CRDC: Chronic Absenteesim

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 Chronic Absenteesim    File -- with only the columns needed

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

fileChA <- "ID 814 SCH - Chronic Absenteeism.csv"

getwd()

## [1] "C:/Users/BlaiseSevier/Desktop/R Work"

ChA.sch <- read.csv(fileChA)

### 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

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.

TOTAL_STUDENTS_REPORTED_M Number of Male—Total enrolled students for all race-ethnicity categories TOTAL_STUDENTS_REPORTED_F Number of Female—Total enrolled students for all race-ethnicity categories

LEP_M Number of Male—Limited English proficient (LEP) Students LEP_F Number of Female—Limited English proficient (LEP) Students

Change Code from 0s to 9s

#Code to change the -9s to 0s

ChA.sch[ChA.sch==-8]<-0
ChA.sch[ChA.sch==-9]<-0
ChA.sch[ChA.sch==-8]<-0
ChA.sch[ChA.sch==-6]<-0
ChA.sch[ChA.sch==-5]<-0
ChA.sch[ChA.sch==-3]<-0


# Reserve code value
# Definition
# -3 | Skip Logic Failure
# -5 | Action Plan
# -6 | Force Certified
# -8 | EDFacts Missing Data
# -9 | Not Applicable / Skipped
# -11 | Suppressed Data

Chronic Absenteesim

TOTAL_STUDENTS_REPORTED_M Number of Male—Total enrolled students for all race-ethnicity categories TOTAL_STUDENTS_REPORTED_F Number of Female—Total enrolled students for all race-ethnicity categories

LEP_M Number of Male—Limited English proficient (LEP) Students LEP_F Number of Female—Limited English proficient (LEP) Students

Non-English Learner Male Students Enrolled Chronically Absent

#Computes the total number of a non-English Learner male students Chronically Absent  courses

#dat$NonELM<- (dat$TOT_ENR_M - dat$TOT_LEPENR_M)

ChA.sch$NonELM <- (ChA.sch$TOTAL_STUDENTS_REPORTED_M - ChA.sch$LEP_M)

sum(ChA.sch$NonELM)

## [1] 3749745

#There are 3,749,745 Non-English Learner Male Students Chronically Absent   

Non-English Learner Female Students Chronically Absent

#Total female non-EL 
#dat$NonELF <-dat$TOT_ENR_F-dat$TOT_LEPENR_F

ChA.sch$NonELF <- (ChA.sch$TOTAL_STUDENTS_REPORTED_F - ChA.sch$LEP_F)
sum(ChA.sch$NonELF)

## [1] 3546373

#There are 3,546,373 Non-English Learner Females Enrolled Chronically Absent courses.

Total Non ELs Chronically Absent

#total non EL
#dat$NonEL<-dat$NonELF + dat$NonELM

ChA.sch$NonEL <- ChA.sch$NonELF + ChA.sch$NonELM

non.el.ChA <- sum(ChA.sch$NonEL)
non.el.ChA

## [1] 7296118

#There are 7,296,118 Non-English Learners Enrolled Chronically Absent  

English Learners Chronically Absent

English Learner Males Chronically Absent

#Total Lep Male Chronically Absent  

#dat$ELMGT <- dat$SCH_GTENR_LEP_M

ChA.sch$ELM.ChA <- ChA.sch$LEP_M

sum(ChA.sch$LEP_M)

## [1] 451893

sum(ChA.sch$ELM.ChA)

## [1] 451893

## There is 451,893 English Learner Males Chronically Absent   

English Learner Females Chronically Absent

#Total LEP Female in GT
#dat$ELFGT <- dat$SCH_GTENR_LEP_F

ChA.sch$ELF.ChA <- ChA.sch$LEP_F

sum(ChA.sch$ELF.ChA)

## [1] 374486

## There are 374,486 EL Females Chronically Absent   

Total English Learners Chronically Absent

#dat$ELEnrol <- dat$TOT_LEPENR_M + dat$TOT_LEPENR_F

ChA.sch$ELEnrolChA<- ChA.sch$ELM.ChA + ChA.sch$ELF.ChA

els.ChA <- sum(ChA.sch$ELEnrolChA)

sum(els.ChA)

## [1] 826379

## There is 826,379 English learners Chronically Absent    

Percentage of ELs and Non ELs Chronically Absent

#Percentage of ELs in Chronically Absent in K-12 15.6%
round(els.ChA/els.k12*100,2)

## [1] 15.6

#Percentage of non-ELs Chronically Absent in K-12 is 15.99%
round(non.el.ChA/nonel.k12*100,2)

## [1] 15.99