Local Election.Rmd

---
title: "Local Election"
author: "Po-Sheng Lee"
date: "2018/12/20"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
library(tidyverse)
library(readxl)
library(magrittr)


```

```{r tidy2018 data}

#台北
taipei_2018 <- read_excel("data/縣表3-1-100(臺北市).xls")
taipei_2018 %<>%
  rename("行政區別" = `107年臺北市市長選舉候選人在各投開票所得票數一覽表`, "里別" = X__1, 
         "民進黨候選人得票數" = X__5, "國民黨候選人得票數" = X__4, "無黨籍候選人得票數" = X__6, 
         "投票數" = X__10,"選舉人數"= X__14) %>%
  map(~gsub('\\s+', '',x = .)) %>% 
  as_data_frame() %>%
  mutate(選舉年份 = 2018, 縣市別 = "台北市", 
             國民黨候選人 = "丁守中", 民進黨候選人 = "姚文智", 無黨籍候選人 = "柯文哲",
             民進黨候選人得票數 = as.numeric(民進黨候選人得票數), 
             國民黨候選人得票數 = as.numeric(國民黨候選人得票數),
             無黨籍候選人得票數 = as.numeric(無黨籍候選人得票數),
             選舉人數 = as.numeric(選舉人數), 
             投票數 = as.numeric(投票數)) %>%
  select(選舉年份, 縣市別, 行政區別, 里別, 國民黨候選人, 民進黨候選人, 無黨籍候選人,
             國民黨候選人得票數, 民進黨候選人得票數, 無黨籍候選人得票數, 投票數, 選舉人數) %>%
  drop_na() %>%
  group_by(行政區別, 里別) %>%
  mutate(國民黨候選人得票數 = sum(國民黨候選人得票數), 民進黨候選人得票數 = sum(民進黨候選人得票數),
                  無黨籍候選人得票數 = sum(無黨籍候選人得票數), 
                  選舉人數 = sum(選舉人數), 投票數 = sum(投票數)) %>%
  distinct(行政區別, 里別, .keep_all = T) %>%
  ungroup()

taipei_2018 %<>%
  mutate(國民黨候選人得票率 = 國民黨候選人得票數/投票數, 民進黨候選人得票率 = 民進黨候選人得票數/投票數,
                  無黨籍候選人得票率 = 無黨籍候選人得票數/投票數, 投票率 = 投票數/選舉人數,
                  政黨色 = case_when(
                    國民黨候選人得票數 > 無黨籍候選人得票數 & 國民黨候選人得票數 > 民進黨候選人得票數 ~ "Blue",
                    無黨籍候選人得票數 > 國民黨候選人得票數 & 無黨籍候選人得票數 > 民進黨候選人得票數 ~ "Grey",
                    民進黨候選人得票數 > 無黨籍候選人得票數 & 民進黨候選人得票數 > 國民黨候選人得票數 ~ "Green"))
write_excel_csv(taipei_2018, "data/2018台北市候選人得票概況（村里別）")

#新北
newtaipei_2018 <- read_excel("data/縣表3-1-200(新北市).xls")
newtaipei_2018 %<>%
  rename("行政區別" = `107年新北市市長選舉候選人在各投開票所得票數一覽表`, "里別" = X__1, 
         "民進黨候選人得票數" = X__3, "國民黨候選人得票數" = X__4, 
         "投票數" = X__7, "選舉人數" = X__11) %>%
  map(~gsub('\\s+', '',x = .)) %>% 
  as_data_frame() %>%
  mutate(選舉年份 = 2018, 縣市別 = "新北市", 國民黨候選人 = "侯友宜", 民進黨候選人 = "蘇貞昌",
             民進黨候選人得票數 = as.numeric(民進黨候選人得票數), 
             國民黨候選人得票數 = as.numeric(國民黨候選人得票數),
             投票數 = as.numeric(投票數),
             選舉人數 = as.numeric(選舉人數)) %>%
  select(選舉年份, 縣市別, 行政區別, 里別, 國民黨候選人, 民進黨候選人, 
             國民黨候選人得票數, 民進黨候選人得票數, 投票數, 選舉人數) %>%
  drop_na() %>%
  group_by(行政區別, 里別) %>%
  mutate(國民黨候選人得票數 = sum(國民黨候選人得票數), 民進黨候選人得票數 = sum(民進黨候選人得票數),
                     投票數 = sum(投票數), 選舉人數 = sum(選舉人數)) %>%
  distinct(行政區別, 里別, .keep_all = T) %>%
  ungroup()

newtaipei_2018 %<>%
  mutate(國民黨候選人得票率 = 國民黨候選人得票數/投票數, 民進黨候選人得票率 = 民進黨候選人得票數/投票數,
                  `得票率差（百分點）` = (國民黨候選人得票率 - 民進黨候選人得票率)*100, 
                  得票數差 = 國民黨候選人得票數 - 民進黨候選人得票數, 投票率 = 投票數/選舉人數,
                  政黨色 = if_else(得票數差 > 0, "Blue", "Green"))
write_excel_csv(newtaipei_2018, "data/2018新北市候選人得票概況（村里別）")

#桃園
taoyuan_2018 <- read_excel("data/縣表3-1-300(桃園市).xls")
taoyuan_2018 %<>%
  rename("行政區別" = `107年桃園市市長選舉候選人在各投開票所得票數一覽表`, "里別" = X__1, 
         "民進黨候選人得票數" = X__7, "國民黨候選人得票數" = X__4, "投票數" = X__10, "選舉人數" = X__14) %>%
  map(~gsub('\\s+', '',x = .)) %>% 
  as_data_frame() %>%
  mutate(選舉年份 = 2018, 縣市別 = "桃園市", 國民黨候選人 = "陳學聖", 民進黨候選人 = "鄭文燦",
             民進黨候選人得票數 = as.numeric(民進黨候選人得票數), 
             國民黨候選人得票數 = as.numeric(國民黨候選人得票數),
             投票數 = as.numeric(投票數),
             選舉人數 = as.numeric(選舉人數)) %>%
  select(選舉年份, 縣市別, 行政區別, 里別, 國民黨候選人, 民進黨候選人, 
             國民黨候選人得票數, 民進黨候選人得票數, 投票數, 選舉人數) %>%
  drop_na() %>%
  group_by(行政區別, 里別) %>%
  mutate(國民黨候選人得票數 = sum(國民黨候選人得票數), 民進黨候選人得票數 = sum(民進黨候選人得票數),
                     投票數 = sum(投票數), 選舉人數 = sum(選舉人數)) %>%
  distinct(行政區別, 里別, .keep_all = T) %>%
  ungroup() %>%
  mutate(國民黨候選人得票率 = 國民黨候選人得票數/投票數, 民進黨候選人得票率 = 民進黨候選人得票數/投票數,
                  `得票率差（百分點）` = (國民黨候選人得票率 - 民進黨候選人得票率)*100, 
                  得票數差 = 國民黨候選人得票數 - 民進黨候選人得票數, 投票率 = 投票數/選舉人數,
                  政黨色 = if_else(得票數差 > 0, "Blue", "Green"))
write_excel_csv(taoyuan_2018, "data/2018桃園市候選人得票概況（村里別）")

#台中
taichung_2018 <- read_excel("data/縣表3-1-400(臺中市).xls")
taichung_2018 %<>%
  rename("行政區別" = `107年臺中市市長選舉候選人在各投開票所得票數一覽表`, "里別" = X__1, 
         "民進黨候選人得票數" = X__4, "國民黨候選人得票數" = X__5, "投票數" = X__8, "選舉人數" = X__12) %>%
  map(~gsub('\\s+', '',x = .)) %>% 
  as_data_frame() %>%
  mutate(選舉年份 = 2018, 縣市別 = "台中市", 國民黨候選人 = "盧秀燕", 民進黨候選人 = "林佳龍",
             民進黨候選人得票數 = as.numeric(民進黨候選人得票數), 
             國民黨候選人得票數 = as.numeric(國民黨候選人得票數),
             投票數 = as.numeric(投票數),
             選舉人數 = as.numeric(選舉人數)) %>%
  select(選舉年份, 縣市別, 行政區別, 里別, 國民黨候選人, 民進黨候選人, 
             國民黨候選人得票數, 民進黨候選人得票數, 投票數, 選舉人數) %>%
  drop_na() %>%
  group_by(行政區別, 里別) %>%
  mutate(國民黨候選人得票數 = sum(國民黨候選人得票數), 民進黨候選人得票數 = sum(民進黨候選人得票數),
                     投票數 = sum(投票數), 選舉人數 = sum(選舉人數)) %>%
  distinct(行政區別, 里別, .keep_all = T) %>%
  ungroup() %>%
  mutate(國民黨候選人得票率 = 國民黨候選人得票數/投票數, 民進黨候選人得票率 = 民進黨候選人得票數/投票數,
                  `得票率差（百分點）` = (國民黨候選人得票率 - 民進黨候選人得票率)*100, 
                  得票數差 = 國民黨候選人得票數 - 民進黨候選人得票數, 投票率 = 投票數/選舉人數,
                  政黨色 = if_else(得票數差 > 0, "Blue", "Green"))
write_excel_csv(taoyuan_2018, "data/2018台中市候選人得票概況（村里別）")

#台南
tainan_2018 <- read_excel("data/縣表3-1-500(臺南市).xls")
tainan_2018 %<>%
  rename("行政區別" = `107年臺南市市長選舉候選人在各投開票所得票數一覽表`, "里別" = X__1, 
         "民進黨候選人得票數" = X__3, "國民黨候選人得票數" = X__4, "投票數" = X__11, "選舉人數" = X__15) %>%
  map(~gsub('\\s+', '',x = .)) %>% 
  as_data_frame() %>%
  mutate(選舉年份 = 2018, 縣市別 = "台南市", 國民黨候選人 = "高思博", 民進黨候選人 = "黃偉哲",
             民進黨候選人得票數 = as.numeric(民進黨候選人得票數), 
             國民黨候選人得票數 = as.numeric(國民黨候選人得票數),
             投票數 = as.numeric(投票數),
             選舉人數 = as.numeric(選舉人數)) %>%
  select(選舉年份, 縣市別, 行政區別, 里別, 國民黨候選人, 民進黨候選人, 
             國民黨候選人得票數, 民進黨候選人得票數, 投票數, 選舉人數) %>%
  drop_na() %>%
  group_by(行政區別, 里別) %>%
  mutate(國民黨候選人得票數 = sum(國民黨候選人得票數), 民進黨候選人得票數 = sum(民進黨候選人得票數),
                     投票數 = sum(投票數), 選舉人數 = sum(選舉人數)) %>%
  distinct(行政區別, 里別, .keep_all = T) %>%
  ungroup() %>%
  mutate(國民黨候選人得票率 = 國民黨候選人得票數/投票數, 民進黨候選人得票率 = 民進黨候選人得票數/投票數,
                  `得票率差（百分點）` = (國民黨候選人得票率 - 民進黨候選人得票率)*100, 
                  得票數差 = 國民黨候選人得票數 - 民進黨候選人得票數, 投票率 = 投票數/選舉人數,
                  政黨色 = if_else(得票數差 > 0, "Blue", "Green"))
write_excel_csv(taoyuan_2018, "data/2018台南市候選人得票概況（村里別）")

#高雄
kaoshung_2018 <- read_excel("data/縣表3-1-600(高雄市).xls")
kaoshung_2018 %<>%
  rename("行政區別" = `107年高雄市市長選舉候選人在各投開票所得票數一覽表`, "里別" = X__1, 
         "民進黨候選人得票數" = X__4, "國民黨候選人得票數" = X__3, "投票數" = X__9, "選舉人數" = X__13) %>%
  # delete the white space for further use 
  # map function shortcut with ~ 
  map(~gsub('\\s+', '',x = .)) %>% 
  as_data_frame() %>%
  mutate(選舉年份 = 2018, 縣市別 = "高雄市", 國民黨候選人 = "韓國瑜", 民進黨候選人 = "陳其邁",
             民進黨候選人得票數 = as.numeric(民進黨候選人得票數), 
             國民黨候選人得票數 = as.numeric(國民黨候選人得票數),
             投票數 = as.numeric(投票數),
             選舉人數 = as.numeric(選舉人數)) %>%
  select(選舉年份, 縣市別, 行政區別, 里別, 國民黨候選人, 民進黨候選人, 
             國民黨候選人得票數, 民進黨候選人得票數, 投票數, 選舉人數) %>%
  drop_na() %>%
  group_by(行政區別, 里別) %>%
  mutate(國民黨候選人得票數 = sum(國民黨候選人得票數), 民進黨候選人得票數 = sum(民進黨候選人得票數),
                     投票數 = sum(投票數), 選舉人數 = sum(選舉人數)) %>%
  distinct(行政區別, 里別, .keep_all = T) %>%
  ungroup() %>%
  mutate(國民黨候選人得票率 = 國民黨候選人得票數/投票數, 民進黨候選人得票率 = 民進黨候選人得票數/投票數,
                  `得票率差（百分點）` = (國民黨候選人得票率 - 民進黨候選人得票率)*100, 
                  得票數差 = 國民黨候選人得票數 - 民進黨候選人得票數, 投票率 = 投票數/選舉人數,
                  政黨色 = if_else(得票數差 > 0, "Blue", "Green"))
write_excel_csv(taoyuan_2018, "data/2018高雄市候選人得票概況（村里別）")

```

```{r data import}

taipei_2010 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2010台北")
taipei_2014 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2014台北")
newtaipei_2010 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2010新北") %>%
  mutate(選舉年份 = as.numeric(選舉年份))
newtaipei_2014 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2014新北")
taoyuan_2009 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2009桃園")
taoyuan_2014 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2014桃園")
taichung_2010 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2010台中")
taichung_2014 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2014台中")
tainan_2010 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2010台南")
tainan_2014 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2014台南")
kaoshung_2010 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2010高雄")
kaoshung_2014 <- read_excel("data/近3屆六都縣市長候選人得票概況（村里別）.xlsx", sheet = "2014高雄")

```

```{r turnout 2010 and 2014}

turnout <- function(path){
  t_2010 <- read_excel(path = path, skip = 1) %>%
  rename(投票數 = `C
投票數

C=A+B`,
選舉人數 = `G
選舉人數
(原領票數)
G=E+F`) %>%
  select(行政區別, 里別, 投票數, 選舉人數) %>%
  map(~gsub('\\s+', '',x = .)) %>%
  as_data_frame() %>%
  mutate(投票數 = as.numeric(投票數), 選舉人數 = as.numeric(選舉人數)) %>%
  drop_na() %>%
  group_by(行政區別, 里別) %>%
  mutate(投票數 = sum(投票數), 選舉人數 = sum(選舉人數)) %>%
  distinct(行政區別, 里別, .keep_all = T) %>%
  ungroup() %>%
  mutate(投票率 = 投票數/選舉人數)
  return(t_2010)
}

turnout_taipei_2010 <- turnout(path = "data/市表3-1-100(10臺北市).xls")
turnout_newtaipei_2010 <- turnout(path = "data/市表3-1-200(10新北市).xls")
turnout_taichung_2010 <- turnout(path = "data/市表3-1-300(10臺中市).xls")
turnout_tainan_2010 <- turnout(path = "data/市表3-1-400(10臺南市).xls")
turnout_kaoshung_2010 <- turnout(path = "data/市表3-1-500(10高雄市).xls")

turnout_taipei_2014 <- turnout(path = "data/縣表3-1-100(14臺北市)-候選人得票數一覽表.xls")
turnout_newtaipei_2014 <- turnout(path = "data/縣表3-1-200(14新北市)-候選人得票數一覽表.xls")
turnout_taoyuan_2014 <- turnout(path = "data/縣表3-1-300(14桃園市)-候選人得票數一覽表.xls")
turnout_taichung_2014 <- turnout(path = "data/縣表3-1-400(14臺中市)-候選人得票數一覽表.xls")
turnout_tainan_2014 <- turnout(path = "data/縣表3-1-500(14臺南市)-候選人得票數一覽表.xls")
turnout_kaoshung_2014 <- turnout(path = "data/縣表3-1-600(14高雄市)-候選人得票數一覽表.xls")

turnout_taoyuan_2010 <- read_excel("data/縣表3-1-303(09桃園縣).xls", skip = 1) %>%
  rename(投票數 = `C
投票數

C=A+B`,
選舉人數 = `G
選舉人數
(原領票數)
G=E+F` ,
行政區別 = 鄉鎮市區別, 里別 = 村里別) %>%
  select(行政區別, 里別, 投票數, 選舉人數) %>%
  map(~gsub('\\s+', '',x = .)) %>%
  as_data_frame() %>%
  mutate(投票數 = as.numeric(投票數), 選舉人數 = as.numeric(選舉人數)) %>%
  drop_na() %>%
  group_by(行政區別, 里別) %>%
  mutate(投票數 = sum(投票數), 選舉人數 = sum(選舉人數)) %>%
  distinct(行政區別, 里別, .keep_all = T) %>%
  ungroup() %>%
  mutate(投票率 = 投票數/選舉人數)



```

```{r combine turnout to extant data}

turnout_combination <- function(city_data, turnout_data){
  x <- city_data %>%
    unite(行政區別, 里別, col = "neighbor", sep = "/")
  y <- turnout_data %>%
    select(-投票數) %>%
    unite(行政區別, 里別, col = "neighbor", sep = "/")
  z <- left_join(x = x, y = y, by = "neighbor") %>%
    separate(neighbor, into = c("行政區別", "里別"))
}

taipei_2010 %<>%
  turnout_combination(turnout_data = turnout_taipei_2010)
taipei_2014 %<>%
  turnout_combination(turnout_data = turnout_taipei_2014)
newtaipei_2010 %<>%
  turnout_combination(turnout_data = turnout_newtaipei_2010)
newtaipei_2014 %<>%
  turnout_combination(turnout_data = turnout_newtaipei_2014)
taoyuan_2009 %<>%
  turnout_combination(turnout_data = turnout_taoyuan_2010)
taoyuan_2014 %<>%
  turnout_combination(turnout_data = turnout_taoyuan_2014)
taichung_2010 %<>%
  turnout_combination(turnout_data = turnout_taichung_2010)
taichung_2014 %<>%
  turnout_combination(turnout_data = turnout_taichung_2014)
tainan_2010 %<>%
  turnout_combination(turnout_data = turnout_tainan_2010)
tainan_2014 %<>%
  turnout_combination(turnout_data = turnout_tainan_2014)
kaoshung_2010 %<>%
  turnout_combination(turnout_data = turnout_kaoshung_2010)
kaoshung_2014 %<>%
  turnout_combination(turnout_data = turnout_kaoshung_2014)

```

```{r combine data}

vote_combination <- function(x_2010,x_2014,x_2018){
  x_2010 %<>%
    select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 民進黨候選人得票數, 投票數, 
              國民黨候選人得票率, 民進黨候選人得票率, 選舉人數, 投票率) %>%
    rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
           KMTvote_2010 = 國民黨候選人得票數, DDPvote_2010 = 民進黨候選人得票數,
           ballot_2010 = 投票數, KMTrate_2010 = 國民黨候選人得票率, DDPrate_2010 = 民進黨候選人得票率,
           electorate_2010 = 選舉人數, turnout_2010 = 投票率) %>%
    unite(city, district, neighbor, col = "neighbor", sep = "/")
  x_2014 %<>%
    select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 民進黨候選人得票數, 投票數, 
              國民黨候選人得票率, 民進黨候選人得票率, 選舉人數, 投票率) %>%
    rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
           KMTvote_2014 = 國民黨候選人得票數, DDPvote_2014 = 民進黨候選人得票數, ballot_2014 = 投票數, 
           KMTrate_2014 = 國民黨候選人得票率, DDPrate_2014 = 民進黨候選人得票率, 
           electorate_2014 = 選舉人數, turnout_2014 = 投票率) %>%
    unite(city, district, neighbor, col = "neighbor", sep = "/")
  x_2018 %<>%
    select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 民進黨候選人得票數, 投票數, 
              國民黨候選人得票率, 民進黨候選人得票率, 選舉人數, 投票率) %>%
    rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
           KMTvote_2018 = 國民黨候選人得票數, DDPvote_2018 = 民進黨候選人得票數, ballot_2018 = 投票數, 
           KMTrate_2018 = 國民黨候選人得票率, DDPrate_2018 = 民進黨候選人得票率, 
           electorate_2018 = 選舉人數, turnout_2018 = 投票率) %>%
    unite(city, district, neighbor, col = "neighbor", sep = "/")
  z <- x_2018 %>%
    left_join(x_2014, by = "neighbor") %>%
    left_join(x_2010, by = "neighbor") %>%
    separate(neighbor, into = c("city", "district", "neighbor"))
  return(z)
}

newtaipei_2010_to_2018 <- vote_combination(newtaipei_2010, newtaipei_2014, newtaipei_2018)
taoyuan_2010_to_2018 <- vote_combination(taoyuan_2009, taoyuan_2014, taoyuan_2018)
taichung_2010_to_2018 <- vote_combination(taichung_2010, taichung_2014, taichung_2018)

kaoshung_working_2010 <- kaoshung_2010 %>%
  select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 民進黨候選人得票數, 無黨籍候選人得票數, 
            投票數, 國民黨候選人得票率, 民進黨候選人得票率, 無黨籍候選人得票率, 選舉人數, 投票率) %>%
  rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
         KMTvote_2010 = 國民黨候選人得票數, DDPvote_2010 = 民進黨候選人得票數, NPvote_2010 = 無黨籍候選人得票數,
         ballot_2010 = 投票數, KMTrate_2010 = 國民黨候選人得票率, DDPrate_2010 = 民進黨候選人得票率, 
         NPrate_2010 = 無黨籍候選人得票率, 
         electorate_2010 = 選舉人數, turnout_2010 = 投票率) %>%
  unite(city, district, neighbor, col = "neighbor", sep = "/") # For joining the data

kaoshung_working_2014 <- kaoshung_2014 %>%
  select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 民進黨候選人得票數, 投票數, 
            國民黨候選人得票率, 民進黨候選人得票率, 選舉人數, 投票率) %>%
  rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
         KMTvote_2014 = 國民黨候選人得票數, DDPvote_2014 = 民進黨候選人得票數, ballot_2014 = 投票數, 
         KMTrate_2014 = 國民黨候選人得票率, DDPrate_2014 = 民進黨候選人得票率, 
         electorate_2014 = 選舉人數, turnout_2014 = 投票率) %>%
  unite(city, district, neighbor, col = "neighbor", sep = "/")

kaoshung_working_2018 <- kaoshung_2018 %>%
  select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 民進黨候選人得票數, 投票數, 
            國民黨候選人得票率, 民進黨候選人得票率, 選舉人數, 投票率) %>%
  rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
         KMTvote_2018 = 國民黨候選人得票數, DDPvote_2018 = 民進黨候選人得票數, ballot_2018 = 投票數, 
         KMTrate_2018 = 國民黨候選人得票率, DDPrate_2018 = 民進黨候選人得票率, 
         electorate_2018 = 選舉人數, turnout_2018 = 投票率) %>%
  unite(city, district, neighbor, col = "neighbor", sep = "/")

kaoshung_2010_to_2018 <- kaoshung_working_2018 %>%
  left_join(kaoshung_working_2014, by = "neighbor") %>%
  left_join(kaoshung_working_2010, by = "neighbor") %>%
  separate(neighbor, into = c("city", "district", "neighbor"))

taipei_working_2010 <- taipei_2010 %>%
  select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 民進黨候選人得票數, 投票數, 
            國民黨候選人得票率, 民進黨候選人得票率, 選舉人數, 投票率) %>%
  rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
         KMTvote_2010 = 國民黨候選人得票數, DDPvote_2010 = 民進黨候選人得票數, ballot_2010 = 投票數, 
         KMTrate_2010 = 國民黨候選人得票率, DDPrate_2010 = 民進黨候選人得票率, 
         electorate_2010 = 選舉人數, turnout_2010 = 投票率) %>%
  unite(city, district, neighbor, col = "neighbor", sep = "/")

taipei_working_2014 <- taipei_2014 %>%
  select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 無黨籍候選人得票數, 
            投票數, 國民黨候選人得票率, 無黨籍候選人得票率, 選舉人數, 投票率) %>%
  rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
         KMTvote_2014 = 國民黨候選人得票數, NPvote_2014 = 無黨籍候選人得票數, ballot_2014 = 投票數, 
         KMTrate_2014 = 國民黨候選人得票率, NPrate_2014 = 無黨籍候選人得票率, 
         electorate_2014 = 選舉人數, turnout_2014 = 投票率) %>%
  unite(city, district, neighbor, col = "neighbor", sep = "/")

taipei_working_2018 <- taipei_2018 %>%
  select(縣市別, 行政區別, 里別, 國民黨候選人得票數, 民進黨候選人得票數, 無黨籍候選人得票數, 
            投票數, 國民黨候選人得票率, 民進黨候選人得票率, 無黨籍候選人得票率, 選舉人數, 投票率) %>%
  rename(city = 縣市別, district = 行政區別, neighbor = 里別, 
         KMTvote_2018 = 國民黨候選人得票數, DDPvote_2018 = 民進黨候選人得票數, NPvote_2018 = 無黨籍候選人得票數,
         ballot_2018 = 投票數, KMTrate_2018 = 國民黨候選人得票率, DDPrate_2018 = 民進黨候選人得票率, 
         NPrate_2018 = 無黨籍候選人得票率, 
         electorate_2018 = 選舉人數, turnout_2018 = 投票率) %>%
  unite(city, district, neighbor, col = "neighbor", sep = "/")

taipei_2010_to_2018 <- taipei_working_2010 %>%
  left_join(taipei_working_2014, by = "neighbor") %>%
  left_join(taipei_working_2018, by = "neighbor") %>%
  separate(neighbor, into = c("city", "district", "neighbor"))

taiwan_2010_to_2018 <- bind_rows(taipei_2010_to_2018, newtaipei_2010_to_2018, 
                                 taoyuan_2010_to_2018, taichung_2010_to_2018,
                                 kaoshung_2010_to_2018) #台南2018年村里重劃，無法放在一起看

write_excel_csv(taiwan_2010_to_2018, "data/台灣六都（台南除外）2010至18年市長選舉結果（村里別）")

taiwan_2018 <- bind_rows(taipei_2018, newtaipei_2018, taoyuan_2018, taichung_2018, tainan_2018, kaoshung_2018)
taiwan_2014 <- bind_rows(taipei_2014, newtaipei_2014, taoyuan_2014, taichung_2014, tainan_2014, kaoshung_2014)
taiwan_2010 <- bind_rows(taipei_2010, newtaipei_2010, taoyuan_2009, taichung_2010, tainan_2010, kaoshung_2010)
```

```{r mutate data}


taiwan_2010_to_2018 %<>%
  mutate(KMTrsp_2010 = KMTvote_2010/electorate_2010, DDPrsp_2010 = DDPvote_2010/electorate_2010, 
         NPrsp_2010 = NPvote_2010/electorate_2010, KMTrsp_2014 = KMTvote_2014/electorate_2014, 
         DDPrsp_2014 = DDPvote_2014/electorate_2014, NPrsp_2014 = NPvote_2014/electorate_2014,
         KMTrsp_2018 = KMTvote_2018/electorate_2018, DDPrsp_2018 = DDPvote_2018/electorate_2018, 
         NPrsp_2018 = NPvote_2018/electorate_2018,
         ballot_10_to_14 = ballot_2014 - ballot_2010, ballot_14_to_18 = ballot_2018 - ballot_2014,
         turnout_10_to_14 = turnout_2014 - turnout_2010, turnout_14_to_18 = turnout_2018 - turnout_2014,
         DDPvote_10_to_14 = DDPvote_2014 - DDPvote_2010, DDPvote_14_to_18 = DDPvote_2018 - DDPvote_2014,
         KMTvote_10_to_14 = KMTvote_2014 - KMTvote_2010, KMTvote_14_to_18 = KMTvote_2018 - KMTvote_2014,
         NPvote_10_to_14 = NPvote_2014 - NPvote_2010, NPvote_14_to_18 = NPvote_2018 - NPvote_2014,
         DDPrate_10_to_14 = DDPrate_2014 - DDPrate_2010, DDPrate_14_to_18 = DDPrate_2018 - DDPrate_2014,
         KMTrate_10_to_14 = KMTrate_2014 - KMTrate_2010, KMTrate_14_to_18 = KMTrate_2018 - KMTrate_2014,
         NPrate_10_to_14 = NPrate_2014 - NPrate_2010, NPrate_14_to_18 = NPrate_2018 - NPrate_2014,
         DDPrsp_10_to_14 = DDPrsp_2014 - DDPrsp_2010, DDPrsp_14_to_18 = DDPrsp_2018 - DDPrsp_2014,
         KMTrsp_10_to_14 = KMTrsp_2014 - KMTrsp_2010, KMTrsp_14_to_18 = KMTrsp_2018 - KMTrsp_2014,
         NPrsp_10_to_14 = NPrsp_2014 - NPrsp_2010, NPrsp_14_to_18 = NPrsp_2018 - NPrsp_2014)
```

```{r 2014 turnout and voting pattern ballot}
#投票數基本圖像 14
taiwan_2010_to_2018 %>%
  ggplot(aes(x = ballot_2010)) + geom_point(aes(y = ballot_2014), color = "red", alpha = 0.1) + 
  geom_point(aes(y = ballot_2018), color = "yellow", alpha = 0.1) + geom_abline() + 
  labs(title = "投票數", x = "2010年各里投票數", y = "14（紅）及18年（黃）投票數") +
  theme(text = element_text(family = "Heiti TC Light")) # 指定繁體中文黑體

#投票數變化與票數 14
taiwan_2010_to_2018 %>%
  filter(city != "台北市") %>%
  ggplot(aes(x = ballot_10_to_14)) + 
  geom_point(aes(y = KMTvote_2014), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTvote_2014), color = "blue", method = "lm", alpha = 0.4 ) + 
  geom_point(aes(y = DDPvote_2014), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPvote_2014), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票人數變化與票數", x = "10-14 投票人數變化", y = "票數") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "台北市") %>%
  ggplot(aes(x = ballot_10_to_14)) + 
  geom_point(aes(y = KMTvote_2014), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTvote_2014), color = "blue", method = "lm", alpha = 0.4) + 
  geom_point(aes(y = NPvote_2014), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = NPvote_2014), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票人數變化與票數（台北市）", x = "10-14 投票人數變化", y = "票數") +
  theme(text = element_text(family = "Heiti TC Light"))

#投票數變化與票數變化
taiwan_2010_to_2018 %>%
  filter(city != c("台北市", "高雄市")) %>%
  ggplot(aes(x = ballot_10_to_14)) + 
  geom_point(aes(y = KMTvote_10_to_14), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTvote_10_to_14), color = "blue", method = "lm", alpha = 0.4 ) + 
  geom_point(aes(y = DDPvote_10_to_14), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPvote_10_to_14), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票人數變化與票數變化", x = "10-14 投票人數變化", y = "票數") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "台北市") %>% #14柯文哲減去10蘇貞昌
  ggplot(aes(x = ballot_10_to_14)) + 
  geom_point(aes(y = KMTvote_10_to_14), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTvote_10_to_14), color = "blue", method = "lm", alpha = 0.4) + 
  geom_point(aes(y = (NPvote_2014 - DDPvote_2010)), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = (NPvote_2014 - DDPvote_2010)), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票人數變化與票數變化（台北市）", x = "10-14 投票人數變化", y = "票數") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>% #14楊秋興減去10黃昭順與楊秋興
  filter(city == "高雄市") %>%
  ggplot(aes(x = ballot_10_to_14)) + 
  geom_point(aes(y = (KMTvote_10_to_14 - NPvote_2010)), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = (KMTvote_10_to_14 - NPvote_2010)), color = "blue", method = "lm", alpha = 0.4) + 
  geom_point(aes(y = DDPvote_10_to_14), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPvote_10_to_14), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票人數變化與票數變化（高雄市）", x = "10-14 投票人數變化", y = "票數") +
  theme(text = element_text(family = "Heiti TC Light"))

```

```{r 2014 turnout and voting rate}
#投票率圖像
taiwan_2010_to_2018 %>%
  ggplot(aes(x = turnout_2010)) + geom_point(aes(y = turnout_2014), color = "red", alpha = 0.1) + 
  geom_point(aes(y = turnout_2018), color = "yellow", alpha = 0.1) + geom_abline() + 
  labs(title = "投票率", x = "2010年各里投票率", y = "14（紅）及18年（黃）投票率") +
  theme(text = element_text(family = "Heiti TC Light")) # 指定繁體中文黑體

#投票率與得票率
taiwan_2014 %>%
  filter(縣市別 != c("台北市")) %>%
  ggplot(aes(x = 投票率)) + 
  geom_point(aes(y = 國民黨候選人得票率), color = "blue", alpha = 0.2 ) +
  geom_smooth(aes(y = 國民黨候選人得票率), color = "blue", method = "lm", alpha = 0.3) +
  geom_point(aes(y = 民進黨候選人得票率), color = "green", alpha = 0.2 ) +
  geom_smooth(aes(y = 民進黨候選人得票率), color = "green", method = "lm", alpha = 0.3) +
  labs(title = "2014年投票率與得票率", x = "投票率", y = "得票率") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2014 %>%
  filter(縣市別 == "台北市") %>%
  ggplot(aes(x = 投票率)) + 
  geom_point(aes(y = 國民黨候選人得票率), color = "blue", alpha = 0.2 ) +
  geom_smooth(aes(y = 國民黨候選人得票率), color = "blue", method = "lm", alpha = 0.3) +
  geom_point(aes(y = 無黨籍候選人得票率), color = "green", alpha = 0.2 ) +
  geom_smooth(aes(y = 無黨籍候選人得票率), color = "green", method = "lm", alpha = 0.3) +
  labs(title = "2014年投票率與得票率（台北市）", x = "投票率", y = "得票率") +
  theme(text = element_text(family = "Heiti TC Light"))

#投票率變化與得票率
taiwan_2010_to_2018 %>%
  filter(city != "台北市") %>%
  ggplot(aes(x = turnout_10_to_14)) + 
  geom_point(aes(y = KMTrate_2014), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTrate_2014), color = "blue", method = "lm", alpha = 0.4 ) + 
  geom_point(aes(y = DDPrate_2014), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPrate_2014), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票率變化與得票率", x = "10-14 投票率變化", y = "14年得票率") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "台北市") %>%
  ggplot(aes(x = turnout_10_to_14)) + 
  geom_point(aes(y = KMTrate_2014), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTrate_2014), color = "blue", method = "lm", alpha = 0.4) + 
  geom_point(aes(y = NPrate_2014), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = NPrate_2014), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票率變化與得票率（台北市）", x = "10-14 投票率變化", y = "14得票率") +
  theme(text = element_text(family = "Heiti TC Light"))

#投票率變化與得票率變化

taiwan_2010_to_2018 %>%
  filter(city != c("台北市", "高雄市")) %>%
  ggplot(aes(x = turnout_10_to_14)) + 
  geom_point(aes(y = KMTrate_10_to_14), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTrate_10_to_14), color = "blue", method = "lm", alpha = 0.4 ) + 
  geom_point(aes(y = DDPrate_10_to_14), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPrate_10_to_14), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票率變化與得票率變化", x = "10-14 投票率變化", y = "得票率變化") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "台北市") %>% #14柯文哲減去10蘇貞昌
  ggplot(aes(x = turnout_10_to_14)) + 
  geom_point(aes(y = KMTrate_10_to_14), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTrate_10_to_14), color = "blue", method = "lm", alpha = 0.4) + 
  geom_point(aes(y = (NPrate_2014 - DDPrate_2010)), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = (NPrate_2014 - DDPrate_2010)), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票率變化與得票率變化（台北市）", x = "10-14 投票率變化", y = "得票率變化") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>% #14楊秋興減去10黃昭順與楊秋興
  filter(city == "高雄市") %>%
  ggplot(aes(x = turnout_10_to_14)) + 
  geom_point(aes(y = (KMTrate_10_to_14 - NPrate_2010)), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = (KMTrate_10_to_14 - NPrate_2010)), color = "blue", method = "lm", alpha = 0.4) + 
  geom_point(aes(y = DDPrate_10_to_14), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPrate_10_to_14), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "14年投票率變化與得票率變化（高雄市）", x = "10-14 投票率變化", y = "得票率變化") +
  theme(text = element_text(family = "Heiti TC Light"))

```

```{r 2018 turnout and voting pattern}

#投票率與得票率
taiwan_2018 %>%
  filter(縣市別 != c("台北市")) %>%
  ggplot(aes(x = 投票率)) + 
  geom_point(aes(y = 國民黨候選人得票率), color = "blue", alpha = 0.2 ) +
  geom_smooth(aes(y = 國民黨候選人得票率), color = "blue", method = "lm", alpha = 0.3) +
  geom_point(aes(y = 民進黨候選人得票率), color = "green", alpha = 0.2 ) +
  geom_smooth(aes(y = 民進黨候選人得票率), color = "green", method = "lm", alpha = 0.3) +
  labs(title = "2018年投票率與得票率", x = "投票率", y = "得票率") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2018 %>%
  filter(縣市別 == "台北市") %>%
  ggplot(aes(x = 投票率)) + 
  geom_point(aes(y = 國民黨候選人得票率), color = "blue", alpha = 0.2 ) +
  geom_smooth(aes(y = 國民黨候選人得票率), color = "blue", method = "lm", alpha = 0.3) +
  geom_point(aes(y = 無黨籍候選人得票率), color = "red", alpha = 0.2 ) +
  geom_smooth(aes(y = 無黨籍候選人得票率), color = "red", method = "lm", alpha = 0.3) +
  geom_point(aes(y = 民進黨候選人得票率), color = "green", alpha = 0.2 ) +
  geom_smooth(aes(y = 民進黨候選人得票率), color = "green", method = "lm", alpha = 0.3) +
  labs(title = "2018年投票率與得票率（台北市）", x = "投票率", y = "得票率") +
  theme(text = element_text(family = "Heiti TC Light"))

#投票率變化與得票率
taiwan_2010_to_2018 %>%
  filter(city != "台北市") %>%
  ggplot(aes(x = turnout_14_to_18)) + 
  geom_point(aes(y = KMTrate_2018), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTrate_2018), color = "blue", method = "lm", alpha = 0.4 ) + 
  geom_point(aes(y = DDPrate_2018), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPrate_2018), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "18年投票率變化與得票率", x = "14-18 投票率變化", y = "18年得票率") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "台北市") %>%
  ggplot(aes(x = turnout_10_to_14)) + 
  geom_point(aes(y = KMTrate_2018), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTrate_2018), color = "blue", method = "lm", alpha = 0.4) + 
  geom_point(aes(y = DDPrate_2018), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPrate_2018), color = "green", method = "lm", alpha = 0.4) +
  geom_point(aes(y = NPrate_2018), color = "red", alpha = 0.3) +
  geom_smooth(aes(y = NPrate_2018), color = "red", method = "lm", alpha = 0.4) +
  labs(title = "18年投票率變化與得票率（台北市）", x = "14-18 投票率變化", y = "18得票率") +
  theme(text = element_text(family = "Heiti TC Light"))

#投票率變化與得票率變化

taiwan_2010_to_2018 %>%
  filter(city != "台北市") %>%
  ggplot(aes(x = turnout_14_to_18)) + 
  geom_point(aes(y = KMTrate_14_to_18), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTrate_14_to_18), color = "blue", method = "lm", alpha = 0.4 ) + 
  geom_point(aes(y = DDPrate_14_to_18), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = DDPrate_14_to_18), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "18年投票率變化與得票率變化", x = "14-18 投票率變化", y = "得票率變化") +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "台北市") %>% #18柯文哲跟姚文智算一起
  ggplot(aes(x = turnout_14_to_18)) + 
  geom_point(aes(y = KMTrate_14_to_18), color = "blue", alpha = 0.3) + 
  geom_smooth(aes(y = KMTrate_14_to_18), color = "blue", method = "lm", alpha = 0.4) + 
  geom_point(aes(y = (NPrate_14_to_18 + DDPrate_2018)), color = "green", alpha = 0.3) +
  geom_smooth(aes(y = (NPrate_14_to_18 + DDPrate_2018)), color = "green", method = "lm", alpha = 0.4) +
  labs(title = "18年投票率變化與得票率變化（台北市）", x = "14-18 投票率變化", y = "得票率變化") +
  theme(text = element_text(family = "Heiti TC Light"))

```

```{r 2014 vote change and 2018 vote change}

taiwan_2010_to_2018 %>%
  filter(city == "台北市") %>%
  ggplot() +
  geom_point(aes(x = KMTrate_10_to_14, y = KMTrate_14_to_18), color = "blue", alpha = 0.2) +
  geom_smooth(aes(x = KMTrate_10_to_14, y = KMTrate_14_to_18), color = "blue", method = "lm", alpha = 0.2) +
  geom_point(aes(x = (NPrate_2014 - DDPrate_2010), y = (NPrate_14_to_18 + DDPrate_2018)), 
             color = "green", alpha = 0.2) +
  geom_smooth(aes(x = (NPrate_2014 - DDPrate_2010), y = (NPrate_14_to_18 + DDPrate_2018)), 
              color = "green", method = "lm", alpha = 0.2) +
  labs(title = "台北三屆選舉得票率變化", x = "10-14年得票率變化", y = "14-18年得票率變化") +
  expand_limits(x = c(-0.3, 0.3), y = c(-0.3, 0.3)) +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "新北市") %>%
  ggplot() +
  geom_point(aes(x = KMTrate_10_to_14, y = KMTrate_14_to_18), color = "blue", alpha = 0.2) +
  geom_smooth(aes(x = KMTrate_10_to_14, y = KMTrate_14_to_18), color = "blue", method = "lm", alpha = 0.2) +
  geom_point(aes(x = DDPrate_10_to_14, y = DDPrate_14_to_18), color = "green", alpha = 0.2) +
  geom_smooth(aes(x = DDPrate_10_to_14, y = DDPrate_14_to_18), color = "green", method = "lm", alpha = 0.2) +
  labs(title = "新北三屆選舉得票率變化", x = "10-14年得票率變化", y = "14-18年得票率變化") +
  expand_limits(x = c(-0.3, 0.3), y = c(-0.3, 0.3)) +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "桃園市") %>%
  ggplot() +
  geom_point(aes(x = KMTrate_10_to_14, y = KMTrate_14_to_18), color = "blue", alpha = 0.2) +
  geom_smooth(aes(x = KMTrate_10_to_14, y = KMTrate_14_to_18), color = "blue", method = "lm", alpha = 0.2) +
  geom_point(aes(x = DDPrate_10_to_14, y = DDPrate_14_to_18), color = "green", alpha = 0.2) +
  geom_smooth(aes(x = DDPrate_10_to_14, y = DDPrate_14_to_18), color = "green", method = "lm", alpha = 0.2) +
  labs(title = "桃園三屆選舉得票率變化", x = "10-14年得票率變化", y = "14-18年得票率變化") +
  expand_limits(x = c(-0.3, 0.3), y = c(-0.3, 0.3)) +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "台中市") %>%
  ggplot() +
  geom_point(aes(x = KMTrate_10_to_14, y = KMTrate_14_to_18), color = "blue", alpha = 0.2) +
  geom_smooth(aes(x = KMTrate_10_to_14, y = KMTrate_14_to_18), color = "blue", method = "lm", alpha = 0.2) +
  geom_point(aes(x = DDPrate_10_to_14, y = DDPrate_14_to_18), color = "green", alpha = 0.2) +
  geom_smooth(aes(x = DDPrate_10_to_14, y = DDPrate_14_to_18), color = "green", method = "lm", alpha = 0.2) +
  labs(title = "台中三屆選舉得票率變化", x = "10-14年得票率變化", y = "14-18年得票率變化") +
  expand_limits(x = c(-0.3, 0.3), y = c(-0.3, 0.3)) +
  theme(text = element_text(family = "Heiti TC Light"))

taiwan_2010_to_2018 %>%
  filter(city == "高雄市") %>%
  ggplot() +
  geom_point(aes(x = (KMTrate_10_to_14 - NPrate_2010), y = KMTrate_14_to_18), color = "blue", alpha = 0.2) +
  geom_smooth(aes(x = (KMTrate_10_to_14 - NPrate_2010), y = KMTrate_14_to_18), color = "blue", 
              method = "lm", alpha = 0.2) +
  geom_point(aes(x = DDPrate_10_to_14, y = DDPrate_14_to_18), color = "green", alpha = 0.2) +
  geom_smooth(aes(x = DDPrate_10_to_14, y = DDPrate_14_to_18), color = "green", method = "lm", alpha = 0.2) +
  labs(title = "高雄三屆選舉得票率變化", x = "10-14年得票率變化", y = "14-18年得票率變化") +
  expand_limits(x = c(-0.3, 0.3), y = c(-0.3, 0.3)) +
  theme(text = element_text(family = "Heiti TC Light"))

```