knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v tibble 3.0.4 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0
## v purrr 0.3.4## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(knitr)
#reading in merged Gifted and Talented File -- with only the columns needed
fileGT <- "Gifted and Talented_CRDC_2017-18.csv"
GT.sch <- read.csv(fileGT)
### How to set your working directory
## In quotation marks, ("C: /Users/BlaiseSevier/Desktop/R Work")
setwd("C:/Users/BlaiseSevier/Desktop/R Work")
els.k12 <-5296851
nonel.k12 <- 45625438
GT.sch[GT.sch==-8]<-0
GT.sch[GT.sch==-9]<-0
GT.sch[GT.sch==-8]<-0
GT.sch[GT.sch==-6]<-0
GT.sch[GT.sch==-5]<-0
GT.sch[GT.sch==-3]<-0For more information about the school form see: https://www2.ed.gov/about/offices/list/ocr/docs/2017-18-crdc-school-form.pdf.
TOT_GTENR_M Gifted and Talented Student Enrollment: Calculated Male Total TOT_GTENR_F Gifted and Talented Student Enrollment: Calculated Female Total SCH_GTENR_LEP_M Gifted and Talented Student Enrollment: LEP Male SCH_GTENR_LEP_F Gifted and Talented Student Enrollment: LEP Female
#Code to change the -9s to 0s
GT.sch[GT.sch==-9]<-0
## important columns 27:30#Computes the total number of a non-English Learner male students in pre school
#dat$NonELM<- (dat$TOT_ENR_M - dat$TOT_LEPENR_M)
GT.sch$NonELM <- (GT.sch$TOT_GTENR_M - GT.sch$SCH_GTENR_LEP_M)
sum(GT.sch$NonELM)## [1] 1618641# 1,618,641 Non English learner males in GT#Total female non-EL
#dat$NonELF <-dat$TOT_ENR_F-dat$TOT_LEPENR_F
GT.sch$NonELF <- (GT.sch$TOT_GTENR_F - GT.sch$SCH_GTENR_LEP_F)
sum(GT.sch$NonELF)## [1] 1630767## There are 1,630,767 Non English Learner Females in Gifted and Talented #total non EL
#dat$NonEL<-dat$NonELF + dat$NonELM
GT.sch$NonEL <- GT.sch$NonELF + GT.sch$NonELM
non.el.GT <- sum(GT.sch$NonEL)
non.el.GT## [1] 3249408#There are 3,249,408 non english learners in Gifted and Talented#Total Lep Male in Gifted and Talented
#dat$ELMGT <- dat$SCH_GTENR_LEP_M
GT.sch$ELMGEOM <- GT.sch$SCH_GTENR_LEP_M
sum(GT.sch$ELMGEOM)## [1] 42932#There are 42,932 English learner males in Gifted and Talented #Total LEP Female in GT
#dat$ELFGT <- dat$SCH_GTENR_LEP_F
GT.sch$ELFGEOM <- GT.sch$SCH_GTENR_LEP_F
sum(GT.sch$ELFGEOM)## [1] 37200#There are 37,200 English learner females in Gifted and Talented #dat$ELEnrol <- dat$TOT_LEPENR_M + dat$TOT_LEPENR_F
GT.sch$ELEnrolGEOM <- GT.sch$ELMGEOM + GT.sch$ELFGEOM
els.GT <- sum(GT.sch$ELEnrolGEOM)
els.GT## [1] 80132#There are 80,132 English learners in Gifted and Talented # Computes the percentage of male English Learners at the school level who are enrolled in a G&T program
#dat$ELMGTPct<-round(dat$SCH_GTENR_LEP_M/dat$TOT_LEPENR_M*100,1)
GT.sch$ELMPct <- round(GT.sch$ELMGEOM/GT.sch$NonELM*100,1)# Computes the percentage of female English Learners at the school level who are enrolled in a G&T program
#dat$ELFGTPct<-round(dat$SCH_GTENR_LEP_F/dat$TOT_LEPENR_F*100,1)
GT.sch$ELFPct <- round(GT.sch$ELFGEOM/GT.sch$NonELF*100,1)#dat$NonELMGTPct<-round(dat$NonELMGT/dat$NonELM*100,1)
GT.sch$NonELMGEOMPct <- round(GT.sch$ELMGEOM/GT.sch$NonELM*100,1)############################
#Percentage of English Learners (Number of ELs in GT / Number of English Learners Total K-12)
(els.GT)/els.k12*100 ## [1] 1.512823### 1.5% of all English Learners are enrolled in Gifted and Talented
#######################Non English Learners in Gifted and Talented
## Percentage of Els in GT / All non-ELs in K-12
non.el.GT/nonel.k12*100## [1] 7.121922##7.12% of Non-Els are in GT.