source | dataset | .html | .RData |
---|---|---|---|
2024-06-20 | 2024-05-06 |
Net debt (% of GDP)
Data - IMF - FM
Info
Data on public debt
source | dataset | .html | .RData |
---|---|---|---|
2024-10-23 | 2024-10-08 | ||
2024-10-23 | 2024-10-08 | ||
2024-10-24 | 2024-10-24 | ||
2024-10-24 | 2024-10-24 | ||
2021-08-22 | 2021-03-01 | ||
2024-06-20 | 2020-03-13 | ||
2024-10-29 | 2024-10-29 | ||
2024-10-29 | 2024-10-29 | ||
2024-06-20 | 2024-05-06 | ||
2024-06-20 | NA | ||
2024-09-15 | 2024-04-15 | ||
2023-06-18 | 2024-09-18 | ||
2023-06-18 | 2024-09-18 | ||
2023-06-18 | 2024-09-18 | ||
2024-09-18 | 2024-09-18 |
LAST_COMPILE
LAST_COMPILE |
---|
2024-10-29 |
Last
TIME_PERIOD | FREQ | Nobs |
---|---|---|
2029 | A | 101 |
FREQ
Code
%>%
GGXWDN_G01_GDP_PT left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
FREQ | Freq | Nobs |
---|---|---|
A | Annual | 3317 |
REF_AREA
Code
%>%
GGXWDN_G01_GDP_PT left_join(REF_AREA, by = "REF_AREA") %>%
group_by(REF_AREA, Ref_area) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
TIME_PERIOD
Code
%>%
GGXWDN_G01_GDP_PT group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
print_table_conditional()
US, Zone Euro
All
Code
<- GGXWDN_G01_GDP_PT %>%
plot filter(REF_AREA %in% c("US", "U2")) %>%
%>%
year_to_date2 filter(date >= as.Date("1999-01-01")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", "United States")) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(color = ifelse(REF_AREA == "US", color2, color)) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
rename(Counterpart_area = Ref_area) %>%
#filter(date <= as.Date("2021-01-01")) %>%
ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
theme_minimal() + xlab("") + ylab("Dette publique nette, % du PIB") +
scale_color_identity() + add_2flags +
scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
labels = percent_format(accuracy = 1))
save(plot, file = "GGXWDN_G01_GDP_PT_files/figure-html/US-U2-1.RData")
plot
Net Government Debt (% of GDP)
Code
%>%
GGXWDN_G01_GDP_PT filter(TIME_PERIOD == "2018") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, OBS_VALUE) %>%
arrange(-OBS_VALUE) %>%
%>%
na.omit mutate_at(vars(3), funs(paste0(round(as.numeric(.), 1), " %"))) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
France, United States, Germany, Japan
Tous
Code
%>%
GGXWDN_G01_GDP_PT filter(REF_AREA %in% c("FR", "US", "DE", "JP")) %>%
%>%
year_to_date2 left_join(REF_AREA, by = "REF_AREA") %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
rename(Counterpart_area = Ref_area) %>%
#filter(date >= as.Date("2015-01-01")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Public debt (years of GDP)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
scale_color_identity() + add_4flags +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 260, 20),
labels = scales::dollar_format(acc = .1, pre = "", su = " y")) +
scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 5), "-01-01")),
labels = date_format("%Y"))
2000-
Code
%>%
GGXWDN_G01_GDP_PT filter(REF_AREA %in% c("FR", "US", "DE", "JP")) %>%
%>%
year_to_date2 left_join(REF_AREA, by = "REF_AREA") %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
rename(Counterpart_area = Ref_area) %>%
filter(date >= as.Date("2000-01-01")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Net public debt (years of GDP)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
scale_color_identity() + add_4flags +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 260, 20),
labels = scales::dollar_format(acc = .1, pre = "", su = " y")) +
scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 2), "-01-01")),
labels = date_format("%Y"))
France, United States, Germany, Japan, Italy, Netherlands
Tous
Code
%>%
GGXWDN_G01_GDP_PT filter(REF_AREA %in% c("FR", "US", "DE", "JP", "IT", "NL")) %>%
%>%
year_to_date2 left_join(REF_AREA, by = "REF_AREA") %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
rename(Counterpart_area = Ref_area) %>%
#filter(date >= as.Date("2015-01-01")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Public debt (years of GDP)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
scale_color_identity() + add_6flags +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 260, 20),
labels = scales::dollar_format(acc = .1, pre = "", su = " y")) +
scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 5), "-01-01")),
labels = date_format("%Y"))
2000-
Code
%>%
GGXWDN_G01_GDP_PT filter(REF_AREA %in% c("FR", "US", "DE", "JP", "IT", "NL")) %>%
%>%
year_to_date2 left_join(REF_AREA, by = "REF_AREA") %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
rename(Counterpart_area = Ref_area) %>%
filter(date >= as.Date("2000-01-01")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Net public debt (years of GDP)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
scale_color_identity() + add_6flags +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 260, 20),
labels = scales::dollar_format(acc = .1, pre = "", su = " y")) +
scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 2), "-01-01")),
labels = date_format("%Y"))