After defining your survey dataset (please refer back to ‘survey’ package blog (1) & (2) ), you could use the functions below to describe your survey data and estimate population.

Let’s still use *apiclus1 *data. After svydesign() function, you have a designed survey dataset, **dclus1**, which we designed in the last week. In this dataset, there are several variables we are going to mention in the following syntax.

api00: continuous variable, integer

api99: continuous variable, integer

enroll: continuous variable, integer

sch.wide: categorical variable, which is also recognized as factor in R

stype: categorical variable, which is also recognized as factor in R

svymean(~api00, dclus1) #calculate survey mean of variable *api00* in defined survey dataset *dclus1*

svyby(~api99, ~stype, dclus1, svymean)# calculate survey mean of variable *api99* by variable* stype*

svychisq(~sch.wide+stype, dclus1) #contingency tables and chisquared tests between sch.wide and stype. The default (statistic=”F”) is the Rao-Scott second-order correction. And there are other options for “statistics”, such as “Wald”, “Lincom”.

svyhist(~enroll, dclus1, main=”Survey weighted”,col=”purple”,ylim=c(0,1.3e-3)) #create a weighted histogram graph for variable *enroll*, named as “Survey weighted”, colored as purple, range of y axis is from zero to 0.0013

svyboxplot(enroll~stype,dclus1,all.outliers=TRUE) #create a boxplot for variable* enroll*, grouped by variable *stype*, and keep all the outliers

svyplot(api00~api99, design=dclus1, style=”bubble”) # create a scatter plot graph for *api00* and *api99 *using bubble as the scatter shape