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library(dplyr)

## 
## 载入程辑包：'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.2 --

## v ggplot2 3.3.5     v purrr   0.3.5
## v tibble  3.1.6     v stringr 1.4.1
## v tidyr   1.2.1     v forcats 0.5.2
## v readr   2.1.3     
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(lubridate)

## 载入需要的程辑包：timechange
## 
## 载入程辑包：'lubridate'
## 
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(ggalt)

## Registered S3 methods overwritten by 'ggalt':
##   method                  from   
##   grid.draw.absoluteGrob  ggplot2
##   grobHeight.absoluteGrob ggplot2
##   grobWidth.absoluteGrob  ggplot2
##   grobX.absoluteGrob      ggplot2
##   grobY.absoluteGrob      ggplot2

load("E:\\e-program\\DATA20230225.Rdata")
industry <- Y %>% 
   filter(`基准指标` == "福建:规模以上工业增加值:当月同比") %>%
   mutate(
     `指标值` = case_when(
       `时间段` == "2020年前" ~ map(
         `指标值`,
         ~ tibble(
           `年月`=ymd("20160101")+months(1:length(.x)-1),
           `指标值` = .x
         )
       ),
       `时间段` == "2020年后" ~ map(
         `指标值`,
         ~ tibble(
           `年月`=ymd("20200101")+months(1:length(.x)-1),
           `指标值` = .x
         )
       ),
     )
    ) %>% 
   unnest(`指标值`)
industry$年月 <- seq(as.Date("2016-01-01"),length = 81,by = "1 month" )
colnames(industry)[1] <- "指标"
colnames(industry)[3] <- "容量"
colnames(industry)[4] <- "业扩类型"
industry$"申请.接电" <- rep(NA,81)
industry$"一级行业" <- rep(NA,81)
d <- c()
for(i in 1:48){
  d[i] <- (industry$容量[i]-min(industry$容量[1:48],na.rm=TRUE))/(max(industry$容量[1:48],na.rm=TRUE)-min(industry$容量[1:48],na.rm=TRUE))
}
for(i in 49:81){
  d[i] <- (industry$容量[i]-min(industry$容量[49:81],na.rm=TRUE))/(max(industry$容量[49:81],na.rm=TRUE)-min(industry$容量[49:81],na.rm=TRUE))
}
industry$容量 <- d
dianwang1 <- read.csv("E:\\e-program\\电网数据-工业、建筑业、批发零售业、房地产业-暂停恢复.csv",sep=",")
dianwang2 <- read.csv("E:\\e-program\\电网数据-2(暂停恢复-行业十、十一）.csv",sep=",")
colnames(dianwang1)[1] <- "申请.接电"
dianwang_re <- rbind(dianwang1,dianwang2)
dianwang_re$年月 <- seq(as.Date("2016-01-01"),length = 83,by = "1 month" )
a <- dianwang_re[as.numeric(format(dianwang_re$年月,format="%Y"))>2019,]

d <- c()
for(i in 1:45){
  m <- 35*i-34
  n <- 35*i
  for(j in m:n){
    d[j] <- (a$容量[j]-min(a$容量[m:n],na.rm=TRUE))/(max(a$容量[m:n],na.rm=TRUE)-min(a$容量[m:n],na.rm=TRUE)) 
  }
}

a$容量 <- d
a_jiedian <- filter(a,`申请.接电`=="接电")
a_shenqing <- filter(a,`申请.接电`=="申请")

plot1 <- function(data){
  if(length(data$年月) > 103){
    k <- paste(data$一级行业[1],data$申请.接电[1],data$业扩类型[1])
    p <- ggplot(data, aes(`年月`, `容量`, color = `指标`)) +
      geom_point() +
      scale_colour_manual(values=c("red","blue","green2","grey3"))+
      geom_xspline(spline_shape=-1, size=0.75)+
      labs(title = bquote(''*.(k)))
  }
  else {
    k <- paste(data$一级行业[1],data$申请.接电[1],data$业扩类型[1])
    p <- ggplot(data, aes(`年月`, `容量`, color = `指标`)) +
      geom_point() +
      scale_colour_manual(values=c("red","grey35","skyblue3"))+
      geom_xspline(spline_shape=-1, size=0.75)+
      labs(title = bquote(''*.(k)))
  }
  return(p)
} 

for(i in 1:6){
  m <- 1 + (i-1)*175
  n <- 70+ (i-1)*175
  b1 <- rbind(a_jiedian[m:n,],industry[49:81,])
   print(plot1(b1))
  u <- 71 + (i-1)*175
  v <- 175 + (i-1)*175
  b2 <- rbind(a_jiedian[u:v,],industry[49:81,])
   print(plot1(b2))
}