Calculate the an unweighted summary statistic from a survey
Calculate unweighted summaries from a survey dataset, just as on
a normal data.frame with summarise. Though it is
possible to use regular functions directly, because the survey package
doesn't always remove rows when filtering (instead setting the weight to 0),
this can sometimes give bad results. See examples for more details.
unweighted(x, ...)
x |
A variable or expression |
... |
Ignored |
Uses tidy evaluation semantics and so if you want to use wrapper functions based on variable names, you must use tidy evaluation, see the examples here, documentation in nse-force, or the dplyr vignette called 'programming' for more information.
library(survey)
library(dplyr)
data(api)
dstrata <- apistrat %>%
as_survey_design(strata = stype, weights = pw)
dstrata %>%
summarise(api99_unw = unweighted(mean(api99)),
n = unweighted(n()))
dstrata %>%
group_by(stype) %>%
summarise(api_diff_unw = unweighted(mean(api00 - api99)))
# Some survey designs, like ones with raked weights, are not removed
# when filtered to preserve the structure. So if you don't use `unweighted()`
# your results can be wrong.
# Declare basic clustered design ----
cluster_design <- as_survey_design(
.data = apiclus1,
id = dnum,
weights = pw,
fpc = fpc
)
# Add raking weights for school type ----
pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018))
pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122))
raked_design <- rake(
cluster_design,
sample.margins = list(~stype,~sch.wide),
population.margins = list(pop.types, pop.schwide)
)
raked_design %>%
filter(cname != "Alameda") %>%
group_by(cname) %>%
summarize(
direct_unw_mean = mean(api99),
wrapped_unw_mean = unweighted(mean(api99))
) %>%
filter(cname == "Alameda")
# Notice how the results are different when using `unweighted()`Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.