knitr::opts_chunk$set(echo = TRUE)

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v tibble  3.0.4     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.0
## v purrr   0.3.4
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(knitr)

#reading in merged Geometry File -- with only the columns needed


### How to set your working directory 
## In quotation marks, ("C: /Users/BlaiseSevier/Desktop/R Work")

setwd("C:/Users/BlaiseSevier/Desktop/R Work/Enrollment Info")

#15,18

# fileXX <- "XX.csv"
# XX<- read.csv(fileXX)
# 
# XX.T <- sum(XX$Total)
# XX.LEP <- sum(XX$LEP)
# ##############################

Alabama - Colorado

fileAL <- "AL.csv"
AL<- read.csv(fileAL)

AL.T <- sum(AL$Total)
AL.LEP <- sum(AL$LEP)


##################################

fileAK <- "AK.csv"
AK<- read.csv(fileAK)

AK.T <- sum(AK$Total)
AK.LEP <- sum(AK$LEP)
##################################

fileAZ <- "AZ.csv"
AZ<- read.csv(fileAZ)

AZ.T <- sum(AZ$Total)
AZ.LEP <- sum(AZ$LEP)
##############################

fileAR <- "AR.csv"
AR<- read.csv(fileAR)

AR.T <- sum(AR$Total)
AR.LEP <- sum(AR$LEP)
##############################

fileCA <- "CA.csv"
CA<- read.csv(fileCA)

CA.T <- sum(CA$Total)
CA.LEP <- sum(CA$LEP)


##############################

fileCO <- "CO.csv"
CO<- read.csv(fileCO)

CO.T <- sum(CO$Total)
CO.LEP <- sum(CO$LEP)


#############################

Connecticut - Idaho

fileCT <- "CT.csv"
CT<- read.csv(fileCT)

CT.T <- sum(CT$Total)
CT.LEP <- sum(CT$LEP)
##############################


fileDE <- "DE.csv"
DE<- read.csv(fileDE)

DE.T <- sum(DE$Total)
DE.LEP <- sum(DE$LEP)
##############################

fileDC <- "DC.csv"
DC<- read.csv(fileDC)

DC.T <- sum(DC$Total)
DC.LEP <- sum(DC$LEP)
##############################


fileFL <- "FL.csv"
FL<- read.csv(fileFL)

FL.T <- sum(FL$Total)
FL.LEP <- sum(FL$LEP)
##############################

fileGA <- "GA.csv"
GA<- read.csv(fileGA)

GA.T <- sum(GA$Total)
GA.LEP <- sum(GA$LEP)
##############################

fileHI <- "HI.csv"
HI<- read.csv(fileHI)

HI.T <- sum(HI$Total)
HI.LEP <- sum(HI$LEP)
##############################

fileID <- "ID.csv"
ID<- read.csv(fileID)

ID.T <- sum(ID$Total)
ID.LEP <- sum(ID$LEP)
##############################

Illinois - Massachusetts

fileIL <- "IL.csv"
IL<- read.csv(fileIL)

IL.T <- sum(IL$Total)
IL.LEP <- sum(IL$LEP)
##############################

fileIN <- "IN.csv"
IN<- read.csv(fileIN)

IN.T <- sum(IN$Total)
IN.LEP <- sum(IN$LEP)
##############################

fileIA <- "IA.csv"
IA<- read.csv(fileIA)

IA.T <- sum(IA$Total)
IA.LEP <- sum(IA$LEP)
##############################

fileKS <- "KS.csv"
KS<- read.csv(fileKS)

KS.T <- sum(KS$Total)
KS.LEP <- sum(KS$LEP)
##############################

fileKY <- "KY.csv"
KY<- read.csv(fileKY)

KY.T <- sum(KY$Total)
KY.LEP <- sum(KY$LEP)
##############################

fileLA <- "LA.csv"
LA<- read.csv(fileLA)

LA.T <- sum(LA$Total)
LA.LEP <- sum(LA$LEP)
##############################

fileME <- "ME.csv"
ME<- read.csv(fileME)

ME.T <- sum(ME$Total)
ME.LEP <- sum(ME$LEP)
##############################
fileMD <- "MD.csv"
MD<- read.csv(fileMD)

MD.T <- sum(MD$Total)
MD.LEP <- sum(MD$LEP)
##############################

fileMA <- "MA.csv"
MA<- read.csv(fileMA)

MA.T <- sum(MA$Total) ## this has an invalid symbol Take out    
#Peabody Veterans Memorial High

MA.LEP <- sum(MA$LEP)
##############################

Michigan - New Mexico

fileMI <- "MI.csv"
MI<- read.csv(fileMI)

MI.T <- sum(MI$Total)
MI.LEP <- sum(MI$LEP)
##############################

fileMN <- "MN.csv"
MN<- read.csv(fileMN)

MN.T <- sum(MN$Total)
MN.LEP <- sum(MN$LEP)
##############################

fileMS <- "MS.csv"
MS<- read.csv(fileMS)

MS.T <- sum(MS$Total)
MS.LEP <- sum(MS$LEP)
##############################

fileMO <- "MO.csv"
MO<- read.csv(fileMO)

MO.T <- sum(MO$Total)
MO.LEP <- sum(MO$LEP)
##############################

fileMT <- "MT.csv"
MT<- read.csv(fileMT)

MT.T <- sum(MT$Total)
MT.LEP <- sum(MT$LEP)
##############################

fileNE <- "NE.csv"
NE<- read.csv(fileNE)

NE.T <- sum(NE$Total)
NE.LEP <- sum(NE$LEP)
##############################

fileNV <- "NV.csv"
NV<- read.csv(fileNV)

NV.T <- sum(NV$Total)
NV.LEP <- sum(NV$LEP)
##############################

fileNH <- "NH.csv"
NH<- read.csv(fileNH)

NH.T <- sum(NH$Total)
NH.LEP <- sum(NH$LEP)
##############################

fileNJ <- "NJ.csv"
NJ<- read.csv(fileNJ)

NJ.T <- sum(NJ$Total)
NJ.LEP <- sum(NJ$LEP)
##############################

fileNM <- "NM.csv"
NM<- read.csv(fileNM)

NM.T <- sum(NM$Total)
NM.LEP <- sum(NM$LEP)
##############################

New York - South Dakota

fileNY <- "NY.csv"
NY<- read.csv(fileNY)

NY.T <- sum(NY$Total)
NY.LEP <- sum(NY$LEP)
##############################

fileNC <- "NC.csv"
NC<- read.csv(fileNC)

NC.T <- sum(NC$Total)
NC.LEP <- sum(NC$LEP)
##############################

fileND <- "ND.csv"
ND<- read.csv(fileND)

ND.T <- sum(ND$Total)
ND.LEP <- sum(ND$LEP)
##############################

fileOH <- "OH.csv"
OH<- read.csv(fileOH)

OH.T <- sum(OH$Total)
OH.LEP <- sum(OH$LEP)
##############################

fileOK <- "OK.csv"
OK<- read.csv(fileOK)

OK.T <- sum(OK$Total)
OK.LEP <- sum(OK$LEP)
##############################

fileOR <- "OR.csv"
OR<- read.csv(fileOR)

OR.T <- sum(OR$Total)
OR.LEP <- sum(OR$LEP)
##############################

filePA <- "PA.csv"
PA<- read.csv(filePA)

PA.T <- sum(PA$Total)
PA.LEP <- sum(PA$LEP)
##############################

fileRI <- "RI.csv"
RI<- read.csv(fileRI)

RI.T <- sum(RI$Total)
RI.LEP <- sum(RI$LEP)
##############################

fileSC <- "SC.csv"
SC<- read.csv(fileSC)

SC.T <- sum(SC$Total)
SC.LEP <- sum(SC$LEP)
##############################
fileSD <- "SD.csv"
SD<- read.csv(fileSD)

SD.T <- sum(SD$Total)
SD.LEP <- sum(SD$LEP)
##############################

Tennessee – Wyoming

fileTN <- "TN.csv"
TN<- read.csv(fileTN)

TN.T <- sum(TN$Total)
TN.LEP <- sum(TN$LEP)
##############################

fileTX <- "TX.csv"
TX<- read.csv(fileTX)

TX.T <- sum(TX$Total)
TX.LEP <- sum(TX$LEP)
##############################

fileUT <- "UT.csv"
UT<- read.csv(fileUT)

UT.T <- sum(UT$Total)
UT.LEP <- sum(UT$LEP)
##############################
fileVT <- "VT.csv"
VT<- read.csv(fileVT)

VT.T <- sum(VT$Total)
VT.LEP <- sum(VT$LEP)
##############################
fileVA <- "VA.csv"
VA<- read.csv(fileVA)

VA.T <- sum(VA$Total)
VA.LEP <- sum(VA$LEP)
##############################
fileWA <- "WA.csv"
WA<- read.csv(fileWA)

WA.T <- sum(WA$Total)
WA.LEP <- sum(WA$LEP)
##############################

fileWV <- "WV.csv"
WV<- read.csv(fileWV)

WV.T <- sum(WV$Total)
WV.LEP <- sum(WV$LEP)
##############################

fileWI <- "WI.csv"
WI<- read.csv(fileWI)

WI.T <- sum(WI$Total)
WI.LEP <- sum(WI$LEP)
##############################

fileWY <- "WY.csv"
WY<- read.csv(fileWY)

WY.T <- sum(WY$Total)
WY.LEP <- sum(WY$LEP)
##############################

Enrollment Totals for HS

There are 13,108,063 non-English learners in High School

tot_enrol_hs <- sum(AL.T,
AK.T,
AZ.T,
AR.T,
CA.T,
CO.T,
CT.T,
DE.T,
DC.T,
FL.T,
GA.T,
HI.T,
ID.T,
IL.T,
IN.T,
IA.T,
KS.T,
KY.T,
LA.T,
ME.T,
MD.T,
MA.T,
MI.T,
MN.T,
MS.T,
MO.T,
MT.T,
NE.T,
NV.T,
NH.T,
NJ.T,
NM.T,
NY.T,
NC.T,
ND.T,
OH.T,
OK.T,
OR.T,
PA.T,
RI.T,
SC.T,
SD.T,
TN.T,
TX.T,
UT.T,
VT.T,
VA.T,
WA.T,
WV.T,
WI.T,
WY.T)

LEP Enrollment

There are 1,075,425 English Learners in High School

tot_lep_hs<-sum(AL.LEP,
AK.LEP,
AZ.LEP,
AR.LEP,
CA.LEP,
CO.LEP,
CT.LEP,
DE.LEP,
DC.LEP,
FL.LEP,
GA.LEP,
HI.LEP,
ID.LEP,
IL.LEP,
IN.LEP,
IA.LEP,
KS.LEP,
KY.LEP,
LA.LEP,
ME.LEP,
MD.LEP,
MA.LEP,
MI.LEP,
MN.LEP,
MS.LEP,
MO.LEP,
MT.LEP,
NE.LEP,
NV.LEP,
NH.LEP,
NJ.LEP,
NM.LEP,
NY.LEP,
NC.LEP,
ND.LEP,
OH.LEP,
OK.LEP,
OR.LEP,
PA.LEP,
RI.LEP,
SC.LEP,
SD.LEP,
TN.LEP,
TX.LEP,
UT.LEP,
VT.LEP,
VA.LEP,
WA.LEP,
WV.LEP,
WI.LEP,
WY.LEP)

tot_lep_hs
## [1] 1075425
non.el.hs <- tot_enrol_hs - tot_lep_hs

non.el.hs
## [1] 13108063

Percentage of High School English Learners

tot_lep_hs/tot_enrol_hs*100
## [1] 7.582232

CRDC Survey Information

For more information about the school form see: https://www2.ed.gov/about/offices/list/ocr/docs/2017-18-crdc-school-form.pdf.