library(hypegrammaR)
#> Loading required package: survey
#> Loading required package: grid
#> Loading required package: Matrix
#> Loading required package: survival
#>
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#>
#> dotchart
#> Loading required package: magrittr
#> Warning: package 'magrittr' was built under R version 3.4.4
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 3.4.4
#> Loading required package: ggthemes
#> Warning: package 'ggthemes' was built under R version 3.4.4
mydata<-read.csv("../tests/testthat/data.csv")define your survey design. Since it’s a simple random design, we just pass the data and that’s all. Now R knows that we’re doing simple random sampling, so no need to bother with cluster/stratification stuff.
mydesign<-map_to_design(mydata)
# hypegrammaR:::barchart_with_error_bars(hypothesis.test.results = result$hypothesis.test,
# summary.statistics = result$summary.statistic)Say my research question is whether there is a difference in nutrition need between different regions. this means I want to get
The function analyse_indicator does the heavy lifting. It wants to know: - the general case of analysis that you want to do. Currently available are - direct_reporting: simply calculate the summary statistic (categorical or numerical) - group_difference: looking for differences between groups (categorical or numerical)
case<-map_to_case(data,
dependent.var = "nutrition_need",
independent.var = "region",hypothesis.type = "group_difference")