleRandomSample:
+data: 200x42 DataFrame
+weights: 31.0, 31.0, 31.0, ..., 31.0
+probs: 0.0323, 0.0323, 0.0323, ..., 0.0323
+fpc: 6194, 6194, 6194, ..., 6194
+popsize: 6194
+sampsize: 200
+sampfraction: 0.0323
+ignorefpc: false
+```
+
+With a `SimpleRandomSample` (as well as with any subtype of [`AbstractSurveyDesign`](@ref)) it is possible to calculate estimates of the mean, population total, etc., for a given variable, along with the corresponding standard errors.
+
+```julia
+julia> mean(:api00, srs)
+1×2 DataFrame
+ Row │ mean     sem     
+     │ Float64  Float64 
+─────┼──────────────────
+   1 │ 656.585  9.24972
 
-## API
-```@docs
-AbstractSurveyDesign
-SimpleRandomSample
-StratifiedSample
-ClusterSample
-design
-load_data
-mean(x::Symbol, design::SimpleRandomSample)
-total(x::Symbol, design::SimpleRandomSample)
-quantile
-by
-colnames(design::AbstractSurveyDesign)
-dim(design::AbstractSurveyDesign)
-dimnames(design::AbstractSurveyDesign)
-plot(design::AbstractSurveyDesign, x::Symbol, y::Symbol; kwargs...)
-boxplot(design::AbstractSurveyDesign, x::Symbol, y::Symbol; kwargs...)
-hist(design::AbstractSurveyDesign, var::Symbol,
-				 bins::Union{Integer, AbstractVector} = freedman_diaconis(design, var);
-				 normalization = :density,
-				 kwargs...
-    			)
-freedman_diaconis
-sturges
+julia> total(:api00, srs)
+1×2 DataFrame
+ Row │ total      se_total 
+     │ Float64    Float64  
+─────┼─────────────────────
+   1 │ 4.06689e6   57292.8
 ```