library(SGP) library(SGPdata) is_html_output = function() { knitr::opts_knit$get("rmarkdown.pandoc.to")=="html" } knitr::opts_chunk$set( collapse=TRUE, comment="", prompt=TRUE, fig.dpi=96) if (is_html_output()) { options(width=1000) }
SGP analyses can be conducted in many ways with several variations. In general, one can always conduct the analyses using the lower level functions
studentGrowthPercentiles
and studentGrowthProjections
.
However, it is often easier to use higher level functions (wrapper functions) for the lower level functions, particularly for operational analyses.
Both approaches to running SGP analyses are discussed below.
Because these steps are almost always conducted simultaneously in operational SGP analyses, the SGP Package has wrapper functions
abcSGP
and updateSGP
that "wrap" the above 6 steps into a single function call, simplifying the source code associated with the analysis.
##################################################################################### ### ### R Script for Demonstration SGP analyses ### ##################################################################################### ### Load SGP & SGPdata packages require(SGP) require(SGPdata) ### abcSGP Demonstration_SGP <- abcSGP( sgp_object=sgpData_LONG, data_supplementary=list(INSTRUCTOR_NUMBER=sgpData_INSTRUCTOR_NUMBER), parallel.config=list(BACKEND="PARALLEL", WORKERS=list(PERCENTILES=4, BASELINE_PERCENTILES=4, PROJECTIONS=4, LAGGED_PROJECTIONS=4, SGP_SCALE_SCORE_TARGETS=4, SUMMARY=4, GA_PLOTS=4, SG_PLOTS=1))) ### Save results save(Demonstration_SGP, file="Data/Demonstration_SGP.Rdata")
Running this script will run the six steps mentioned earlier by performing the following:
prepareSGP
Take exemplar LONG data, sgpData_LONG, and INSTRUCTOR-STUDENT lookup files, sgpData_INSTRUCTOR_NUMBER and create the SGP object Demonstration_SGP that includes them.analyzeSGP
Performs SGP analyses for all years and content areas including: student growth percentiles, baseline student growth percentiles, student growth projections, baseline student growth projections, lagged student growth projections, lagged baseline student growth projections.combineSGP
Merges results derived from analyzeSGP back into the master longitudinal record, Demonstration_SGP@Data, and calculates/creates scale scores associated with SGP targets.summarizeSGP
Calculates summary tables and puts them results into the Demonstration_SGP@Summary slot.visualizeSGP
Creates state level summary bubble plots, growth achievement plots (wave charts), and demonstration student growth plots.outputSGP
Exports data in multiple formats:The parallel.config argument is used to take advantage of computing resources with multiple processors/cores. In general, parallel computation works better on Linux/OSX than on Windows, though the package is tested on all three operating systems. Removing the parallel.config argument will result in the analyses being run sequentially --- same results, just takes longer.
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