prepareSGP
, analyzeSGP
and combineSGP
.summarizeSGP(sgp_object,
years,
content_areas,
state="DEMO",
sgp.summaries=list(MEDIAN_SGP="median_na(SGP)",
MEDIAN_SGP_COUNT="num_non_missing(SGP)",
PERCENT_AT_ABOVE_PROFICIENT=
"percent_in_category(ACHIEVEMENT_LEVEL, list(c(1,4)), list(1:5))",
PERCENT_AT_ABOVE_PROFICIENT_COUNT=
"num_non_missing(ACHIEVEMENT_LEVEL)"),
summary.groups=list(institution=c("STATE", "SCHOOL_NUMBER"),
content="CONTENT_AREA",
time="YEAR",
institution_level="GRADE",
demographic=c("GENDER", "ETHNICITY", "FREE_REDUCED_LUNCH_STATUS",
"ELL_STATUS", "CATCH_KEEP_UP"),
institution_inclusion=list(STATE="OCTOBER_ENROLLMENT_STATUS",
SCHOOL_NUMBER="OCTOBER_ENROLLMENT_STATUS")),
confidence.interval.groups=list(institution="SCHOOL_NUMBER",
content="CONTENT_AREA",
time="YEAR",
institution_level= NULL,
demographic=NULL,
institution_inclusion=list(STATE=NULL,
SCHOOL_NUMBER="OCTOBER_ENROLLMENT_STATUS")))
Student
slot. If summaries of student growth percentiles are requested, those quantities must first be produced (possibly by first using analyzeSGP
stateData
.summary.group
argument. The default summaries include the group level MEDIAN_SGP
, MEDIAN_SGP_COUNT
(the number of students used to compute institution
(e.g. state, districts and schools), content area
, time
, institution_level
(usually grade), de
analyzeSGP
for more information). List slSummary
slot of the SGP data object. Each institution
has a slot in the Summary
list.foreach
package to parallel process summary tables of student data. It is the user's responsibility to register a parallel backend of their choice before running the summarizeSGP
function. The proper choice may be dependent upon the user's operating system, software and system memory capacity. Please see the foreach
documentation for details. If no parallel backend is specified, the function will process the summary tables sequentially.prepareSGP
, analyzeSGP
, combineSGP
## analyzeSGP is Step 4 of 5
DEMO_Data <- prepareSGP(sgpData_LONG)
DEMO_Data <- analyzeSGP(DEMO_Data)
DEMO_Data <- combineSGP(DEMO_Data)
DEMO_Data <- summarizeSGP(DEMO_Data)
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