```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = FALSE)
```
```{r}
library(ggplot2)
library(tidyr)
library(lattice)
library(gridExtra)
library(flextable)#制作表格
library(bestglm)
library(leaps)
library(car)
library(corrplot)
library(lmtest)
library("ivreg")
library("AER") # 用于线性回归检验
library("stargazer")# 提供相关异方差稳健标准误
library(tidyverse)
library(modelr)
library(broom)
library(dplyr)
```
```{r}
d1<-read.csv(file="GII.csv")
# 创建数据
x <- c(1990:2021)
y<-c(0:1)
y1 <- d1$Gender.Inequality.Index[d1$Entity == "High human development (UNDP)"]
y2 <- d1$Gender.Inequality.Index[d1$Entity == "Low human development (UNDP)"]
y3 <- d1$Gender.Inequality.Index[d1$Entity == "Medium human development (UNDP)"]
y4 <- d1$Gender.Inequality.Index[d1$Entity == "Very high human development (UNDP)"]
y5 <- d1$Gender.Inequality.Index[d1$Entity == "World"]
x1 <- d1$Year[d1$Entity == "High human development (UNDP)"]
x2 <- d1$Year[d1$Entity == "Low human development (UNDP)"]
x3 <- d1$Year[d1$Entity == "Medium human development (UNDP)"]
x4 <- d1$Year[d1$Entity == "Very high human development (UNDP)"]
# 绘制折线图
plot(x, y5, ylim=c(0.1, 0.72),type="o", col="cornflowerblue", pch=16, xlab="Year", ylab="Gender.Inequality.Index", main="Overview of Gender Inequality Indicators")
lines(x1, y1, type="o",pch=15, col="chartreuse4")
lines(x2, y2, type="o",pch=8, col="chartreuse4")
lines(x3, y3, type="o", pch=2,col="chartreuse4")
lines(x4, y4, type="o", pch=3,col="chartreuse4")
legend("bottomleft", legend=c("High human development", "Low human development", "Medium human development", "Very high human development", "World"), col=c("chartreuse4", "chartreuse4", "chartreuse4", "chartreuse4","cornflowerblue"), lty=1, pch=c(15,8,2,3,16), cex=0.6)
```
```{r}
d<-read.csv(file="GDI.csv")
d<-d[1:40,]
rownames(d) = d[,1]
d = d[,-1]
x<-subset(d,Degree.of.national.development<=4)
x <- x[order(x$Gender.Development.Index,decreasing = TRUE),]
x$Degree.of.national.development <- factor(x$Degree.of.national.development)
x$color[x$Degree.of.national.development==1] <- "chartreuse3"
x$color[x$Degree.of.national.development==2] <- "cadetblue1"
x$color[x$Degree.of.national.development==3] <- "yellow2"
x$color[x$Degree.of.national.development==4] <- "plum1"
dotchart(x$Gender.Development.Index,labels = row.names(x),cex=.7,groups = x$Degree.of.national.development,gcolor ="black",color = x$color,pch=19,
main = "Overview of countries represented in the Gender Development Index in 2021",
xlab = "Gender Development Index")
```
```{r}
d2<-read.csv(file="HDI.csv")
# 创建数据
x <- seq(1950, 2000, by=10)
y<-seq(50,80, by=5)
y1 <- d2$Regional.averages.of.the.composite.gender.equality.index[d2$Entity == "East Asia"]
y2 <- d2$Regional.averages.of.the.composite.gender.equality.index[d2$Entity == "Eastern Europe"]
y3 <- d2$Regional.averages.of.the.composite.gender.equality.index[d2$Entity == "Latin America and Caribbean"]
y4 <- d2$Regional.averages.of.the.composite.gender.equality.index[d2$Entity == "Middle East and North Africa"]
y5 <- d2$Regional.averages.of.the.composite.gender.equality.index[d2$Entity == "South and South-East Asia"]
y6 <- d2$Regional.averages.of.the.composite.gender.equality.index[d2$Entity == "Sub-Sahara Africa"]
y7 <- d2$Regional.averages.of.the.composite.gender.equality.index[d2$Entity == "Western Europe"]
y8 <- d2$Regional.averages.of.the.composite.gender.equality.index[d2$Entity == "Western Offshoots"]
# 绘制折线图
plot(x, y1, ylim=c(50,80),type="o", col="gold", pch=16, xlab="Year", ylab="Historical Gender Equality Index", main="Overview of Historical Gender Equality Index for 1950-2000")
lines(x, y2, type="o",pch=8, col="coral2")
lines(x, y3, type="o", pch=2,col="darkolivegreen3")
lines(x, y4, type="o", pch=3,col="black")
lines(x, y5, type="o",pch=5, col="darkviolet")
lines(x, y6, type="o",pch=7, col="deepskyblue2")
lines(x, y7, type="o", pch=9,col="chartreuse4")
lines(x, y8, type="o",pch=12,col="darkorange1")
legend("bottomright", legend=c("East Asia", "Eastern Europe", "Latin America and Caribbean", "Middle East and North Africa", "South and South-East Asia","Sub-Sahara Africa","Western Europe","Western Offshoots"), col=c("gold", "coral2", "darkolivegreen3", "black","darkviolet","deepskyblue2","chartreuse4","darkorange1"), lty=1, pch=c(16,8,2,3,5,7,9,12), cex=0.5)
```
```{r}
d3<-read.csv(file="ILO1.csv")
d3 <- d3[order(d3$obs_value, decreasing = TRUE), ]
par(mar=c(5, 13, 3, 5))
barplot(d3$obs_value~d3$ref_area.label,main="Organisation or continental area in 2021 Gender Pay Gap",xlab="Gender Income Gap (%)",ylab="",horiz=TRUE,col="olivedrab3", las=1)
```
```{r}
d4<-read.csv(file="ILO.csv")
# 创建数据
x <- c(2011:2021)
y1 <- d4$obs_value[d4$ref_area.label == "World"]
y2 <- d4$obs_value[d4$ref_area.label == "World: Low income"]
y3 <- d4$obs_value[d4$ref_area.label == "World: Lower-middle income"]
y4 <- d4$obs_value[d4$ref_area.label == "World: Upper-middle income"]
y5 <- d4$obs_value[d4$ref_area.label == "World: High income"]
# 绘制折线图
plot(x, y5, ylim=c(25,60),type="o", col="chartreuse4", pch=16, xlab="Year", ylab="Gender Income Gap (%)", main="Overview of the gender income gap 2011-2021")
lines(x, y1, type="o",pch=15, col="chartreuse4")
lines(x, y2, type="o",pch=8, col="chartreuse4")
lines(x, y3, type="o", pch=2,col="chartreuse4")
lines(x, y4, type="o", pch=3,col="chartreuse4")
legend("bottomright", legend=c("World", "World: Low income", "World: Lower-middle income", "World: Upper-middle income", "World: High income"), col=c("chartreuse4", "chartreuse4", "chartreuse4", "chartreuse4","chartreuse4"), lty=1, pch=c(15,8,2,3,16), cex=0.6)
```
```{r}
# 创建一个示例数据集
d5<-read.csv(file="GDP1.csv")
d5$Income.classifications <- as.factor(d5$Income.classifications)
# 使用ggplot2创建点图并添加名称标签
ggplot(d5, aes(x = GDP.per.capita, y = Gender.wage.gap..., shape = Income.classifications, label = Country)) +
geom_point(size = 4, color = "chartreuse4") +
geom_text(hjust = -0.2, vjust = -0.3,size=2.8) + # 调整标签位置
labs(title = "Graph of changes in GDP per capita and gender pay gap", x = "GDP per capita ($)", y = "Gender Wage Gap (%)")+
scale_shape_manual(values = c(15,16,17)) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))+
scale_color_gradient(low = "lightblue", high = "chartreuse4")+
theme(legend.position = c(0.8, 0.2)) + # 图例位置
guides(shape = guide_legend(title = "Income Classifications")) # 图例标题
```
```{r}
# 使用ggplot2创建点图并添加名称标签
ggplot(d5, aes(x = GDP.per.capita, y = Gender.wage.gap..., shape = Continents, label = Country)) +
geom_point(size = 4, color = "chartreuse4") +
geom_text(hjust = -0.2, vjust = -0.3,size=2.8) + # 调整标签位置
labs(title = "Graph of changes in GDP per capita and gender pay gap", x = "GDP per capita ($)", y = "Gender Wage Gap (%)")+
scale_shape_manual(values = c(18,15,16,17)) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))+
scale_color_gradient(low = "lightblue", high = "chartreuse4")+
theme(legend.position = c(0.8, 0.2)) + # 图例位置
guides(shape = guide_legend(title = "Continents")) # 图例标题
```
```{r}
x<-read.csv(file="linear regression.csv")
x$GDI[x$GDI=='..'] <- NA
x=na.omit(x)
f1<-x$Labour.force.participation.rate..Female
m1<-x$Labour.force.participation.rate..Male
x1<- x$GDI
plot(x1,f1,pch=11,col="darkgreen",xlab="Gender Development Index",ylab="labor force participation rate (%)",ylim = c(0,100))
title("Comparison of male and female labor force participation rates in 2021")
points(x1,m1,pch=10,col="skyblue",cex=2)
x$GDI=as.numeric(x$GDI)
x$Labour.force.participation.rate..Female=as.numeric(x$Labour.force.participation.rate..Female)
x$Labour.force.participation.rate..Male=as.numeric(x$Labour.force.participation.rate..Male)
a<-round(mean(x$GDI),2)
b<-round(mean(x$Labour.force.participation.rate..Female),2)
c<-round(mean(x$Labour.force.participation.rate..Male),2)
abline(h=b,v=a,col="gold",lty=2)
points(a,b,cex=2,col="gold",pch=18)
abline(h=c,v=a,col="gold",lty=2)
points(a,c,cex=2,col="gold",pch=18)
text(a,b,paste("mean of female:",b),pos=2,cex=0.8)
text(a,c,paste("mean of male:",c),pos=2,cex=0.8)
legend("bottomleft",c("Female","Male","mean"),pch=c(11,10,18),col=c("darkgreen","skyblue","gold"))
```
```{r}
par(mfrow=c(3,4))
opar<-par(no.readonly = TRUE)
p<-read.csv(file="The proportion of women in the base salary.csv")
year<-c(2010:2021)
col<-c(rep(c("lightblue1"),4),rep(c("skyblue2"),4),rep(c("steelblue3"),4))
for (i in 1:12) {
j<-(subset(p,Year==year[i],select = Proportion.of.women.among.low.wage.workers))$Proportion.of.women.among.low.wage.workers
plot(density(j),main=paste("low-paid women ",year[i]),cex=0.9,xlim=c(0,100))
polygon(density(j),col=col[i],border="gold")
rug(j,col="blue")
}
par(opar)
```
```{r}
opar<-par(no.readonly = TRUE)
m<-read.csv(file="Earnings of women and men.csv")
par(mfrow=c(2,3))
m1<-subset(m,Country=="Brazil")
m1$Sex<-factor(m1$Sex,levels = c("Female","Male"),labels = c("Female","Male"))
m1$Year<-factor(m1$Year,levels = c("2017","2018","2019","2020","2021"),labels = c("2017","2018","2019","2020","2021"))
boxplot(Average.monthly.income~Sex*Year,data=m1,varwidth=TRUE,col=c("pink2","skyblue"),main="Brazil's gender income",xlab="Categorized by sex and year",ylab = "Average monthly income")
m2<-subset(m,Country=="India")
m2$Sex<-factor(m2$Sex,levels = c("Female","Male"),labels = c("Female","Male"))
m2$Year<-factor(m2$Year,levels = c("2017","2018","2019","2020","2021"),labels = c("2017","2018","2019","2020","2021"))
boxplot(Average.monthly.income~Sex*Year,data=m2,varwidth=TRUE,col=c("pink2","skyblue"),main="India's gender income",xlab="Categorized by sex and year",ylab = "Average monthly income")
m3<-subset(m,Country=="Italy")
m3$Sex<-factor(m3$Sex,levels = c("Female","Male"),labels = c("Female","Male"))
m3$Year<-factor(m3$Year,levels = c("2017","2018","2019","2020","2021"),labels = c("2017","2018","2019","2020","2021"))
boxplot(Average.monthly.income~Sex*Year,data=m3,varwidth=TRUE,col=c("pink2","skyblue"),main="Italy's gender income",xlab="Categorized by sex and year",ylab = "Average monthly income")
m4<-subset(m,Country=="Mexico")
m4$Sex<-factor(m4$Sex,levels = c("Female","Male"),labels = c("Female","Male"))
m4$Year<-factor(m4$Year,levels = c("2017","2018","2019","2020","2021"),labels = c("2017","2018","2019","2020","2021"))
boxplot(Average.monthly.income~Sex*Year,data=m4,varwidth=TRUE,col=c("pink2","skyblue"),main="Mexico's gender income",xlab="Categorized by sex and year",ylab = "Average monthly income")
m5<-subset(m,Country=="Armenia")
m5$Sex<-factor(m5$Sex,levels = c("Female","Male"),labels = c("Female","Male"))
m5$Year<-factor(m5$Year,levels = c("2017","2018","2019","2020","2021"),labels = c("2017","2018","2019","2020","2021"))
boxplot(Average.monthly.income~Sex*Year,data=m5,varwidth=TRUE,col=c("pink2","skyblue"),main="Armenia's gender income",xlab="Categorized by sex and year",ylab = "Average monthly income")
m6<-subset(m,Country=="Peru")
m6$Sex<-factor(m6$Sex,levels = c("Female","Male"),labels = c("Female","Male"))
m6$Year<-factor(m6$Year,levels = c("2017","2018","2019","2020","2021"),labels = c("2017","2018","2019","2020","2021"))
boxplot(Average.monthly.income~Sex*Year,data=m6,varwidth=TRUE,col=c("pink2","skyblue"),main="Peru's gender income",xlab="Categorized by sex and year",ylab = "Average monthly income")
par(opar)
```
```{r}
# 创建一个示例数据集，包含国家名、收入0.1%前的比例和收入10%前的比例
d7<-read.csv(file="top income.csv")
d7<-d7[,-1]
d71<-d7[1:7,]
d72<-d7[8:14,]
d73<-d7[15:21,]
d74<-d7[22:28,]
# 将数据从宽格式转换为长格式
library(tidyr)
data_long1 <- gather(d71, key = "IncomeLevel", value = "IncomePercentage", -Country)
data_long2 <- gather(d72, key = "IncomeLevel", value = "IncomePercentage", -Country)
data_long3 <- gather(d73, key = "IncomeLevel", value = "IncomePercentage", -Country)
data_long4 <- gather(d74, key = "IncomeLevel", value = "IncomePercentage", -Country)
# 使用ggplot2创建柱状图，一个国家对应两根柱子，并用不同颜色区分
library(ggplot2)
p1<-ggplot(data_long1, aes(x = Country, y = IncomePercentage, fill = IncomeLevel)) +
geom_bar(stat = "identity", position = "dodge") +
theme(axis.text.x = element_text(size = 7, angle = 45, hjust = 1),
axis.ticks.x = element_line(size = 1))+
labs(title = "Year-2000", x="", y = "Income Percentage(%)") +
scale_fill_manual(values = c("Income_1" = "lightblue", "Income_10" = "darkblue")) # 为不同的柱子分配不同的颜色
# 使用ggplot2创建柱状图，一个国家对应两根柱子，并用不同颜色区分
p2<-ggplot(data_long2, aes(x = Country, y = IncomePercentage, fill = IncomeLevel)) +
geom_bar(stat = "identity", position = "dodge") +
theme(axis.text.x = element_text(size = 7, angle = 45, hjust = 1),
axis.ticks.x = element_line(size = 1))+
labs(title = "Year-2005", x="", y = "Income Percentage(%)") +
scale_fill_manual(values = c("Income_1" = "lightblue", "Income_10" = "darkblue")) # 为不同的柱子分配不同的颜色
# 使用ggplot2创建柱状图，一个国家对应两根柱子，并用不同颜色区分
p3<-ggplot(data_long3, aes(x = Country, y = IncomePercentage, fill = IncomeLevel)) +
geom_bar(stat = "identity", position = "dodge") +
theme(axis.text.x = element_text(size = 7, angle = 45, hjust = 1),
axis.ticks.x = element_line(size = 1))+
labs(title = "Year-2010", x="",y = "Income Percentage(%)") +
scale_fill_manual(values = c("Income_1" = "lightblue", "Income_10" = "darkblue")) # 为不同的柱子分配不同的颜色
# 使用ggplot2创建柱状图，一个国家对应两根柱子，并用不同颜色区分
p4<-ggplot(data_long4, aes(x = Country, y = IncomePercentage, fill = IncomeLevel)) +
geom_bar(stat = "identity", position = "dodge") +
theme(axis.text.x = element_text(size = 7, angle = 45, hjust = 1),
axis.ticks.x = element_line(size = 1))+
labs(title = "Year-2015",x="", y = "Income Percentage(%)") +
scale_fill_manual(values = c("Income_1" = "lightblue", "Income_10" = "darkblue")) # 为不同的柱子分配不同的颜色
grid.arrange(p1, p2, p3, p4, ncol = 2)
```
```{r}
#2021年25-64岁的总体至少受中等教育及以上的比例
x1<-read.csv(file="education1.csv")
opar<-par(no.readonly = TRUE)
par(mfrow=c(2,2))
a1<-x1$Population.with.at.least.some.secondary.education.Femlale.
h<-hist(a1,freq=FALSE,breaks=12,col="lightpink",xlab="Population with at least some secondary education-Female(%)",main="Density plot for females in 2021",cex.lab= 0.9)
rug(jitter(a1))
lines(density(a1),col="olivedrab3",lwd=2)
box()
a2<-x1$Population.with.at.least.some.secondary.education.Male.
h<-hist(a2,freq=FALSE,breaks=12,col="lightblue2",xlab="Population with at least some secondary education-Male(%)",main="Density plot for males in 2021",cex.lab= 0.9)
rug(jitter(a2))
lines(density(a2),col="olivedrab3",lwd=2)
box()
h<-hist(a1,breaks=12,col="lightpink",xlab="Population with at least some secondary education-Female(%)",main="Frequency plot for females in 2021",cex.lab= 0.9)
x1fit<-seq(min(a1),max(a1),length=40)
y1fit<-dnorm(x1fit,mean=mean(a1),sd=sd(a1))
y1fit<-y1fit*diff(h$mids[1:2])*length(a1)
lines(x1fit,y1fit,col="olivedrab3",lwd=2)
box()
h<-hist(a2,breaks=12,col="lightblue2",xlab="Population with at least some secondary education-Male(%)",main="Frequency plot for males in 2021",cex.lab= 0.9)
x2fit<-seq(min(a2),max(a2),length=40)
y2fit<-dnorm(x2fit,mean=mean(a2),sd=sd(a2))
y2fit<-y2fit*diff(h$mids[1:2])*length(a2)
lines(x2fit,y2fit,col="olivedrab3",lwd=2)
box()
par(opar)
```
```{r}
# 创建示例数据
df <- read.csv(file="education2.csv")
df<-df[,1:5]
# 重塑数据
df_long <- tidyr::gather(df, key = "AgeGroup", value = "Value", -Gender)
# 创建箱型图
p <- ggplot(df_long, aes(x = AgeGroup, y = Value, fill = Gender)) +
geom_boxplot(position = position_dodge(width = 0.8), width = 0.7) +
scale_fill_manual(values = c( "pink","lightblue") ,labels = c("women", "men")) + # 设置颜色
labs(title = "Age Group Distribution by Gender in 2021", x = "Age Group", y = "Population with at least some secondary education", fill = "Gender") + # 设置标题和标签
theme_minimal() + # 设置主题为minimal风格
theme(legend.position = "top",plot.title = element_text(hjust = 0.5))# 设置图例位置为顶部
# 显示图形
print(p)
```
```{r}
# 创建示例数据
df <- read.csv(file="education3.csv")
# 将数据从宽格式转换为长格式
df_long <- gather(df, key = "Category", value = "Value")
# 创建箱型图
p <- ggplot(df_long, aes(x = Category, y = Value, fill = Category)) +
geom_boxplot() +
labs(title = "Women's income relative to men by level of education in 2021", x = "Educational Level", y = "Women's income relative to men(men=100)") +
theme_minimal() + # 设置主题为minimal风格
theme(plot.title = element_text(hjust = 0.5))+
scale_fill_manual(values = rep(c( "darkblue","lightblue","lightblue","lightblue"), length(unique(df_long$Category)))) +
theme(axis.text.x = element_text(size = 10, angle = 5, hjust = 1),
axis.ticks.x = element_line(size = 1))+
guides(fill = FALSE) # 隐藏图例
# 显示图形
print(p)
```
```{r}
opar<-par(no.readonly = TRUE)
g<-read.csv(file="GIDDB2019_15062022103259579.csv")
Region<- factor(g$Region, levels=c("Africa", "Americas", "Asia","Europe"),
labels=c("Africa", "Americas", "Asia","Europe"))
Income<- factor(g$Income, levels=c("High income","Upper middle income","Lower middle income ","Low income"),
labels=c("High income","Upper middle income","Lower middle income ","Low income"))
histogram(~g$Value|Region*Income,
main="Early marriage of women",
xlab="Proportion of women aged 15 to 19 who marry early",ylab="The percentage of the total population of the type",col="powderblue",bg="lightpink",xlim=c(0,60))
```
```{r}
df <- read.csv(file="marrige.csv")
a1<-df$Country1
a2<-df$Country2
d1<-c()
d2<-c()
d3<-c()
for(i in a1){
if(i %in% a2){
a<-which(a2==i)
b<-which(a1==i)
d1<-c(d1,i)
d2<-c(d2,df$early.marrige.rate...[b])
d3<-c(d3,df$GII[a])
}
}
d<-data.frame(
Country = d1,
rate = d2,
GII= d3
)
d$GII<-as.numeric(d$GII)
ggplot(d, aes(x =rate, y = GII, label = Country)) +
geom_point(size = 3.5, color = "chartreuse4") +
geom_text(hjust = -0.2, vjust = -0.3,size=2.3) + # 调整标签位置
labs(title = "Plot of changes in the proportion of early marriages and GII", x = "Early Marrige Rate", y = "GII")+
scale_y_continuous(breaks = c(0.1, 0.2,0.3,0.4,0.5,0.6,0.7, 0.8)) +
scale_color_gradient(low = "lightblue", high = "chartreuse4")+
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.y = element_text(size = 10))
#scale_y_discrete(breaks = c(0,0.5,1))+
#geom_hline(yintercept = 0.5, linetype = "dashed", color = "red")
```
```{r}
d9<-read.csv(file="marrige2.csv")
# 创建数据
x <- c(1973:2021)
y1 <- d9$Estimated.average.age.at.marriage..women..UN.[d9$Entity == "Hungary"]
y2 <- d9$Estimated.average.age.at.marriage..women..UN.[d9$Entity == "Denmark"]
y3 <- d9$Estimated.average.age.at.marriage..women..UN.[d9$Entity == "Netherlands"]
y4 <- d9$Estimated.average.age.at.marriage..women..UN.[d9$Entity == "Norway"]
y5 <- d9$Estimated.average.age.at.marriage..women..UN.[d9$Entity == "Sweden"]
y6 <- d9$Estimated.average.age.at.marriage..women..UN.[d9$Entity == "Iceland"]
y7 <- d9$Estimated.average.age.at.marriage..women..UN.[d9$Entity == "Finland"]
x1 <- d9$Year[d9$Entity == "Hungary"]
x2 <- d9$Year[d9$Entity == "Denmark"]
x3 <- d9$Year[d9$Entity == "Netherlands"]
x4 <- d9$Year[d9$Entity == "Norway"]
x5 <- d9$Year[d9$Entity == "Sweden"]
x6 <- d9$Year[d9$Entity == "Iceland"]
x7 <- d9$Year[d9$Entity == "Finland"]
# 绘制折线图
plot(x1, y1, xlim=c(1973,2021),ylim=c(20,33), col="chartreuse4", pch=16, xlab="Year", ylab="average age at marriage", main="Overview of average age at marriage 1973-2021")
points(x2, y2,pch=8, col="chartreuse4")
points(x3, y3, pch=2,col="chartreuse4")
points(x4, y4, pch=3,col="chartreuse4")
points(x5, y5,pch=15, col="chartreuse4")
points(x6, y6, pch=7,col="chartreuse4")
points(x7, y7, pch=9,col="chartreuse4")
legend("bottomright", legend=c("Hungary", "Denmark", "Netherlands", "Norway","Sweden","Iceland","Finland"), col=c("chartreuse4", "chartreuse4", "chartreuse4", "chartreuse4","chartreuse4","chartreuse4","chartreuse4"), lty=1, pch=c(16,8,2,3,15,7,9), cex=0.8)
```
```{r}
opar<-par(no.readonly = TRUE)
v<-read.csv(file="Women's political voice.csv")
rownames(v) = v[,2]
v = v[,-2]
Region<- factor(v$Region, levels=c("Asia","Americas","Europe","Africa"), labels=c(1,2,3,4))
ggplot(data=v, aes(x=Value, y=Income, colour=Region)) +
geom_point() +labs(title="The impact of women's political participation on local women's income", x="Proportion of women in politics", y="Women's earnings") +
geom_smooth(method="lm", color="burlywood4", linetype=4)+
geom_text(aes(Value,Income,label=rownames(v)),color="pink3",check_overlap = T)
```
```{r}
df <- read.csv(file="politics.csv")
a1<-df$Country1
a2<-df$Country2
a3<-df$Country3
d1<-c()
d2<-c()
d3<-c()
d4<-c()
for(i in a1){
if(i %in% a2 & i %in% a3){
a<-which(a1==i)
b<-which(a2==i)
c<-which(a3==i)
d1<-c(d1,i)
d2<-c(d2,df$GII[a])
d3<-c(d3,df$Value[b])
d4<-c(d4,df$continents[c])
}
}
d<-data.frame(
Country = d1,
GII = d2,
Value= d3,
Continents=d4
)
d$GII<-as.numeric(d$GII)
d$Continents <- as.factor(d$Continents)
ggplot(d, aes(x =Value, y = GII,shape = Continents, label = Country)) +
geom_point(size = 4, color = "chartreuse4") +
geom_text(hjust = -0.2, vjust = -0.3,size=2.3) + # 调整标签位置
labs(title = "Women in managerial positions vs GII", x = "Proportion of women in managerial positions(%)", y = "GII")+
scale_y_continuous(breaks = c(0.0,0.1, 0.2,0.3,0.4,0.5,0.6)) +
scale_color_gradient(low = "lightblue", high = "chartreuse4")+
scale_shape_manual(values = c(18,15,16,17,10,11)) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.y = element_text(size = 10))+
theme(legend.position = c(0.92, 0.2)) + # 图例位置
guides(shape = guide_legend(title = "Continents")) # 图例标题
#scale_y_discrete(breaks = c(0,0.5,1))+
#geom_hline(yintercept = 0.5, linetype = "dashed", color = "red")
```
```{r}
d9<-read.csv(file="politics1.csv")
# 创建数据
x <- c(2000:2021)
y1 <- d9$Value[d9$Region == "World"]
y2 <- d9$Value[d9$Region == "Asia"]
y3 <- d9$Value[d9$Region == "Europe"]
y4 <- d9$Value[d9$Region == "Africa"]
y5 <- d9$Value[d9$Region == "Americas"]
y6 <- d9$Value[d9$Region == "Oceania"]
x1 <- d9$Year[d9$Region == "World"]
x2 <- d9$Year[d9$Region == "Asia"]
x3 <- d9$Year[d9$Region == "Europe"]
x4 <- d9$Year[d9$Region == "Africa"]
x5 <- d9$Year[d9$Region == "Americas"]
x6 <- d9$Year[d9$Region == "Oceania"]
# 绘制折线图
plot(x1, y1, xlim=c(2000,2021),ylim=c(16,41), col="chartreuse4", pch=16, xlab="Year", ylab="Proportion of women in managerial positions", main="Overview of women in managerial positions 2000-2021")
points(x2, y2,pch=8, col="chartreuse4")
points(x3, y3, pch=2,col="chartreuse4")
points(x4, y4, pch=3,col="chartreuse4")
points(x5, y5,pch=15, col="chartreuse4")
points(x6, y6, pch=7,col="chartreuse4")
legend("bottomright", legend=c("World", "Asia", "Europe","Afirca","Americas","Oceania"), col=c("chartreuse4", "chartreuse4", "chartreuse4", "chartreuse4","chartreuse4","chartreuse4"), lty=1, pch=c(16,8,2,3,15,7,9), cex=0.8)
```
```{r}
df <- read.csv(file="politics2.csv")
a1<-df$Country1
a2<-df$Country2
d1<-c()
d2<-c()
d3<-c()
for(i in a2){
if(i %in% a1){
a<-which(a2==i)
b<-which(a1==i)
d1<-c(d1,i)
d2<-c(d2,df$GDI[b])
d3<-c(d3,df$wom_emp_vdem_owid[a])
}
}
d<-data.frame(
Country = d1,
GDI = d2,
wom_emp_vdem_owid= d3
)
d$GDI<-as.numeric(d$GDI)
ggplot(d, aes(x =wom_emp_vdem_owid, y = GDI, label = Country)) +
geom_point(size = 4, color = "chartreuse4") +
geom_text(hjust = -0.2, vjust = -0.3,size=2.3) + # 调整标签位置
labs(title = "Female Political Empowerment Index vs GDI", x = "Female Political Empowerment Index", y = "GDI")+
scale_y_continuous(breaks = c(0.4,0.5,0.6,0.7,0.8,0.9,1.0)) +
scale_color_gradient(low = "lightblue", high = "chartreuse4")+
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.y = element_text(size = 10))
#scale_y_discrete(breaks = c(0,0.5,1))+
#geom_hline(yintercept = 0.5, linetype = "dashed", color = "red")
```
```{r}
#Law mandates equal remuneration for females and males for work of equal value (1=yes; 0=no)
df<-read.csv(file="Society.csv")
a1<-df$Country1
a2<-df$Country2
d1<-c()
d2<-c()
d3<-c()
for(i in a2){
if(i %in% a1){
a<-which(a2==i)
b<-which(a1==i)
d1<-c(d1,i)
d2<-c(d2,df$GII[b])
d3<-c(d3,df$Law[a])
}
}
x<-data.frame(
Country = d1,
GII = d2,
Law= d3
)
a1<-subset(x, Law == 0)
a2<-subset(x, Law == 1)
a1[a1=='..'] <- NA
a1=na.omit(a1)
a2[a2=='..'] <- NA
a2=na.omit(a2)
# 将'a1$GII'转换为数值数据类型
a1$GII <- as.numeric(as.character(a1$GII))
a2$GII <- as.numeric(as.character(a2$GII))
opar<-par(no.readonly = TRUE)
par(mfrow=c(1,2))
h1<-hist(a1$GII,breaks=6,col="lightblue2",xlab="GII",main="Frequency plot for law=0")
x1fit<-seq(min(a1$GII),max(a1$GII),length=40)
y1fit<-dnorm(x1fit,mean=mean(a1$GII),sd=sd(a1$GII))
y1fit<-y1fit*diff(h1$mids[1:2])*length(a1$GII)
lines(x1fit,y1fit,col="olivedrab3",lwd=2)
box()
h2<-hist(a2$GII,breaks=6,col="darkgreen",xlab="GII",main="Frequency plot for law=1")
x2fit<-seq(min(a2$GII),max(a2$GII),length=40)
y2fit<-dnorm(x2fit,mean=mean(a2$GII),sd=sd(a2$GII))
y2fit<-y2fit*diff(h2$mids[1:2])*length(a2$GII)
lines(x2fit,y2fit,col="olivedrab3",lwd=2)
box()
par(opar)
```
```{r}
df<-read.csv(file="Female-to-male ratio of time devoted to unpaid care work.csv")
a<-df$Female.to.male.ratio.of.time.devoted.to.unpaid.care.work..OECD..2014..
h<-hist(a,breaks=12,col="lightblue2",xlab="Ratio of Unpaid Care Work",main="Frequency plot for time spent by women and men on household chores")
ticks <- seq(min(a), max(a), length.out = nclass.Sturges(a))
formatted_ticks <- sprintf("%.2f", ticks)
axis(side = 1, at = ticks, labels = formatted_ticks)
x1fit<-seq(min(a),max(a),length=40)
y1fit<-dnorm(x1fit,mean=mean(a),sd=sd(a))
y1fit<-y1fit*diff(h$mids[1:2])*length(a)
lines(x1fit,y1fit,col="olivedrab3",lwd=2)
box()
```
```{r}
df<-read.csv(file="Society1.csv")
df<-df[,1:2]
# 绘制箱型图
boxplot(df, col = c("lightblue", "lightpink"), names = c("Male", "Female"),
xlab = "Gender", ylab = "Proportion of bank accounts with access to ownership", main = "Boxplot of bank account ownership in 2021")
```
```{r}
data <- read.csv("linear regression.csv")
data$GII[data$GII=='..'|data$Expected.years.of.schooling.Female=='..'] <- NA
data=na.omit(data)
rownames(data)=data[,1] #取出第一列
data=data[,-1] #将第一列删除
head(data)
```
```{r}
par(mfrow=c(1,2))
hist(as.numeric(data$GII),main="histogram of GII",col="darkgreen",xlab="GII")
hist(log(as.numeric(data$GII)),main="histogram of log(GII)",col="dark green",xlab="log(GII)")
```
```{r}
data$degree.of.development<-as.factor(data$degree.of.development)
data$GII<-as.numeric(data$GII)
data$Maternal.mortality.ratio<-as.numeric(data$Maternal.mortality.ratio)
data$Adolescent.birth.rate<-as.numeric(data$Adolescent.birth.rate)
data$Share.of.seats.in.parliament<-as.numeric(data$Share.of.seats.in.parliament)
data$Population.with.at.least.some.secondary.education.Female<-as.numeric(data$Population.with.at.least.some.secondary.education.Female)
data$Population.with.at.least.some.secondary.education.Male<-as.numeric(data$Population.with.at.least.some.secondary.education.Male)
data$Labour.force.participation.rate..Female<-as.numeric(data$Labour.force.participation.rate..Female)
data$Labour.force.participation.rate..Male<-as.numeric(data$Labour.force.participation.rate..Male)
data$Life.expectancy.at.birth.Female<-as.numeric(data$Life.expectancy.at.birth.Female)
data$Life.expectancy.at.birth.Male<-as.numeric(data$Life.expectancy.at.birth.Male)
data$Expected.years.of.schooling.Female<-as.numeric(data$Expected.years.of.schooling.Female)
data$Expected.years.of.schooling.Male<-as.numeric(data$Expected.years.of.schooling.Male)
data$Mean.years.of.schooling.Female<-as.numeric(data$Mean.years.of.schooling.Female)
data$Mean.years.of.schooling.Male<-as.numeric(data$Mean.years.of.schooling.Male)
data$Estimated..gross.national.income.per.capita.Female<-as.numeric(data$Estimated..gross.national.income.per.capita.Female)
data$Estimated..gross.national.income.per.capita.Male<-as.numeric(data$Estimated..gross.national.income.per.capita.Male)
summary(data)
```
```{r}
#全模型-可看单个系数显著性
data <- cbind(data, model.matrix(~ degree.of.development - 1, data = data))
Full.lm1 = lm(data$GII ~data$degree.of.development1+data$degree.of.development2+data$degree.of.development3+data$degree.of.development4+data$Maternal.mortality.ratio+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Population.with.at.least.some.secondary.education.Female+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Life.expectancy.at.birth.Female+data$Expected.years.of.schooling.Female+data$Mean.years.of.schooling.Female+data$Estimated..gross.national.income.per.capita.Female+data$Population.with.at.least.some.secondary.education.Male+data$Labour.force.participation.rate..Male+data$Life.expectancy.at.birth.Male+data$Life.expectancy.at.birth.Male+data$Expected.years.of.schooling.Male+data$Mean.years.of.schooling.Male+data$Estimated..gross.national.income.per.capita.Male,data)
summary(Full.lm1)
as_flextable(Full.lm1)
```
```{r}
#残差检验通过，符合正态性
aov1 <- aov(Full.lm1)
#summary(aov1)
plot(aov1$fitted.values, aov1$residuals,
pch = 22, bg = "darkgrey",
xlab = "Predicted", ylab = "Residuals",
sub="Plot of residuals versus fitted values")
abline(h = 0)
hist(aov1$residuals,main="histogram of residuals",col="darkgreen",xlab="residuals")
```
```{r}
Restrict.lm1 = lm(data$GII ~data$degree.of.development1+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Labour.force.participation.rate..Male+data$Estimated..gross.national.income.per.capita.Male,data)
summary(Restrict.lm1)
as_flextable(Restrict.lm1)
```
```{r}
a1<-c()
for(i in aov1$residuals){a1<-c(i^2,a1)}
RSS1<-sum(a1)
aov2 <- aov(Restrict.lm1)
a2<-c()
for(i in aov2$residuals){a2<-c(i^2,a2)}
RSS2<-sum(a2)
RSS1
RSS2
```
```{r}
new_data <-data[,c(2:21)]
bestglm(new_data)
```
```{r}
Restrict1.lm1 = lm(data$GII ~data$degree.of.development1+data$degree.of.development2+data$degree.of.development3+data$Maternal.mortality.ratio+data$Labour.force.participation.rate..Female+data$Expected.years.of.schooling.Female+data$Mean.years.of.schooling.Female,data)
summary(Restrict1.lm1)
as_flextable(Restrict1.lm1)
```
```{r}
Restrict2.lm1 = lm(data$GII ~data$degree.of.development1+data$Maternal.mortality.ratio+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Labour.force.participation.rate..Male+data$Estimated..gross.national.income.per.capita.Male+data$Expected.years.of.schooling.Female+data$Mean.years.of.schooling.Female,data)
summary(Restrict2.lm1)
as_flextable(Restrict2.lm1)
```
```{r}
Restrict.lm1 = lm(data$GII ~data$degree.of.development1+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Labour.force.participation.rate..Male+data$Estimated..gross.national.income.per.capita.Male+data$Mean.years.of.schooling.Female,data)
summary(Restrict.lm1)
as_flextable(Restrict.lm1)
```
```{r}
aov2 <- aov(Restrict.lm1)
summary(aov2)
```
```{r}
confint(Restrict.lm1)
predict(Restrict.lm1, interval="confidence", level=0.95)[1:5,]
predict(Restrict.lm1, interval="prediction", level=0.95)[1:5,]
```
```{r}
pred.int <- predict(Restrict.lm1, interval="prediction", level=0.95)
mydata <- cbind(data, pred.int)
# 2. 绘制回归线和置信区间
p <- ggplot(mydata, aes(data$degree.of.development1+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Labour.force.participation.rate..Male+data$Estimated..gross.national.income.per.capita.Male+data$Mean.years.of.schooling.Female,data$GII)) +
labs(x='Linear combination of explanatory variables',y=" Response Variables")+
geom_point() +
stat_smooth(method = lm)
# 3. 绘制预测区间
p + geom_line(aes(y = mydata$lwr), color = "darkgreen", linetype = "dashed")+
geom_line(aes(y = mydata$upr), color = "darkgreen", linetype = "dashed")
```
```{r}
vif(Restrict.lm1)
```
```{r}
data2<-cbind(data$degree.of.development1,data$Adolescent.birth.rate,data$Share.of.seats.in.parliament,data$Labour.force.participation.rate..Female,data$Life.expectancy.at.birth.Female,data$Labour.force.participation.rate..Male,data$Estimated..gross.national.income.per.capita.Male,data$Mean.years.of.schooling.Female)
df_cor=cor(data2)
kappa(df_cor, exact=T)
```
```{r}
corr<-cor(data2);corr
corrplot(corr,method="number",type="full" ,bg="black",tl.col = "blue")
```
```{r}
aaa<-step(Restrict.lm1,direction="both")
as_flextable(aaa)
```
```{r}
#异方差性检验
rst <- rstandard(Restrict.lm1)#标准化残差
fit<-predict(Restrict.lm1)#预测值
mn<-data.frame(rst,fit)
ggplot(mn,aes(fit,rst))+geom_point()+geom_hline(yintercept = 2, linetype = "dashed")+geom_hline(yintercept = -2, linetype = "dashed")+geom_hline(yintercept = 3, linetype = "solid")+geom_hline(yintercept = -3, linetype = "solid")+scale_y_continuous( breaks = c(-3,-2,0,2,3))
```
```{r}
as_flextable(gqtest(Restrict.lm1))
# 运行White检验
bptest(Restrict.lm1)
# 运行Heteroscedasticity-Glesjer检验
ncvTest(Restrict.lm1)
```
```{r}
e<-resid(Restrict.lm1)#e是标准化残差
b1<-lm(data$GII ~data$degree.of.development1+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Labour.force.participation.rate..Male+data$Estimated..gross.national.income.per.capita.Male+data$Mean.years.of.schooling.Female,weights=1/abs(e),data=data)
b1
summary(b1)
as_flextable(b1)
Restrict.lm1<-b1
```
```{r}
df<-read.csv(file="endogenous.csv")
a1<-df$Country1
a2<-df$Country2
a3<-df$Entity
d1<-c()
d2<-c()
d3<-c()
d4<-c()
d5<-c()
d6<-c()
d7<-c()
d8<-c()
d9<-c()
d10<-c()
d11<-c()
d12<-c()
for(i in a1){
if(i %in% a2 & i %in% a3){
a<-which(a1==i)
b<-which(a2==i)
c<-which(a3==i)
d1<-c(d1,i)
d2<-c(d2,df$GII[a])
d3<-c(d3,df$continents[b])
d4<-c(d4,df$Population[c])
d5<-c(d5,df$Adolescent.birth.rate[a])
d6<-c(d6,df$Share.of.seats.in.parliament[a])
d7<-c(d7,df$Labour.force.participation.rate..Female[a])
d8<-c(d8,df$Labour.force.participation.rate..Male[a])
d9<-c(d9,df$Life.expectancy.at.birth.Female[a])
d10<-c(d10,df$Mean.years.of.schooling.Female[a])
d11<-c(d11,df$Estimated..gross.national.income.per.capita.Male[a])
d12<-c(d12,df$degree.of.development1[a])
}
}
x<-data.frame(
Country = d1,
GII = d2,
Continents= d3,
Population=d4,
Adolescent.birth.rate=d5,
Share.of.seats.in.parliament=d6,
Labour.force.participation.rate..Female=d7,
Labour.force.participation.rate..Male=d8,
Life.expectancy.at.birth.Female=d9,
Mean.years.of.schooling.Female=d10,
Estimated..gross.national.income.per.capita.Male=d11,
degree.of.development1=d12)
```
```{r}
#两阶段最小二乘估计--two-stage least squares (TSLS)
m_iv1 <- ivreg(GII ~ Mean.years.of.schooling.Female+Share.of.seats.in.parliament+Adolescent.birth.rate + Labour.force.participation.rate..Female +Labour.force.participation.rate..Male+Life.expectancy.at.birth.Female+Estimated..gross.national.income.per.capita.Male+degree.of.development1|Continents+Population+Adolescent.birth.rate + Labour.force.participation.rate..Female + Labour.force.participation.rate..Male+Life.expectancy.at.birth.Female+Estimated..gross.national.income.per.capita.Male+degree.of.development1, data = x)
coeftest(m_iv1,vcov=vcovHC,type="HC1")# 异方差稳健标准误
summary(m_iv1,test = TRUE) # 诊断
as_flextable(m_iv1)
```
```{r}
data.frame(
RMSE=rmse(Restrict.lm1,data=data),
MAE=mae(Restrict.lm1,data=data)
)
glance(Restrict.lm1)
data.frame(
RMSE=rmse(m_iv1,data=x),
MAE=mae(m_iv1,data=x)
)
glance(m_iv1)
```
```{r}
data.frame(
RMSE=rmse(robust,data=data),
MAE=mae(robust,data=data)
)
glance(robust)
```
```{r}
model1<- lm(data$GII ~data$degree.of.development1+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Labour.force.participation.rate..Male+data$Estimated..gross.national.income.per.capita.Male+data$Mean.years.of.schooling.Female+data$Adolescent.birth.rate:data$Labour.force.participation.rate..Female+data$Adolescent.birth.rate:data$Labour.force.participation.rate..Male+data$Estimated..gross.national.income.per.capita.Female:data$Mean.years.of.schooling.Female+data$Estimated..gross.national.income.per.capita.Male:data$Mean.years.of.schooling.Male+data$Estimated..gross.national.income.per.capita.Female:data$Life.expectancy.at.birth.Female+data$Estimated..gross.national.income.per.capita.Male:data$Life.expectancy.at.birth.Male+data$Expected.years.of.schooling.Female:data$Labour.force.participation.rate..Female+data$Expected.years.of.schooling.Male:data$Labour.force.participation.rate..Male+data$Share.of.seats.in.parliament:data$Labour.force.participation.rate..Female,data=data)
summary(model1)
as_flextable(model1)
```
```{r}
model21<- lm(data$GII ~data$degree.of.development1+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Labour.force.participation.rate..Male+data$Mean.years.of.schooling.Female+data$Labour.force.participation.rate..Female:data$Adolescent.birth.rate+data$Labour.force.participation.rate..Male:data$Adolescent.birth.rate+data$Share.of.seats.in.parliament:data$Labour.force.participation.rate..Female,data=data)
summary(model21)
as_flextable(model21)
```
```{r}
model2<- lm(data$GII ~data$degree.of.development1+data$Adolescent.birth.rate+data$Share.of.seats.in.parliament+data$Labour.force.participation.rate..Female+data$Life.expectancy.at.birth.Female+data$Labour.force.participation.rate..Male+data$Mean.years.of.schooling.Female+data$Labour.force.participation.rate..Female:data$Adolescent.birth.rate+data$Share.of.seats.in.parliament:data$Labour.force.participation.rate..Female,data=data)
summary(model2)
as_flextable(model2)
```
```{r}
data.frame(
RMSE=rmse(model2,data=data),
MAE=mae(model2,data=data)
)
#一次性做完所有指标的
#p值低意味着模型显著性更大
glance(model2)
```
```{r}
Restrict.lm1<-model2
```
```{r}
# 创建一个示例数据集
set.seed(123)
datap <- read.csv(file="Prediction.csv")
# 定义一个函数来进行线性拟合和预测
linear_predict <- function(df, value_type) {
model <- lm(df[[value_type]] ~ year, data = df)
future_years <- data.frame(year = 2022:2033)
predicted_values <- predict(model, newdata = future_years)
return(predicted_values)
}
# 针对每个地域和每个value进行预测
predicted_data <- datap %>%
group_by(region) %>%
do({
new_values <- cbind(year = 2022:2033,
sapply(c("Life.expectancy.at.birth.Female", "Mean.years.of.schooling.Female", "Adolescent.birth.rate","Share.of.seats.in.parliament", "Labour.force.participation.rate..Female", "Labour.force.participation.rate..Male"), function(value_type) linear_predict(., value_type)))
data.frame(region = rep(unique(.$region), each = 12), new_values)
})
# 合并预测的数据和原始数据
combined_data <- rbind(datap, predicted_data)
# 绘制2014到2033年每个地域对应值的散点图（以Life.Expectancy.at.Birth.female为例）
ggplot(combined_data %>% filter(year >= 2014), aes(x = year, y = Life.expectancy.at.birth.Female, color = region)) +
geom_point() +
labs(title = "Life expectancy at birth Female Changes from 2014 to 2033 by Region", x = "Year", y = "Life expectancy at birth Female") +
theme_minimal()
# 绘制2014到2033年每个地域对应值的散点图（以Mean.years.of.schooling.Female为例）
ggplot(combined_data %>% filter(year >= 2014), aes(x = year, y = Mean.years.of.schooling.Female, color = region)) +
geom_point() +
labs(title = "Mean years of schooling Female Changes from 2014 to 2033 by Region", x = "Year", y = "Mean years of schooling Female") +
theme_minimal()
```
```{r}
d1<-c(rep(0,36),rep(1,12),rep(0,12))
predicted_data1<-cbind(predicted_data,"degree.of.development1"=d1)[,c(3:9)]
f<-c()
for(i in 1:60){
d<-predicted_data1[c(i),]
y<-(1.020e+00)-(6.612e-02)*d[7]+(3.595e-03)*d[3]-(8.674e-03)*d[4]-(2.679e-03)*d[5]-(6.817e-03)*d[1]+(1.504e-03)*d[6]-(1.260e-02)*d[2]-(3.372e-05)*d[5]*d[3]+(1.008e-04)*d[4]*d[5]
y<-unname(y)
y<-unlist(y, recursive = FALSE)
f<-c(f,y)
}
predicted_data<-cbind(predicted_data,"GII"=f)
# 绘制散点图
ggplot(predicted_data, aes(x = year, y = GII, color = region)) +
geom_point() +
labs(title = "GII over Years by Region", x = "Year", y = "GII") +
scale_x_continuous(breaks = seq(2022, 2033, 1)) +
theme_minimal()+
theme(plot.title = element_text(hjust = 0.5))
```