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.
edsurvey.data.frame.list(datalist, cov = NULL, labels = NULL)append.edsurvey.data.frame.list(sdfA, sdfB, labelsA = NULL, labelsB = NULL)
edsurvey.data.frame.list
returns an edsurvey.data.frame.list
with
elements
a list of edsurvey.data.frame
objects
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
a list of edsurvey.data.frame
objects
a character vector of key variables that vary within
the edsurvey.data.frame.list
.
When labels are included, they will be included in
covs
.
a list of edsurvey.data.frame
s to be combined
a character vector that indicates what varies across
the edsurvey.data.frame
objects.
Guessed if not supplied. For example,
if several edsurvey.data.frame
s for several
different countries are supplied, then cov
would
be set to the country.
a character vector that specifies labels. Must be the
same length
as datalist
. Not needed if cov
exists or can be
guessed. See Examples.
an edsurvey.data.frame
or an edsurvey.data.frame.list
to be combined
an edsurvey.data.frame
or an edsurvey.data.frame.list
to be combined
a character vector that specifies labels
for sdfA
when creating
the new edsurvey.data.frame.list
.
a character vector that specifies labels
for sdfB
when creating
the new edsurvey.data.frame.list
.
Paul Bailey, Huade Huo
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.frame
s, 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.
if (FALSE) {
# read in the example data (generated, not real student data)
sdf <- readNAEP(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(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$covs
# get the gaps between Male and Female for each data set
gap1 <- gap("composite", sdfl, dsex=="Male", dsex=="Female")
gap1
# make combine sdfA and sdfB
sdfl1a <- edsurvey.data.frame.list(list(sdfA, sdfB),
labels=c("A locations",
"B locations"))
# combine sdfC and sdfD
sdfl1b <- edsurvey.data.frame.list(list(sdfC, sdfD),
labels=c("C locations",
"D locations"))
# append to make sdf3 the same as sdfl
sdfl3 <- append.edsurvey.data.frame.list(sdfl1a, sdfl1b)
identical(sdfl, sdfl3) #TRUE
# append to make sdf4 the same as sdfl
sdfl4 <- append.edsurvey.data.frame.list(
append.edsurvey.data.frame.list(sdfl1a, sdfC, labelsB = "C locations"),
sdfD,
labelsB = "D locations")
identical(sdfl, sdfl4) #TRUE
# show label deconflicting
downloadTIMSS(root="~/", years=c(2011, 2015))
t11 <- readTIMSS("~/TIMSS/2011", countries = c("fin", "usa"), gradeLvl = 4)
t15 <- readTIMSS("~/TIMSS/2015", countries = c("fin", "usa"), gradeLvl = 4)
# these would not be unique
t11$covs
t15$covs
# resulting values includes year now
t11_15 <- append.edsurvey.data.frame.list(t11, t15)
t11_15$covs
}
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