#----------------------------
# 関数の読み込み（plot1_1.R）
#----------------------------

source("plot1_1.R")　#図1-4のコードも参照

#------------------
# データの読み込み
#------------------

dat1_1 <- read.csv("approve_economy.csv", fileEncoding = "SJIS") %>%
  mutate(
    sociotropic_gd = q5c1 + q5c2,
    sociotropic_bd = q5c4 + q5c5,
    liv_gd         = q3c1 + q3c2,
    liv_bd         = q3c4 + q3c5
  )

df_keiki <- dat1_1 %>%
  select(yearmon, year_graph, sociotropic_gd, sociotropic_bd)

df_kurashi <- dat1_1 %>%
  select(yearmon, year_graph, liv_gd, liv_bd)

#------------------
#  プロットの作成
#------------------

g1_1 <- plot1_1(df_keiki, "sociotropic_bd", "sociotropic_gd", "景気への評価(-)", "景気への評価(+)")
g1_2 <- plot1_1(df_kurashi, "liv_bd", "liv_gd", "暮らし向きへの評価(-)", "暮らし向きへの評価(+)")


grid.arrange(g1_1[[1]], g1_1[[2]], g1_1[[3]], ncol = 3, widths = c(4/9, 1/9, 4/9))
grid.arrange(g1_2[[1]], g1_2[[2]], g1_2[[3]], ncol = 3, widths = c(4/9, 1/9, 4/9))

#---------------------------
# 関数の読み込み(plot1_3.R)
#---------------------------

source("plot1_3.R")

#------------------
# データの読み込み
#------------------

dat1_3 <- read.csv("df_ec_keiki.csv") %>%
  mutate(否定 = 否定*100,
         肯定 = 肯定*100)
  
#----------------------
# プロットの作成と表示
#----------------------

g1_3 <- plot1_3(dat1_3, "景気への評価(-)", "景気への評価(+)")

grid.arrange(g1_3[[1]], g1_3[[2]], g1_3[[3]], ncol = 3, widths = c(4/9, 1/9, 4/9))
#景気評価に関する図は出力を省略。

#------------------
# データの読み込み
#------------------

dat1_4 <- read_csv("approve_economy.csv", locale = locale(encoding = "SJIS")) %>%
  dplyr::select(yearmon, year_graph, ruling, opposite, indep)
#（上述の"approve_economy.csv"データに依拠、データの閲覧・入手については筆者にご相談ください）

#---------------------------------------
# プロットの作成(plot1_1.Rの内部も参照)
#---------------------------------------

g.mid<-ggplot(dat1_4,aes(x=1,y=year_graph))+
  geom_text(aes(label=year_graph))+
  geom_segment(aes(x=0.94,xend=0.96,yend=year_graph))+
  geom_segment(aes(x=1.04,xend=1.065,yend=year_graph))+
  ggtitle("")+
  ylab(NULL)+
  theme(axis.title=element_blank(),
        panel.grid=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        panel.background=element_blank(),
        axis.text.x=element_text(color=NA),
        axis.ticks.x=element_line(color=NA),
        plot.margin = unit(c(1,-1,1,-1), "mm")) +
  scale_x_reverse()


g1 <- ggplot(data = dat1_4, aes(x = as.Date(yearmon,"%Y-%m-%d"), y = ruling)) +
  geom_bar(stat = "identity") +  ggtitle("与党支持率") +
  theme(axis.title.x = element_blank(), 
        axis.title.y = element_blank(), 
        axis.text.y = element_blank(), 
        axis.ticks.y = element_blank(), 
        plot.margin = unit(c(1,-1,1,0), "mm")) +
  coord_flip()+
  ylim(0, 75)+
  xlab("年")+
  ylab("与党支持率")+
  theme(text = element_text(family = "jpfont"))+
  theme_bw()


g2 <- ggplot(data = dat1_4, aes(x = as.Date(yearmon,"%Y-%m-%d"), y = opposite)) +
  geom_bar(stat = "identity") + ggtitle("野党支持率") +
  theme(axis.title.x = element_blank(), 
        axis.title.y = element_blank(), 
        axis.text.y = element_blank(), 
        axis.ticks.y = element_blank(), 
        plot.margin = unit(c(1,-1,1,0), "mm")) +
  coord_flip()+
  ylim(0, 75)+
  xlab("年")+
  ylab("野党支持率")+
  theme(text = element_text(family = "jpfont"))+
    theme_bw()


g3 <- ggplot(data = dat1_4, aes(x = as.Date(yearmon,"%Y-%m-%d"), y = indep)) +
  geom_bar(stat = "identity") + ggtitle("無党派層割合") +
  theme(axis.title.x = element_blank(), 
        axis.title.y = element_blank(), 
        axis.text.y = element_blank(), 
        axis.ticks.y = element_blank(), 
        plot.margin = unit(c(1,-1,1,0), "mm")) +
  coord_flip()+
  xlab("年")+
  ylab("無党派割合")+
  ylim(0, 75)+
  theme(text = element_text(family = "jpfont"))+
    theme_bw()


gg1 <- ggplot_gtable(ggplot_build(g1))
gg2 <- ggplot_gtable(ggplot_build(g2))
gg3 <- ggplot_gtable(ggplot_build(g3))

gg.mid <- ggplot_gtable(ggplot_build(g.mid))

grid.arrange(gg1,gg.mid,gg2,gg.mid,gg3,ncol=5,widths=c(4/14,1/14,4/14,1/14,4/14))

#------------------
# データの読み込み
#------------------

dat1_5 <- read_csv("psupport_move.csv")

#------------
# 要件の設定
#------------

dat1_5 <- dat1_5 %>%
  mutate(Condition = factor(Condition, levels = c("残留: 与党→与党", "残留: 野党→野党", 
                                                  "離脱: 与党→野党", "離脱: 野党→与党")))

#----------------
# プロットの作成
#----------------

ggplot(dat1_5, aes(x = 年, y = Ratio * 100, fill = Condition)) +
  geom_bar(stat = "identity", position = position_dodge(), color = "black") +
  geom_text(aes(label = sprintf("%.1f", Ratio * 100)), 
            position = position_dodge(width = 0.9), 
            vjust = -0.5, size = 4, color = "black") +
  facet_wrap(~Condition, ncol = 2) +
  scale_fill_grey(start = 0.5, end = 0.8) + 
  labs(title = "", x = "年", y = "割合(%) ") +
  theme_bw(base_size = 14) +
  theme(
    legend.position = "none", 
    panel.grid.major = element_line(size = 0.5),
    panel.grid.minor = element_blank(),
    strip.text = element_text(face = "bold", size = 12) 
  ) +
  geom_label(aes(label = sprintf("%.1f", Ratio * 100)), 
             position = position_dodge(width = 0.9), 
             vjust = -0.5, size = 4, fill = "white", color = "black", label.size = 0.3, 
             label.padding = unit(0.2, "lines"))

#------------------
# データの読み込み
#------------------

jes2019 <- read_csv("2019jes.csv", locale = locale(encoding = "SJIS"))

#-------------------------------
# 「与党―与党」のダミー変数作成
#-------------------------------

jes2019 <- jes2019 %>%
  mutate(
    rul_rul_dum2019 = case_when(
      (Q2 %in% c("自民党", "公明党")) & (aQ7 %in% c("自民党", "公明党")) ~ 1,
      TRUE ~ 0
    )
  )

#-------------------------------
# 「与党―野党」のダミー変数作成
#-------------------------------

jes2019 <- jes2019 %>%
  mutate(
    rul_op_dum2019 = case_when(
      (aQ7 %in% c("自民党", "公明党")) &
      (Q2 %in% c("立憲民主", "社民党", "共産党", "次世代の", "みんなの",
                 "希望の党", "新党改革", "日本未来の党", "日本維新", "その他")) ~ 1,
      TRUE ~ 0
    )
  )

library(tidyverse)

#------------------
# データの読み込み
#------------------

dat1_6 <- read_csv("df_temp.csv", locale = locale(encoding = "SJIS"))

dat1_6_1 <- dat1_6 %>%
  select(yearmon, sociotropic_gd_rul, sociotropic_bd_rul) %>%
  mutate(政党支持 = "与党派")

dat1_6_2 <- dat1_6 %>%
  select(yearmon, sociotropic_gd_op, sociotropic_bd_op) %>%
  mutate(政党支持 = "野党派")

dat1_6_3 <- dat1_6 %>%
  select(yearmon, sociotropic_gd_indep, sociotropic_bd_indep) %>%
  mutate(政党支持 = "無党派")

colnames(dat1_6_1) <- colnames(dat1_6_2) <- colnames(dat1_6_3) <- c("年", "肯定", "否定", "政党支持")
dat1_6 <- bind_rows(dat1_6_1, dat1_6_2, dat1_6_3) %>%
  mutate(across(c(肯定, 否定), ~ . * 100))

dat1_6 <- dat1_6 %>%
  pivot_longer(cols = c(肯定, 否定), names_to = "景気評価", values_to = "値") %>%
  mutate(政党支持 = factor(政党支持, levels = c("与党派", "野党派", "無党派")))

#------------------
# プロットの作成
#------------------

ggplot(data = dat1_6, aes(x = as.Date(年,"%Y-%m-%d"), y = 値)) +
  geom_line(aes(color = 景気評価), size = 1) +
  scale_color_manual(values = c("grey", "black")) +
  xlab("年")+
  facet_wrap(~政党支持,ncol=1)+
  ylab("割合(%)")+ 
  theme_bw()+
theme(text = element_text(family = "jpfont"))

#----------------------------
#　関数の読み込み(plot1_7.R)
#----------------------------

source("plot1_7.R")

#------------------------------------------------
# データの読み込みとプロットの作成: 社会志向評価
#------------------------------------------------

dat1_7socio <- read.csv("df_ec_partisan.csv")
g1_7socio <- plot1_7(dat1_7socio, "景気評価", "景気")


grid.arrange(g1_7socio$g1, g1_7socio$g.mid, g1_7socio$g2, 
             g1_7socio$g.mid, g1_7socio$g3, 
             ncol = 5, widths = c(4/14, 1/14, 4/14, 1/14, 4/14))

#------------------
# モジュールの導入
#------------------


import statsmodels.api as sm
import pandas

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import pandas_datareader as pdr
from statsmodels.regression.rolling import RollingOLS
import matplotlib.pyplot as plt
import seaborn
seaborn.set_style('darkgrid')
pd.plotting.register_matplotlib_converters()
%matplotlib inline


#------------------
# データの読み込み
#------------------

df = pd.read_csv('df_fecm.csv',
                  index_col='yearmon', parse_dates=True,encoding='cp932')
print(df)

#------------------
# 内生変数の設定
#------------------

endog = df.approve
endog

#---------------------------------------
# 外生変数の設定：社会志向の経済評価(+)
#---------------------------------------

exog = sm.add_constant(df['ruling'])

#----------------------
# 推定と推定結果の確認
#----------------------

rols_ruling = RollingOLS(endog, exog, window=60)
rres_ruling = rols_ruling.fit()
params = rres_ruling.params
print(params.head())
print(params.tail())

#------------------
# プロットの作成
#------------------

fig_ruling= rres_dis_liv.plot_recursive_coefficient(variables=['ruling'], figsize=(14,6))

#---------------------------
# 関数の読み込み(plot2_2.R)
#---------------------------

source("plot2_2.R")

#------------------
# データの読み込み
#------------------

dat2_2 <- read.csv("approve_economy.csv", fileEncoding = "SJIS")


# 暮らし向き
cols1 <- c("q3c1", "q3c2", "q3c3", "q3c4", "q3c5", "q3c6")
labels1 <- c("大変楽になった", "やや楽になった", "どちらでもない", "やや苦しくなった", "大変苦しくなった", "わからない")
p2_2_1 <- plot2_1(dat2_2, cols1, labels1, "暮らし向き")

# 物価
cols2 <- c("q4c3", "q4c1", "q4c2", "q4c4")
labels2 <- c("今より下がると思う", "落ち着いてきたと思う", "上がると思う", "わからない")
p2_2_2 <- plot2_1(dat2_2, cols2, labels2, "物価")

# 景気
cols3 <- c("q5c1", "q5c2", "q5c3", "q5c4", "q5c5", "q5c6")
labels3 <- c("確かに良くなってきたと思う", "やや良くなってきたと思う", "どちらでもない", "やや悪くなってきたと思う", "確かに悪くなってきたと思う", "わからない")
p2_2_3 <- plot2_1(dat2_2, cols3, labels3, "景気")

# これから先の生活
cols4 <- c("q6c1", "q6c2", "q6c3", "q6c4")
labels4 <- c("良くなっていく", "変わりない", "悪くなっていく", "どちらともいえない")
p2_2_4 <- plot2_1(dat2_2, cols4, labels4, "これから先の生活")

#------------------
# プロットの作成
#------------------

gridExtra::grid.arrange(p2_2_1, p2_2_2, p2_2_3, p2_2_4, ncol = 2, nrow = 2)

#--------------------------------- 
# FIの計算：社会志向の経済評価(+)
#--------------------------------- 

fdGPH(na.omit(sociotropic_gd))

#--------------------------------- 
# FIの計算：社会志向の経済評価(-)
#--------------------------------- 

fdGPH(na.omit(sociotropic_bd))

#--------------------------------- 
# FIの計算：個人志向の経済評価(+)
#--------------------------------- 

fdGPH(na.omit(liv_gd))

#--------------------------------- 
# FIの計算：個人志向の経済評価(-)
#--------------------------------- 

fdGPH(na.omit(liv_bd))

#---------------
# データの設定
#--------------- 

vars <- list(
  "社会志向(+)" = sociotropic_gd,
  "社会志向(-)" = sociotropic_bd,
  "個人志向(+)" = liv_gd,
  "個人志向(-)" = liv_bd
)

#---------------- 
# 関数の読み込み
#----------------

source("adf_kpss_test.R")


#---------------- 
# 分析結果の表化
#----------------

df_results <- map_dfr(
  names(vars),
  ~ adf_kpss_test(vars[[.x]]) %>%
    mutate(Variable = .x)   
) %>%
  select(Variable, everything())  

#----------- 
# 表の出力
#----------- 

df_results %>%
  kbl(
    format = "html",
    digits = 4,                     
    caption = "ADF & KPSS 検定結果一覧"
  ) %>%
  kable_styling(full_width = FALSE)

#---------------------------- 
# 関数の読み込み(plot2_3.R)
#---------------------------- 

source(plot2_3.R)

#------------------
# データの読み込み
#------------------

dat2_3 <- read.csv("approve_economy.csv", fileEncoding = "SJIS") %>%
  mutate(
    sociotropic_gd = q5c1 + q5c2,
    sociotropic_bd = q5c4 + q5c5,
    liv_gd         = q3c1 + q3c2,
    liv_bd         = q3c4 + q3c5
  )

#---------------- 
# イベントの設定
#----------------

events <- tibble(
  date = as.Date(c("1973-10-01", "1989-04-01", "1990-01-01", "1997-07-01", "2008-09-01",
                   "2011-03-01", "2012-12-01")),
  label = c("第1次石油ショック(1973)", "消費税導入(1989)", "バブル経済崩壊(1990)", 
            "アジア通貨危機(1997)", "グローバル経済危機(2008)", "東日本大震災(2011)", 
            "アベノミクス導入(2012)")
)


#-------------- 
# データの整形
#--------------

dat2_3keiki <- dat2_3 %>%
  select(yearmon, sociotropic_gd, sociotropic_bd) %>%
  pivot_longer(cols = -yearmon, names_to = "経済評価", values_to = "value") %>%
  mutate(経済評価 = recode(経済評価, sociotropic_gd = "肯定", sociotropic_bd = "否定"))

dat2_3kurashi <- dat2_3 %>%
  select(yearmon, liv_gd, liv_bd) %>%
  pivot_longer(cols = -yearmon, names_to = "経済評価", values_to = "value") %>%
  mutate(経済評価 = recode(経済評価, liv_gd = "肯定", liv_bd = "否定"))

#--------------
# プロット作成
#--------------

p1 <- plot2_3(dat2_3keiki , "A:社会志向の経済評価の推移と政治経済的出来事", 45)
p2 <- plot2_3(dat2_3kurashi, "B:個人志向の経済評価の推移と政治経済的出来事", 28)

windows(30, 20)
grid.arrange(p1, p2, nrow = 2)

#---------------------------- 
# 関数の読み込み(plot2_4.R)
#---------------------------- 

source("plot2_4.R")

#------------------
# データの読み込み
#------------------

dat2_4 <- read.csv("approve_economy.csv", fileEncoding = "SJIS") %>%
  mutate(
    sociotropic_gd = q5c1 + q5c2,
    sociotropic_bd = q5c4 + q5c5,
    liv_gd         = q3c1 + q3c2,
    liv_bd         = q3c4 + q3c5
  )

#---------------------------- 
# 景気循環のセグメントを定義
#---------------------------- 

business_cycles <- data.frame(
  start = as.Date(c("1963-12-01", "1965-10-01", "1971-12-01", "1975-03-01",
                    "1977-10-01", "1983-02-01", "1986-11-01", "1993-10-01",
                    "1999-12-01", "2002-01-01", "2009-03-01", "2012-11-01")),
  end = as.Date(c("1964-10-01", "1970-07-01", "1973-11-01", "1977-01-01",
                  "1980-02-01", "1985-06-01", "1991-02-01", "1997-05-01",
                  "2000-11-01", "2008-02-01", "2012-03-01", "2020-05-01"))
)



#----------------
# プロットの作成
#----------------

p1 <- plot2_4(dat2_4, "sociotropic_gd", "sociotropic_bd", 
              "A: 社会志向の経済評価の推移と景気循環", 48, "社会志向の経済評価")

p2 <- plot2_4(dat2_4, "liv_gd", "liv_bd", 
              "B: 個人志向の経済評価の推移と景気循環", 18, "個人志向の経済評価")

windows(width = 240, height = 180)
grid.arrange(p1, p2, nrow = 2)

#------------------ 
# データの読み込み
#------------------

dat2_3 <- read_csv("approve_economy.csv", locale = locale(encoding = "SJIS")) %>%
  mutate(
    与党派_社会志向_否定 = sociotropic_bd_rul,
    野党派_社会志向_否定 = sociotropic_bd_op,
    無党派_社会志向_否定 = sociotropic_bd_indep,
    与野党派間差異 = sociotropic_bd_rul - sociotropic_bd_op,
    与無党派間差異 = sociotropic_bd_rul - sociotropic_bd_indep,
    野無党派間差異 = sociotropic_bd_op - sociotropic_bd_indep
  ) %>%
  select(与党派_社会志向_否定, 野党派_社会志向_否定, 無党派_社会志向_否定, 
         与野党派間差異, 与無党派間差異, 野無党派間差異)

#----------------
# 記述統計の抽出
#----------------

stats <- dat2_3 %>%
  describe() %>%
  select(mean, sd, min, max, skew, kurtosis) %>%
  rename(平均 = mean, 標準偏差 = sd, 最小値 = min, 最大値 = max, 歪度 = skew, 尖度 = kurtosis)

#---------- 
# 表の作成
#---------- 

stats %>%
  knitr::kable(
    format = "html",
    booktabs = TRUE, 
    caption = "党派別の社会志向の肯定的経済評価・党派性差異の記述統計",
    digits = 3
  ) %>%
  add_header_above(header = c(" " = 1, "統計量" = 6)) %>%
  kable_styling(bootstrap_options = "striped", full_width = FALSE)

#-----------------
# データの読み込み
#-----------------

dat2_4 <- read_csv("approve_economy.csv", locale = locale(encoding = "SJIS"))

#-------------------
# データリストの作成
#-------------------

dat2_4 <- list(
  dat$sociotropic_gd_rul, dat$sociotropic_gd_op, dat$sociotropic_gd_indep,
  dat$sociotropic_bd_rul, dat$sociotropic_bd_op, dat$sociotropic_bd_indep,
  dat$egotropic_gd_rul,   dat$egotropic_gd_op,   dat$egotropic_gd_indep,
  dat$egotropic_bd_rul,   dat$egotropic_bd_op,   dat$egotropic_bd_indep
)

labels <- c(
  "与党派・社会志向(+)", "野党派・社会志向(+)", "無党派・社会志向(+)",
  "与党派・社会志向(-)", "野党派・社会志向(-)", "無党派・社会志向(-)",
  "与党派・個人志向(+)", "野党派・個人志向(+)", "無党派・個人志向(+)",
  "与党派・個人志向(-)", "野党派・個人志向(-)", "無党派・個人志向(-)"
)

#----------
# FIの計算
#----------

results2_4 <- lapply(data_list2_4, function(data) {
  res <- fdGPH(na.omit(data))
  c(round(res$d, 3), round(res$sd.reg, 3))
})


#------------
# 結果の表化
#------------

results_df2_4 <- results %>%
  do.call(rbind, .) %>%
  as.data.frame() %>%
  setNames(c("d", "sd.reg")) %>%
  mutate(Variable = labels) %>%
  select(Variable, everything())

#-----------------
# データの読み込み
#-----------------

data_list2_4 <- read_csv("approve_economy.csv", locale = locale(encoding = "SJIS")) %>%
  select(
    sociotropic_gd_rul, sociotropic_gd_op, sociotropic_gd_indep,
    sociotropic_bd_rul, sociotropic_bd_op, sociotropic_bd_indep,
    egotropic_gd_rul,   egotropic_gd_op,   egotropic_gd_indep,
    egotropic_bd_rul,   egotropic_bd_op,   egotropic_bd_indep
  ) %>%
  as.list()

#----------------------
#各変数に対応するラベル
#----------------------

labels <- c(
  "与党派・社会志向(+)", "野党派・社会志向(+)", "無党派・社会志向(+)",
  "与党派・社会志向(-)", "野党派・社会志向(-)", "無党派・社会志向(-)",
  "与党派・個人志向(+)", "野党派・個人志向(+)", "無党派・個人志向(+)",
  "与党派・個人志向(-)", "野党派・個人志向(-)", "無党派・個人志向(-)"
)

#----------------
# 分析結果の表化
#----------------

df_results2_4 <- map2_dfr(data_list2_4, labels, ~{
  series <- na.omit(.x)         # NA を除去
  
  # ADF 検定
  adf_res <- adf.test(series)
  adf_stat <- round(adf_res$statistic, 3)
  adf_pval <- round(adf_res$p.value, 4)
  
  # KPSS 検定
  kpss_res <- kpss.test(series, null = "Level")
  kpss_stat <- round(kpss_res$statistic, 3)
  kpss_pval <- round(kpss_res$p.value, 4)
  
  tibble(
    Variable  = .y,         
    ADF_Stat  = adf_stat,
    ADF_pval  = adf_pval,
    KPSS_Stat = kpss_stat,
    KPSS_pval = kpss_pval
  )
})

#----------
# 表の出力
#----------

df_results2_4 %>%
  kbl(
    format = "html",
    caption = "ADF & KPSS検定の結果",
    digits = 3
  ) %>%
  kable_styling(full_width = FALSE)

#------------------ 
# 関数の読み込み
#------------------

source("plot2_5df.R")
source("plot2_5_1.R")
source("plot2_5_2.R")


#------------------ 
# データの読み込み
#------------------

dat2_5 <- read.csv("approve_economy.csv", fileEncoding = "SJIS")
attach(dat2_5)
year <- read.csv("year.csv")
attach(year)


#------------------ 
# ハイライトの設定
#------------------

temp <- data.frame(
  start = as.Date( '2009-09-01'), 
  end   = as.Date('2012-11-01'))


#------------------ 
# プロットの作成
#------------------

#---- 社会志向 (+)：原系列
df_parec1 <- plot2_5df(year$yearmon, cbind(sociotropic_gd_rul, sociotropic_gd_op), c("年", "与党派", "野党派"))
g1 <- plot2_5_1(df_parec1, "A:党派別の社会志向の経済評価(+)","年", "value", "党派別経済効果")

#---- 社会志向 (+): 差異
socposdiff = sociotropic_gd_rul-sociotropic_gd_op
df_parec2 <- create_dataframe(year$yearmon, socposdiff, c("年", "党派性差異"))
g2 <- plot2_5_2(df_parec2, "B:党派別の社会志向の党派性差異(+)", "党派性差異", "value")


grid.arrange(g1, g2, ncol = 1)

#------------------ 
# プロットの作成
#------------------

# 社会志向 (-)：2系列併記
df_parec3 <- plot2_5df(year$yearmon, cbind(sociotropic_bd_rul, sociotropic_bd_op), c("年", "与党派", "野党派"))
g3 <- plot2_5_1(df_parec3, "A:党派別の社会志向の経済評価(-)","年", "value", "党派別経済効果")

# 社会志向 (-): 差異
socnegdiff = sociotropic_bd_rul-sociotropic_bd_op
df_parec4 <- plot2_5df(year$yearmon, socnegdiff, c("年", "党派性差異"))
g4 <- plot2_5_2(df_parec4, "B:党派別の社会志向の党派性差異(-)", "党派性差異", "value")


grid.arrange(g3, g4, ncol = 1)

#------------------------
# データの読み込みと整形
#------------------------


dat3_1 <- read_csv("sentiment_data_includingNEWS.csv", locale = locale(encoding = "SJIS")) %>%
  na.omit() %>%
  group_by(newspaper) %>%
  summarise(across(c(positive, negative), mean, na.rm = TRUE)) %>%
  ungroup() %>%
  pivot_longer(cols = c(positive, negative), names_to = "sentiment", values_to = "value") %>%
  mutate(
    sentiment = recode(sentiment, "positive" = "肯定", "negative" = "否定"),
    newspaper = recode(newspaper, "asahi" = "朝日", "nikkei" = "日経", "yomiuri" = "読売"),
    newspaper = factor(newspaper, levels = c("朝日", "日経", "読売")),
    sentiment = factor(sentiment, levels = c("肯定", "否定"))
  )

#-----------
# ANOVA検定
#-----------

anova_result <- dat3_1 %>% aov(value ~ newspaper * sentiment, data = .)
summary(anova_result)

#----------------
# Tukey HSD 検定
#----------------

tukey_result <- TukeyHSD(anova_result)
print(tukey_result)


#----------------
# プロットの作成
#----------------

windows(10, 8)
dodge <- position_dodge(width = 0.8)

ggplot(dat3_1, aes(x = newspaper, y = value, fill = sentiment, group = sentiment)) +
    geom_bar(stat = "identity", position = dodge, width = 0.7) +  
    geom_label(aes(label = round(value, 2)),
             position = dodge, vjust = -0.2, size = 3, fill = "white", color = "black", label.size = 0.25) +
  xlab("新聞社") +
  ylab("肯定的／否定的情報スコア") +
  geom_signif(comparisons = list(c("朝日", "日経"), c("日経", "読売"), c("朝日", "読売")),
              map_signif_level = TRUE, y_position = c(0.34, 0.34, 0.36),
              annotation = c("***.", "***", "*"), tip_length = 0) +
  geom_signif(
    y_position = c(0.25, 0.28, 0.32),
    xmin = c(0.8, 1.8, 2.8),
    xmax = c(1.2, 2.2, 3.2),
    annotation = c("NS.", "***", "***"),
    tip_length = 0
  ) +
  theme_bw() +
  scale_fill_grey() +
  labs(fill = "情報")