データの加工
df_office <- df_office |>
mutate(
office = parse_number(as.character(office)),
forecast = parse_number(as.character(forecast)),
after_forecast = parse_number(as.character(after_forecast))
) |>
mutate(log_office = log(office), log_forecast = log(forecast), log_after_forecast = log(after_forecast)) |>
mutate(growth_log_office = log_office - lag(log_office), growth_log_forecast = log_forecast - lag(log_forecast), growth_log_after_forecast = log_after_forecast - lag(log_after_forecast)) |>
mutate(growth_log_combined = coalesce(growth_log_forecast, growth_log_after_forecast))
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_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_pop <- df_pop |>
mutate(pop_jpn = parse_number(as.character(pop_jpn)), wapop_jpn = parse_number(as.character(wapop_jpn)), pop_tokyo = parse_number(as.character(pop_tokyo)), wapop_tokyo = parse_number(as.character(wapop_tokyo))) |>
mutate(pop_jpn = pop_jpn*1000, wapop_jpn = wapop_jpn*1000) |>
mutate(log_growth_pop_jpn = log(pop_jpn/lag(pop_jpn, order_by = year, n = 1)), log_growth_pop_tokyo = log(pop_tokyo/lag(pop_tokyo))) |>
mutate(log_pop_jpn = log(pop_jpn), log_wapop_jpn = log(wapop_jpn), log_pop_tokyo = log(pop_tokyo), log_wapop_tokyo = log(wapop_tokyo)) |>
mutate(growth_log_pop_jpn = log_pop_jpn - lag(log_pop_jpn), growth_log_wapop_jpn = log_wapop_jpn - lag(log_wapop_jpn), growth_log_pop_tokyo = log_pop_tokyo - lag(log_pop_tokyo), growth_log_wapop_tokyo = log_wapop_tokyo - lag(log_wapop_tokyo))
グラフ
df_growth_forecast |>
ggplot() +
geom_line(aes(x = year, y = actualGDP_growth_rate, color = "actualGDP_growth_rate")) +
geom_line(aes(x = year, y = gov, color = "gov")) +
geom_line(aes(x = year, y = min, color = "min")) +
geom_line(aes(x = year, y = 4, color = "4%forecast")) +
scale_x_continuous(breaks = seq(1980, 2023, 5)) +
scale_y_continuous(breaks = seq(0, 5, 1)) +
labs(x = "年", y = "実質GDP成長率", title = "実質GDP成長率と予測") +
scale_color_manual(values = c( "red", "black", "blue", "green")) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5), legend.title = element_blank())
df_office |>
ggplot() +
geom_line(aes(x = year, y = growth_log_office, color = "office面積")) +
geom_line(aes(x = year, y = growth_log_forecast, color = "forecast")) +
geom_line(aes(x = year, y = growth_log_after_forecast, color = "forecast"), linetype = "dashed") +
scale_x_continuous(breaks = seq(1980, 2023, 5)) +
scale_y_continuous(breaks = seq(0, 5, 1)) +
labs(x = "年", y = "実質GDP成長率", title = "office面積成長率と予測") +
scale_color_manual(values = c("black", "red")) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5), legend.title = element_blank())
df_actualGDP_jpn_tokyo |>
ggplot() +
geom_line(aes(x = year, y = growth_log_jpn, color ="jpn")) +
geom_line(aes(x = year, y = growth_log_tokyo, color = "tokyo")) +
geom_line(aes(x = year, y = 0)) +
scale_x_continuous(breaks = seq(1980, 2022, 5)) +
scale_y_continuous(breaks = seq(-0.1, 0.1, 0.01)) +
labs(x = "年", y = "実質GDP成長率", title = "日本と東京都の実質GDP成長率") +
theme_minimal() +
theme(legend.title = element_blank())
df_pop |>
ggplot() +
geom_line(aes(x = year, y = log_growth_pop_jpn, color ="jpn")) +
geom_line(aes(x = year, y = log_growth_pop_tokyo, color = "tokyo")) +
geom_line(aes(x = year, y = 0)) +
scale_x_continuous(breaks = seq(1980, 2022, 5)) +
scale_y_continuous(limits = c(-0.015, 0.015, 0.01)) +
labs(x = "年", y = "実質GDP成長率", title = "日本と東京都の人口成長率") +
theme_minimal() +
theme(legend.title = element_blank())
df_pop |>
ggplot() +
geom_line(aes(x = year, y = growth_log_wapop_jpn, color ="jpn")) +
geom_line(aes(x = year, y = growth_log_wapop_tokyo, color = "tokyo")) +
geom_line(aes(x = year, y = 0)) +
ylim(-0.15, 0.15) +
scale_x_continuous(breaks = seq(1980, 2022, 5)) +
scale_y_continuous(breaks = seq(-0.15, 0.15, 0.01)) +
labs(x = "年", y = "実質GDP成長率", title = "日本と東京都生産年齢人口成長率") +
theme_minimal() +
theme(legend.title = element_blank())
df_pop |>
ggplot() +
geom_line(aes(x = year, y = pop_jpn, color ="jpn")) +
geom_line(aes(x = year, y = pop_tokyo, color = "tokyo")) +
geom_line(aes(x = year, y = 0)) +
scale_x_continuous(breaks = seq(1980, 2022, 5)) +
scale_y_continuous(breaks = seq(0, 150000000, 10000000)) +
labs(x = "年", y = "人口(人)", title = "日本と東京都の人口") +
theme_minimal() +
theme(legend.title = element_blank())
CAGR <- function(start, end, year){
(end/start)^(1/year) - 1
}
actualGDP = CAGR(df_GDP$GDP[df_GDP$year == 1983], df_GDP$GDP[df_GDP$year == 2023], 2023-1990)
actualGDP_s = CAGR(df_GDP$GDP[df_GDP$year == 1983], df_GDP$GDP[df_GDP$year == 1990], 1990-1983)
office_l = CAGR(df_office$office[df_office$year == 1983], df_office$office[df_office$year == 2023], 2023-1983)
office_s = CAGR(df_office$office[df_office$year == 1983], df_office$office[df_office$year == 1990], 1990-1983)
pop_l = CAGR(df_pop$pop_jpn[df_pop$pop_jpn == 1980], df_office$office[df_office$year == 2020], 2020-1980)
pop_s = CAGR(df_pop$pop_jpn[df_pop$pop_jpn == 1980], df_office$office[df_office$year == 1990], 1990-1980)
df_growth_rate <- data.frame("期間" = c("1983-1990", "full sample"), GDP = c(actualGDP, actualGDP_s))
df_growth_rate <- df_growth_rate |>
mutate(
"office" = c(office_l, office_s)
) #|>
#mutate(
#"人口" = c(pop_l, pop_s))
print(df_growth_rate)
## 期間 GDP office
## 1 1983-1990 0.01841021 0.02432754
## 2 full sample 0.04844745 0.04771538