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WIDA_GA_SGP_FORMATTED_OUTPUT_2021.R
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WIDA_GA_SGP_FORMATTED_OUTPUT_2021.R
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###############################################################################
### ###
### Formatted text output for WIDA_GA from 2021 long data ###
### ###
###############################################################################
require(data.table)
require(cfaTools)
### Load 2021 Data
load("Data/WIDA_GA_SGP.Rdata")
load("Data/WIDA_GA_SGP_LONG_Data.Rdata")
# Other Data Questions:
# a. Elements GaDOE wants to include in the data file, if possible:
# i. PRIOR Composite Proficiency Level (reported to the tenth)
# ii. PRIOR Grade associated with the above CPL
# iii. Prior YEAR associated with the above prior CPL
# b. In the 2021 data, we noted that on the ACHIEVEMENT_LEVEL and ACHIEVEMENT_LEVEL_PRIOR
# there is mostly Level 1, Level 2, etc., but there is also a Level 4.3? Can we get
# rid of that for 2022, only preserving the actual achievement level in this field?
# c. I wanted to confirm the interpretation of the fields associated with the percentiles
# 1, 35, 65, 99: The values in these fields represent the current years cut scores
# (Composite SS) for categorizing the student’s observed SGP into an SGP LEVEL (low,
# typical, high).
### Check SGP_LEVEL (c.)
# WIDA_GA_SGP_LONG_Data[!is.na(SGP),
# .(MIN = min(SGP), MAX = max(SGP)),
# keyby = c("GRADE", "SGP_LEVEL")]
# WIDA_GA_SGP_LONG_Data[!is.na(SGP_BASELINE),
# .(MIN = min(SGP_BASELINE), MAX = max(SGP_BASELINE)),
# keyby = c("GRADE", "SGP_LEVEL_BASELINE")]
### Remove level 4.3 from current and lagged variables (b.)
WIDA_GA_SGP_LONG_Data[ACHIEVEMENT_LEVEL == "Level 4.3", ACHIEVEMENT_LEVEL := "Level 4"]
WIDA_GA_SGP_LONG_Data[ACHIEVEMENT_LEVEL_PRIOR == "Level 4.3", ACHIEVEMENT_LEVEL_PRIOR := "Level 4"]
# table(WIDA_GA_SGP_LONG_Data[, ACHIEVEMENT_LEVEL, ACHIEVEMENT_LEVEL_ORIGINAL])
### Remove "BASELINE" Prior Scale Scores (redundant)
# table(WIDA_GA_SGP_LONG_Data[,
# SCALE_SCORE_PRIOR == SCALE_SCORE_PRIOR_BASELINE], exclude = NULL)
# table(WIDA_GA_SGP_LONG_Data[,
# SCALE_SCORE_PRIOR_STANDARDIZED == SCALE_SCORE_PRIOR_STANDARDIZED_BASELINE],
# exclude = NULL)
WIDA_GA_SGP_LONG_Data[, SCALE_SCORE_PRIOR_BASELINE := NULL]
WIDA_GA_SGP_LONG_Data[, SCALE_SCORE_PRIOR_STANDARDIZED_BASELINE := NULL]
### Create requested lagged variables (a.)
## Change VALID_CASE to allow for correct lagging (save original in separate variable)
## Check for duplicates within GRADE - take highest grade level/score within grade
WIDA_GA_SGP_LONG_Data[, VC := VALID_CASE]
setkey(WIDA_GA_SGP_LONG_Data, VALID_CASE, CONTENT_AREA, YEAR, ID, GRADE, SCALE_SCORE)
setkey(WIDA_GA_SGP_LONG_Data, VALID_CASE, CONTENT_AREA, YEAR, ID)
WIDA_GA_SGP_LONG_Data[which(duplicated(WIDA_GA_SGP_LONG_Data, by = key(WIDA_GA_SGP_LONG_Data))) - 1, VALID_CASE := "INVALID_CASE"]
setkey(WIDA_GA_SGP_LONG_Data, VALID_CASE, CONTENT_AREA, YEAR, ID)
shift.key <- c("ID", "CONTENT_AREA", "YEAR", "GRADE", "VALID_CASE")
setkeyv(WIDA_GA_SGP_LONG_Data, shift.key)
TMP_INVALID_CASES <- WIDA_GA_SGP_LONG_Data[VALID_CASE == "INVALID_CASE"]
WIDA_GA_SGP_LONG_Data <- WIDA_GA_SGP_LONG_Data[VALID_CASE == "VALID_CASE"]
getShiftedValues(WIDA_GA_SGP_LONG_Data, shift_amount = c(1L, 2L),
shift_variable = c("YEAR", "GRADE", "ACHIEVEMENT_LEVEL_ORIGINAL"))
# Clean up - rename according to old conventions
setnames(WIDA_GA_SGP_LONG_Data, gsub("LAG_1", "PRIOR_1_YEAR", names(WIDA_GA_SGP_LONG_Data)))
setnames(WIDA_GA_SGP_LONG_Data, gsub("LAG_2", "PRIOR_2_YEAR", names(WIDA_GA_SGP_LONG_Data)))
# table(WIDA_GA_SGP_LONG_Data[YEAR=='2021', ACHIEVEMENT_LEVEL_PRIOR, ACHIEVEMENT_LEVEL_ORIGINAL_PRIOR_1_YEAR], exclude=NULL)
WIDA_GA_SGP_LONG_Data <- rbindlist(list(WIDA_GA_SGP_LONG_Data, TMP_INVALID_CASES), fill = TRUE)
WIDA_GA_SGP_LONG_Data[, VALID_CASE := VC]
WIDA_GA_SGP_LONG_Data[, VC := NULL]
# table(WIDA_GA_SGP_LONG_Data[, YEAR, is.na(ACHIEVEMENT_LEVEL_ORIGINAL_PRIOR_1_YEAR)], exclude=NULL)
# table(WIDA_GA_SGP_LONG_Data[, GRADE, is.na(ACHIEVEMENT_LEVEL_ORIGINAL_PRIOR_1_YEAR)], exclude=NULL)
# Remove 1_YEAR priors for Grade 0 - repeaters
# WIDA_GA_SGP_LONG_Data[GRADE == 0 | YEAR == "2019", ACHIEVEMENT_LEVEL_ORIGINAL_PRIOR_1_YEAR := NA]
### Add in CURRENT Projections
proj.var.names <- c("ID", "GRADE",
"LEVEL_4_SGP_TARGET_YEAR_1_CURRENT",
"P1_PROJ_YEAR_1_CURRENT", "P35_PROJ_YEAR_1_CURRENT",
"P66_PROJ_YEAR_1_CURRENT", "P99_PROJ_YEAR_1_CURRENT")
tmp.list.current <- list()
my.projection.table.names <- c("READING.2020", "READING.2021")
for (i in my.projection.table.names) {
tmp.list.current[[i]] <- data.table(
VALID_CASE = "VALID_CASE",
YEAR = unlist(strsplit(i, "\\."))[2],
WIDA_GA_SGP@SGP$SGProjections[[i]][, proj.var.names, with = FALSE])
}
### Merge projection/target data in.
tmp.projections.c <- data.table(rbindlist(tmp.list.current), key = c("ID", "GRADE"))
pctl.names <- names(WIDA_GA_SGP_LONG_Data)
pjct.names <- names(tmp.projections.c)
setkeyv(tmp.projections.c, c("VALID_CASE", "YEAR", "ID", "GRADE"))
setkeyv(WIDA_GA_SGP_LONG_Data, c("VALID_CASE", "YEAR", "ID", "GRADE"))
WIDA_GA_SGP_LONG_Data <- tmp.projections.c[WIDA_GA_SGP_LONG_Data]
### Final arrangement of variables
setcolorder(WIDA_GA_SGP_LONG_Data, union(pctl.names, pjct.names))
setkeyv(WIDA_GA_SGP_LONG_Data, c("VALID_CASE", "YEAR", "GRADE", "ID"))
### Clean up names
wida.ga.names <- c(
"VALID_CASE", "CONTENT_AREA", "GTID", "SCHOOL_YEAR", "Grade",
"CompositeOverallScaleScore", "CSEMOverall", "CompositeOverallProficiencyLevel",
"DistrictNumber", "DistrictName", "SchoolNumber", "SchoolName",
"StudentLastName", "StudentFirstName", "BirthDate", "NativeLanguage",
"Gender", "IEPStatus", "TitleIIIStatus", "LengthofTimeinLEPELLProgram")
setnames(WIDA_GA_SGP_LONG_Data,
c("VALID_CASE", "CONTENT_AREA", "ID", "YEAR", "GRADE",
"SCALE_SCORE", "SCALE_SCORE_CSEM", "ACHIEVEMENT_LEVEL_ORIGINAL",
"DISTRICT_NUMBER", "DISTRICT_NAME", "SCHOOL_NUMBER", "SCHOOL_NAME",
"LAST_NAME", "FIRST_NAME", "BIRTH_DATE", "NATIVE_LANGUAGE",
"GENDER", "IEP_STATUS", "TITLE_III_STATUS", "TIME_IN_ELL_PROGRAM"),
wida.ga.names)
prior.cpl.names <- grep("ACHIEVEMENT_LEVEL_ORIGINAL", names(WIDA_GA_SGP_LONG_Data), value = TRUE)
setnames(WIDA_GA_SGP_LONG_Data,
c(prior.cpl.names, "YEAR_PRIOR_1_YEAR", "YEAR_PRIOR_2_YEAR", "GRADE_PRIOR_1_YEAR", "GRADE_PRIOR_2_YEAR"),
c(gsub("ACHIEVEMENT_LEVEL_ORIGINAL", "CompositeOverallProficiencyLevel", prior.cpl.names),
"SCHOOL_YEAR_PRIOR_1_YEAR", "SCHOOL_YEAR_PRIOR_2_YEAR", "Grade_PRIOR_1_YEAR", "Grade_PRIOR_2_YEAR"))
setnames(WIDA_GA_SGP_LONG_Data,
"LEVEL_4_SGP_TARGET_YEAR_1_CURRENT",
"MIN_EXIT_CRITERIA_SGP_TARGET_YEAR_1_CURRENT")
### Save results
assign("WIDA_GA_SGP_LONG_Data_FORMATTED", WIDA_GA_SGP_LONG_Data)
# save(WIDA_GA_SGP_LONG_Data_FORMATTED, file = "Data/WIDA_GA_SGP_LONG_Data_FORMATTED.Rdata")
fwrite(WIDA_GA_SGP_LONG_Data_FORMATTED, file = "Data/WIDA_GA_SGP_LONG_Data_FORMATTED.txt", sep = "|")
zip(zipfile = "Data/WIDA_GA_SGP_LONG_Data_FORMATTED.txt.zip", files = "Data/WIDA_GA_SGP_LONG_Data_FORMATTED.txt")
unlink("Data/WIDA_GA_SGP_LONG_Data_FORMATTED.txt")