Last observation: 2031 (N = 189) · Q2 2026 (N = 48)
First observation: 1980 (N = 145) · Q1 1990 (N = 10)
Last data update: 20 sept. 2026, 23:01
Last compile: 20 sept. 2026, 23:18
Main Datasets
Javascript
Code
INDICATOR |>
filter(INDICATOR %in% gsub(".qmd$", "", list.files(pattern = "\\.qmd$"))) |>
mutate(html = paste0('<a target=_blank href=', INDICATOR, '.html > html </a>')) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Flat
Code
INDICATOR |>
filter(INDICATOR %in% gsub(".qmd$", "", list.files(pattern = "\\.qmd$"))) |>
mutate(html = paste0("[html](", INDICATOR, '.html)')) %>%
{if (is_html_output()) print_table(.) else .}| INDICATOR | Indicator | html |
|---|---|---|
| BGS_BP6_USD | Current Account, Goods and Services, Net, US Dollars | [html](BGS_BP6_USD.html) |
| BCA_BP6_USD | Current Account, Total, Net, US Dollars | [html](BCA_BP6_USD.html) |
| BOP_BP6_USD | Net Errors and Omissions, US Dollars | [html](BOP_BP6_USD.html) |
| IFR_BP6_USD | Net International Investment Position (With Fund Record), US Dollars | [html](IFR_BP6_USD.html) |
Other Datasets
Code
INDICATOR %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Public Debt VS IIPs (% of GDP)
2011
Code
GGXWDG_GDP |>
select(TIME_PERIOD, REF_AREA, GGXWDG_GDP = OBS_VALUE) |>
left_join(IFR_BP6_USD, by = c("TIME_PERIOD", "REF_AREA")) |>
year_to_date2() |>
rename(iso2c = REF_AREA) |>
left_join(NY.GDP.MKTP.CD |>
mutate(date = as.Date(paste0(year, "-01-01"))) |>
select(date, iso2c, NY.GDP.MKTP.CD = value),
by = c("date", "iso2c")) |>
filter(date == as.Date("2011-01-01")) |>
mutate(IFR_BP6_USD_GDP = -(10^(UNIT_MULT))*OBS_VALUE / NY.GDP.MKTP.CD) |>
left_join(iso2c, by = "iso2c") |>
ggplot() + theme_minimal() +
geom_point(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP)) +
xlab("2011 Debt/GDP (%)") + ylab("2011 External Debt/GDP (%)") +
scale_x_continuous(breaks = 0.01*seq(-100, 600, 20),
labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(breaks = 0.01*seq(-600, 600, 50),
labels = scales::percent_format(accuracy = 1)) +
geom_text_repel(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP, label = Iso2c)) +
stat_smooth(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP),
linetype = 2, method = "lm", color = viridis(4)[2])
Code
GGXWDG_GDP |>
select(TIME_PERIOD, REF_AREA, GGXWDG_GDP = OBS_VALUE) |>
left_join(IFR_BP6_USD, by = c("TIME_PERIOD", "REF_AREA")) |>
year_to_date2() |>
rename(iso2c = REF_AREA) |>
left_join(NY.GDP.MKTP.CD |>
mutate(date = as.Date(paste0(year, "-01-01"))) |>
select(date, iso2c, NY.GDP.MKTP.CD = value),
by = c("date", "iso2c")) |>
filter(date == as.Date("2011-01-01")) |>
mutate(IFR_BP6_USD_GDP = -(10^(UNIT_MULT))*OBS_VALUE / NY.GDP.MKTP.CD) |>
left_join(iso2c, by = "iso2c") %>%
lm(IFR_BP6_USD_GDP ~ GGXWDG_GDP, data = .) |>
summary()#
# Call:
# lm(formula = IFR_BP6_USD_GDP ~ GGXWDG_GDP, data = .)
#
# Residuals:
# Min 1Q Median 3Q Max
# -3.4898 -0.1825 0.0793 0.3816 4.4664
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -0.110479 0.120077 -0.920 0.35912
# GGXWDG_GDP 0.005695 0.002138 2.663 0.00864 **
# ---
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#
# Residual standard error: 0.8093 on 140 degrees of freedom
# (45 observations effacées parce que manquantes)
# Multiple R-squared: 0.04822, Adjusted R-squared: 0.04142
# F-statistic: 7.093 on 1 and 140 DF, p-value: 0.0086442011 Europe
Code
GGXWDG_GDP |>
select(TIME_PERIOD, REF_AREA, GGXWDG_GDP = OBS_VALUE) |>
left_join(IFR_BP6_USD, by = c("TIME_PERIOD", "REF_AREA")) |>
year_to_date2() |>
rename(iso2c = REF_AREA) |>
left_join(NY.GDP.MKTP.CD |>
mutate(date = as.Date(paste0(year, "-01-01"))) |>
select(date, iso2c, NY.GDP.MKTP.CD = value),
by = c("date", "iso2c")) |>
filter(date == as.Date("2011-01-01"),
iso2c %in% c("FR", "DE", "IT", "PT", "ES", "GR", "NL", "IE",
"AT", "BE", "DK", "SE", "FI", "GB")) |>
mutate(IFR_BP6_USD_GDP = -(10^6)*OBS_VALUE / NY.GDP.MKTP.CD) |>
left_join(iso2c, by = "iso2c") |>
ggplot() + theme_minimal() +
geom_point(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP)) +
xlab("2011 Debt/GDP (%)") + ylab("2011 External Debt/GDP (%)") +
scale_x_continuous(breaks = 0.01*seq(-100, 600, 20),
labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(breaks = 0.01*seq(-600, 600, 50),
labels = scales::percent_format(accuracy = 1)) +
geom_text_repel(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP, label = Iso2c)) +
stat_smooth(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP),
linetype = 2, method = "lm", color = viridis(4)[2])
Without Names
Code
GGXWDG_GDP |>
select(TIME_PERIOD, REF_AREA, GGXWDG_GDP = OBS_VALUE) |>
left_join(IFR_BP6_USD, by = c("TIME_PERIOD", "REF_AREA")) |>
year_to_date2() |>
rename(iso2c = REF_AREA) |>
left_join(NY.GDP.MKTP.CD |>
mutate(date = as.Date(paste0(year, "-01-01"))) |>
select(date, iso2c, NY.GDP.MKTP.CD = value),
by = c("date", "iso2c")) |>
filter(date == as.Date("2011-01-01"),
iso2c %in% c("FR", "DE", "IT", "PT", "ES", "GR", "NL", "IE",
"AT", "BE", "DK", "SE", "FI", "GB")) |>
mutate(IFR_BP6_USD_GDP = -(10^6)*OBS_VALUE / NY.GDP.MKTP.CD) |>
left_join(iso2c, by = "iso2c") |>
ggplot() + theme_minimal() +
geom_point(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP)) +
xlab("Government debt (%)") + ylab("External Debt (%)") +
scale_x_continuous(breaks = 0.01*seq(-100, 600, 20),
labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(breaks = 0.01*seq(-600, 600, 50),
labels = scales::percent_format(accuracy = 1)) +
stat_smooth(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP),
linetype = 2, method = "lm", color = viridis(4)[2])
With Names
Code
GGXWDG_GDP |>
select(TIME_PERIOD, REF_AREA, GGXWDG_GDP = OBS_VALUE) |>
left_join(IFR_BP6_USD, by = c("TIME_PERIOD", "REF_AREA")) |>
year_to_date2() |>
rename(iso2c = REF_AREA) |>
left_join(NY.GDP.MKTP.CD |>
mutate(date = as.Date(paste0(year, "-01-01"))) |>
select(date, iso2c, NY.GDP.MKTP.CD = value),
by = c("date", "iso2c")) |>
filter(date == as.Date("2011-01-01"),
iso2c %in% c("FR", "DE", "IT", "PT", "ES", "GR", "NL", "IE",
"AT", "BE", "DK", "SE", "FI", "GB")) |>
mutate(IFR_BP6_USD_GDP = -(10^6)*OBS_VALUE / NY.GDP.MKTP.CD) |>
left_join(iso2c, by = "iso2c") |>
ggplot() + theme_minimal() +
geom_point(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP)) +
xlab("Government debt (%)") + ylab("External Debt (%)") +
scale_x_continuous(breaks = 0.01*seq(-100, 600, 20),
labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(breaks = 0.01*seq(-600, 600, 50),
labels = scales::percent_format(accuracy = 1)) +
geom_text_repel(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP, label = Iso2c)) +
stat_smooth(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP),
linetype = 2, method = "lm", color = viridis(4)[2])
With Codes
Code
GGXWDG_GDP |>
select(TIME_PERIOD, REF_AREA, GGXWDG_GDP = OBS_VALUE) |>
left_join(IFR_BP6_USD, by = c("TIME_PERIOD", "REF_AREA")) |>
year_to_date2() |>
rename(iso2c = REF_AREA) |>
left_join(NY.GDP.MKTP.CD |>
mutate(date = as.Date(paste0(year, "-01-01"))) |>
select(date, iso2c, NY.GDP.MKTP.CD = value),
by = c("date", "iso2c")) |>
filter(date == as.Date("2011-01-01"),
iso2c %in% c("FR", "DE", "IT", "PT", "ES", "GR", "NL", "IE",
"AT", "BE", "DK", "SE", "FI", "GB")) |>
mutate(IFR_BP6_USD_GDP = -(10^6)*OBS_VALUE / NY.GDP.MKTP.CD) |>
left_join(iso2c, by = "iso2c") |>
ggplot() + theme_minimal() +
geom_point(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP)) +
xlab("Government debt (%)") + ylab("External Debt (%)") +
scale_x_continuous(breaks = 0.01*seq(-100, 600, 20),
labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(breaks = 0.01*seq(-600, 600, 50),
labels = scales::percent_format(accuracy = 1)) +
geom_text_repel(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP, label = iso2c)) +
stat_smooth(aes(x = GGXWDG_GDP/100, y = IFR_BP6_USD_GDP),
linetype = 2, method = "lm", color = viridis(4)[2])
Balance of Goods and Services
United States, United Kingdom, France
Annual
Code
BG_BP6_USD |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA %in% c("US", "FR", "GB")) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) |>
filter(n() == 2) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(REF_AREA, by = "REF_AREA") |>
year_to_date2() |>
add_flag_color("Ref_area") |>
ggplot() + scale_color_identity() + add_flags + theme_minimal() + xlab("") + ylab("% of GDP") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1))
Goods, Services
Nobs
Code
BMS_BP6_USD |>
bind_rows(BXS_BP6_USD) |>
bind_rows(BXG_BP6_USD) |>
bind_rows(BMG_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
TIME_PERIOD == "2018") |>
mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, Goods and Services, ", "", Indicator),
Indicator = gsub(", US Dollars", "", Indicator)) |>
left_join(REF_AREA, by = "REF_AREA") |>
group_by(REF_AREA, Ref_area) |>
mutate(OBS_VALUE = round(100*OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"], 2)) |>
filter(!(INDICATOR == "NGDP_USD")) |>
select(-INDICATOR, -TIME_PERIOD) |>
spread(Indicator, OBS_VALUE) |>
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 .}Italy
Code
BMS_BP6_USD |>
bind_rows(BXS_BP6_USD) |>
bind_rows(BXG_BP6_USD) |>
bind_rows(BMG_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "IT") |>
mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.23, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
labels = percent_format(accuracy = 1),
limits = c(0, 0.4))
China
Code
BMS_BP6_USD |>
bind_rows(BXS_BP6_USD) |>
bind_rows(BXG_BP6_USD) |>
bind_rows(BMG_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "CN") |>
mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.25, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1),
limits = c(0, 0.06))
United States
Code
BMS_BP6_USD |>
bind_rows(BXS_BP6_USD) |>
bind_rows(BXG_BP6_USD) |>
bind_rows(BMG_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "US") |>
mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.25, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1))
Germany
Code
BMS_BP6_USD |>
bind_rows(BXS_BP6_USD) |>
bind_rows(BXG_BP6_USD) |>
bind_rows(BMG_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "DE") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.23, 0.83)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
labels = percent_format(accuracy = 1))
Current Account Decomposition (% of GDP)
United States
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "US") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1))
Germany
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "DE") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1))
Netherlands
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "NL") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1))
Spain
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "ES") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1))
Greece
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "GR") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.15)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
labels = percent_format(accuracy = 1))
Italy
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "IT") |>
mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
labels = percent_format(accuracy = 1))
Switzerland
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "CH") |>
mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
labels = percent_format(accuracy = 1))
Ireland
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "IR") |>
mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
labels = percent_format(accuracy = 1))
Current Account Decomposition (USD)
United Kingdom
% of GDP
Annual
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "GB") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1)) +
xlab("") + ylab("% of GDP")
Germany
% of GDP
Annual
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "DE") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1)) +
xlab("") + ylab("Net Current Account (% of GDP)")
Quarterly
Code
BGS_BP6_USD |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "Q",
REF_AREA == "DE") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
quarter_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator, linetype = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1)) +
xlab("") + ylab("Net Current Account (% of GDP)")
USD
Annual
Code
BGS_BP6_USD |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
filter(FREQ == "A",
REF_AREA == "DE") |>
left_join(INDICATOR, by = "INDICATOR") |>
year_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator, linetype = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 10),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account")
Quarterly
Code
BGS_BP6_USD |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
filter(FREQ == "Q",
REF_AREA == "DE") |>
left_join(INDICATOR, by = "INDICATOR") |>
quarter_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator, linetype = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 10),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account")
France
USD
Annual
Code
BGS_BP6_USD |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
filter(FREQ == "A",
REF_AREA == "FR") |>
left_join(INDICATOR, by = "INDICATOR") |>
year_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator, linetype = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.8)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 10),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account")
Quarterly
Code
BGS_BP6_USD |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
filter(FREQ == "Q",
REF_AREA == "FR") |>
left_join(INDICATOR, by = "INDICATOR") |>
quarter_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator, linetype = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.9)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 10),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account")
% of GDP
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "FR") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.9)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1)) +
xlab("") + ylab("Net Current Account (% of GDP)")
Argentina
% of GDP
Code
BG_BP6_USD |>
bind_rows(BS_BP6_USD) |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
bind_rows(NGDP_USD) |>
filter(FREQ == "A",
REF_AREA == "AR") |>
select(TIME_PERIOD, INDICATOR, REF_AREA, OBS_VALUE) |>
spread(INDICATOR, OBS_VALUE) |>
na.omit() |>
gather(INDICATOR, OBS_VALUE, -TIME_PERIOD, -REF_AREA) |>
group_by(TIME_PERIOD, REF_AREA) |>
mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[INDICATOR == "NGDP_USD"]) |>
filter(!(INDICATOR == "NGDP_USD")) |>
left_join(INDICATOR, by = "INDICATOR") |>
mutate(Indicator = gsub("Current Account, ", "", Indicator),
Indicator = gsub(", Net, US Dollars", "", Indicator)) |>
year_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.9)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
labels = percent_format(accuracy = 1)) +
xlab("") + ylab("Net Current Account (% of GDP)")
Annual
Code
BGS_BP6_USD |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
filter(FREQ == "A",
REF_AREA == "AR") |>
left_join(INDICATOR, by = "INDICATOR") |>
year_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator, linetype = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.9)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 5),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account")
Quarterly
Code
BGS_BP6_USD |>
bind_rows(BIP_BP6_USD) |>
bind_rows(BIS_BP6_USD) |>
filter(FREQ == "Q",
REF_AREA == "AR") |>
left_join(INDICATOR, by = "INDICATOR") |>
quarter_to_date2() |>
ggplot() + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicator, linetype = Indicator)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.9)) +
scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 1),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account")