man/examples/cor.sdf.R

\dontrun{
# read in the example data (generated, not real student data)
sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))

# for two categorical variables any of the following work
c1_pears <- cor.sdf(x="b017451", y="b003501", data=sdf, method="Pearson",
                    weightVar="origwt")
c1_spear <- cor.sdf(x="b017451", y="b003501", data=sdf, method="Spearman",
                    weightVar="origwt")
c1_polyc <- cor.sdf(x="b017451", y="b003501", data=sdf, method="Polychoric",
                    weightVar="origwt")

c1_pears
c1_spear
c1_polyc

# for categorical variables, users can either keep the original numeric levels of the variables
# or condense the levels (default)
# The following call condenses the levels of the variable 'c046501'
cor.sdf(x="c046501", y="c044006", data=sdf)

# The following call keeps the original levels of the variable 'c046501'
cor.sdf(x="c046501", y="c044006", data=sdf, condenseLevels = FALSE)

# these take awhile to calculate for large datasets, so limit to a subset
sdf_dnf <- subset(sdf, b003601 == 1)

# for a categorical variable and a scale score any of the following work
c2_pears <- cor.sdf(x="composite", y="b017451", data=sdf_dnf, method="Pearson",
                    weightVar="origwt")
c2_spear <- cor.sdf(x="composite", y="b017451", data=sdf_dnf, method="Spearman",
                    weightVar="origwt")
c2_polys <- cor.sdf(x="composite", y="b017451", data=sdf_dnf, method="Polyserial",
                    weightVar="origwt")

c2_pears
c2_spear
c2_polys

# recode two variables
cor.sdf(x="c046501", y="c044006", data=sdf, method="Spearman", weightVar="origwt",
        recode=list(c046501=list(from="0%",to="None"),
                    c046501=list(from=c("1-5%", "6-10%", "11-25%", "26-50%",
                                        "51-75%", "76-90%", "Over 90%"),
                                 to="Between 0% and 100%"),
                    c044006=list(from=c("1-5%", "6-10%", "11-25%", "26-50%",
                                        "51-75%", "76-90%", "Over 90%"),
                                 to="Between 0% and 100%")))

# reorder two variables
cor.sdf(x="b017451", y="sdracem", data=sdf, method="Spearman", weightVar="origwt", 
        reorder=list(sdracem=c("White", "Hispanic", "Black", "Asian/Pacific Island",
                               "Amer Ind/Alaska Natv", "Other"),
                     b017451=c("Every day", "2 or 3 times a week", "About once a week",
                               "Once every few weeks", "Never or hardly ever")))

# recode two variables and reorder
cor.sdf(x="pared", y="b013801", data=subset(sdf, !pared %in% "I Don\'t Know"),
        method="Spearman", weightVar = "origwt",
        recode=list(pared=list(from="Some ed after H.S.", to="Graduated H.S."), 
                    pared=list(from="Graduated college", to="Graduated H.S."),
                    b013801=list(from="0-10", to="Less than 100"), 
                    b013801=list(from="11-25", to="Less than 100"),
                    b013801=list(from="26-100", to="Less than 100")),
        reorder=list(b013801=c("Less than 100", ">100")))
}

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EdSurvey documentation built on May 2, 2019, 7:30 a.m.