edsurvey.data.frame.list: EdSurvey Dataset Vectorization

In EdSurvey: Analysis of NCES Education Survey and Assessment Data

EdSurvey Dataset Vectorization

Description

The edsurvey.data.frame.list function creates an edsurvey.data.frame.list object from a series of edsurvey.data.frame objects. append.edsurvey.data.frame.list creates an edsurvey.data.frame.list from two edsurvey.data.frame or edsurvey.data.frame.list objects.

An edsurvey.data.frame.list is useful for looking at data, for example, across time or graphically, and reduces repetition in function calls. The user may specify a variable that varies across the edsurvey.data.frame objects that is then included in further output.

Usage

edsurvey.data.frame.list(datalist, cov = NULL, labels = NULL)

append.edsurvey.data.frame.list(sdfA, sdfB, labelsA = NULL, labelsB = NULL)

Arguments

datalist	a list of edsurvey.data.frames to be combined
cov	a character vector that indicates what varies across the edsurvey.data.frame objects. Guessed if not supplied. For example, if several edsurvey.data.frames for several different countries are supplied, then cov would be set to the country.
labels	a character vector that specifies labels. Must be the same length as datalist. Not needed if cov exists or can be guessed. See Examples.
sdfA	an edsurvey.data.frame or an edsurvey.data.frame.list to be combined
sdfB	an edsurvey.data.frame or an edsurvey.data.frame.list to be combined
labelsA	a character vector that specifies labels for sdfA when creating the new edsurvey.data.frame.list.
labelsB	a character vector that specifies labels for sdfB when creating the new edsurvey.data.frame.list.

Details

The edsurvey.data.frame.list can be used in place of an edsurvey.data.frame in function calls, and results are returned for each of the component edsurvey.data.frames, with the organization of the results varying by the particular method.

An edsurvey.data.frame.list can be created from several edsurvey.data.frame objects that are related; for example, all are NAEP mathematics assessments but have one or more differences (e.g., they are all from different years). Another example could be data from multiple countries for an international assessment.

When cov and labels are both missing, edsurvey.data.frame.list attempts to guess what variables may be varying and uses those. When there are no varying covariates, generic labels are automatically generated.

Value

edsurvey.data.frame.list returns an edsurvey.data.frame.list with elements

datalist	a list of edsurvey.data.frame objects
covs	a character vector of key variables that vary within the edsurvey.data.frame.list. When labels are included, they will be included in covs. In the unusual circumstance that sdfA or sdfB is an edsurvey.data.frame.list has covs, and labels are not supplied, the covs are simply pasted together with colons between them.

append.edsurvey.data.frame.list returns an edsurvey.data.frame.list with elements

datalist	a list of edsurvey.data.frame objects
covs	a character vector of key variables that vary within the edsurvey.data.frame.list. When labels are included, they will be included in covs.

Author(s)

Paul Bailey, Huade Huo

Examples

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

# NOTE: the following code would not normally have to be run but is used here
# to generate demo data.
# Specifically, make subsets of sdf by the scrpsu variable,
# "Scrambled PSU and school code"
sdfA <- subset(sdf, scrpsu %in% c(5,45,56))
sdfB <- subset(sdf, scrpsu %in% c(75,76,78))
sdfC <- subset(sdf, scrpsu %in% 100:200)
sdfD <- subset(sdf, scrpsu %in% 201:300)

# construct an edsurvey.data.frame.list from these four data sets
sdfl <- edsurvey.data.frame.list(datalist=list(sdfA, sdfB, sdfC, sdfD),
                                 labels=c("A locations",
                                          "B locations",
                                          "C locations",
                                          "D locations"))

# alternative method of building
sdfl2 <- sdfA + sdfB + sdfC

# check contents
sdfA %in% sdfl
# note %in% checks by survey (NAEP 2005 Math for sdf,
# sdfA, sdfB, sdfC, and sdfD) not by subset, so this also return TRUE
sdfD %in% sdfl2

# this shows how these datasets will be described
sdfl$covs 
# get the gaps between Male and Female for each data set
gap1 <- gap(variable="composite", data=sdfl, dsex=="Male", dsex=="Female")
gap1

# make combine sdfA and sdfB
sdfl1a <- edsurvey.data.frame.list(datalist=list(sdfA, sdfB),
                                   labels=c("A locations",
                                            "B locations"))

# combine sdfC and sdfD
sdfl1b <- edsurvey.data.frame.list(datalist=list(sdfC, sdfD),
                                   labels=c("C locations",
                                            "D locations"))

# append to make sdf3 the same as sdfl
sdfl3 <- append.edsurvey.data.frame.list(sdfA=sdfl1a, sdfB=sdfl1b)
identical(sdfl, sdfl3) #TRUE

# append to make sdf4 the same as sdfl
sdfl4 <- append.edsurvey.data.frame.list(
  append.edsurvey.data.frame.list(sdfA=sdfl1a, sdfB=sdfC, labelsB = "C locations"),
  sdfD,
  labelsB = "D locations")
identical(sdfl, sdfl4) #TRUE

# show label deconflicting
downloadTIMSS(root="~/", years=c(2011, 2015))
t11 <- readTIMSS(path="~/TIMSS/2011", countries = c("fin", "usa"), gradeLvl = 4)
t15 <- readTIMSS(path="~/TIMSS/2015", countries = c("fin", "usa"), gradeLvl = 4)
# these would not be unique
t11$covs
t15$covs
# resulting values includes year now
t11_15 <- append.edsurvey.data.frame.list(sdfA=t11, sdfB=t15)
t11_15$covs


## End(Not run)

EdSurvey documentation built on June 27, 2024, 5:10 p.m.