PISA Analizi 8 - fen Derslerindeki Disiplin Ortami
library(gridExtra)## Warning: package 'gridExtra' was built under R version 3.2.5library(grid)
library(car)## Warning: package 'car' was built under R version 3.2.5library(intsvy)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.2.5# Veri setlerini yeniden hesaplanmis ESCS degerleri ile birlestirme
# 2003
colnames(escs_2003)[1:3] <- c("CNT","SCHOOLID","STIDSTD")
pisa_2003_stu <- merge(pisa_2003_stu,escs_2003,by=c("CNT","SCHOOLID","STIDSTD") ,all=TRUE)
# 2006
levels(escs_2006$cnt)## [1] "ARG" "AUS" "AUT" "BEL" "BGR" "BRA" "CAN" "CHE" "CHL" "COL" "CZE"
## [12] "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR" "GRC" "HKG" "HRV" "HUN"
## [23] "IDN" "IRL" "ISL" "ISR" "ITA" "JOR" "JPN" "KOR" "LTU" "LUX" "LVA"
## [34] "MAC" "MEX" "MNE" "NLD" "NOR" "NZL" "POL" "PRT" "QAT" "ROU" "RUS"
## [45] "SVK" "SVN" "SWE" "TAP" "THA" "TUN" "TUR" "URY" "USA" levels(escs_2006$cnt) <- c("Argentina","Australia","Austria","Belgium","Bulgaria"
,"Brazil","Canada","Switzerland","Chile","Colombia",
"Czech Republic","Germany","Denmark","Spain","Estonia",
"Finland","France","United Kingdom","Greece",
"Hong Kong-China","Croatia","Hungary","Indonesia","Ireland",
"Iceland","Israel", "Italy","Jordan","Japan","Korea",
"Lithuania","Luxembourg","Latvia","Macao-China",
"Mexico","Montenegro","Netherlands","Norway","New Zealand",
"Poland","Portugal","Qatar","Romania","Russian Federation",
"Slovak Republic","Slovenia ","Sweden",
"Chinese Taipei","Thailand","Tunisia","Turkey","Uruguay",
"United States")
levels(escs_2006$cnt)## [1] "Argentina" "Australia" "Austria"
## [4] "Belgium" "Bulgaria" "Brazil"
## [7] "Canada" "Switzerland" "Chile"
## [10] "Colombia" "Czech Republic" "Germany"
## [13] "Denmark" "Spain" "Estonia"
## [16] "Finland" "France" "United Kingdom"
## [19] "Greece" "Hong Kong-China" "Croatia"
## [22] "Hungary" "Indonesia" "Ireland"
## [25] "Iceland" "Israel" "Italy"
## [28] "Jordan" "Japan" "Korea"
## [31] "Lithuania" "Luxembourg" "Latvia"
## [34] "Macao-China" "Mexico" "Montenegro"
## [37] "Netherlands" "Norway" "New Zealand"
## [40] "Poland" "Portugal" "Qatar"
## [43] "Romania" "Russian Federation" "Slovak Republic"
## [46] "Slovenia " "Sweden" "Chinese Taipei"
## [49] "Thailand" "Tunisia" "Turkey"
## [52] "Uruguay" "United States" colnames(escs_2006)[1:3] <- c("CNT","SCHOOLID","STIDSTD")
pisa_2006_stu <- merge(pisa_2006_stu,escs_2006,by=c("CNT","SCHOOLID","STIDSTD") ,all=TRUE)
# 2009
levels(escs_2009$cnt)## [1] "ALB" "ARE" "ARG" "AUS" "AUT" "BEL" "BGR" "BRA" "CAN" "CHE" "CHL"
## [12] "COL" "CRI" "CZE" "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR" "GEO"
## [23] "GRC" "HKG" "HRV" "HUN" "IDN" "IRL" "ISL" "ISR" "ITA" "JOR" "JPN"
## [34] "KAZ" "KOR" "LTU" "LUX" "LVA" "MAC" "MDA" "MEX" "MLT" "MNE" "MYS"
## [45] "NLD" "NOR" "NZL" "PER" "POL" "PRT" "QAT" "ROU" "RUS" "SGP" "SVK"
## [56] "SVN" "SWE" "TAP" "THA" "TTO" "TUN" "TUR" "URY" "USA" levels(escs_2009$cnt) <- c("Albania","United Arab Emirates","Argentina","Australia",
"Austria","Belgium","Bulgaria","Brazil","Canada",
"Switzerland","Chile","Colombia","Costa Rica",
"Czech Republic","Germany","Denmark",
"Spain","Estonia","Finland","France","United Kingdom",
"Georgia","Greece","Hong Kong-China","Croatia","Hungary",
"Indonesia","Ireland","Iceland","Israel", "Italy","Jordan",
"Japan","Kazakhstan","Korea","Lithuania","Luxembourg",
"Latvia","Macao-China","Republic of Moldova","Mexico",
"Malta","Montenegro","Malaysia","Netherlands","Norway",
"New Zealand","Peru","Poland","Portugal","Qatar","Romania",
"Russian Federation","Singapore","Slovak Republic",
"Slovenia","Sweden","Chinese Taipei","Thailand",
"Trinidad and Tobago","Tunisia","Turkey","Uruguay",
"United States")
levels(escs_2009$cnt)## [1] "Albania" "United Arab Emirates" "Argentina"
## [4] "Australia" "Austria" "Belgium"
## [7] "Bulgaria" "Brazil" "Canada"
## [10] "Switzerland" "Chile" "Colombia"
## [13] "Costa Rica" "Czech Republic" "Germany"
## [16] "Denmark" "Spain" "Estonia"
## [19] "Finland" "France" "United Kingdom"
## [22] "Georgia" "Greece" "Hong Kong-China"
## [25] "Croatia" "Hungary" "Indonesia"
## [28] "Ireland" "Iceland" "Israel"
## [31] "Italy" "Jordan" "Japan"
## [34] "Kazakhstan" "Korea" "Lithuania"
## [37] "Luxembourg" "Latvia" "Macao-China"
## [40] "Republic of Moldova" "Mexico" "Malta"
## [43] "Montenegro" "Malaysia" "Netherlands"
## [46] "Norway" "New Zealand" "Peru"
## [49] "Poland" "Portugal" "Qatar"
## [52] "Romania" "Russian Federation" "Singapore"
## [55] "Slovak Republic" "Slovenia" "Sweden"
## [58] "Chinese Taipei" "Thailand" "Trinidad and Tobago"
## [61] "Tunisia" "Turkey" "Uruguay"
## [64] "United States" colnames(escs_2009)[1:3] <- c("CNT","SCHOOLID","StIDStd")
pisa_2009_stu <- merge(pisa_2009_stu,escs_2009,by=c("CNT","SCHOOLID","StIDStd") ,all=TRUE)
# 2012
levels(escs_2012$cnt)## [1] "ALB" "ARE" "ARG" "AUS" "AUT" "BEL" "BGR" "BRA" "CAN" "CHE" "CHL"
## [12] "COL" "CRI" "CZE" "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR" "GRC"
## [23] "HKG" "HRV" "HUN" "IDN" "IRL" "ISL" "ISR" "ITA" "JOR" "JPN" "KAZ"
## [34] "KOR" "LTU" "LUX" "LVA" "MAC" "MEX" "MNE" "MYS" "NLD" "NOR" "NZL"
## [45] "PER" "POL" "PRT" "QAT" "QUC" "ROU" "RUS" "SGP" "SVK" "SVN" "SWE"
## [56] "TAP" "THA" "TUN" "TUR" "URY" "USA" "VNM" levels(escs_2012$cnt) <- c("Albania","United Arab Emirates","Argentina","Australia",
"Austria","Belgium",
"Bulgaria","Brazil","Canada",
"Switzerland","Chile","Colombia","Costa Rica",
"Czech Republic","Germany","Denmark",
"Spain","Estonia","Finland","France","United Kingdom",
"Greece","Hong Kong-China",
"Croatia","Hungary","Indonesia","Ireland","Iceland",
"Israel","Italy","Jordan","Japan","Kazakhstan","Korea",
"Lithuania","Luxembourg","Latvia","Macao-China",
"Mexico","Montenegro","Malaysia","Netherlands","Norway",
"New Zealand","Peru","Poland","Portugal","Qatar",
"Shanghai-China","Romania","Russian Federation","Singapore",
"Slovak Republic","Slovenia","Sweden","Chinese Taipei",
"Thailand","Tunisia","Turkey",
"Uruguay","United States of America","Viet Nam")
levels(escs_2012$cnt)## [1] "Albania" "United Arab Emirates"
## [3] "Argentina" "Australia"
## [5] "Austria" "Belgium"
## [7] "Bulgaria" "Brazil"
## [9] "Canada" "Switzerland"
## [11] "Chile" "Colombia"
## [13] "Costa Rica" "Czech Republic"
## [15] "Germany" "Denmark"
## [17] "Spain" "Estonia"
## [19] "Finland" "France"
## [21] "United Kingdom" "Greece"
## [23] "Hong Kong-China" "Croatia"
## [25] "Hungary" "Indonesia"
## [27] "Ireland" "Iceland"
## [29] "Israel" "Italy"
## [31] "Jordan" "Japan"
## [33] "Kazakhstan" "Korea"
## [35] "Lithuania" "Luxembourg"
## [37] "Latvia" "Macao-China"
## [39] "Mexico" "Montenegro"
## [41] "Malaysia" "Netherlands"
## [43] "Norway" "New Zealand"
## [45] "Peru" "Poland"
## [47] "Portugal" "Qatar"
## [49] "Shanghai-China" "Romania"
## [51] "Russian Federation" "Singapore"
## [53] "Slovak Republic" "Slovenia"
## [55] "Sweden" "Chinese Taipei"
## [57] "Thailand" "Tunisia"
## [59] "Turkey" "Uruguay"
## [61] "United States of America" "Viet Nam" colnames(escs_2012)[1:3] <- c("CNT","SCHOOLID","StIDStd")
pisa_2012_stu <- merge(pisa_2012_stu,escs_2012,by=c("CNT","SCHOOLID","StIDStd") ,all=TRUE)
# Simdide 2003, 2006, 2009, 2012,2015 yillarinda 2015 icin yaptigimiz gibi Turkiye verisini suzelim.
pisa_2015_stu_TUR <- subset(pisa_2015_stu,CNT=='Turkey')
pisa_2012_stu_TUR <- subset(pisa_2012_stu,CNT=="Turkey")
pisa_2009_stu_TUR <- subset(pisa_2009_stu,CNT=='Turkey')
pisa_2006_stu_TUR <- subset(pisa_2006_stu,CNT=='Turkey')
pisa_2003_stu_TUR <- subset(pisa_2003_stu,CNT=='TUR')
# OECD verisini suzelim
pisa_2015_stu_OECD <- subset(pisa_2015_stu,OECD=="Yes")
pisa_2012_stu_OECD <- subset(pisa_2012_stu,OECD=="OECD")
pisa_2009_stu_OECD <- subset(pisa_2009_stu,OECD=="OECD")
pisa_2006_stu_OECD <- subset(pisa_2006_stu,OECD=="OECD")
pisa_2003_stu_OECD <- subset(pisa_2003_stu,OECD=="OECD country")
# AB verisini suzelim
AB <- c("AUT","BEL","BGR","HRV","CYP","CZE","DNK","EST","FIN","FRA","DEU","GRC","HUN",
"IRL","ITA","LVA","LTU","LUX","MLT","NLD","POL","PRT","ROU","SVK","SVN","ESP",
"SWE")
AB2 <- c("Austria","Belgium","Bulgaria","Croatia","Cyprus","Czech Republic","Denmark",
"Estonia","Finland","France","Germany","Greece","Hungary","Ireland","Italy",
"Latvia","Lithuania","Luxembourg","Malta","Netherlands","Poland","Portugal",
"Romania","Slovak Republic","Slovenia ","Spain","Sweden")
AB3 <- c("Austria","Belgium","Bulgaria","Croatia","Cyprus","Czech Republic","Denmark",
"Estonia","Finland","France","Germany","Greece","Hungary","Ireland","Italy",
"Latvia","Lithuania","Luxembourg","Malta","Netherlands","Poland","Portugal",
"Romania","Slovak Republic","Slovenia","Spain","Sweden")
pisa_2003_stu_AB <- subset(pisa_2003_stu, CNT %in% AB)
pisa_2006_stu_AB <- subset(pisa_2006_stu, CNT %in% AB2)
pisa_2009_stu_AB <- subset(pisa_2009_stu, CNT %in% AB3)
pisa_2012_stu_AB <- subset(pisa_2012_stu, CNT %in% AB3)
pisa_2015_stu_AB <- subset(pisa_2015_stu, CNT %in% AB3)
# Simdide 2003, 2006, 2009, 2012,2015 yillarinda Okul seviyesinde Turkiye verisini suzelim
pisa_2015_sch_TUR <- subset(pisa_2015_sch,CNT=='Turkey')
pisa_2012_sch_TUR <- subset(pisa_2012_sch,CNT=="Turkey")
pisa_2009_sch_TUR <- subset(pisa_2009_sch,CNT=='Turkey')
pisa_2006_sch_TUR <- subset(pisa_2006_sch,CNT=='Turkey')
pisa_2003_sch_TUR <- subset(pisa_2003_sch,CNT=='TUR')
pisa_2015_stu_TUR$bolge <- NA
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 01: TR1 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 02: TR1 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 03: TR1 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "ISTANBUL"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 04: TR2 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 05: TR2 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 06: TR2 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "BatiMarmara"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 07: TR3 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 08: TR3 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 09: TR3 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "Ege"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 10: TR4 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 11: TR4 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 12: TR4 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "DoguMarmara"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 13: TR5 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 14: TR5 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 15: TR5 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "BatiAnadolu"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 16: TR6 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 17: TR6 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 18: TR6 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "Akdeniz"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 19: TR7 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 20: TR7 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 21: TR7 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "OrtaAnadolu"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 22: TR8 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 23: TR8 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 24: TR8 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "BatiKaradeniz"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 25: TR9 BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 26: TR9 GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 27: TR9 VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "DoguKaradeniz"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 28: TRA BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 29: TRA GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 30: TRA VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "KuzeydoguAnadolu"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 31: TRB BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 32: TRB GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 33: TRB VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "OrtadoguAnadolu"
pisa_2015_stu_TUR[pisa_2015_stu_TUR$STRATUM=="TUR - stratum 34: TRC BASIC EDUCATION" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 35: TRC GENERAL SECONDARY" |
pisa_2015_stu_TUR$STRATUM=="TUR - stratum 36: TRC VOCATIONAL AND TECHNICAL SECONDARY",]$bolge <- "GuneydoguAnadolu"
pisa_2015_stu_TUR$bolge <- factor(pisa_2015_stu_TUR$bolge)
# Okul ve Ogrenci seviyesindeki verileri birlestirelim
pisa_2003_TUR <- merge(pisa_2003_stu_TUR,
pisa_2003_sch_TUR,
by=c("CNT","SCHOOLID") ,all=TRUE)
pisa_2006_TUR <- merge(pisa_2006_stu_TUR,
pisa_2006_sch_TUR,
by=c("CNT","SCHOOLID") ,all=TRUE)
pisa_2009_TUR <- merge(pisa_2009_stu_TUR,
pisa_2009_sch_TUR,
by=c("CNT","SCHOOLID") ,all=TRUE)
pisa_2012_TUR <- merge(pisa_2009_stu_TUR,
pisa_2009_sch_TUR,
by=c("CNT","SCHOOLID") ,all=TRUE)
pisa_2015_TUR <- merge(pisa_2015_stu_TUR,
pisa_2015_sch_TUR,
by=c("CNT","CNTSCHID") ,all=TRUE)
pisa_2015 <- merge(pisa_2015_stu,
pisa_2015_sch,
by=c("CNT","CNTSCHID") ,all=TRUE)
pisa_2015_OECD <- subset(pisa_2015,OECD.x=="Yes")
pisa_2015_AB <- subset(pisa_2015, CNT %in% AB3)cor(pisa_2015_stu_TUR\(DISCLISCI,as.numeric(pisa_2015_stu_TUR\)ST097Q01TA),use=“pairwise.complete.obs”) cor(pisa_2015_stu_TUR\(DISCLISCI,as.numeric(pisa_2015_stu_TUR\)ST097Q02TA),use=“pairwise.complete.obs”) cor(pisa_2015_stu_TUR\(DISCLISCI,as.numeric(pisa_2015_stu_TUR\)ST097Q03TA),use=“pairwise.complete.obs”) cor(pisa_2015_stu_TUR\(DISCLISCI,as.numeric(pisa_2015_stu_TUR\)ST097Q04TA),use=“pairwise.complete.obs”) cor(pisa_2015_stu_TUR\(DISCLISCI,as.numeric(pisa_2015_stu_TUR\)ST097Q05TA),use=“pairwise.complete.obs”)
ST097Q01TA- Sinifta ögrenciler ögretmeni dinlemiyor
tur <- pisa2015.table(variable="ST097Q01TA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST097Q01TA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST097Q01TA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST097Q01TA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST097Q01TA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST097Q01TA <- factor(q$ST097Q01TA,
levels=c("Every lesson","Most lessons","Some lessons","Never or hardly ever"),
labels=c("Her zaman","Çogu zaman","Bazen","Hiç")
)
q## ST097Q01TA Percentage tip
## 1 Her zaman 9.15000 Türkiye
## 2 Çogu zaman 18.35000 Türkiye
## 3 Bazen 54.33000 Türkiye
## 4 Hiç 18.17000 Türkiye
## 5 Her zaman 10.95200 OECD Ülkeleri Ortalamasi
## 6 Çogu zaman 21.21629 OECD Ülkeleri Ortalamasi
## 7 Bazen 49.46171 OECD Ülkeleri Ortalamasi
## 8 Hiç 18.37029 OECD Ülkeleri Ortalamasi
## 9 Her zaman 11.88231 AB Ülkeleri Ortalamasi
## 10 Çogu zaman 23.50808 AB Ülkeleri Ortalamasi
## 11 Bazen 50.03077 AB Ülkeleri Ortalamasi
## 12 Hiç 14.57846 AB Ülkeleri Ortalamasi plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST097Q01TA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,100))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Sinifta ögrenciler ögretmeni dinlemiyor",
x = "", y = "Yüzde",
fill="Olma Durumu")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 18),
legend.text=element_text(size = 12)) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 3.3, xmax = 3.3, ymin = 95, ymax = 95)
plot
## png
## 2ST097Q02TA- Sinifta gürültü ve kargasa oluyor
tur <- pisa2015.table(variable="ST097Q02TA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST097Q02TA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST097Q02TA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST097Q02TA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST097Q02TA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST097Q02TA <- factor(q$ST097Q02TA,
levels=c("Every lesson","Most lessons","Some lessons","Never or hardly ever"),
labels=c("Her zaman","Çogu zaman","Bazen","Hiç")
)
q## ST097Q02TA Percentage tip
## 1 Her zaman 10.40000 Türkiye
## 2 Çogu zaman 20.12000 Türkiye
## 3 Bazen 51.64000 Türkiye
## 4 Hiç 17.85000 Türkiye
## 5 Her zaman 10.90714 OECD Ülkeleri Ortalamasi
## 6 Çogu zaman 22.23486 OECD Ülkeleri Ortalamasi
## 7 Bazen 48.16429 OECD Ülkeleri Ortalamasi
## 8 Hiç 18.69343 OECD Ülkeleri Ortalamasi
## 9 Her zaman 10.75423 AB Ülkeleri Ortalamasi
## 10 Çogu zaman 22.71231 AB Ülkeleri Ortalamasi
## 11 Bazen 48.42154 AB Ülkeleri Ortalamasi
## 12 Hiç 18.11154 AB Ülkeleri Ortalamasi plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST097Q02TA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,100))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Sinifta gürültü ve kargasa oluyor",
x = "", y = "Yüzde",
fill="Olma Durumu")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 14),
legend.text=element_text(size = 12)) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 3.3, xmax = 3.3, ymin = 100, ymax = 100)
plot
## png
## 2ST097Q03TA - Ögretmen ögrencilerin susmasi için uzun süre beklemek zorunda kaliyor
tur <- pisa2015.table(variable="ST097Q03TA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST097Q03TA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST097Q03TA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST097Q03TA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST097Q03TA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST097Q03TA <- factor(q$ST097Q03TA,
levels=c("Every lesson","Most lessons","Some lessons","Never or hardly ever"),
labels=c("Her zaman","Çogu zaman","Bazen","Hiç")
)
q## ST097Q03TA Percentage tip
## 1 Her zaman 11.600000 Türkiye
## 2 Çogu zaman 18.780000 Türkiye
## 3 Bazen 47.900000 Türkiye
## 4 Hiç 21.720000 Türkiye
## 5 Her zaman 9.661143 OECD Ülkeleri Ortalamasi
## 6 Çogu zaman 19.085143 OECD Ülkeleri Ortalamasi
## 7 Bazen 44.380000 OECD Ülkeleri Ortalamasi
## 8 Hiç 26.875143 OECD Ülkeleri Ortalamasi
## 9 Her zaman 9.989615 AB Ülkeleri Ortalamasi
## 10 Çogu zaman 20.025000 AB Ülkeleri Ortalamasi
## 11 Bazen 44.977692 AB Ülkeleri Ortalamasi
## 12 Hiç 25.009231 AB Ülkeleri Ortalamasi plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST097Q03TA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,100))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Ögretmen ögrencilerin susmasi için uzun süre beklemek zorunda kaliyor",
x = "", y = "Yüzde",
fill="Olma Durumu")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 18),
legend.text=element_text(size = 12)) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 3.3, xmax = 3.3, ymin =100, ymax =100)
plot
## png
## 2ST097Q04TA - Ögrenciler sinifta iyi çalisamiyor
tur <- pisa2015.table(variable="ST097Q04TA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST097Q04TA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST097Q04TA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST097Q04TA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST097Q04TA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST097Q04TA <- factor(q$ST097Q04TA,
levels=c("Every lesson","Most lessons","Some lessons","Never or hardly ever"),
labels=c("Her zaman","Çogu zaman","Bazen","Hiç")
)
q## ST097Q04TA Percentage tip
## 1 Her zaman 11.060000 Türkiye
## 2 Çogu zaman 23.150000 Türkiye
## 3 Bazen 49.060000 Türkiye
## 4 Hiç 16.730000 Türkiye
## 5 Her zaman 6.828000 OECD Ülkeleri Ortalamasi
## 6 Çogu zaman 14.696571 OECD Ülkeleri Ortalamasi
## 7 Bazen 44.546000 OECD Ülkeleri Ortalamasi
## 8 Hiç 33.929429 OECD Ülkeleri Ortalamasi
## 9 Her zaman 6.993846 AB Ülkeleri Ortalamasi
## 10 Çogu zaman 15.384615 AB Ülkeleri Ortalamasi
## 11 Bazen 45.133846 AB Ülkeleri Ortalamasi
## 12 Hiç 32.488462 AB Ülkeleri Ortalamasi plot <- ggplot(q, aes(x=tip, y=Percentage,fill=q$ST097Q04TA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,100))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Ögrenciler sinifta iyi çalisamiyor",
x = "", y = "Yüzde",
fill="Olma Durumu")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 18),
legend.text=element_text(size = 12)) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 3.3, xmax = 3.3, ymin = 100, ymax = 100)
plot
## png
## 2ST097Q05TA - Ders basladiktan uzun süre sonra bile ögrenciler çalismaya baslamiyor
tur <- pisa2015.table(variable="ST097Q05TA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST097Q05TA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST097Q05TA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST097Q05TA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST097Q05TA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST097Q05TA <- factor(q$ST097Q05TA,
levels=c("Every lesson","Most lessons","Some lessons","Never or hardly ever"),
labels=c("Her zaman","Çogu zaman","Bazen","Hiç")
)
q## ST097Q05TA Percentage tip
## 1 Her zaman 11.020000 Türkiye
## 2 Çogu zaman 20.240000 Türkiye
## 3 Bazen 45.180000 Türkiye
## 4 Hiç 23.560000 Türkiye
## 5 Her zaman 8.716571 OECD Ülkeleri Ortalamasi
## 6 Çogu zaman 16.982571 OECD Ülkeleri Ortalamasi
## 7 Bazen 42.036857 OECD Ülkeleri Ortalamasi
## 8 Hiç 32.264571 OECD Ülkeleri Ortalamasi
## 9 Her zaman 9.243077 AB Ülkeleri Ortalamasi
## 10 Çogu zaman 17.650000 AB Ülkeleri Ortalamasi
## 11 Bazen 41.651154 AB Ülkeleri Ortalamasi
## 12 Hiç 31.454231 AB Ülkeleri Ortalamasi plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST097Q05TA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,100))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Ders basladiktan uzun süre sonra bile ögrenciler çalismaya baslamiyor",
x = "", y = "Yüzde",
fill="Olma Durumu")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 18),
legend.text=element_text(size = 12)) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 3.3, xmax = 3.3, ymin = 100, ymax = 100)
plot
## png
## 2DIGER ULKELERLE KARSILASTIRMA
a <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_stu,by="CNT")
a$Mean <- -a$Mean
a <- a[order(a[,3],decreasing=T),]
a <- na.omit(a)
a$Mean2 <- round(a$Mean,2)
a$cnt <- NA
for(i in 1:nrow(a)) {
cod = substr(unique(pisa_2015_stu[which(pisa_2015_stu$CNT==a[i,1]),]$CNTSCHID)[1],1,3)
if(length(which(country.code[,2]==as.numeric(cod)))!=0){
a[i,]$cnt=as.character(country.code[which(country.code[,2]==as.numeric(cod)),1])
}
}
a[which(a$CNT=="B-S-J-G (China)"),]$cnt="CHN"
a[which(a$CNT=="Belgium"),]$cnt="BEL"
a[which(a$CNT=="Brazil"),]$cnt="BRA"
a[which(a$CNT=="Australia"),]$cnt="IDN"
a[which(a$CNT=="Austria"),]$cnt="AUT"
a <- na.omit(a)
a$rank <- 1:nrow(a)
plot <- ggplot(a, aes(x=rank, y=Mean2,width=.5)) +
geom_bar(stat='identity',position=position_dodge(1.5),fill="white",colour="black") +
scale_y_continuous(limit = c(-1,1.1))+
theme_bw()+
labs(title ="Fen Derslerindeki Disiplin Ortami",
x = "", y = "Standard Puan",
fill=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 18),
legend.text=element_text(size = 12)) +
geom_text(aes(y=rep(0,66),label=cnt),angle=90,size=4,
stat= "identity", position=position_dodge(1),vjust =0.2,hjust=c(rep(1.25,34),rep(-.25,32)))+
geom_text(aes(y=Mean2,label=Mean2),angle=90,
stat= "identity", position=position_dodge(1),vjust =0.2,hjust=c(rep(-.25,34),rep(1.25,32)))+
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 54, xmax = 54, ymin = .4, ymax = .4)
plot
## png
## 2Cografi Bolgeler
tur <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_stu_TUR,by="bolge")
tur$Mean <- -tur$Mean
tur <- tur[order(tur[,3]),]
tur[,1] <- factor(tur[,1],levels=tur[,1],labels=tur[,1])
plot <- ggplot(tur, aes(x=bolge, y=Mean)) +
geom_bar(stat='identity',position=position_dodge(),width=.7,fill="bisque2") +
# geom_errorbar(aes(ymin=Mean-1.96*s.e., ymax=Mean+1.96*s.e.),
# position = position_dodge(.2),
# lty=2,
# colour="gray50",
# width=0.05)+
theme_bw()+
scale_y_continuous(limit = c(-.1,.4)) +
geom_text(aes(y=Mean,label=Mean),
stat= "identity", position=position_dodge(.2),
hjust=1.2,vjust = -.6,size = 5) +
labs(title = "Fen Derslerindeki Disiplin Ortami",
x = "COGRAFI BOLGE", y = "STANDARD PUAN",
shape=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1,size=13),
title = element_text(size = 20),
legend.justification=c(-0.5,-0.2),
legend.position=c(0,0),
legend.text=element_text(size = 12)
) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin =1, xmax = 1, ymin = 0.95, ymax = 0.95)
plot
## png
## 2SINIF DUZEYI
tur <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_stu_TUR,by="ST001D01T")
tur <- tur[2:4,]
tur## ST001D01T Freq Mean s.e. SD s.e
## 2 Grade 8 65 -0.19 0.14 0.84 0.08
## 3 Grade 9 1161 -0.18 0.03 0.94 0.03
## 4 Grade 10 3968 -0.11 0.03 0.92 0.02 oecd <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_stu_OECD,by=c("CNT","ST001D01T"))
oecd <- aggregate(Mean ~ ST001D01T, data=oecd,mean)
oecd <- oecd[2:4,]
oecd## ST001D01T Mean
## 2 Grade 8 -0.2251724
## 3 Grade 9 -0.1115625
## 4 Grade 10 0.1142857 ab <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_stu_AB,by=c("CNT","ST001D01T"))
ab <- aggregate(Mean ~ ST001D01T,data=ab,mean)
ab<- ab[2:4,]
ab## ST001D01T Mean
## 2 Grade 8 -0.22240000
## 3 Grade 9 -0.11615385
## 4 Grade 10 0.09115385 q <- rbind(tur,tur,tur)
q$tip <- c(rep("Türkiye",3),rep("OECD Ülkeleri Ortalamasi",3),rep("AB Ülkeleri Ortalamasi",3))
q[4:6,]$Mean <- oecd$Mean
q[4:6,]$s.e. <- 0
q[7:9,]$Mean <- ab$Mean
q[7:9,]$s.e. <- 0
q$Mean <- -q$Mean
q$Mean2 <- round(q$Mean,2)
q$ST001D01T <- factor(q$ST001D01T,
levels=c("Grade 8","Grade 9","Grade 10"),
labels=c("8. Sinif","9. Sinif","10. Sinif"))
q## ST001D01T Freq Mean s.e. SD s.e tip
## 2 8. Sinif 65 0.19000000 0.14 0.84 0.08 Türkiye
## 3 9. Sinif 1161 0.18000000 0.03 0.94 0.03 Türkiye
## 4 10. Sinif 3968 0.11000000 0.03 0.92 0.02 Türkiye
## 21 8. Sinif 65 0.22517241 0.00 0.84 0.08 OECD Ülkeleri Ortalamasi
## 31 9. Sinif 1161 0.11156250 0.00 0.94 0.03 OECD Ülkeleri Ortalamasi
## 41 10. Sinif 3968 -0.11428571 0.00 0.92 0.02 OECD Ülkeleri Ortalamasi
## 22 8. Sinif 65 0.22240000 0.00 0.84 0.08 AB Ülkeleri Ortalamasi
## 32 9. Sinif 1161 0.11615385 0.00 0.94 0.03 AB Ülkeleri Ortalamasi
## 42 10. Sinif 3968 -0.09115385 0.00 0.92 0.02 AB Ülkeleri Ortalamasi
## Mean2
## 2 0.19
## 3 0.18
## 4 0.11
## 21 0.23
## 31 0.11
## 41 -0.11
## 22 0.22
## 32 0.12
## 42 -0.09 plot <- ggplot(q, aes(x=tip, y=Mean,fill=ST001D01T)) +
geom_bar(stat='identity',position=position_dodge(),width=.75) +
geom_errorbar(aes(ymin=Mean-1.96*s.e., ymax=Mean+1.96*s.e.),
position = position_dodge(0.8),
lty=2,
colour="gray50",
width=c(.05,.05,.05,0,0,0,0,0,0))+
scale_y_continuous(limit = c(-.2,.5))+
theme_bw()+
geom_text(aes(y=Mean2,label=Mean2),
stat= "identity", position=position_dodge(.8),
vjust = c(1.2,-1,-1,1.5,-1,-1,-.5,-.5,-.5),
hjust = c(.6,.6,.6,.6,.6,.6,1.2,1.2,1.2),
size = 6)+
labs(title = "Fen Derslerindeki Disiplin Ortami",
x = "", y = "Standard Puan",
fill=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 20),
legend.text=element_text(size = 12)
) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin =0.7, xmax = 0.7, ymin = 0.5, ymax = 0.5)
plot
## png
## 2Okul Tipi
pisa_2015_TUR$tur <- ifelse(pisa_2015_TUR$SCHLTYPE=="Private Independent" |
pisa_2015_TUR$SCHLTYPE=="Private Government-dependent",1,0)
tur <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_TUR,by="tur")
tur <- tur[1:2,]
pisa_2015_OECD$tur <- ifelse(pisa_2015_OECD$SCHLTYPE=="Private Independent" |
pisa_2015_OECD$SCHLTYPE=="Private Government-dependent",1,0)
oecd <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_OECD,by=c("CNT","tur"))
oecd <- aggregate(Mean ~ tur, data=oecd,mean)
pisa_2015_AB$tur <- ifelse(pisa_2015_AB$SCHLTYPE=="Private Independent" |
pisa_2015_AB$SCHLTYPE=="Private Government-dependent",1,0)
ab <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_AB,by=c("CNT","tur"))
ab <- aggregate(Mean ~ tur, data=ab,mean)
q <- rbind(tur,tur,tur)
q$tip <- c(rep("Türkiye",2),rep("OECD Ülkeleri Ortalamasi",2),rep("AB Ülkeleri Ortalamasi",2))
q[3:4,]$Mean <- oecd$Mean
q[3:4,]$s.e. <- 0
q[5:6,]$Mean <- ab$Mean
q[5:6,]$s.e. <- 0
q$tur <- factor(q$tur,levels=c(0,1),labels=c("Devlet Okulu","Ozel Okul"))
q$Mean <- -q$Mean
q$Mean2 <- round(q$Mean,2)
q## tur Freq Mean s.e. SD s.e tip
## 1 Devlet Okulu 5017 0.12000000 0.02 0.90 0.01 Türkiye
## 2 Ozel Okul 222 0.24000000 0.15 1.05 0.06 Türkiye
## 3 Devlet Okulu 5017 0.02676471 0.00 0.90 0.01 OECD Ülkeleri Ortalamasi
## 4 Ozel Okul 222 -0.09235294 0.00 1.05 0.06 OECD Ülkeleri Ortalamasi
## 5 Devlet Okulu 5017 0.07692308 0.00 0.90 0.01 AB Ülkeleri Ortalamasi
## 6 Ozel Okul 222 -0.01923077 0.00 1.05 0.06 AB Ülkeleri Ortalamasi
## Mean2
## 1 0.12
## 2 0.24
## 3 0.03
## 4 -0.09
## 5 0.08
## 6 -0.02 plot <- ggplot(q, aes(x=tip, y=Mean,fill=tur)) +
geom_bar(stat='identity',position=position_dodge(),width=.75) +
geom_errorbar(aes(ymin=Mean-1.96*s.e., ymax=Mean+1.96*s.e.),
position = position_dodge(0.8),
lty=2,
colour="gray50",
width=c(.05,.05,0,0,0,0))+
scale_y_continuous(limit = c(-.3,.8))+
theme_bw()+
geom_text(aes(y=Mean2,label=Mean2),
stat= "identity", position=position_dodge(1),
vjust = c(1.2,1.2,1.8,1.2,-.3,-.3),
hjust = c(.6,.6,.6,.6,1.5,1.2),
size = 6)+
labs(title ="Fen Derslerindeki Disiplin Ortami",
x = "", y = "Standard Puan",
fill=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 18),
legend.text=element_text(size = 12)) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 3.3, xmax = 3.3, ymin = .8, ymax = .8)
plot
## png
## 2Okul Turu
tur <- pisa2015.mean(variable="DISCLISCI",data=pisa_2015_stu_TUR,by="PROGN")
tur$PROGN <- as.character(tur$PROGN)
tur$PROGN<- factor(tur$PROGN,
levels=c("Turkey: Basic Education","Turkey: Vocational and Technical Secondary Education",
"Turkey: General Secondary Education"),
labels=c("Ortaogretim","Mesleki ve Teknik Lise","Genel Lise"))
tur## PROGN Freq Mean s.e. SD s.e
## 1 Ortaogretim 75 -0.15 0.16 0.82 0.07
## 2 Genel Lise 2973 -0.06 0.03 0.90 0.02
## 3 Mesleki ve Teknik Lise 2216 -0.22 0.03 0.93 0.02 tur$Mean <- -tur$Mean
plot <- ggplot(tur, aes(x=PROGN, y=Mean)) +
geom_bar(stat='identity',position=position_dodge(),width=.7,fill="bisque2") +
geom_errorbar(aes(ymin=Mean-1.96*s.e., ymax=Mean+1.96*s.e.),
position = position_dodge(.2),
lty=2,
colour="gray50",
width=0.05)+
theme_bw()+
geom_text(aes(y=Mean,label=Mean),
stat= "identity", position=position_dodge(.2),
hjust=1.2,vjust = -.6,size = 5) +
labs(title = "Okul Turune Gore Fen Derslerindeki Disiplin Ortami Puani",
x = "", y = "Standard Puan",
fill=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 20),
legend.text=element_text(size = 12)
) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin =3.3, xmax = 3.3, ymin = -0.15, ymax = -0.15)
plot
## png
## 2