I am using the survey package in R. I am working with survey data and using the svydesign() and update() functions to manipulate the dataset (create new variables, etc).
Here is how I am getting the weighted crosstabs:
## Build svytable
Drinks_Sex <- svytable(~ Sex + Drinks, design=x)
## Cell Totals
round(addmargins(Drinks_Sex),0)
## Drinks
## Sex 0 1 Sum
## Female 6501 213 6714
## Male 5254 157 5411
## Sum 11755 370 12125
Is there a way for me to get the unweighted crosstabs using the survey package? I know how to get the unweighted crosstabs using base R on the original dataset, but the problem is that doing so would not allow me to analyze any variables using update().
Alternately: is there any way for me to propagate the work I've done using update() into the original dataset (in csv format) so I can work it using base R?
update is the same function as transform. if you want to just replace-all your update functions with transform functions and at the same time replace your survey.design with a data.frame then it will work.
library(survey)
data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
# variables in the data.frame object inside the design are here: dclus1$variables
# so
head( dclus1$variables )
# is the same as
head( apiclus1 )
# make whatever new variable
dclus1 <- update( dclus1 , new_col = target * 3 )
# equivalent to
# apiclus1 <- transform( apiclus1 , new_col = target * 3 )
# so long as you are working with a data.frame object instead of a survey.design
# just access the `variables` attribute of the survey.design object
# as if it were a data.frame object. note: this will not work on some very complicated designs (possibly will not work on database-backed or multiply-imputed or calibrated designs)
# single unweighted table
table( dclus1$variables[ , 'new_col' ] )
# crosstabs
table( dclus1$variables[ , c( 'awards' , 'new_col' ) ] )