.Rhistory

PicData$PosNeg <- as.factor(PicData$PosNeg)
# 避免部分异常值太大或太小，从而图形大小比例失衡而进行的截尾处理
PicData[PicData$poten_y_rate > quantile(PicData$poten_y_rate,0.97,na.rm = T),'poten_y_rate'] <- 0.4
PicData <- PicData[abs(PicData$poten_y_rate) > 0.05,]
# 绘制图4气泡图
## 定义要素约束、非充分发展、非重要行业
library(reshape2)
leading <- openxlsx::read.xlsx('data-raw/主导产业.xlsx',1) %>% melt(id.vars = 'province')
names(leading)[2:3] <- c('leading','code_name')
leading$leading <- 'LeadingIndus'
PicData <- merge(PicData, leading[,], by = c('province','code_name'),all.x = T)
PicData$IndusCls <- NA
PicData$IndusCls[PicData$PosNeg == 0] <- 'const'
PicData$IndusCls[PicData$PosNeg == 1 & is.na(PicData$leading)] <- 'NotImp'
PicData$IndusCls[is.na(PicData$IndusCls)] <- 'NotSuffi'
## 绘图
p <- ggplot(data = PicData,aes(x=province,y=code_name,size = abs(poten_y_rate),fill=PosNeg,shape = IndusCls)) + geom_point()+
labs(x=NULL,y=NULL) + scale_shape_manual(values = c(23,21,22))
p + scale_size_area(max_size = 10,guide=FALSE)+guides(fill = FALSE,shape = FALSE)+
scale_fill_grey(start = 0.4,end = 0.8) + theme_bw() +
theme(axis.text.x = element_text(angle = 90, size = rel(1.4)),
axis.text.y = element_text(size = rel(1.4)))
Sys.time()
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(randomForest)
devtools::load_all()
ans <- Sys.time()
RltFinal <- Prelnd(FactorEndw,Ind0111,IndCode,CityEmp,CityWage, n = 500)
Sys.time() - ans
PicData <- group_by(Since2009[Since2009$year == 2016 |
Since2009$year == 2015|
Since2009$year == 2014,],province,code_name) %>%
summarise(poten_y_rate_down = mean(poten_y_rate_down),
poten_y_rate_up = mean(poten_y_rate_up),
poten_y_rate = mean(poten_y_rate))
# 只绘显著的
PicData <- PicData[PicData$poten_y_rate_down * PicData$poten_y_rate_up > 0,]
PicData$PosNeg <- 0
PicData$PosNeg[PicData$poten_y_rate < 0] <- 1
PicData$PosNeg <- as.factor(PicData$PosNeg)
# 避免部分异常值太大或太小，从而图形大小比例失衡而进行的截尾处理
PicData[PicData$poten_y_rate > quantile(PicData$poten_y_rate,0.97,na.rm = T),'poten_y_rate'] <- 0.4
PicData <- PicData[abs(PicData$poten_y_rate) > 0.05,]
# 绘制图4气泡图
## 定义要素约束、非充分发展、非重要行业
library(reshape2)
leading <- openxlsx::read.xlsx('data-raw/主导产业.xlsx',1) %>% melt(id.vars = 'province')
names(leading)[2:3] <- c('leading','code_name')
leading$leading <- 'LeadingIndus'
PicData <- merge(PicData, leading[,], by = c('province','code_name'),all.x = T)
PicData$IndusCls <- NA
PicData$IndusCls[PicData$PosNeg == 0] <- 'const'
PicData$IndusCls[PicData$PosNeg == 1 & is.na(PicData$leading)] <- 'NotImp'
PicData$IndusCls[is.na(PicData$IndusCls)] <- 'NotSuffi'
## 绘图
p <- ggplot(data = PicData,aes(x=province,y=code_name,size = abs(poten_y_rate),fill=PosNeg,shape = IndusCls)) + geom_point()+
labs(x=NULL,y=NULL) + scale_shape_manual(values = c(23,21,22))
p + scale_size_area(max_size = 10,guide=FALSE)+guides(fill = FALSE,shape = FALSE)+
scale_fill_grey(start = 0.4,end = 0.8) + theme_bw() +
theme(axis.text.x = element_text(angle = 90, size = rel(1.4)),
axis.text.y = element_text(size = rel(1.4)))
Since2009 <- RltFinal[['RltPredict']]
Since2009 <- bind_rows(Since2009)
PicData <- group_by(Since2009[Since2009$year == 2016 |
Since2009$year == 2015|
Since2009$year == 2014,],province,code_name) %>%
summarise(poten_y_rate_down = mean(poten_y_rate_down),
poten_y_rate_up = mean(poten_y_rate_up),
poten_y_rate = mean(poten_y_rate))
# 只绘显著的
PicData <- PicData[PicData$poten_y_rate_down * PicData$poten_y_rate_up > 0,]
PicData$PosNeg <- 0
PicData$PosNeg[PicData$poten_y_rate < 0] <- 1
PicData$PosNeg <- as.factor(PicData$PosNeg)
# 避免部分异常值太大或太小，从而图形大小比例失衡而进行的截尾处理
PicData[PicData$poten_y_rate > quantile(PicData$poten_y_rate,0.97,na.rm = T),'poten_y_rate'] <- 0.4
PicData <- PicData[abs(PicData$poten_y_rate) > 0.05,]
# 绘制图4气泡图
## 定义要素约束、非充分发展、非重要行业
library(reshape2)
leading <- openxlsx::read.xlsx('data-raw/主导产业.xlsx',1) %>% melt(id.vars = 'province')
names(leading)[2:3] <- c('leading','code_name')
leading$leading <- 'LeadingIndus'
PicData <- merge(PicData, leading[,], by = c('province','code_name'),all.x = T)
PicData$IndusCls <- NA
PicData$IndusCls[PicData$PosNeg == 0] <- 'const'
PicData$IndusCls[PicData$PosNeg == 1 & is.na(PicData$leading)] <- 'NotImp'
PicData$IndusCls[is.na(PicData$IndusCls)] <- 'NotSuffi'
## 绘图
p <- ggplot(data = PicData,aes(x=province,y=code_name,size = abs(poten_y_rate),fill=PosNeg,shape = IndusCls)) + geom_point()+
labs(x=NULL,y=NULL) + scale_shape_manual(values = c(23,21,22))
p + scale_size_area(max_size = 10,guide=FALSE)+guides(fill = FALSE,shape = FALSE)+
scale_fill_grey(start = 0.4,end = 0.8) + theme_bw() +
theme(axis.text.x = element_text(angle = 90, size = rel(1.4)),
axis.text.y = element_text(size = rel(1.4)))
ncol(FactorEndw)
view(FactorEndw)
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(randomForest)
devtools::load_all()
load('data-raw/Since2009_20181107JJYJ.RData')
rm(CityEmp, CityWage, FactorEndw, Ind0111, LstData)
rm(poten_emp, poten_tech,RltFinal)
rm(i,prv)
save.image('data-raw/myrlt.rdata')
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/main.R', encoding = 'UTF-8', echo=TRUE)
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(randomForest)
devtools::load_all()
# 若设置TRUE, 则在CPU是i9,8核下大约跑22分钟左右。因为是随机抽样，每次跑完结果略有差异。
# 若设置FALSE，则调用以前跑完存储的数据集。
if (FALSE){
ans <- Sys.time()
RltFinal <- Prelnd(FactorEndw,Ind0111,IndCode,CityEmp,CityWage, n = 500)
Sys.time() - ans
Since2009 <- RltFinal[['RltPredict']]
Since2009 <- bind_rows(Since2009)
}else {
load('data-raw/myrlt.RData')
}
rm(PadMissData)
ls()
rm(Prelnd)
save.image('data-raw/myrlt.rdata')
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/main.R', encoding = 'UTF-8', echo=TRUE)
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(randomForest)
devtools::load_all()
# 若设置TRUE, 则在CPU是i9,8核下大约跑22分钟左右。因为是随机抽样，每次跑完结果略有差异。
# 若设置FALSE，则调用以前跑完存储的数据集。
if (FALSE){
ans <- Sys.time()
RltFinal <- Prelnd(FactorEndw,Ind0111,IndCode,CityEmp,CityWage, n = 500)
Sys.time() - ans
Since2009 <- RltFinal[['RltPredict']]
Since2009 <- bind_rows(Since2009)
}else {
load('data-raw/myrlt.RData')
}
rm(IndCode)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/main.R', encoding = 'UTF-8', echo=TRUE)
devtools::load_all()
# 绘偏效应图
# 通用设备
DrawIndus <- 'C34'
PicName <- 'PartialEffect_ou_Ready_TY_JJYJ.emf' %>% paste('.\\RltForPapers\\',.,sep = '')
EndwImptn <- c('CityEmp','LandMining','RD','K')
EndwImptn_CHN <- c('行业就业人员年末数','城市工业用地','研究与试验发展人员全时当量','物质资本存量')
p <- DrawFun(DrawIndus,PicName,EndwImptn,EndwImptn_CHN,RltFinal,T)
load("E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/Since2009_20181107JJYJ.RData")
save(Since2009, RltFinal, file = 'data-raw/myrlt.rdata')
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(cowplot)
library(randomForest)
devtools::load_all()
# 若设置TRUE, 则在CPU是i9,8核下大约跑22分钟左右。因为是随机抽样，每次跑完结果略有差异。
# 若设置FALSE，则调用以前跑完存储的数据集。
if (FALSE){
ans <- Sys.time()
RltFinal <- Prelnd(FactorEndw,Ind0111,IndCode,CityEmp,CityWage, n = 500)
Sys.time() - ans
Since2009 <- RltFinal[['RltPredict']]
Since2009 <- bind_rows(Since2009)
}else {
load('data-raw/myrlt.RData')
}
# 绘偏效应图
# 通用设备
DrawIndus <- 'C34'
PicName <- 'PartialEffect_ou_Ready_TY_JJYJ.emf' %>% paste('.\\RltForPapers\\',.,sep = '')
EndwImptn <- c('CityEmp','LandMining','RD','K')
EndwImptn_CHN <- c('行业就业人员年末数','城市工业用地','研究与试验发展人员全时当量','物质资本存量')
p <- DrawFun(DrawIndus,PicName,EndwImptn,EndwImptn_CHN,RltFinal,T)
p <- DrawFun(DrawIndus,PicName,EndwImptn,EndwImptn_CHN,RltFinal,FALSE)
# draw arrow et al.
p1 <- p$p1 + geom_segment(x = 2000,xend = 50000,y = 1500, yend = 1100,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 300000,xend = 250000,y = 2500, yend = 2500,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 200000,xend = 155000,y = 1600, yend = 1800,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(2500,300000,200000),y = c(1580,2400,1500),
label = c('1','2','斜率最大'),size = 6,color = I('grey55')) +
labs(x = 'CE')
p2 <- p$p2 + geom_segment(x = 500,xend = 300,y = 1200, yend = 1200,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 750,xend = 600,y = 1600, yend = 1600,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 500,xend = 750,y = 2000, yend = 2000,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(550,500,1000),y = c(1200,2100,1600),
label = c('1','2','斜率最大'),size = 6,color = I('grey55')) + labs(x = 'LM')
p3 <- p$p3 + geom_segment(x = 30000,xend = 30000,y = 1500, yend = 1300,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 350000,xend = 300000,y = 2000, yend = 2000,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 300000,xend = 260000,y = 1700, yend = 1700,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(30000,359000,330000),y = c(1580,2000,1750),
label = c('1','2','斜率最大'),size = 6,color = I('grey55'))
p4 <- p$p4 + geom_segment(x = 5000,xend = 7500,y = 1500, yend = 1350,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 20000,xend = 28000,y = 1650, yend = 1650,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 40000,xend = 38000,y = 1760, yend = 1760,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(5000,43000,10000),y = c(1530,1750,1650),
label = c('1','2','斜率最大'),size = 6,color = I('grey55'))
plot_grid(p1,p2,p3,p4,ncol = 2)
p
PicName
DrawIndus <- 'C39'
PicName <- 'data-raw/partial_effect1.png'
EndwImptn <- c('CityEmp','LandMining','RD','patent')
EndwImptn_CHN <- c('行业就业人员年末数','城市工业用地','研究与试验发展人员全时当量',
'国内专利申请授权数')
p <- DrawFun(DrawIndus,PicName,EndwImptn,EndwImptn_CHN,RltFinal,FALSE)
p1 <- p$p1 + geom_segment(x = 40000,xend = 40000,y = 3000, yend = 1700,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 2000000,xend = 2000000,y = 8000, yend = 9000,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 800000,xend = 650000,y = 6000, yend = 6000,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(49000,2000000,1500000),y = c(3500,7500,6000),
label = c('1','2','斜率最大'),size = 6,color = I('grey55')) +
labs(x = 'CE')
p2 <- p$p2 + geom_segment(x = 500,xend = 500,y = 3500, yend = 2600,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 1250,xend = 1250,y = 4500, yend = 5500,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 1000,xend = 900,y = 3800, yend = 3800,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(500,1200,1200),y = c(3600,4500,3800),
label = c('1','2','斜率最大'),size = 6,color = I('grey55')) + labs(x = 'LM')
p3 <- p$p3 + geom_segment(x = 50000,xend = 50000,y = 3500, yend = 2500,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 470000,xend = 470000,y = 4800, yend = 5800,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 350000,xend = 380000,y = 5500, yend = 5500,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(52000,470000,250000),y = c(3500,4500,5500),
label = c('1','2','斜率最大'),size = 6,color = I('grey55'))
p4 <- p$p4 + geom_segment(x = 50000,xend = 10000,y = 2500, yend = 2500,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 200000,xend = 200000,y = 3250, yend = 3550,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 80000,xend = 110000,y = 3200, yend = 3200,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(51000,195000,40000),y = c(2550,3250,3200),
label = c('1','2','斜率最大'),size = 6,color = I('grey55'))
plot_grid(p1,p2,p3,p4,ncol = 2)
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(cowplot)
library(randomForest)
library(signal)
devtools::load_all()
ans <- Sys.time()
RltFinal <- Prelnd(FactorEndw,Ind0111,IndCode,CityEmp,CityWage, n = 500)
Sys.time() - ans
Since2009 <- RltFinal[['RltPredict']]
Since2009 <- bind_rows(Since2009)
DrawIndus <- 'C34'
PicName <- 'data-raw/partial_effect1.png'
EndwImptn <- c('CityEmp','LandMining','RD','K')
EndwImptn_CHN <- c('行业就业人员年末数','城市工业用地','研究与试验发展人员全时当量','物质资本存量')
p <- DrawFun(DrawIndus,PicName,EndwImptn,EndwImptn_CHN,RltFinal,FALSE)
# 绘制箭头等
p1 <- p$p1 + geom_segment(x = 2000,xend = 50000,y = 1500, yend = 1100,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 300000,xend = 250000,y = 2500, yend = 2500,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 200000,xend = 155000,y = 1600, yend = 1800,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(2500,300000,200000),y = c(1580,2400,1500),
label = c('1','2','斜率最大'),size = 6,color = I('grey55')) +
labs(x = 'CE')
p2 <- p$p2 + geom_segment(x = 500,xend = 300,y = 1200, yend = 1200,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 750,xend = 600,y = 1600, yend = 1600,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 500,xend = 750,y = 2000, yend = 2000,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(550,500,1000),y = c(1200,2100,1600),
label = c('1','2','斜率最大'),size = 6,color = I('grey55')) + labs(x = 'LM')
p3 <- p$p3 + geom_segment(x = 30000,xend = 30000,y = 1500, yend = 1300,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 350000,xend = 300000,y = 2000, yend = 2000,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 300000,xend = 260000,y = 1700, yend = 1700,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(30000,359000,330000),y = c(1580,2000,1750),
label = c('1','2','斜率最大'),size = 6,color = I('grey55'))
p4 <- p$p4 + geom_segment(x = 5000,xend = 7500,y = 1500, yend = 1350,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 20000,xend = 28000,y = 1650, yend = 1650,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = 40000,xend = 38000,y = 1760, yend = 1760,arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = c(5000,43000,10000),y = c(1530,1750,1650),
label = c('1','2','斜率最大'),size = 6,color = I('grey55'))
plot_grid(p1,p2,p3,p4,ncol = 2)
pos <- data.frame(x = c(2000, 300000, 200000), xend = c(50000,25000,155000),
y = c(1500, 2500,1600), yend = c(1100, 2500, 1800))
pos <- data.frame(x = c(2000, 300000, 200000), xend = c(50000,25000,155000),
y = c(1500, 2500,1600), yend = c(1100, 2500, 1800),
xtext = c(2500,300000,200000),
ytext = c(1580,2400,1500))
p1 <- p$p1 + geom_segment(x = pos$x[1],xend = pos$xend[1],y = pos$y[1], yend = pos$yend[1],
arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = pos$x[2],xend = pos$xend[2],y = pos$y[2], yend = pos$yend[2],
arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = pos$x[3],xend = pos$xend[3],y = pos$y[3], yend = pos$yend[3],
arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = pos$xtext,y = pos$ytext,
label = c('1','2','斜率最大'),size = 6,color = I('grey55')) +
labs(x = 'CE')
p1
pos <- data.frame(x = c(2000, 300000, 200000), xend = c(50000,250000,155000),
y = c(1500, 2500,1600), yend = c(1100, 2500, 1800),
xtext = c(2500,300000,200000),
ytext = c(1580,2400,1500))
p$p1 + geom_segment(x = pos$x[1],xend = pos$xend[1],y = pos$y[1], yend = pos$yend[1],
arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = pos$x[2],xend = pos$xend[2],y = pos$y[2], yend = pos$yend[2],
arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
geom_segment(x = pos$x[3],xend = pos$xend[3],y = pos$y[3], yend = pos$yend[3],
arrow = arrow(length = unit(0.3,'cm')),
size = 1,color = I('grey55')) +
annotate('text',x = pos$xtext,y = pos$ytext,
label = c('1','2','斜率最大'),size = 6,color = I('grey55')) +
labs(x = 'CE')
devtools::load_all()
p1 <- draw_arrow(p, pos)
p1
devtools::load_all()
pos <- data.frame(x = c(500, 750, 500), xend = c(300,600,750),
y = c(1200, 1600,2000), yend = c(1200, 1600, 2000),
xtext = c(550,500,1000),
ytext = c(1200,2100,1600)) # 设定箭头和文本的坐标
draw_arrow(p$p2, pos)
p2 <- draw_arrow(p$p2, pos)
pos <- data.frame(x = c(500, 750, 500), xend = c(300,600,750),
y = c(1200, 1600,2000), yend = c(1200, 1600, 2000),
xtext = c(550,500,1000),
ytext = c(1200,2100,1600)) # 设定箭头和文本的坐标
p2 <- draw_arrow(p$p2, pos)
p2
pos <- data.frame(x = c(30000, 350000, 300000), xend = c(30000,300000,260000),
y = c(1500, 2000,1700), yend = c(1300, 2000, 1700),
xtext = c(30000,359000,330000),
ytext = c(1580,2000,1750)) # 设定箭头和文本的坐标
draw_arrow(p$p3, pos)
draw_arrow(p$p3, pos)
draw_arrow(p$p3, pos)
p3 <- draw_arrow(p$p3, pos)
p3
pos <- data.frame(x = c(5000, 20000, 40000), xend = c(7500,28000,38000),
y = c(1500, 1650,1760), yend = c(1350, 1650, 1760),
xtext = c(5000,43000,10000),
ytext = c(1530,1750,1650)) # 设定箭头和文本的坐标
p4 <- draw_arrow(p$p4, pos)
p4
plot_grid(p1,p2,p3,p4,ncol = 2)
DrawIndus <- 'C39'
PicName <- 'data-raw/partial_effect1.png'
EndwImptn <- c('CityEmp','LandMining','RD','patent')
EndwImptn_CHN <- c('行业就业人员年末数','城市工业用地','研究与试验发展人员全时当量',
'国内专利申请授权数')
p <- DrawFun(DrawIndus,PicName,EndwImptn,EndwImptn_CHN,RltFinal,FALSE)
p_computer <- DrawFun(DrawIndus,PicName,EndwImptn,EndwImptn_CHN,RltFinal,FALSE)
# 绘制箭头
pos <- data.frame(x = c(40000, 2000000, 800000), xend = c(40000,2000000,650000),
y = c(3000, 8000,6000), yend = c(1700, 9000, 6000),
xtext = c(49000,2000000,1500000),
ytext = c(3500,7500,6000)) # 设定箭头和文本的坐标
p1 <- draw_arrow(p_computer, pos)
p1 <- draw_arrow(p_computer, pos)
p1 <- draw_arrow(p_computer$p1, pos)
p1
pos <- data.frame(x = c(500, 1250, 1000), xend = c(500,1250,900),
y = c(3500, 4500,3800), yend = c(2600, 5500, 3800),
xtext = c(500,1200,1200),
ytext = c(3600,4500,3800)) # 设定箭头和文本的坐标
p2 <- draw_arrow(p_computer$p2, pos)
p2
pos <- data.frame(x = c(50000, 470000, 350000), xend = c(50000,470000,380000),
y = c(3500, 4800,5500), yend = c(2500, 5800, 5500),
xtext = c(52000,470000,250000),
ytext = c(3500,4500,5500)) # 设定箭头和文本的坐标
p3 <- draw_arrow(p_computer$p3, pos)
p3
pos <- data.frame(x = c(50000, 200000, 80000), xend = c(10000,200000,110000),
y = c(2500, 3250,3200), yend = c(2500, 3550, 3200),
xtext = c(51000,195000,40000),
ytext = c(2550,3250,3200)) # 设定箭头和文本的坐标
p4 <- draw_arrow(p_computer$p4, pos)
p4
plot_grid(p1,p2,p3,p4,ncol = 2)
RltForest <- RltFinal[['RltForest']]
PicData <- NULL
for (i in 1:length(RltForest)) {
PicData <- data.frame(endow = row.names(importance(RltForest[[i]])),
IncNodePurity = importance(RltForest[[i]])[,1]/sum(importance(RltForest[[i]])[,1]),
industry = names(RltForest)[i]) %>%
rbind(PicData,.)
}
library(randomForest)
for (i in 1:length(RltForest)) {
PicData <- data.frame(endow = row.names(importance(RltForest[[i]])),
IncNodePurity = importance(RltForest[[i]])[,1]/sum(importance(RltForest[[i]])[,1]),
industry = names(RltForest)[i]) %>%
rbind(PicData,.)
}
for (i in 1:length(RltForest)) {
if (is.null(RltForest[[i]])) next
PicData <- data.frame(endow = row.names(importance(RltForest[[i]])),
IncNodePurity = importance(RltForest[[i]])[,1]/sum(importance(RltForest[[i]])[,1]),
industry = names(RltForest)[i]) %>%
rbind(PicData,.)
}
PicData <- merge(PicData,unique(Ind0111[,c('IndCode','industry')]),by.x = 'industry',by.y = 'IndCode',all.x = T)
PicData$endow_abb <- PicData$endow %>% as.character()
PicData <- PicData[!(PicData$endow %in% 'river'),]
PicData$endow_abb[PicData$endow %in% 'CityEmp'] <- 'CE'
PicData$endow_abb[PicData$endow %in% 'CityWage'] <- 'CW'
PicData$endow_abb[PicData$endow %in% 'RDIntensity'] <- 'RDI'
PicData$endow_abb[PicData$endow %in% 'CropArea'] <- 'CrpA'
PicData$endow_abb[PicData$endow %in% 'CyCntr'] <- 'DIF'
PicData$endow_abb[PicData$endow %in% 'TechImp'] <- 'TI'
PicData$endow_abb[PicData$endow %in% 'InputFee'] <- 'IF'
PicData$endow_abb[PicData$endow %in% 'LandMining'] <- 'LM'
# 绘制要素禀赋重要性图
p <- ggplot(PicData,aes(x=endow_abb,y=industry.y,size = IncNodePurity)) +
geom_point(shape=21,fill = 'grey') + labs(x=NULL,y=NULL)
p + scale_size_area(max_size = 15,guide=FALSE)+guides(fill = FALSE) +
scale_fill_grey(start = 0.4,end = 0.8) + theme_bw()+
theme(axis.text.x = element_text(angle = 90, size = rel(1.4)),
axis.text.y = element_text(size = rel(1.4)))
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/draw_importance.R', encoding = 'UTF-8', echo=TRUE)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/draw_importance.R', encoding = 'UTF-8', echo=TRUE)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/draw_importance.R', encoding = 'UTF-8', echo=TRUE)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/R/draw_partial_effect.R', encoding = 'UTF-8', echo=TRUE)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/R/draw_partial_effect.R', encoding = 'UTF-8', echo=TRUE)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/R/draw_partial_effect.R', encoding = 'UTF-8', echo=TRUE)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/R/draw_partial_effect.R', encoding = 'UTF-8', echo=TRUE)
# 绘图4，图5偏效应图
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(cowplot)
library(randomForest)
library(signal)
devtools::load_all()
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(cowplot)
library(randomForest)
library(signal)
devtools::load_all()
load('data-raw/myrlt.RData')
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/main.R', encoding = 'UTF-8', echo=TRUE)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/draw_partial_effect.R', encoding = 'UTF-8', echo=TRUE)
source('E:/做过的研究/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/draw_importance.R', encoding = 'UTF-8', echo=TRUE)
view(FactorEndw)
data("FactorEndw")
View(FactorEndw)
knitr::opts_chunk$set(echo = TRUE)
data("Ind0111")
knitr::opts_chunk$set(echo = TRUE)
data("Ind0111")
head(Ind0111)
data("Ind0111")
knitr::opts_chunk$set(echo = TRUE)
getwd()
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
devtools::load_all()
data("Ind0111")
head(Ind0111)
devtools::load_all()
data("Ind0111")
head(Ind0111)
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
devtools::load_all()
data("Ind0111")
head(Ind0111)
# 不分行业
data("FactorEndw")
head(FactorEndw[,1:6])
# 分行业的工资和就业
data("CityEmp")
data("CityWage")
head(CityEmp[,1:5])
head(CityWage[,1:5])
source('E:/CompletedResearch/z_FactorAndIndustry/FollowingEffects/FactorandIndustry/data-raw/draw_importance.R', encoding = 'UTF-8', echo=TRUE)