visualizeSGP( sgp_object, plot.types=c("bubblePlot", "studentGrowthPlot", "growthAchievementPlot"), state, bPlot.years=NULL, bPlot.content_areas=NULL, bPlot.districts=NULL, bPlot.schools=NULL, bPlot.instructors=NULL, bPlot.styles=c(1), bPlot.levels=NULL, bPlot.level.cuts=NULL, bPlot.full.academic.year=TRUE, bPlot.minimum.n=10, bPlot.anonymize=FALSE, bPlot.prior.achievement=TRUE, bPlot.draft=FALSE, bPlot.demo=FALSE, bPlot.output="PDF", bPlot.format="print", bPlot.folder="Visualizations/bubblePlots", sgPlot.save.sgPlot.data=FALSE, sgPlot.years=NULL, sgPlot.content_areas=NULL, sgPlot.districts=NULL, sgPlot.schools=NULL, sgPlot.reports.by.school=TRUE, sgPlot.instructors=NULL, sgPlot.reports.by.instructor=FALSE, sgPlot.students=NULL, sgPlot.reports.by.student=FALSE, sgPlot.reports.group.vars=
list(DISTRICT_NUMBER="DISTRICT_NUMBER", SCHOOL_NUMBER="SCHOOL_NUMBER"), sgPlot.header.footer.color="#4CB9CC", sgPlot.front.page=NULL, sgPlot.folder="Visualizations/studentGrowthPlots", sgPlot.folder.names="number", sgPlot.fan=TRUE,
sgPlot.sgp.targets=FALSE,
sgPlot.sgp.targets.timeframe=3, sgPlot.anonymize=FALSE, sgPlot.cleanup=TRUE, sgPlot.demo.report=FALSE, sgPlot.produce.plots=TRUE, sgPlot.baseline=NULL, sgPlot.zip=TRUE, sgPlot.output.format="PDF",
sgPlot.year.span=5, sgPlot.plot.test.transition=TRUE, gaPlot.years=NULL, gaPlot.content_areas=NULL, gaPlot.students=NULL, gaPlot.format="print", gaPlot.baseline=NULL, gaPlot.max.order.for.progression=NULL,
gaPlot.start.points="Achievement Level Cuts", gaPlot.folder="Visualizations/growthAchievementPlots", parallel.config=NULL)@Data slot that will be used for the production of student growth
and achievement plots and system growth and achievement plots, summary data from summarizeSGP in the Summary slot for bubble plots. Alternatively, a properly formatted WIDE data set can be provided for production of student growth plots. Such data sets are produced from an SGP object when the sgPlot.save.sgPlot.data argument is set to TRUE.
bubblePlot,
studentGrowthPlot, and growthAchievementPlot.
SGPstateData.
bubblePlots using data available in sgp_object. If missing the
function will use the last year available in the data to produce bubblePlots.
bubblePlots using data available in sgp_object. If missing the
function will produce plots for all available content areas provided in the data.
bubblePlots using data available in sgp_object. Consult bubblePlot
styles to determine which bubblePlots styles accept specification for districts. Default is to produce plots for all available districts in the data.
bubblePlots using data available in sgp_object. Consult bubblePlot
styles to determine which bubblePlot styles accept specification for schools. Default is to produce plots for all available schools in the data.
bubblePlots using data available in sgp_object. If missing the
function will produce plots for all available instructors provided in the data where schools and districts represent relevant units to be represented by the specific bubblePlot style.
bubblePlots to produce using data available in sgp_object.
See associated documentation for example plots.
bubblePlot. See associated documentation for example plots.
bubblePlot. See associated
documentation for example plots.
bubblePlots should use full academic year results if available.
bubblePlots.
bubblePlots school and district names that appear in the
plots and data tips of the plots. For student level anonymization, the function utilizes the randomNames package to produce gender and ethnic correct names based
upon gender and ethnicity codes available in sgp_object@Data.
bubblePlots using prior achievement as well as current
achievement as the vertical dimension of the bubblePlot.
bubblePlots should be placed. Default folder is "Visualizations/bubblePlots".
studentGrowthPlots. The supplied year indicates the final year associated with each
student's studentGrowthPlot. If missing the function will use the last year available in the data to produce studentGrowthPlots.
studentGrowthPlots. If missing, the function will utilize all available
content areas.
studentGrowthPlots for. If missing the function will use all available
districts in the data to produce studentGrowthPlots.
studentGrowthPlots for. If missing the function will use all available
schools in the data to produce studentGrowthPlots. If both sgPlot.districts and sgPlot.schools are provided the function produces
studentGrowthPlots for ALL students in the districts and schools provided.
district/school/grade folder hierarchy. The default is TRUE which puts the reports into their appropriate district/school/grade folder.
studentGrowthPlots for. If NULL and the argument sgPlot.reports.by.instructor is TRUE,
the argument function will use all available instructors in the data to produce studentGrowthPlots. If sgPlot.districts and/or sgPlot.schools are
provided the function produces studentGrowthPlots for ALL students in the districts and/or schools provided.
district/school/grade folder hierarchy. The default is TRUE which puts the reports into their appropriate district/school/grade folder.
IDs indicating which students to produce studentGrowthPlots for. If missing the function will
use all available students in the data to produce studentGrowthPlots.
list(DISTRICT_NUMBER='DISTRICT_NUMBER', SCHOOL_NUMBER='SCHOOL_NUMBER')
district/school/grade folder hierarchy. The default is FALSE which puts the reports into their appropriate district/school/grade slot.
studentGrowthPlot. Another good color is goldenrod2.
studentGrowthPlot. The default is missing so that no front page is
attached to the studentGrowthPlot.
studentGrowthPlots should be placed. Note that studentGrowthPlots are placed
within nested folders within this folder. Default folder is "Visualizations/studentGrowthPlots".
studentGrowthPlots.
studentGrowthPlots.
studentGrowthPlots.
studentGrowthPlots student, school and district names.
For student level anonymization, the function utilizes the randomNames package to produce gender and ethnicity based names based upon gender and ethnicity
codes available in sgp_object@Data.
studentGrowthPlot catalogs.
studentGrowthPlot catalogs. Note: When producing
studentGrowthPlots for an entire state, considerable resources are required to produce this many reports. We are actively working on parallelizing this
functionality to reduce report production time by two orders of magnitude.
studentGrowthPlots. Useful when one just wants to produce
wide formatted data without the actual student growth plots.
SGPstateData which contains information on whether state is a cohort or baseline referenced system.
studentGrowthPlots.
growthAchievementPlots. If missing the function will use the last year available
in the data to produce the growthAchievementPlots.
growthAchievementPlots using data available in sgp_object.
If missing the function will produce plots for all available content areas provided in the data.
IDs indicating which students to produce growthAchievementPlots for. If missing the function will
use all available students in the data to produce growthAchievementPlots.
SGPstateData which contains information on whether state is a cohort or
baseline referenced system.
growthAchievementPlots should be placed. The default folder is 'Visualizations/growthAchievementPlots'.
FOREACH or PARALLEL. Please consult the manuals and vignettes for information of these packages! The analyzeSGP help page contains more thorough explanation and examples of the parallel.config setup. TYPE is a third element of the parallel.config list that provides necessary information when using FOREACH or PARALLEL packages as the backend. With BACKEND="FOREACH", the TYPE element specifies the flavor of 'foreach' backend. As of version 1.0-1.0, only "doParallel" is supported. TYPE=NA (default) produces summaries sequentially. If BACKEND = "PARALLEL", the parallel package will be used. This package combines deprecated parallel packages snow and multicore. Using the "snow" implementation of parallel the function will create a cluster object based on the TYPE element specified and the number of workers requested (see WORKERS list description below). The TYPE element indicates the users preferred cluster type (either "PSOCK" for socket cluster of "MPI" for an OpenMPI cluster). If Windows is the operating system, this "snow" implementation must be used and the TYPE element must = "PSOCK". Defaults are assigned based on operating system if TYPE is missing based on system OS. Unix/Mac OS defaults to the "multicore" to avoid worker node pre-scheduling and appears to be more efficient in these operating systems.
The WORKERS element is a list with GA_PLOTS (growth achievement plots) and SG_PLOTS (student growth plots) specifying the number of processors to be used. NOTE: choice of the number of cores is a balance between the number of processors available and the amount of RAM a system has; each system will be different and may require some adjustment.
Default is FOREACH as the back end, TYPE=NA and both plot WORKERS=1, which produces plots sequentially: 'list(BACKEND="FOREACH", TYPE=NA, WORKERS=list(GA_PLOTS=1, SG_PLOTS=1))'
Examples of various parallel configurations can be found in the examples for analyzeSGP and summarizeSGP.
Betebenner, D. W. (2009). Norm- and criterion-referenced student growth. Educational Measurement: Issues and Practice, 28(4):42-51.
bubblePlot, bubblePlot_Styles, studentGrowthPlot, growthAchievementPlot## Not run:
# ## visualizeSGP is Step 5 of 5 of abcSGP
# Demonstration_SGP <- sgpData_LONG
# Demonstration_SGP <- prepareSGP(Demonstration_SGP)
# Demonstration_SGP <- analyzeSGP(Demonstration_SGP)
# Demonstration_SGP <- combineSGP(Demonstration_SGP)
# Demonstration_SGP <- summarizeSGP(Demonstration_SGP)
# visualizeSGP(Demonstration_SGP)
#
# ## Produce a DEMO catalog of student growth plots
#
# visualizeSGP(
# sgp_object=Demonstration_SGP,
# plot.types="studentGrowthPlot",
# state="DEMO",
# sgPlot.demo.report=TRUE)
#
# ## Production of sample student growth and achievement plots
#
# visualizeSGP(
# sgp_object=Demonstration_SGP,
# plot.types="studentGrowthPlot",
# state="DEMO",
# sgPlot.districts=470,
# sgPlot.schools=c(6418, 8008),
# sgPlot.header.footer.color="#4CB9CC")
# ## End(Not run)
Run the code above in your browser using DataLab