In tpemartin/econDV2:

library(tidyverse)
library(readxl)
library(lubridate)
library(ggplot2)
library(stringr)

Final Project 主題

大主題環繞在能源上，想問的問題是，台灣的能源政策和國際之間究竟差異在哪? 1. 各國的能源供需與經濟成長 2. 各國的能源依賴與經濟成長 3. 各國的再生能源政策狀態 4. 台灣的能源依賴現況

"https://www.iea.org/data-and-statistics/data-browser?country=TAIPEI&fuel=Energy%20supply&indicator=TESbySource" 這邊是iea的各國能源供給(依source分類)

圖一資料、簡介

圖一進度：目前基本架構ok，但右軸定位怪怪的且需要調整顏色、text、圖例及標題

#引入資料~
##用INSERT EXCEL導進GITHUB的DATA，這樣老師比較方便~

finalProj <- list()
finalProj$BureauEnergy$source <- "https://www.moeaboe.gov.tw/ECW/populace/content/SubMenu.aspx?menu_id=3010"

#台灣能源總消費量
##單位：公秉油當量kloe, kiloliter of oil equivalent
url <- "https://github.com/hereisjulia/R_Visualization/blob/main/Tasks/Final%20Project/DomesticEnergyConsumption.xlsx?raw=true"
destfile <- "DomesticEnergyConsumption.xlsx"
curl::curl_download(url, destfile)
finalProj$BureauEnergy$data$Dom_Energy_Consumption <- read_excel(destfile, skip = 1)

#台灣進口能源比例
##單位：公秉油當量
url <- "https://github.com/hereisjulia/R_Visualization/blob/main/Tasks/Final%20Project/ImpEnergySource.xlsx?raw=true"
destfile <- "ImpEnergySource.xlsx"
curl::curl_download(url, destfile)
finalProj$BureauEnergy$data$Imp_EnergySource<- read_excel(destfile, skip = 1)

#台灣對進口能源依賴比率
url <- "https://github.com/hereisjulia/R_Visualization/blob/main/Tasks/Final%20Project/Import_Reliance.xlsx?raw=true"
destfile <- "Import_Reliance.xlsx"
curl::curl_download(url, destfile)
finalProj$BureauEnergy$data$Imp_EnergyRelience <- read_excel(destfile, skip = 1)

View(finalProj$BureauEnergy$data$Imp_EnergyRelience)

圖一

/內容包含：台灣能源總量+進口量+依賴進口量 /data格式：2019年1月到2022年3月的月資料 /aes 設計：呈現總能源量>凸顯對進口能源依賴>顯示進口能源類別 ---用area+線圖呈現

DataTidying

Data1: 台灣能源總量

但現在我們的資料形式是wide form, 先把它改成long.

pivot_longer(
  finalProj$BureauEnergy$data$Dom_Energy_Consumption,
  cols = -c(1),
  names_to = "month",
  values_to = "Consump_kloe"
) -> finalProj$BureauEnergy$data$Dom_Energy_Consumption

現在一個問題是等等要畫圖時的x軸，我想要用時間的格式，所以要把資料改成時間。so, 這邊做法： /因為date最小單位必須要是日期，所以要把日期加一下 /接下來就可以直接用lubridate轉資料類別了~

paste(finalProj$BureauEnergy$data$Dom_Energy_Consumption$month, "-01") -> finalProj$BureauEnergy$data$Dom_Energy_Consumption$month
lubridate::ymd(finalProj$BureauEnergy$data$Dom_Energy_Consumption$month) -> finalProj$BureauEnergy$data$Dom_Energy_Consumption$month

Data2: 進口量

pivot_longer(
  finalProj$BureauEnergy$data$Imp_EnergySource,
  cols = -c(1),
  names_to = "month",
  values_to = "Import_kloe"
) -> finalProj$BureauEnergy$data$Imp_EnergySource

#轉date
paste(finalProj$BureauEnergy$data$Imp_EnergySource$month, "-01") -> finalProj$BureauEnergy$data$Imp_EnergySource$month
lubridate::ymd(finalProj$BureauEnergy$data$Imp_EnergySource$month) -> finalProj$BureauEnergy$data$Imp_EnergySource$month

names(finalProj$BureauEnergy$data$Imp_EnergySource)[[1]] <- "EnergySource"
View(finalProj$BureauEnergy$data$Imp_EnergySource)

Data3: 進口能源依賴

pivot_longer(
  finalProj$BureauEnergy$data$Imp_EnergyRelience,
  cols = -c(1),
  names_to = "month",
  values_to = "relience"
) -> finalProj$BureauEnergy$data$Imp_EnergyRelience

#轉date
paste(finalProj$BureauEnergy$data$Imp_EnergyRelience$month, "-01") -> finalProj$BureauEnergy$data$Imp_EnergyRelience$month
lubridate::ymd(finalProj$BureauEnergy$data$Imp_EnergyRelience$month) -> finalProj$BureauEnergy$data$Imp_EnergyRelience$month

ggplot start

總之現在就開始畫圖~從area兩張+一張線圖開始！

#先確定一下資料格式正確：month是date，consump是numeric
finalProj$BureauEnergy$data$Dom_Energy_Consumption$month|>class()
finalProj$BureauEnergy$data$Dom_Energy_Consumption$Consump_kloe |>class()

#先確定一下資料格式正確：month是date，Import_kloe量是numeric
finalProj$BureauEnergy$data$Imp_EnergySource$month|>class()
finalProj$BureauEnergy$data$Imp_EnergySource$Import_kloe|>class()

雙軸

finalProj$plot1 <- list()
finalProj$plot1$Dom_Energy_Consumption <- function(){
  geom_area(
    data = finalProj$BureauEnergy$data$Dom_Energy_Consumption,
    mapping = aes(
      x=month,
      y=Consump_kloe
    ),
    fill = "#5CADAD"
)
}
finalProj$plot1$Imp_EnergySource <- function(){
  geom_area(
    data = finalProj$BureauEnergy$data$Imp_EnergySource,
    mapping = aes(
      x=month,
      y=Import_kloe,
      group = EnergySource,
      fill= EnergySource,
      alpha=0.5
    ),
    position= "stack"
  )}
finalProj$plot1$Imp_EnergyRelience <- function(){
  geom_line(
    data = finalProj$BureauEnergy$data$Imp_EnergyRelience,
    mapping = aes(
      x= month,
      y= transferInv_leftbreaks(relience)
    ),
    size=1,
    color="#272727"
  )
}
view(finalProj$BureauEnergy$data$Imp_EnergyRelience)

#調整右軸的位置
transfer_leftBreaks = function(breaks){
      scales::rescale(breaks, 
        from=c(0,8200000), to=c(90,100))
}
#調整要放在右軸的資料
transferInv_leftbreaks = function(breaks){
  scales::rescale(breaks,
        to=c(0,8200000), from=c(90,100))
}


finalProj$plot1$scale_y_area <- function(...){
  scale_y_continuous(
    name="Energy Consumption(kloe)",
    limits = c(0, 8200000),
    breaks = seq(0, 8200000, by=2000000),
    labels = seq(0, 8200000, by=2000000),
    sec.axis = sec_axis(name = "Import Relience",
                        trans = transfer_leftBreaks,
                        breaks = (seq(90,100, by=2)))
  )
}

finalProj$plot1$twoplot <- function(){
  ggplot()+
    finalProj$plot1$Dom_Energy_Consumption()+
    finalProj$plot1$Imp_EnergySource()+
    finalProj$plot1$Imp_EnergyRelience()+
    finalProj$plot1$scale_y_area()+
    scale_fill_discrete(name = "能源種類")
}

##未加入雙軸前，能源種類都有被呈現出來
ggplot()+finalProj$plot1$Imp_EnergySource()

##加入後，有幾個被消失了
finalProj$plot1$twoplot()

圖二資料、簡介

目前考慮，比較國內外對再生能源的使用程度。 "https://www.iea.org/data-and-statistics/data-browser/?country=WORLD&fuel=Renewables%20and%20waste&indicator=SDG72" 這是renewable對總能源用量的占比，還有風和太陽能發電的量

line graph呈現各個國家renewable能源占總能源用量的比例

finalProj$IEA$source <- "https://www.iea.org/data-and-statistics/data-browser?country=TAIPEI&fuel=Renewables%20and%20waste&indicator=SDG72"
ReNew_World <- read_csv("https://raw.githubusercontent.com/hereisjulia/R_Visualization/main/Tasks/Final%20Project/Renewable%20share%20in%20final%20energy%20consumption%20(SDG%207.2)%20%20-%20World.csv", skip = 3)
Renew_Taiwan <- read_csv("https://raw.githubusercontent.com/hereisjulia/R_Visualization/main/Tasks/Final%20Project/Renewable%20share%20in%20final%20energy%20consumption%20(SDG%207.2)%20%20-%20Chinese%20Taipei.csv", skip = 3)
ReNew_EU <- read_csv("https://raw.githubusercontent.com/hereisjulia/R_Visualization/main/Tasks/Final%20Project/Renewable%20share%20in%20final%20energy%20consumption%20(SDG%207.2)%20%20-%20European%20Union%20-%2028.csv",skip=3)
ReNew_USA <- read_csv("https://raw.githubusercontent.com/hereisjulia/R_Visualization/main/Tasks/Final%20Project/Renewable%20share%20in%20final%20energy%20consumption%20(SDG%207.2)%20%20-%20United%20States.csv", skip = 3)
ReNew_China <- read_csv("https://raw.githubusercontent.com/hereisjulia/R_Visualization/main/Tasks/Final%20Project/Renewable%20share%20in%20final%20energy%20consumption%20(SDG%207.2)%20%20-%20China%20(People's%20Republic%20of%20China%20and%20Hong%20Kong%20China).csv", skip=3)

這邊做一個簡單的quickMerge function，因為我的data set都一個個國家的所以加速合併。

quickMerge <- function(df1, df2){
  names(df1)[[1]] <- "Year"
  names(df2)[[1]] <- "Year"
  a <- merge(x=df1, y=df2, by= "Year")
  select(a, -contains("Units"))
}
a <- quickMerge(ReNew_World,Renew_Taiwan)
a <- quickMerge(a, ReNew_EU)
names(a) <- c("Year", "World", "Taiwan", "EU")
a <- quickMerge(a, ReNew_USA)
a <- quickMerge(a, ReNew_China)
names(a) <- c("Year", "World", "Taiwan", "EU", "USA","China")
finalProj$IEA$data$ReNew_EnCon <-a

pivot_longer(
  finalProj$IEA$data$ReNew_EnCon,
  cols = -c(1),
  names_to = "Country",
  values_to = "Renewable share"
) -> finalProj$IEA$data$ReNew_EnCon

圖二

lineColor= c("#3C3C3C", "#3C3C3C", "#4F9D9D", "#3C3C3C", "#3C3C3C")

ggplot()+
  geom_line(
    data = finalProj$IEA$data$ReNew_EnCon,
    mapping = aes(
      x=Year,
      y=`Renewable share`,
      group = Country,
      color = Country
      ),
    size =1
  )+
  scale_x_continuous(
    breaks= seq(1990, 2020, by=5),
    labels = seq(1990, 2020, by=5)
  )

圖三資料、簡介

想嘗試用地圖+指定國家的renewalbe能源來源(圓餅圖?) data: https://www.iea.org/data-and-statistics/data-browser?country=TAIPEI&fuel=Renewables%20and%20waste&indicator=RenewGenBySource