#Vize ödevi-Selin#

#1. adım: Datayı çağırma

library(readxl)
data1=read_excel("C:\\Rvize\\data01570.xlsx", col_names = FALSE)
dim(data1)

#2.adım: Datadan çalışmak için örneklem seçmek
#Not: Her defasında data ismini değiştiryoruz. Bu sayede hata yaptığımızda
#direkt ana dataya geri gitmemize gerek kalmıyor.

data2=cbind(data1[3:4], data1[10:11], data1[18], data1[20])
data2=rbind(data2[1:110,], data2[800:1000,])

#6 değişken ve 311 gözlenenden oluşan data seçimini tamamladık.dim ile kontrol
dim(data2)
is.matrix(data2)
#n ve k tanımla
k=ncol(data2)
k

n=nrow(data2)
n

data3=matrix(c(rep(0,n*k)),n,k)
data3=as.matrix(data2)
is.matrix(data3)
summary(data3)

out1<-matrix(c(rep(0,311*6)),311,6)
colnames(out1)=c(colnames(data2))
rownames(out1)=c("p", "q", "p.q", "ss", "pbs", "c.pbs", "reli")

#madde güçlüğü hesaplama
p.stats=colMeans(data3)
p.stats

q.stats=1-p.stats
q.stats
out1[1,]=round(p.stats,3)
out1[2,]=round(q.stats,3)

var.i=p.stats*q.stats
var.i
ss.i=sqrt(var.i)
ss.i
out1[3,]=round(var.i,3)
out1[4,]=round(ss.i,3)

total=rowSums(data3)
total
sum(total)
total.ort=mean(total)
total.ort
summary(total)

data5=cbind(data3,total)

library(polycor)
for(i in 1:k){
  out1[5,i] = round(polychor(data3[,i],rowSums(data3)),3)
  data4=data3[,-i]
  out1[6,i]=round(polychor(data3[,i],rowSums(data4)),3)
  p.cor=colMeans(data4)
  p.var=p.cor*(1-p.cor)
  reli.if.del=round((k/(k-1))*(1-(sum(p.var)/(var(rowSums(data4))))),3)
  out1[7,i]=reli.if.del
}

#standart hatayı hesaplamak

var.tot=var(total)
ss.tot=sqrt(var.tot)
reli=round((k/(k-1))*(1-(sum(var.i)/(var.tot))),3)
ste=ss.tot*sqrt(1-reli)
ste

a=min(rowSums(data3))
b=max(rowSums(data3))
hist(rowSums(data3)+0.001, ylim = c(0,150),
     breaks = b-a,
     main= "histogram of total scores",
     xlab= "total scores by groups",
     col = "green", border = "blue")

z.scores.tot=(((data5[,7])-total.ort)/ss.tot)
z.scores.tot

plot(z.scores.tot, type="o", col="blue")

t.scores.tot=(z.scores.tot*10)+50
t.scores.tot

hist(t.scores.tot, type="o", col="blue")

t.0=data5[data5[,7]== '0',]
t.1=data5[data5[,7]== '1',]
t.2=data5[data5[,7]== '2',]
t.3=data5[data5[,7]== '3',]
t.4=data5[data5[,7]== '4',]
t.5=data5[data5[,7]== '5',]
t.6=data5[data5[,7]== '6',]

p.t.0=colMeans(t.0[,1:6])
p.t.1=colMeans(t.1[,1:6])
p.t.2=colMeans(t.2[,1:6])
p.t.3=colMeans(t.3[,1:6])
p.t.4=colMeans(t.4[,1:6])
p.t.5=colMeans(t.5[,1:6])
p.t.6=colMeans(t.6[,1:6])

p.tot=rbind(p.t.0, p.t.1, p.t.2, p.t.3, p.t.4, p.t.5, p.t.6)

plot(p.tot[,1],main="itemdifficulty of i3", col="magenta",
     xlab="total scores by groups", ylab="item.difficulty",
     xlim= c(0,6), ylim= c(0,1))
plot(p.tot[,2],main="itemdifficulty of i4", col="blue",
     xlab="total scores by groups", ylab="item.difficulty",
     xlim= c(0,6), ylim= c(0,1))
plot(p.tot[,3],main="itemdifficulty of i10", col="green",
     xlab="total scores by groups", ylab="item.difficulty",
     xlim= c(0,6), ylim= c(0,1))
plot(p.tot[,4],main="itemdifficulty of i11", col="purple",
     xlab="total scores by groups", ylab="item.difficulty",
     xlim= c(0,6), ylim= c(0,1))
plot(p.tot[,5],main="itemdifficulty of i18", col="red",
     xlab="total scores by groups", ylab="item.difficulty",
     xlim= c(0,6), ylim= c(0,1))
plot(p.tot[,6],main="itemdifficulty of i20", col="black",
     xlab="total scores by groups", ylab="item.difficulty",
     xlim= c(0,6), ylim= c(0,1))

item.discrimination<-out1[6,]
item.difficulty<-out1[1,]
plot(item.discrimination,
     type="p",
     pch=1,
     cex=4,
     col="blue",
     ylab = "item-total correlation/item difficulty",
     xlab ="item number",
     ylim = c(0,1), xlim = c(1,6),
     main = "test item discriminations"
     )

#hocam tüm aktardıklarınız için teşekkür ederiz :) bu yolda daha da gelişmek 
#dileğiyle