The R Script is full of information of how both tasks are completed using R programming language capable of manipulating unstructured datasets from text mining to image mining. It is a standard practice for maintaining data integrity and ensuring that research is reproducible. There are two plots that are interactive that can be clicked and rescaled. The coding lines that can be seen below are the processes of tidy dataset and created plots. The coding process has been completed for both tasks. If you want to know more information about how raw datasets are chosen in PowerStat, you can see it in my GitHub account: https://github.com/Jwinninghoff/NDC-First-Round. However, the final task used Excel for creating a vlookup, as instructed, can be seen in a separated email attachment.
This instruction is to say to show the change over time in remedial course-taking among deaf undergraduate students from 1996-2016. Develop a way to visually represent this data that might be used by our social media team to spotlight this information. This can simply be a sketch or it can be a graph or visualization that you create.
## First step is to obtain raw datasets
C <- c("NCES-1996.csv","NCES-2000.csv","NCES-2004.csv","NCES-2008.csv","NCES-2012.csv",
"NCES-2016.csv")
for(i in C){paste(getwd(),"/Raw datasets/",C,sep="")->files}
read.csv(files[1])->doc1;read.csv(files[2])->doc2;read.csv(files[3])->doc3;
read.csv(files[4])->doc4;read.csv(files[5])->doc5;read.csv(files[6])->doc6
## Clean datasets
library(dplyr)
slice(doc1,15)$X.1%>%as.character()%>%as.numeric()*.01->value96;
slice(doc2,15)$X.1%>%as.character()%>%as.numeric()*.01->value00;
slice(doc3,14)$X.1%>%as.character()%>%as.numeric()*.01->value04;
slice(doc4,15)$X.1%>%as.character()%>%as.numeric()*.01->value08;
slice(doc5,15)$X.1%>%as.character()%>%as.numeric()*.01->value12;
slice(doc6,15)$X.1%>%as.character()%>%as.numeric()*.01->value16
Year <- c(1996,2000,2004,2008,2012,2016)
`Percentage of Remedial Course Taking` <- c(value96,value00,value04,value08,value12,
value16)
round(`Percentage of Remedial Course Taking`,digits=2)->`Percentage of Remedial Course Taking`
cbind(Year,`Percentage of Remedial Course Taking`)%>%as.data.frame->dat
## Create visualized plot
library(ggplot2)
library(plotly)
ggplotly(ggplot(dat,aes(x=Year,y=`Percentage of Remedial Course Taking`))+
geom_line(col=alpha("#EC5937",0.6),size=2)+geom_point(col="#930606",size=3.2)+
labs(title="Remedial Course-Taking Among Deaf Undergraduate Students")+
theme_minimal(base_family = "courier"))This instruction is to say to provide a descriptive data report related to employment and educational outcomes, and disparities thereof between deaf and hearing people.
## Obtain raw dataset
paste(getwd(),"/Raw datasets/Census.csv",sep="")->read
read.csv(read)->doc7
## Clean dataset and then separate and categorize datasets
library(dplyr)
slice(doc7,c(6:7,9:12,14:16,22,24:27,29:32,34:36))->doc7
slice(doc7,3:5)->nodiploma
slice(doc7,7:9)->highdiploma
slice(doc7,11:13)->BA
slice(doc7,15:17)->MA
slice(doc7,19:21)->PHD
## Convert numeric vectors into percentage based on employment-population ratio
sapply(nodiploma[,2:3],as.character)->nodiploma[,2:3]
sapply(nodiploma[,2:3],as.numeric)->nodiploma[,2:3]
for(i in 2:3){
nodiploma[,i]/sum(nodiploma[,i])->nodiploma[,i]
}
sapply(highdiploma[,2:3],as.character)->highdiploma[,2:3]
sapply(highdiploma[,2:3],as.numeric)->highdiploma[,2:3]
for(i in 2:3){
highdiploma[,i]/sum(highdiploma[,i])->highdiploma[,i]
}
sapply(BA[,2:3],as.character)->BA[,2:3]
sapply(BA[,2:3],as.numeric)->BA[,2:3]
for(i in 2:3){
BA[,i]/sum(BA[,i])->BA[,i]
}
sapply(MA[,2:3],as.character)->MA[,2:3]
sapply(MA[,2:3],as.numeric)->MA[,2:3]
for(i in 2:3){
MA[,i]/sum(MA[,i])->MA[,i]
}
sapply(PHD[,2:3],as.character)->PHD[,2:3]
sapply(PHD[,2:3],as.numeric)->PHD[,2:3]
for(i in 2:3){
PHD[,i]/sum(PHD[,i])->PHD[,i]
}
## From now on, set up the barplots
library(plotly)
`Education Level`<-c("No Diploma","High School Diploma","Bachelor's Degree","Master's Degree","Doctorate Degree")
Hearing <- c(round(unlist(nodiploma)[7],digits=2),round(unlist(highdiploma)[7],digits=2),
round(unlist(BA)[7],digits=2),round(unlist(MA)[7],digits=2),round(unlist(PHD)[7],digits=2))
Deaf <- c(round(unlist(nodiploma)[4],digits=2),round(unlist(highdiploma)[4],digits=2),
round(unlist(BA)[4],digits=2),round(unlist(MA)[4],digits=2),round(unlist(PHD)[4],digits=2))
dat <- data.frame(`Education Level`,Deaf,Hearing)
plot_ly(dat, x = ~`Education Level`, y = ~Deaf, type = 'bar', name = 'Deaf') %>%
add_trace(y = ~Hearing, name = 'Hearing') %>%
layout(yaxis = list(title = 'Percentage'),xaxis=list(title='',categoryorder = "array",
categoryarray = ~`Education Level`),title='Employment Group Ratio: Compare Deaf
or Hard of Hearing to Hearing',barmode = 'group')