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Colorado_Data_LONG_2021.R
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176 lines (138 loc) · 9.94 KB
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#########################################################################
### ###
### Prep CMAS/PSAT/SAT data for 2021 Colorado LONG data ###
### ###
#########################################################################
### Load Packages
require(data.table)
Colorado_Data_LONG_CMAS_2021 <- fread(file="Data/Base_Files/CMAS_readin_2021.txt", colClasses=rep('character', 26), quote="'")
### Tidy up CMAS Data
# Rename YR
setnames(Colorado_Data_LONG_CMAS_2021, "YR", "YEAR")
# Re-lable CONTENT_AREA values
Colorado_Data_LONG_CMAS_2021[, CONTENT_AREA := factor(CONTENT_AREA)]
setattr(Colorado_Data_LONG_CMAS_2021$CONTENT_AREA, "levels", c("ELA", "MATHEMATICS", "SLA"))
Colorado_Data_LONG_CMAS_2021[, CONTENT_AREA := as.character(CONTENT_AREA)]
# Convert SCALE_SCORE variable to numeric
Colorado_Data_LONG_CMAS_2021[, SCALE_SCORE := as.numeric(SCALE_SCORE)]
# Convert names to factors (temporary to change levels vs values for time/memory saving)
Colorado_Data_LONG_CMAS_2021[, DISTRICT_NAME:=factor(DISTRICT_NAME)]
Colorado_Data_LONG_CMAS_2021[, SCHOOL_NAME:=factor(SCHOOL_NAME)]
Colorado_Data_LONG_CMAS_2021[, FIRST_NAME:=factor(FIRST_NAME)]
Colorado_Data_LONG_CMAS_2021[, LAST_NAME:=factor(LAST_NAME)]
# Clean up LAST_NAME and FIRST_NAME
setattr(Colorado_Data_LONG_CMAS_2021$LAST_NAME, "levels", sapply(levels(Colorado_Data_LONG_CMAS_2021$LAST_NAME), SGP::capwords))
setattr(Colorado_Data_LONG_CMAS_2021$FIRST_NAME, "levels", sapply(levels(Colorado_Data_LONG_CMAS_2021$FIRST_NAME), SGP::capwords))
####
# Clean up SCHOOL_NAME and DISTRICT_NAME
# Check levels first to confirm special.words - Clean Well for ISRs
####
### Schools
new.sch.levs <- levels(Colorado_Data_LONG_CMAS_2021$SCHOOL_NAME)
new.sch.levs <- gsub("/", " / ", new.sch.levs)
new.sch.levs <- sapply(new.sch.levs, SGP::capwords, special.words = c('AIM', 'AXL', 'CCH', 'CMS', 'DC', 'DCIS', 'DSST', 'DSST:', 'ECE-8', 'GVR', 'IB', 'KIPP', 'PK', 'PK-8', 'PK-12', 'PSD', 'LEAP', 'MHCD', 'STEM', 'TCA', 'VSSA'), USE.NAMES=FALSE)
new.sch.levs <- gsub(" / ", "/", new.sch.levs)
new.sch.levs <- gsub("''", "'", new.sch.levs)
new.sch.levs <- gsub("Prek", "PreK", new.sch.levs)
new.sch.levs <- gsub("Pk-8", "PK-8", new.sch.levs)
new.sch.levs <- gsub("Mcauliffe", "McAuliffe", new.sch.levs)
new.sch.levs <- gsub("Mcglone", "McGlone", new.sch.levs)
new.sch.levs <- gsub("Mcgraw", "McGraw", new.sch.levs)
new.sch.levs <- gsub("Mckinley", "McKinley", new.sch.levs)
new.sch.levs <- gsub("Mcmeen", "McMeen", new.sch.levs)
new.sch.levs <- gsub("Mc Clave", "McClave", new.sch.levs)
new.sch.levs <- gsub("Mc Elwain", "McElwain", new.sch.levs)
new.sch.levs <- gsub("Mc Ginnis", "McGinnis", new.sch.levs)
new.sch.levs <- gsub("Achieve Online", "ACHIEVE Online", new.sch.levs)
setattr(Colorado_Data_LONG_CMAS_2021$SCHOOL_NAME, "levels", new.sch.levs)
### Districts
grep("J", levels(Colorado_Data_LONG_CMAS_2021$DISTRICT_NAME), value=T)
new.dst.levs <- levels(Colorado_Data_LONG_CMAS_2021$DISTRICT_NAME)
new.dst.levs <- gsub("/", " / ", new.dst.levs)
new.dst.levs <- gsub("[-]", " - ", new.dst.levs)
new.dst.levs <- sapply(new.dst.levs, SGP::capwords, special.words = c('1J', '2J', '3J', '4J', '5J', '6J', '11J', '22J', '27J', '28J', '29J', '31J', '33J', '100J', 'JT', '32J', 'RJ', '26J', '49JT', '4A', 'RD', 'RE', 'RE1J'), USE.NAMES=FALSE)
new.dst.levs <- gsub(" / ", "/", new.dst.levs)
new.dst.levs <- gsub(" - ", "-", new.dst.levs)
new.dst.levs <- gsub("Mc Clave", "McClave", new.dst.levs)
grep("j", new.dst.levs, value=T) # Should only leave * Conejos
setattr(Colorado_Data_LONG_CMAS_2021$DISTRICT_NAME, "levels", new.dst.levs)
# Resolve duplicates
setkey(Colorado_Data_LONG_CMAS_2021, VALID_CASE, CONTENT_AREA, YEAR, ID, GRADE, SCALE_SCORE)
setkey(Colorado_Data_LONG_CMAS_2021, VALID_CASE, CONTENT_AREA, YEAR, ID, GRADE)
# dups <- data.table(Colorado_Data_LONG_CMAS_2021[unique(c(which(duplicated(Colorado_Data_LONG_CMAS_2021, by=key(Colorado_Data_LONG_CMAS_2021)))-1, which(duplicated(Colorado_Data_LONG_CMAS_2021, by=key(Colorado_Data_LONG_CMAS_2021))))), ], key=key(Colorado_Data_LONG_CMAS_2021))
# table(dups$VALID_CASE) # All 2021 duplicates within GRADE are already INVALID_CASEs
# Colorado_Data_LONG_CMAS_2021[which(duplicated(Colorado_Data_LONG_CMAS_2021, by=key(Colorado_Data_LONG_CMAS_2021))), VALID_CASE:="INVALID_CASE"]
## Missing cases in 2021
round(prop.table(table(Colorado_Data_LONG_CMAS_2021[CONTENT_AREA != "SLA", is.na(SCALE_SCORE), GRADE]),1)*100, 1)
Colorado_Data_LONG_CMAS_2021 <- Colorado_Data_LONG_CMAS_2021[CONTENT_AREA != "SLA"]
## Save 2021 Data - combine with P/SAT data and save as one file below
# save(Colorado_Data_LONG_CMAS_2021, file="Data/Colorado_Data_LONG_CMAS_2021.Rdata")
#############################################
### PSAT/SAT
#############################################
Colorado_Data_LONG_PSAT_SAT_2021 <- fread(file="Data/Base_Files/SAT_Growth_Readin_2021.txt", colClasses=rep('character', 25), quote="'")
Colorado_Data_LONG_PSAT_SAT_2021[, EDW_DATA_SOURCE:=NULL]
# Rename YR
setnames(Colorado_Data_LONG_PSAT_SAT_2021, "YR", "YEAR")
Colorado_Data_LONG_PSAT_SAT_2021[, CONTENT_AREA := fcase(
CONTENT_AREA == "ELA" & GRADE == 9, "ELA_PSAT_9",
CONTENT_AREA == "ELA" & GRADE == 10, "ELA_PSAT_10",
CONTENT_AREA == "ELA" & GRADE == 11, "ELA_SAT",
CONTENT_AREA == "MAT" & GRADE == 9, "MATHEMATICS_PSAT_9",
CONTENT_AREA == "MAT" & GRADE == 10, "MATHEMATICS_PSAT_10",
CONTENT_AREA == "MAT" & GRADE == 11, "MATHEMATICS_SAT")]
# Convert SCALE_SCORE variable to numeric
Colorado_Data_LONG_PSAT_SAT_2021[, SCALE_SCORE := as.numeric(SCALE_SCORE)]
# Convert names to factors (temporary to change levels vs values for time/memory saving)
Colorado_Data_LONG_PSAT_SAT_2021[, DISTRICT_NAME := factor(DISTRICT_NAME)]
Colorado_Data_LONG_PSAT_SAT_2021[, SCHOOL_NAME := factor(SCHOOL_NAME)]
Colorado_Data_LONG_PSAT_SAT_2021[, FIRST_NAME := factor(FIRST_NAME)]
Colorado_Data_LONG_PSAT_SAT_2021[, LAST_NAME := factor(LAST_NAME)]
# Clean up LAST_NAME and FIRST_NAME
setattr(Colorado_Data_LONG_PSAT_SAT_2021$LAST_NAME, "levels", sapply(levels(Colorado_Data_LONG_PSAT_SAT_2021$LAST_NAME), SGP::capwords))
setattr(Colorado_Data_LONG_PSAT_SAT_2021$FIRST_NAME, "levels", sapply(levels(Colorado_Data_LONG_PSAT_SAT_2021$FIRST_NAME), SGP::capwords))
# Clean up SCHOOL_NAME and DISTRICT_NAME
new.sch.levs <- toupper(levels(Colorado_Data_LONG_PSAT_SAT_2021$SCHOOL_NAME))
new.sch.levs <- gsub("/", " / ", new.sch.levs)
new.sch.levs <- sapply(new.sch.levs, SGP::capwords, special.words = c('AIM', 'AXL', 'CCH', 'CMS', 'DC', 'DCIS', 'DSST', 'DSST:', 'ECE-8', 'GVR', 'IB', 'KIPP', 'PK', 'PK-8', 'PK-12', 'PSD', 'LEAP', 'MHCD', 'STEM', 'TCA', 'VSSA'), USE.NAMES=FALSE)
new.sch.levs <- gsub(" / ", "/", new.sch.levs)
new.sch.levs <- gsub("''", "'", new.sch.levs)
new.sch.levs <- gsub("Prek", "PreK", new.sch.levs)
new.sch.levs <- gsub("Pk-8", "PK-8", new.sch.levs)
new.sch.levs <- gsub("Mcauliffe", "McAuliffe", new.sch.levs)
new.sch.levs <- gsub("Mcglone", "McGlone", new.sch.levs)
new.sch.levs <- gsub("Mcgraw", "McGraw", new.sch.levs)
new.sch.levs <- gsub("Mckinley", "McKinley", new.sch.levs)
new.sch.levs <- gsub("Mcmeen", "McMeen", new.sch.levs)
new.sch.levs <- gsub("Mc Clave", "McClave", new.sch.levs)
new.sch.levs <- gsub("Mc Elwain", "McElwain", new.sch.levs)
new.sch.levs <- gsub("Mc Ginnis", "McGinnis", new.sch.levs)
new.sch.levs <- gsub("Achieve Online", "ACHIEVE Online", new.sch.levs)
setattr(Colorado_Data_LONG_PSAT_SAT_2021$SCHOOL_NAME, "levels", new.sch.levs)
### Districts
grep("J", levels(Colorado_Data_LONG_PSAT_SAT_2021$DISTRICT_NAME), value=T)
new.dst.levs <- toupper(levels(Colorado_Data_LONG_PSAT_SAT_2021$DISTRICT_NAME))
new.dst.levs <- gsub("/", " / ", new.dst.levs)
new.dst.levs <- gsub("[-]", " - ", new.dst.levs)
new.dst.levs <- sapply(new.dst.levs, SGP::capwords, special.words = c('1J', '2J', '3J', '4J', '5J', '6J', '11J', '22J', '27J', '28J', '29J', '31J', '33J', '100J', 'JT', '32J', 'RJ', '26J', '49JT', '4A', 'RD', 'RE', 'RE1J'), USE.NAMES=FALSE)
new.dst.levs <- gsub(" / ", "/", new.dst.levs)
new.dst.levs <- gsub(" - ", "-", new.dst.levs)
new.dst.levs <- gsub("Mc Clave", "McClave", new.dst.levs)
grep("j", new.dst.levs, value=T) # Should only leave * Conejos
setattr(Colorado_Data_LONG_PSAT_SAT_2021$DISTRICT_NAME, "levels", new.dst.levs)
# Establish ACHIEVEMENT_LEVEL for PSAT/SAT
Colorado_Data_LONG_PSAT_SAT_2021 <- SGP:::getAchievementLevel(Colorado_Data_LONG_PSAT_SAT_2021, state="CO")
Colorado_Data_LONG_PSAT_SAT_2021[ACHIEVEMENT_LEVEL=='NA', ACHIEVEMENT_LEVEL := "No Score"]
table(Colorado_Data_LONG_PSAT_SAT_2021[, ACHIEVEMENT_LEVEL, VALID_CASE])
# Resolve duplicates
# setkey(Colorado_Data_LONG_PSAT_SAT_2021, VALID_CASE, CONTENT_AREA, YEAR, ID, GRADE, SCALE_SCORE)
# setkey(Colorado_Data_LONG_PSAT_SAT_2021, VALID_CASE, CONTENT_AREA, YEAR, ID, GRADE)
# dups <- data.table(Colorado_Data_LONG_PSAT_SAT_2021[unique(c(which(duplicated(Colorado_Data_LONG_PSAT_SAT_2021, by=key(Colorado_Data_LONG_PSAT_SAT_2021)))-1, which(duplicated(Colorado_Data_LONG_PSAT_SAT_2021, by=key(Colorado_Data_LONG_PSAT_SAT_2021))))), ], key=key(Colorado_Data_LONG_PSAT_SAT_2021))
# table(dups$VALID_CASE) # All 2021 duplicates within GRADE are already INVALID_CASEs
# Colorado_Data_LONG_PSAT_SAT_2021[which(duplicated(Colorado_Data_LONG_PSAT_SAT_2021, by=key(Colorado_Data_LONG_PSAT_SAT_2021))), VALID_CASE:="INVALID_CASE"]
## Save 2021 PSAT_SAT Data
# save(Colorado_Data_LONG_PSAT_SAT_2021, file="Data/Colorado_Data_LONG_PSAT_SAT_2021.Rdata")
### Combine CMAS and P/SAT data objects and save
Colorado_Data_LONG_2021 <- rbindlist(list(Colorado_Data_LONG_CMAS_2021, Colorado_Data_LONG_PSAT_SAT_2021), fill=TRUE)
setkey(Colorado_Data_LONG_2021, VALID_CASE, CONTENT_AREA, YEAR, GRADE, ID)
save(Colorado_Data_LONG_2021, file="Data/Colorado_Data_LONG_2021.Rdata")