Outputting the N's using the survey package (svymean) - r

Question

I have data such as this, I am trying to use the survey package to apply weights and find the means, SE and the N from each variable.
I was able to find the mean and SE, but I don't know how to pull the N for each variable.
library(survey)
data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
vector_of_variables <- c( 'api00' , 'api99' )
result <-
lapply(
vector_of_variables ,
function( w ) svymean( as.formula( paste( "~" , w ) ) , dclus1 , na.rm = TRUE )
)
result <- lapply( result , function( v ) data.frame( variable = names( v ) , mean = coef( v ) , se = as.numeric( SE( v ) ) ) )
do.call( rbind , result )
Any suggestions?
EDIT
I've adapted the answer given below to expand my question:
library(survey)
data(api)
apiclus1 <-
apiclus1 %>%
mutate(pw2 = pw*0.8) %>%
mutate(part = case_when(full<80 ~"part 1", TRUE~"part 2"))
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
dclus2 <- svydesign(id=~dnum, weights=~pw2, data=apiclus1, fpc=~fpc)
meanseN<-function(variable,design, part,shc.wide){
formula<-make.formula(variable)
m <-svymean(formula, subset(design, part==part, shc.wide = shc.wide),na.rm=TRUE)
N<-unwtd.count(formula, subset(design, part==part, shc.wide = shc.wide),na.rm=TRUE)
c(mean=coef(m), se=SE(m), N=coef(N))
}
vector_of_variables <- c("acs.k3","api00")
sapply(vector_of_variables, meanseN, "part 1","No",design=dclus1)
acs.k3 api00
mean.acs.k3 20.0347222 644.16940
se 0.5204887 23.54224
N.counts 144.0000000 183.00000
As you can see I subset the data (dclus1), so the N's I expect to see for each design should be:
table(apiclus1$sch.wide, apiclus1$part)
part 1 part 2
No 4 19
Yes 30 130
unwtd.count is returning the count for the full sample of data, instead of the subset.... Any idea's why this might be happening?

Answer 1

You don't actually need the survey package functions to do this. The number of observations is whatever it is, it's not a population estimate based on the design. However, the pacakage does have the function unwtd.count to get unweighted count of non-missing observations, eg
> unwtd.count(~api00, dclus1)
counts SE
counts 183 0
If you want all three things in a loop like you were doing before, then rather than doing it in one line it's easiest to write a little function
meanseN<-function(variable,design){
formula<-make.formula(variable)
m <-svymean(formula, design,na.rm=TRUE)
N<-unwtd.count(formula, design)
c(mean=coef(m), se=SE(m), N=coef(N))
}
and do something like
> sapply(vector_of_variables, meanseN,design=dclus1)
api00 api99
mean.api00 644.16940 606.97814
se 23.54224 24.22504
N.counts 183.00000 183.00000