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WIDA_CO_Data_LONG_2019.R
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WIDA_CO_Data_LONG_2019.R
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###############################################################################
###
### Data prep and cleaning for WIDA Colorado - 2019
###
###############################################################################
### Load packages
require(data.table)
require(SGP)
### Load data
WIDA_CO_Data_LONG_2019 <- fread("Data/Base_Files/NCIEA (Marie Huchton)/WIDA_Readin_2019_EDWD_06.04.19.csv", colClasses=rep("character", 24))
### Tidy up data
WIDA_CO_Data_LONG_2019[, FIRST_NAME := as.factor(FIRST_NAME)]
WIDA_CO_Data_LONG_2019[, LAST_NAME := as.factor(LAST_NAME)]
levels(WIDA_CO_Data_LONG_2019$FIRST_NAME) <- sapply(levels(WIDA_CO_Data_LONG_2019$FIRST_NAME), capwords)
levels(WIDA_CO_Data_LONG_2019$LAST_NAME) <- sapply(levels(WIDA_CO_Data_LONG_2019$LAST_NAME), capwords)
WIDA_CO_Data_LONG_2019[, SCALE_SCORE := as.numeric(SCALE_SCORE)]
# WIDA_CO_Data_LONG_2019[ACHIEVEMENT_LEVEL == "", ACHIEVEMENT_LEVEL := NA] # table(WIDA_CO_Data_LONG_2019[, is.na(SCALE_SCORE), ACHIEVEMENT_LEVEL])
WIDA_CO_Data_LONG_2019[, ACHIEVEMENT_LEVEL := as.factor(ACHIEVEMENT_LEVEL)]
levels(WIDA_CO_Data_LONG_2019$ACHIEVEMENT_LEVEL) <- c("L1", "L2", "L3", "L4", "L5", "L6", "NO SCORE")
WIDA_CO_Data_LONG_2019[, ACHIEVEMENT_LEVEL := as.character(ACHIEVEMENT_LEVEL)]
WIDA_CO_Data_LONG_2019[GENDER=="MISSING", GENDER := NA]
WIDA_CO_Data_LONG_2019[, GENDER := as.factor(GENDER)]
levels(WIDA_CO_Data_LONG_2019$GENDER) <- sapply(levels(WIDA_CO_Data_LONG_2019$GENDER), capwords)
WIDA_CO_Data_LONG_2019[ETHNICITY=="MISSING", ETHNICITY := NA]
WIDA_CO_Data_LONG_2019[, ETHNICITY := as.factor(ETHNICITY)]
levels(WIDA_CO_Data_LONG_2019$ETHNICITY) <- sapply(levels(WIDA_CO_Data_LONG_2019$ETHNICITY), capwords)
WIDA_CO_Data_LONG_2019[FREE_REDUCED_LUNCH_STATUS == "MISSING", FREE_REDUCED_LUNCH_STATUS := NA]
WIDA_CO_Data_LONG_2019[, FREE_REDUCED_LUNCH_STATUS := as.factor(FREE_REDUCED_LUNCH_STATUS)]
WIDA_CO_Data_LONG_2019[ELL_STATUS == "Missing", ELL_STATUS := NA] # NOTE change in 2019 to camel case
WIDA_CO_Data_LONG_2019[, ELL_STATUS := as.factor(ELL_STATUS)]
WIDA_CO_Data_LONG_2019[IEP_STATUS == "MISSING", IEP_STATUS := NA]
WIDA_CO_Data_LONG_2019[, IEP_STATUS := as.factor(IEP_STATUS)]
WIDA_CO_Data_LONG_2019[GIFTED_TALENTED_PROGRAM_STATUS == "MISSING", GIFTED_TALENTED_PROGRAM_STATUS := NA]
WIDA_CO_Data_LONG_2019[, GIFTED_TALENTED_PROGRAM_STATUS := as.factor(GIFTED_TALENTED_PROGRAM_STATUS)]
WIDA_CO_Data_LONG_2019[, SCHOOL_NAME := as.factor(SCHOOL_NAME)]
levels(WIDA_CO_Data_LONG_2019$SCHOOL_NAME) <- sapply(levels(WIDA_CO_Data_LONG_2019$SCHOOL_NAME), capwords)
WIDA_CO_Data_LONG_2019[, SCHOOL_ENROLLMENT_STATUS := as.factor(SCHOOL_ENROLLMENT_STATUS)]
WIDA_CO_Data_LONG_2019[, DISTRICT_ENROLLMENT_STATUS := as.factor(DISTRICT_ENROLLMENT_STATUS)]
### Setkey and invalidate duplicate cases (0 new invalidated in 2019 - 2000 already done by Marie)
table(WIDA_CO_Data_LONG_2019[, VALID_CASE])
setkey(WIDA_CO_Data_LONG_2019, VALID_CASE, CONTENT_AREA, YEAR, GRADE, ID, SCALE_SCORE)
setkey(WIDA_CO_Data_LONG_2019, VALID_CASE, CONTENT_AREA, YEAR, GRADE, ID)
WIDA_CO_Data_LONG_2019[which(duplicated(WIDA_CO_Data_LONG_2019, by=key(WIDA_CO_Data_LONG_2019)))-1, VALID_CASE:="INVALID_CASE"]
### Invalidate cases with missing SCALE_SCOREs and IDs
WIDA_CO_Data_LONG_2019[is.na(SCALE_SCORE), VALID_CASE := "INVALID_CASE"]
WIDA_CO_Data_LONG_2019[is.na(ID), VALID_CASE := "INVALID_CASE"]
### Save results
save(WIDA_CO_Data_LONG_2019, file="Data/WIDA_CO_Data_LONG_2019.Rdata")