library(intsvy)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.2.5
library(gridExtra)
## Warning: package 'gridExtra' was built under R version 3.2.5
library(grid)
library(car)
## Warning: package 'car' 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)
ST118Q01NA - Siklikla bir sinava girmenin cok zor oldugundan endiseleniyorum.
tur <- pisa2015.table(variable="ST118Q01NA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST118Q01NA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST118Q01NA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST118Q01NA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST118Q01NA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST118Q01NA <- factor(q$ST118Q01NA,
levels=c("Strongly disagree","Disagree","Agree","Strongly agree"),
labels=c("Tamamen Katilmiyorum","Katilmiyorum",
"Katiliyorum","Tamamen Katiliyorum")
)
q
## ST118Q01NA Percentage tip
## 1 Tamamen Katilmiyorum 9.68000 Türkiye
## 2 Katilmiyorum 20.54000 Türkiye
## 3 Katiliyorum 46.97000 Türkiye
## 4 Tamamen Katiliyorum 22.82000 Türkiye
## 5 Tamamen Katilmiyorum 10.54714 OECD Ülkeleri Ortalamasi
## 6 Katilmiyorum 30.11857 OECD Ülkeleri Ortalamasi
## 7 Katiliyorum 44.06114 OECD Ülkeleri Ortalamasi
## 8 Tamamen Katiliyorum 15.27486 OECD Ülkeleri Ortalamasi
## 9 Katiliyorum 43.62042 AB Ülkeleri Ortalamasi
## 10 Katilmiyorum 30.43792 AB Ülkeleri Ortalamasi
## 11 Tamamen Katiliyorum 14.57875 AB Ülkeleri Ortalamasi
## 12 Tamamen Katilmiyorum 11.36375 AB Ülkeleri Ortalamasi
plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST118Q01NA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,60))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Bir sinava girmenin çok zor oldugundan siklikla endiseleniyorum",
x = "", y = "Yüzde",
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 = 60, ymax = 60)
plot
## png
## 2
ST118Q02NA - Okulda kotu notlar alacagimdan endiseleniyorum.
tur <- pisa2015.table(variable="ST118Q02NA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST118Q02NA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST118Q02NA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST118Q02NA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST118Q02NA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST118Q02NA <- factor(q$ST118Q02NA,
levels=c("Strongly disagree","Disagree","Agree","Strongly agree"),
labels=c("Tamamen Katilmiyorum","Katilmiyorum",
"Katiliyorum","Tamamen Katiliyorum")
)
q
## ST118Q02NA Percentage tip
## 1 Tamamen Katilmiyorum 8.300000 Türkiye
## 2 Katilmiyorum 17.270000 Türkiye
## 3 Katiliyorum 49.190000 Türkiye
## 4 Tamamen Katiliyorum 25.250000 Türkiye
## 5 Tamamen Katilmiyorum 9.195143 OECD Ülkeleri Ortalamasi
## 6 Katilmiyorum 25.101143 OECD Ülkeleri Ortalamasi
## 7 Katiliyorum 44.353429 OECD Ülkeleri Ortalamasi
## 8 Tamamen Katiliyorum 21.349714 OECD Ülkeleri Ortalamasi
## 9 Katiliyorum 45.211667 AB Ülkeleri Ortalamasi
## 10 Katilmiyorum 25.667917 AB Ülkeleri Ortalamasi
## 11 Tamamen Katiliyorum 19.454583 AB Ülkeleri Ortalamasi
## 12 Tamamen Katilmiyorum 9.663750 AB Ülkeleri Ortalamasi
plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST118Q02NA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,60))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Okulda kötü notlar alacagimdan endiseleniyorum",
x = "", y = "Yüzde",
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 = 60, ymax = 60)
plot
## png
## 2
ST118Q03NA - Sinava cok iyi hazirlanmis olsam da hala cok kaygi duyuyorum.
tur <- pisa2015.table(variable="ST118Q03NA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST118Q03NA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST118Q03NA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST118Q03NA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST118Q03NA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST118Q03NA <- factor(q$ST118Q03NA,
levels=c("Strongly disagree","Disagree","Agree","Strongly agree"),
labels=c("Tamamen Katilmiyorum","Katilmiyorum",
"Katiliyorum","Tamamen Katiliyorum")
)
q
## ST118Q03NA Percentage tip
## 1 Tamamen Katilmiyorum 14.12000 Türkiye
## 2 Katilmiyorum 27.11000 Türkiye
## 3 Katiliyorum 37.76000 Türkiye
## 4 Tamamen Katiliyorum 21.02000 Türkiye
## 5 Tamamen Katilmiyorum 14.42771 OECD Ülkeleri Ortalamasi
## 6 Katilmiyorum 30.08429 OECD Ülkeleri Ortalamasi
## 7 Katiliyorum 37.78857 OECD Ülkeleri Ortalamasi
## 8 Tamamen Katiliyorum 17.69886 OECD Ülkeleri Ortalamasi
## 9 Katiliyorum 36.65917 AB Ülkeleri Ortalamasi
## 10 Katilmiyorum 31.05750 AB Ülkeleri Ortalamasi
## 11 Tamamen Katiliyorum 16.43958 AB Ülkeleri Ortalamasi
## 12 Tamamen Katilmiyorum 15.84333 AB Ülkeleri Ortalamasi
plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST118Q03NA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,60))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Sinava çok iyi hazirlanmis olsam da hala çok kaygi duyuyorum",
x = "", y = "Yüzde",
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 = 60, ymax = 60)
plot
## png
## 2
ST118Q04NA - Bir sinava çalisirken çok geriliyorum.
tur <- pisa2015.table(variable="ST118Q04NA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST118Q04NA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST118Q04NA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST118Q04NA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST118Q04NA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST118Q04NA <- factor(q$ST118Q04NA,
levels=c("Strongly disagree","Disagree","Agree","Strongly agree"),
labels=c("Tamamen Katilmiyorum","Katilmiyorum",
"Katiliyorum","Tamamen Katiliyorum")
)
q
## ST118Q04NA Percentage tip
## 1 Tamamen Katilmiyorum 13.700000 Türkiye
## 2 Katilmiyorum 30.330000 Türkiye
## 3 Katiliyorum 38.320000 Türkiye
## 4 Tamamen Katiliyorum 17.640000 Türkiye
## 5 Tamamen Katilmiyorum 22.031143 OECD Ülkeleri Ortalamasi
## 6 Katilmiyorum 41.360000 OECD Ülkeleri Ortalamasi
## 7 Katiliyorum 26.660286 OECD Ülkeleri Ortalamasi
## 8 Tamamen Katiliyorum 9.948000 OECD Ülkeleri Ortalamasi
## 9 Katiliyorum 25.130417 AB Ülkeleri Ortalamasi
## 10 Katilmiyorum 42.206250 AB Ülkeleri Ortalamasi
## 11 Tamamen Katiliyorum 8.649167 AB Ülkeleri Ortalamasi
## 12 Tamamen Katilmiyorum 24.014167 AB Ülkeleri Ortalamasi
plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST118Q04NA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,60))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Bir sinava çalisirken çok geriliyorum",
x = "", y = "Yüzde",
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 = 60, ymax = 60)
plot
## png
## 2
ST118Q05NA - Okulda bir ödevin nasil yapilacagini bilemedigim zaman endiseleniyorum.
tur <- pisa2015.table(variable="ST118Q05NA",data=pisa_2015_stu_TUR)
oecd <- pisa2015.table(variable="ST118Q05NA",data=pisa_2015_stu_OECD,by="CNT")
oecd <- aggregate(Percentage ~ ST118Q05NA, dat=oecd,mean)
ab <- pisa2015.table(variable="ST118Q05NA",data=pisa_2015_stu_AB,by="CNT")
ab <- aggregate(Percentage ~ ST118Q05NA, dat=ab,mean)
q <- rbind(tur[,c(1,3)],oecd,ab)
q$tip <- c(rep("Türkiye",4),rep("OECD Ülkeleri Ortalamasi",4),rep("AB Ülkeleri Ortalamasi",4))
q$ST118Q05NA <- factor(q$ST118Q05NA,
levels=c("Strongly disagree","Disagree","Agree","Strongly agree"),
labels=c("Tamamen Katilmiyorum","Katilmiyorum",
"Katiliyorum","Tamamen Katiliyorum")
)
q
## ST118Q05NA Percentage tip
## 1 Tamamen Katilmiyorum 10.85000 Türkiye
## 2 Katilmiyorum 19.73000 Türkiye
## 3 Katiliyorum 35.31000 Türkiye
## 4 Tamamen Katiliyorum 34.11000 Türkiye
## 5 Tamamen Katilmiyorum 15.28143 OECD Ülkeleri Ortalamasi
## 6 Katilmiyorum 32.99143 OECD Ülkeleri Ortalamasi
## 7 Katiliyorum 35.19829 OECD Ülkeleri Ortalamasi
## 8 Tamamen Katiliyorum 16.52943 OECD Ülkeleri Ortalamasi
## 9 Katiliyorum 34.31333 AB Ülkeleri Ortalamasi
## 10 Katilmiyorum 33.43792 AB Ülkeleri Ortalamasi
## 11 Tamamen Katiliyorum 15.62750 AB Ülkeleri Ortalamasi
## 12 Tamamen Katilmiyorum 16.62250 AB Ülkeleri Ortalamasi
plot <- ggplot(q, aes(x=tip, y=Percentage,fill=ST118Q05NA)) +
geom_bar(stat='identity',position=position_dodge()) +
scale_y_continuous(limit = c(0,60))+
theme_bw()+
geom_text(aes(y=Percentage,label=scales::percent(q$Percentage/100)),
stat= "identity", position=position_dodge(1),vjust = -1)+
labs(title ="Okulda bir ödevin nasil yapilacagini bilemedigim zaman endiseleniyorum.",
x = "", y = "Yüzde",
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 = 60, ymax = 60)
plot
## png
## 2
DIGER ULKELERLE KARSILASTIRMA
a <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu,by="CNT")
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 <- na.omit(a)
a$rank <- 1:nrow(a)
ggplot(a, aes(x=rank, y=Mean2,width=.5)) +
geom_bar(stat='identity',position=position_dodge(1.5),fill="white",colour="black") +
scale_y_continuous(limit = c(-.7,.7))+
theme_bw()+
labs(title =" TEST KAYGISI",
x = "", y = "Standard Puan",
fill=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 18),
legend.text=element_text(size = 12)) +
geom_text(aes(y=rep(0,55),label=cnt),angle=90,size=4,
stat= "identity", position=position_dodge(1),vjust =0.2,hjust=c(rep(1.25,34),rep(-.25,21)))+
geom_text(aes(y=Mean2,label=Mean2),angle=90,
stat= "identity", position=position_dodge(1),vjust =0.2,hjust=c(rep(-.25,34),rep(1.25,21)))+
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 54, xmax = 54, ymin = .4, ymax = .4)
CINSIYET
tur <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_TUR,by="ST004D01T")
oecd <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_OECD,by=c("CNT","ST004D01T"))
oecd <- aggregate(Mean ~ ST004D01T, data=oecd,mean)
ab <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_AB,by=c("CNT","ST004D01T"))
ab <- aggregate(Mean ~ ST004D01T,data=ab,mean)
q <- rbind(tur,tur,tur)
q$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$ST004D01T <- factor(q$ST004D01T,levels=c("Female","Male"),labels=c("Kiz","Erkek"))
q$Mean2 <- round(q$Mean,2)
q
## ST004D01T Freq Mean s.e. SD s.e tip Mean2
## 1 Kiz 2917 0.5200000 0.02 1.03 0.02 Türkiye 0.52
## 2 Erkek 2900 0.0900000 0.03 1.02 0.02 Türkiye 0.09
## 3 Kiz 2917 0.2262857 0.00 1.03 0.02 OECD Ülkeleri Ortalamasi 0.23
## 4 Erkek 2900 -0.2140000 0.00 1.02 0.02 OECD Ülkeleri Ortalamasi -0.21
## 5 Kiz 2917 0.1641667 0.00 1.03 0.02 AB Ülkeleri Ortalamasi 0.16
## 6 Erkek 2900 -0.2708333 0.00 1.02 0.02 AB Ülkeleri Ortalamasi -0.27
plot <- ggplot(q, aes(x=tip, y=Mean,fill=ST004D01T)) +
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(-.6,.6))+
theme_bw()+
geom_text(aes(y=Mean2,label=Mean2),
stat= "identity", position=position_dodge(0.5),vjust = -.6,size = 6)+
labs(title ="Cinsiyete Gore Test Kaygisi Puani",
x = "", y = "Test Kaygisi Puani",
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 = .6, ymax = .6)
plot
## png
## 2
Cografi Bolgeler
tur <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_TUR,by="bolge")
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()+
geom_text(aes(y=Mean,label=Mean),
stat= "identity", position=position_dodge(.2),
hjust=1.2,vjust = -.6,size = 5) +
labs(title = " COGRAFI BOLGE VE TEST KAYGISI",
x = "COGRAFI BOLGE", y = "TEST KAYGISI PUANI",
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 =12, xmax = 12, ymin = 0.6, ymax = 0.6)
plot
## png
## 2
SINIF DUZEYI
tur <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_TUR,by="ST001D01T")
tur <- tur[2:4,]
tur
## ST001D01T Freq Mean s.e. SD s.e
## 2 Grade 8 95 0.20 0.18 1.14 0.12
## 3 Grade 9 1247 0.25 0.04 1.06 0.02
## 4 Grade 10 4268 0.33 0.02 1.04 0.02
oecd <- pisa2015.mean(variable="ANXTEST",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 0.07965517
## 3 Grade 9 0.01218750
## 4 Grade 10 -0.01228571
ab <- pisa2015.mean(variable="ANXTEST",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 0.04791667
## 3 Grade 9 -0.02958333
## 4 Grade 10 -0.08666667
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,]$s.e. <- 0
q$Mean2 <- round(q$Mean,2)
q$ST001D01T <- factor(q$ST001D01T,
levels=c("Grade 8","Grade 9","Grade 10"),
labels=c("8. Sinif","9. Sinif","10. Sinif"))
q
## ST001D01T Freq Mean s.e. SD s.e tip
## 2 8. Sinif 95 0.20000000 0.18 1.14 0.12 Türkiye
## 3 9. Sinif 1247 0.25000000 0.04 1.06 0.02 Türkiye
## 4 10. Sinif 4268 0.33000000 0.02 1.04 0.02 Türkiye
## 21 8. Sinif 95 0.07965517 0.00 1.14 0.12 OECD Ülkeleri Ortalamasi
## 31 9. Sinif 1247 0.01218750 0.00 1.06 0.02 OECD Ülkeleri Ortalamasi
## 41 10. Sinif 4268 -0.01228571 0.00 1.04 0.02 OECD Ülkeleri Ortalamasi
## 22 8. Sinif 95 0.04791667 0.00 1.14 0.12 AB Ülkeleri Ortalamasi
## 32 9. Sinif 1247 -0.02958333 0.00 1.06 0.02 AB Ülkeleri Ortalamasi
## 42 10. Sinif 4268 -0.08666667 0.00 1.04 0.02 AB Ülkeleri Ortalamasi
## Mean2
## 2 0.20
## 3 0.25
## 4 0.33
## 21 0.08
## 31 0.01
## 41 -0.01
## 22 0.05
## 32 -0.03
## 42 -0.09
plot <- ggplot(q, aes(x=tip, y=Mean,fill=ST001D01T)) +
geom_bar(stat='identity',position=position_dodge(),width=.75) +
geom_errorbar(aes(ymin=Mean-1.96*s.e., ymax=Mean+1.96*s.e.),
position = position_dodge(0.8),
lty=2,
colour="gray50",
width=c(.05,.05,.05,0,0,0,0,0,0))+
scale_y_continuous(limit = c(-.2,.6))+
theme_bw()+
geom_text(aes(y=Mean2,label=Mean2),
stat= "identity", position=position_dodge(.8),hjust=1.05,vjust = -.5,size = 6)+
labs(title = " SINIF DUZEYI VE TEST KAYGISI",
x = "", y = "TEST KAYGISI PUANI",
fill=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 20),
legend.text=element_text(size = 12)
) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin =3.2, xmax = 3.2, ymin = 0.6, ymax = 0.6)
plot
## png
## 2
Okul Tipi
pisa_2015_TUR$tur <- ifelse(pisa_2015_TUR$SCHLTYPE=="Private Independent" |
pisa_2015_TUR$SCHLTYPE=="Private Government-dependent",1,0)
tur <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_TUR,by="tur")
tur <- tur[1:2,]
pisa_2015_OECD$tur <- ifelse(pisa_2015_OECD$SCHLTYPE=="Private Independent" |
pisa_2015_OECD$SCHLTYPE=="Private Government-dependent",1,0)
oecd <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_OECD,by=c("CNT","tur"))
oecd <- aggregate(Mean ~ tur, data=oecd,mean)
pisa_2015_AB$tur <- ifelse(pisa_2015_AB$SCHLTYPE=="Private Independent" |
pisa_2015_AB$SCHLTYPE=="Private Government-dependent",1,0)
ab <- pisa2015.mean(variable="ANXTEST",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$tur,levels=c(0,1),labels=c("Devlet Okulu","Ozel Okul"))
q$Mean2 <- round(q$Mean,2)
q
## tur Freq Mean s.e. SD s.e tip
## 1 Devlet Okulu 5543 0.32000000 0.02 1.05 0.01 Türkiye
## 2 Ozel Okul 240 0.13000000 0.08 1.05 0.10 Türkiye
## 3 Devlet Okulu 5543 0.02088235 0.00 1.05 0.01 OECD Ülkeleri Ortalamasi
## 4 Ozel Okul 240 0.01205882 0.00 1.05 0.10 OECD Ülkeleri Ortalamasi
## 5 Devlet Okulu 5543 -0.04541667 0.00 1.05 0.01 AB Ülkeleri Ortalamasi
## 6 Ozel Okul 240 -0.07416667 0.00 1.05 0.10 AB Ülkeleri Ortalamasi
## Mean2
## 1 0.32
## 2 0.13
## 3 0.02
## 4 0.01
## 5 -0.05
## 6 -0.07
plot <- ggplot(q, aes(x=tip, y=Mean,fill=tur)) +
geom_bar(stat='identity',position=position_dodge(),width=.75) +
geom_errorbar(aes(ymin=Mean-1.96*s.e., ymax=Mean+1.96*s.e.),
position = position_dodge(0.8),
lty=2,
colour="gray50",
width=c(.05,.05,0,0,0,0))+
scale_y_continuous(limit = c(-.1,.4))+
theme_bw()+
geom_text(aes(y=Mean2,label=Mean2),
stat= "identity", position=position_dodge(1),hjust=.5,vjust = -.6,size = 6)+
labs(title ="Okul Turune Gore Test Kaygisi Puani",
x = "", y = "Test Kaygisi Puani",
fill=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 18),
legend.text=element_text(size = 12)) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin = 3.3, xmax = 3.3, ymin = .4, ymax = .4)
plot
## png
## 2
Sosyo-ekonomik kulturel statu
pisa_2015_stu_TUR$ESCS_cat <- factor(recode(pisa_2015_stu_TUR$ESCS,
recodes="lo:-1='Dusuk (ESCS < -1)';
-1:0='Orta (-1 < ESCS < 0)';
0:hi='Yuksek(ESCS>0)'"))
tur <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_TUR,by="ESCS_cat")
tur <- tur[1:3,]
tur
## ESCS_cat Freq Mean s.e. SD s.e
## 1 Dusuk (ESCS < -1) 3796 0.32 0.02 1.05 0.02
## 2 Orta (-1 < ESCS < 0) 1273 0.28 0.03 1.07 0.03
## 3 Yuksek(ESCS>0) 747 0.30 0.04 1.01 0.03
pisa_2015_stu_OECD$ESCS_cat <- factor(recode(pisa_2015_stu_OECD$ESCS,
recodes="lo:-1='Dusuk (ESCS < -1)';
-1:0='Orta (-1 < ESCS < 0)';
0:hi='Yuksek(ESCS>0)'"))
oecd <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_OECD,by=c("CNT","ESCS_cat"))
oecd <- aggregate(Mean ~ ESCS_cat, data=oecd,mean)
pisa_2015_stu_AB$ESCS_cat <- factor(recode(pisa_2015_stu_AB$ESCS,
recodes="lo:-1='Dusuk (ESCS < -1)';
-1:0='Orta (-1 < ESCS < 0)';
0:hi='Yuksek(ESCS>0)'"))
ab <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_AB,by=c("CNT","ESCS_cat"))
ab <- aggregate(Mean ~ ESCS_cat, data=ab,mean)
q <- rbind(tur,tur,tur)
q$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$Mean2 <- round(q$Mean,2)
q
## ESCS_cat Freq Mean s.e. SD s.e
## 1 Dusuk (ESCS < -1) 3796 0.32000000 0.02 1.05 0.02
## 2 Orta (-1 < ESCS < 0) 1273 0.28000000 0.03 1.07 0.03
## 3 Yuksek(ESCS>0) 747 0.30000000 0.04 1.01 0.03
## 4 Dusuk (ESCS < -1) 3796 0.10342857 0.00 1.05 0.02
## 5 Orta (-1 < ESCS < 0) 1273 0.04400000 0.00 1.07 0.03
## 6 Yuksek(ESCS>0) 747 -0.03771429 0.00 1.01 0.03
## 7 Dusuk (ESCS < -1) 3796 0.02166667 0.00 1.05 0.02
## 8 Orta (-1 < ESCS < 0) 1273 -0.01375000 0.00 1.07 0.03
## 9 Yuksek(ESCS>0) 747 -0.10083333 0.00 1.01 0.03
## tip Mean2
## 1 Türkiye 0.32
## 2 Türkiye 0.28
## 3 Türkiye 0.30
## 4 OECD Ülkeleri Ortalamasi 0.10
## 5 OECD Ülkeleri Ortalamasi 0.04
## 6 OECD Ülkeleri Ortalamasi -0.04
## 7 AB Ülkeleri Ortalamasi 0.02
## 8 AB Ülkeleri Ortalamasi -0.01
## 9 AB Ülkeleri Ortalamasi -0.10
plot <- ggplot(q, aes(x=tip, y=Mean,fill=ESCS_cat)) +
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),hjust=0.4,vjust = 1,size = 5) +
labs(title = "SOSYO-EKONOMIK STATU VE TEST KAYGISI",
x = "", y = "TEST KAYGISI PUANI",
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 =3.2, xmax = 3.2, ymin = 0.5, ymax = 0.5)
plot
## png
## 2
Okul Turu
tur <- pisa2015.mean(variable="ANXTEST",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 111 0.14 0.13 1.10 0.14
## 2 Genel Lise 3210 0.34 0.02 1.04 0.02
## 3 Mesleki ve Teknik Lise 2496 0.27 0.04 1.06 0.02
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 Test Kaygisi Puani",
x = "", y = "TEST KAYGISI PUANI",
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 = -0.1, ymax = -0.1)
plot
## png
## 2
Okul Oncesi Egitim
pisa_2015_stu_TUR$durecec <- NA
pisa_2015_stu_TUR[which(pisa_2015_stu_TUR$ST125Q01NA=="I did not attend <ISCED 0>"),]$durecec <- 0
pisa_2015_stu_TUR[which(pisa_2015_stu_TUR$ST125Q01NA=="1 year or younger" |
pisa_2015_stu_TUR$ST125Q01NA=="2 years" |
pisa_2015_stu_TUR$ST125Q01NA=="3 years" |
pisa_2015_stu_TUR$ST125Q01NA=="4 years" |
pisa_2015_stu_TUR$ST125Q01NA=="5 years" |
pisa_2015_stu_TUR$ST125Q01NA=="6 years or older"),]$durecec <- 1
pisa_2015_stu_OECD$durecec <- NA
pisa_2015_stu_OECD[which(pisa_2015_stu_OECD$ST125Q01NA=="I did not attend <ISCED 0>"),]$durecec <- 0
pisa_2015_stu_OECD[which(pisa_2015_stu_OECD$ST125Q01NA=="1 year or younger" |
pisa_2015_stu_OECD$ST125Q01NA=="2 years" |
pisa_2015_stu_OECD$ST125Q01NA=="3 years" |
pisa_2015_stu_OECD$ST125Q01NA=="4 years" |
pisa_2015_stu_OECD$ST125Q01NA=="5 years" |
pisa_2015_stu_OECD$ST125Q01NA=="6 years or older"),]$durecec <- 1
pisa_2015_stu_AB$durecec <- NA
pisa_2015_stu_AB[which(pisa_2015_stu_AB$ST125Q01NA=="I did not attend <ISCED 0>"),]$durecec <- 0
pisa_2015_stu_AB[which(pisa_2015_stu_AB$ST125Q01NA=="1 year or younger" |
pisa_2015_stu_AB$ST125Q01NA=="2 years" |
pisa_2015_stu_AB$ST125Q01NA=="3 years" |
pisa_2015_stu_AB$ST125Q01NA=="4 years" |
pisa_2015_stu_AB$ST125Q01NA=="5 years" |
pisa_2015_stu_AB$ST125Q01NA=="6 years or older"),]$durecec <- 1
tur <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_TUR,by="durecec")
tur <- tur[1:2,]
oecd <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_OECD,by=c("CNT","durecec"))
oecd <- aggregate(Mean ~ durecec, data=oecd,mean)
ab <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_AB,by=c("CNT","durecec"))
ab <- aggregate(Mean ~ durecec, 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,]$s.e. <- 0
q$Mean2 <- round(q$Mean,2)
q
## durecec Freq Mean s.e. SD s.e tip Mean2
## 1 0 2612 0.34000000 0.02 1.04 0.02 Türkiye 0.34
## 2 1 2645 0.28000000 0.03 1.06 0.02 Türkiye 0.28
## 3 0 2612 -0.01617647 0.00 1.04 0.02 OECD Ülkeleri Ortalamasi -0.02
## 4 1 2645 0.02705882 0.00 1.06 0.02 OECD Ülkeleri Ortalamasi 0.03
## 5 0 2612 -0.13130435 0.00 1.04 0.02 AB Ülkeleri Ortalamasi -0.13
## 6 1 2645 -0.02391304 0.00 1.06 0.02 AB Ülkeleri Ortalamasi -0.02
q$durecec <- factor(q$durecec,levels=c(0,1),labels=c("HAYIR","EVET"))
plot <- ggplot(q, aes(x=tip, y=Mean,fill=durecec)) +
geom_bar(stat='identity',position=position_dodge(),width=.75) +
geom_errorbar(aes(ymin=Mean-1.96*s.e., ymax=Mean+1.96*s.e.),
position = position_dodge(0.8),
lty=2,
colour="gray50",
width=c(.05,.05,0,0,0,0))+
scale_y_continuous(limit = c(-.2,.5))+
theme_bw()+
geom_text(aes(y=Mean2,label=Mean2),
stat= "identity", position=position_dodge(.8),
hjust =c(0.4,.4,.4,.4,-0.1,-0.1),
vjust =c(1.2,1.2,2.5,1.2,-.2,-.2),size = 5) +
labs(title = "OKUL ONCESI EGITIM VE TEST KAYGISI",
x = "", y = "TEST KAYGISI PUANI",
fill=" ")+
theme(axis.title= element_text(size = 20),
axis.text= element_text(size = 12),
title = element_text(size = 20),
legend.text=element_text(size = 12)
) +
annotation_custom(grob = textGrob("@pisa_turkiye"),
xmin =3.2, xmax = 3.2, ymin = 0.5, ymax = 0.5)
plot
## png
## 2
Sinif Tekrari
tur <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_TUR,by="REPEAT")
tur <- tur[1:2,]
oecd <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_OECD,by=c("CNT","REPEAT"))
oecd <- aggregate(Mean ~ REPEAT, data=oecd,mean)
ab <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_stu_AB,by=c("CNT","REPEAT"))
ab <- aggregate(Mean ~ REPEAT, data=ab,mean)
q <- rbind(tur,tur,tur)
q$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$Mean2 <- round(q$Mean,2)
q
## REPEAT Freq Mean s.e. SD s.e
## 1 Did not repeat a <grade> 5202 0.32000000 0.02 1.05 0.01
## 2 Repeated a <grade> 611 0.23000000 0.06 1.08 0.05
## 3 Did not repeat a <grade> 5202 -0.01545455 0.00 1.05 0.01
## 4 Repeated a <grade> 611 0.08969697 0.00 1.08 0.05
## 5 Did not repeat a <grade> 5202 -0.06500000 0.00 1.05 0.01
## 6 Repeated a <grade> 611 0.03250000 0.00 1.08 0.05
## tip Mean2
## 1 Türkiye 0.32
## 2 Türkiye 0.23
## 3 OECD Ülkeleri Ortalamasi -0.02
## 4 OECD Ülkeleri Ortalamasi 0.09
## 5 AB Ülkeleri Ortalamasi -0.06
## 6 AB Ülkeleri Ortalamasi 0.03
q$REPEAT<- factor(q$REPEAT,levels=c("Did not repeat a <grade>",
"Repeated a <grade>"),
labels=c("HAYIR","EVET"))
plot <- ggplot(q, aes(x=tip, y=Mean,fill=REPEAT)) +
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(-.2,.5))+
theme_bw()+
geom_text(aes(y=Mean2,label=Mean2),
stat= "identity", position=position_dodge(.8),
hjust =c(0.4,.4,.4,.4,-0.1,-0.1),
vjust =c(2.7,1.4,5.5,1.2,-.3,-.3),size = 5) +
labs(title = "SINIF TEKRARI VE TEST KAYGISI",
x = "", y = "TEST KAYGISI PUANI",
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 =3.2, xmax = 3.2, ymin = 0.5, ymax = 0.5)
plot
## png
## 2
Okulun bulundugu yerin nufusu
tur <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_TUR,by="SC001Q01TA")
tur <- tur[1:5,]
oecd <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_OECD,by=c("CNT","SC001Q01TA"))
oecd <- aggregate(Mean ~ SC001Q01TA, data=oecd,mean)
ab <- pisa2015.mean(variable="ANXTEST",data=pisa_2015_AB,by=c("CNT","SC001Q01TA"))
ab <- aggregate(Mean ~ SC001Q01TA, data=ab,mean)
q <- rbind(tur,tur,tur)
q$tip <- c(rep("Türkiye",5),rep("OECD Ülkeleri Ortalamasi",5),rep("AB Ülkeleri Ortalamasi",5))
q[6:10,]$Mean <- oecd$Mean
q[6:10,]$s.e. <- 0
q[11:15,]$Mean <- ab$Mean
q[11:15,]$s.e. <- 0
q$Mean2 <- round(q$Mean,2)
q
## SC001Q01TA Freq
## 1 A village, hamlet or rural area (fewer than 3 000 people) 37
## 2 A small town (3 000 to about 15 000 people) 368
## 3 A town (15 000 to about 100 000 people) 1746
## 4 A city (100 000 to about 1 000 000 people) 1268
## 5 A large city (with over 1 000 000 people) 2364
## 6 A village, hamlet or rural area (fewer than 3 000 people) 37
## 7 A small town (3 000 to about 15 000 people) 368
## 8 A town (15 000 to about 100 000 people) 1746
## 9 A city (100 000 to about 1 000 000 people) 1268
## 10 A large city (with over 1 000 000 people) 2364
## 11 A village, hamlet or rural area (fewer than 3 000 people) 37
## 12 A small town (3 000 to about 15 000 people) 368
## 13 A town (15 000 to about 100 000 people) 1746
## 14 A city (100 000 to about 1 000 000 people) 1268
## 15 A large city (with over 1 000 000 people) 2364
## Mean s.e. SD s.e tip Mean2
## 1 -0.060000000 0.20 0.97 0.13 Türkiye -0.06
## 2 0.200000000 0.06 1.09 0.07 Türkiye 0.20
## 3 0.360000000 0.03 1.06 0.03 Türkiye 0.36
## 4 0.310000000 0.05 1.05 0.02 Türkiye 0.31
## 5 0.290000000 0.03 1.03 0.02 Türkiye 0.29
## 6 0.006060606 0.00 0.97 0.13 OECD Ülkeleri Ortalamasi 0.01
## 7 -0.014705882 0.00 1.09 0.07 OECD Ülkeleri Ortalamasi -0.01
## 8 0.002647059 0.00 1.06 0.03 OECD Ülkeleri Ortalamasi 0.00
## 9 0.024411765 0.00 1.05 0.02 OECD Ülkeleri Ortalamasi 0.02
## 10 0.118260870 0.00 1.03 0.02 OECD Ülkeleri Ortalamasi 0.12
## 11 -0.068181818 0.00 0.97 0.13 AB Ülkeleri Ortalamasi -0.07
## 12 -0.053043478 0.00 1.09 0.07 AB Ülkeleri Ortalamasi -0.05
## 13 -0.062608696 0.00 1.06 0.03 AB Ülkeleri Ortalamasi -0.06
## 14 -0.042608696 0.00 1.05 0.02 AB Ülkeleri Ortalamasi -0.04
## 15 0.031333333 0.00 1.03 0.02 AB Ülkeleri Ortalamasi 0.03
q$SC001Q01TA<- factor(q$SC001Q01TA,levels=c("A village, hamlet or rural area (fewer than 3 000 people)",
"A small town (3 000 to about 15 000 people)",
"A town (15 000 to about 100 000 people)",
"A city (100 000 to about 1 000 000 people)",
"A large city (with over 1 000 000 people)"),
labels=c("Koy",
"Kucuk Kasaba",
"Kasaba",
"Sehir",
"Buyuk Sehir")
)
plot <- ggplot(q, aes(x=tip, y=Mean,fill=SC001Q01TA)) +
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,.05,.05,0,0,0,0,0,0,0,0,0,0))+
scale_y_continuous(limit = c(-.2,.5))+
theme_bw()+
geom_text(aes(y=Mean2,label=Mean2),
stat= "identity", position=position_dodge(.8),
hjust =c(.4,.4,.4,.4,.4,.4,.4,.4,.4,.4,-0.2,-0.2,-.2,-.2,-.2),
vjust =c(-.5,1.2,1.2,1.2,1.2,-.5,-.6,1.4,1.4,1.6,-0.3,-0.3,-.3,-.3,1.2),
size = 4) +
labs(title = "YERLESIM YERI NUFUSU VE TEST KAYGISI",
x = "", y = "TEST KAYGISI PUANI",
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 =3.2, xmax = 3.2, ymin = 0.5, ymax = 0.5)
plot
## Warning: Removed 1 rows containing missing values (geom_errorbar).
## Warning: Removed 1 rows containing missing values (geom_errorbar).
## png
## 2