20250930_報告
horii miu
2025-09-25
library("tidyverse")
library("readr")
library("dplyr")
library("ggrepel")
#install.packages("tinytex")
#tinytex::install_tinytex()
library("tinytex")
#tinytex::tlmgr_install(c("collection-langjapanese", "ipaex", "zxjatype", "pxjahyper"))
library(knitr)データの読み込み
"①"## [1] "①"df_GDP <- read.csv(here::here("data\\cleaned\\20250920_GDP_forecast.csv"))
df_office <- read.csv(here::here("data\\cleaned\\20250922_office_forecast.csv"))
"②"## [1] "②"df_actualGDP_jpn_tokyo <- read.csv(here::here("data\\cleaned\\20250925_actualGDP_jpn_tokyo.csv"))
#print(df_actualGDP_jpn_tokyo)
df_nominalGDP_jpn_tokyo <- read.csv(here::here("data\\cleaned\\20250925_nominalGDP_jpn_tokyo.csv"))
#print(df_nominalGDP_jpn_tokyo)
df_GDP_pop <- read.csv(here::here("data\\cleaned\\20250929_GDP_pop.csv"))
#print(df_GDP_pop)データの加工
①
df_GDP <- df_GDP |>
mutate(GDP = parse_number(as.character(GDP))) |>
mutate(log_GDP = log(GDP), log_forecast = log(forecast), log_after_forecast = log(after_forecast)) |>
mutate(growth_log_GDP = log_GDP - lag(log_GDP), growth_log_forecast = log_forecast -lag(log_forecast), growth_log_after_forecast = log_after_forecast - lag(log_after_forecast))
df_office <- df_office |>
mutate(office = parse_number(as.character(office))) |>
mutate(forecast = parse_number(as.character(forecast))) |>
mutate(log_office = log(office), log_forecast = log(forecast), log_after_forecast = log(after_forecast))②
df_actualGDP_jpn_tokyo <- df_actualGDP_jpn_tokyo |>
mutate(jpn = parse_number(as.character(jpn))) |>
mutate(tokyo = parse_number(as.character(tokyo))) |>
mutate(tokyo = tokyo/1000) |>
mutate(log_jpn = log(jpn), log_tokyo = log(tokyo)) |>
mutate(growth_log_jpn = log_jpn - lag(log_jpn), growth_log_tokyo = log_tokyo -lag(log_tokyo)) |>
group_by(period = if_else(year <= 1992, "1980-1992", "1993-2022"))
df_nominalGDP_jpn_tokyo <- df_nominalGDP_jpn_tokyo |>
mutate(jpn = parse_number(as.character(jpn))) |>
mutate(tokyo = parse_number(as.character(tokyo))) |>
mutate(tokyo = tokyo/1000) |>
mutate(log_jpn = log(jpn), log_tokyo = log(tokyo)) |>
mutate(growth_log_jpn = log_jpn - lag(log_jpn), growth_log_tokyo = log_tokyo -lag(log_tokyo)) |>
group_by(period = if_else(year <= 1992, "1980-1992", "1993-2022"))
df_GDP_pop <- df_GDP_pop|>
mutate(log_tokyo_GDP_pop = log(tokyo_GDP_pop), log_jpn_GDP_pop = log(jpn_GDP_pop), log_tokyo_GDP_working_age_pop = log(tokyo_GDP_working_age_pop), log_jpn_GDP_working_age_pop = log(jpn_GDP_working_age_pop)) |>
mutate(growth_log_tokyo_GDP_pop = log_tokyo_GDP_pop - lag(log_tokyo_GDP_pop), growth_log_jpn_GDP_pop = log_jpn_GDP_pop -lag(log_jpn_GDP_pop), growth_log_tokyo_GDP_working_age_pop = log_tokyo_GDP_working_age_pop -lag(log_tokyo_GDP_working_age_pop), growth_log_jpn_GDP_working_age_pop = log_jpn_GDP_working_age_pop -lag(log_jpn_GDP_working_age_pop)) |>
group_by(period = if_else(year <= 1992, "1980-1992", "1993-2022"))グラフ
①
df_GDP |>
ggplot() +
geom_line(aes(x = year, y = log_GDP, color = "GDP")) +
geom_line(aes(x = year, y = log_forecast, color = "GDP forecast")) +
geom_line(aes(x = year, y = log_after_forecast, color = "GDP forecast"), linetype = "dashed") +
scale_x_continuous(breaks = seq(1980, 2023, 5)) +
scale_y_continuous(breaks = seq(0, 15, 0.5)) +
labs(x = "年", y = "実質GDP(対数化)", title = "GDPと予測") +
scale_color_manual(values = c("black", "red")) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5), legend.title = element_blank())
df_office |>
ggplot() +
geom_line(aes(x = year, y = log_office, color = "office")) +
geom_line(aes(x = year, y = log_forecast, color = "office forecast")) +
geom_line(aes(x = year, y = log_after_forecast, color = "office forecast"), linetype = "dashed") +
scale_x_continuous(breaks = seq(1980, 2023, 5)) +
scale_y_continuous(breaks = seq(0, 15, 0.5)) +
labs(x = "年", y = "office(対数化)", title = "オフィス床面積と予測") +
scale_color_manual(values = c("black", "red")) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5), legend.title = element_blank())
②
日本と東京のGDP比較
df_actualGDP_jpn_tokyo |>
ggplot() +
geom_line(aes(x = year, y = jpn, color ="jpn")) +
geom_line(aes(x = year, y = tokyo, color = "tokyo")) +
scale_x_continuous(breaks = seq(1980, 2022, 5)) +
scale_y_continuous(breaks = seq(0, 600000, 100000)) +
labs(x = "年", y = "実質GDP(100万円)", title = "日本と東京都の実質GDP") +
theme_minimal() +
theme(legend.title = element_blank())
df_nominalGDP_jpn_tokyo |>
ggplot() +
geom_line(aes(x = year, y = jpn, color ="jpn")) +
geom_line(aes(x = year, y = tokyo, color = "tokyo")) +
scale_x_continuous(breaks = seq(1980, 2022, 5)) +
scale_y_continuous(breaks = seq(0, 600000, 100000)) +
labs(x = "年", y = "実質GDP(100万円)", title = "日本と東京都の名目GDP") +
theme_minimal() +
theme(legend.title = element_blank())
df_GDP_pop |>
ggplot() +
geom_line(aes(x = year, y = jpn_GDP_pop, color ="jpn_GDP_pop")) +
geom_line(aes(x = year, y = tokyo_GDP_pop, color = "tokyo_GDP_pop")) +
scale_x_continuous(breaks = seq(1982, 2020, 3)) +
scale_y_continuous(breaks = seq(0, 10, 1)) +
labs(x = "年", y = "実質GDP(10億円)", title = "実質GDP/人口") +
theme_minimal() +
theme(legend.title = element_blank())
df_GDP_pop |>
ggplot() +
geom_line(aes(x = year, y = jpn_GDP_working_age_pop, color ="jpn_GDP_working_age_pop")) +
geom_line(aes(x = year, y = tokyo_GDP_working_age_pop, color = "tokyo_GDP_working_age_pop")) +
scale_x_continuous(breaks = seq(1982, 2020, 3)) +
scale_y_continuous(breaks = seq(0, 20, 1)) +
labs(x = "年", y = "実質GDP(10億円)", title = "実質GDP/生産年齢人口") +
theme_minimal() +
theme(legend.title = element_blank())
成長率 散布図
df_nominalGDP_jpn_tokyo |>
ggplot() +
geom_point(aes(x = growth_log_jpn, y = growth_log_tokyo, color = period)) +
geom_abline(intercept = 0, slope = 1) +
scale_x_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
scale_y_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
labs(x = "日本の成長率", y = "東京の成長率", title = "日本と東京の名目GDP成長率", color = "色分け") +
theme_bw()
df_actualGDP_jpn_tokyo |>
ggplot() +
geom_point(aes(x = growth_log_jpn, y = growth_log_tokyo, color = period)) +
geom_abline(intercept = 0, slope = 1) +
scale_x_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
scale_y_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
labs(x = "日本の成長率", y = "東京の成長率", title = "日本と東京の実質GDP成長率", color = "色分け") +
theme_bw()
df_GDP_pop |>
ggplot() +
geom_point(aes(x = growth_log_jpn_GDP_pop, y = growth_log_tokyo_GDP_pop, color = period)) +
geom_abline(intercept = 0, slope = 1) +
geom_text(aes(x = growth_log_jpn_GDP_pop, y = growth_log_tokyo_GDP_pop, color = period, label = year), size = 3) +
scale_x_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
scale_y_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
labs(x = "日本の成長率", y = "東京の成長率", title = "日本と東京の一人当たり実質GDP成長率", color = "色分け") +
theme_bw()
df_GDP_pop |>
ggplot() +
geom_point(aes(x = growth_log_jpn_GDP_working_age_pop, y = growth_log_tokyo_GDP_working_age_pop, color = period)) +
geom_text(aes(x = growth_log_jpn_GDP_working_age_pop, y = growth_log_tokyo_GDP_working_age_pop, label = year, color = period), size = 3) +
geom_abline(intercept = 0, slope = 1) +
scale_x_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
scale_y_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
labs(x = "日本の成長率", y = "東京の成長率", title = "日本と東京の生産年齢人口一人当たり実質GDP成長率", color = "色分け") +
theme_bw()
相関係数
cor_table <- tibble(
"データ" = c("名目GDP成長率", "実質GDP成長率", "一人当たり実質GDP成長率", "生産年齢人口一人当たり実質GDP成長率"),
"相関係数" = c(cor(df_nominalGDP_jpn_tokyo$growth_log_jpn, df_nominalGDP_jpn_tokyo$growth_log_tokyo, use = "complete.obs"), cor(df_actualGDP_jpn_tokyo$growth_log_jpn, df_actualGDP_jpn_tokyo$growth_log_tokyo, use = "complete.obs"), cor(df_GDP_pop$growth_log_jpn_GDP_pop, df_GDP_pop$growth_log_tokyo_GDP_pop, use = "complete.obs"), cor(df_GDP_pop$growth_log_jpn_GDP_working_age_pop, df_GDP_pop$growth_log_tokyo_GDP_working_age_pop, use = "complete.obs"))
)
print(cor_table)## # A tibble: 4 × 2
## データ 相関係数
## <chr> <dbl>
## 1 名目GDP成長率 0.895
## 2 実質GDP成長率 0.864
## 3 一人当たり実質GDP成長率 0.476
## 4 生産年齢人口一人当たり実質GDP成長率 0.394