Current Account, Goods and Services, Net, US Dollars - BGS_BP6_USD
Data - IMF - BOP
François Geerolf
FREQ
BGS_BP6_USD %>%
group_by(FREQ) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| FREQ | Nobs |
|---|---|
| Q | 15941 |
| A | 7081 |
REF_AREA
BGS_BP6_USD %>%
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
Current Accounts
Largest CA Deficits
BGS_BP6_USD %>%
filter(FREQ == "A",
TIME_PERIOD %in% c("2018", "2019", "2020")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
filter(abs(`2018`) > 10000, `2018` < 0) %>%
arrange(`2018`) %>%
mutate_at(vars(-1, -2), funs(round(./1000))) %>%
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 .}Largest CA Surpluses
Javascript
BGS_BP6_USD %>%
filter(FREQ == "A",
TIME_PERIOD %in% c("2018", "2019", "2020")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
filter(abs(`2018`) > 10000, `2018` > 0) %>%
arrange(-`2018`) %>%
mutate_at(vars(-1, -2), funs(round(./1000))) %>%
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 .}
Germany, Europe, China
All
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("DE", "U2", "CN")) %>%
year_to_date2 %>%
select(date, OBS_VALUE, REF_AREA) %>%
left_join(NY.GDP.MKTP.CD %>%
filter(iso2c == "1W") %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(date, NY.GDP.MKTP.CD),
by = c("date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
ggplot(.) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme_minimal() +
scale_color_manual(values = c("#DE2910", "#003399", "#000000")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.9)) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.1),
labels = scales::percent_format(accuracy = .1)) +
xlab("") + ylab("Current Account (% of World GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
1990-
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("DE", "U2", "CN")) %>%
year_to_date2 %>%
select(date, OBS_VALUE, REF_AREA) %>%
left_join(NY.GDP.MKTP.CD %>%
filter(iso2c == "1W") %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(date, NY.GDP.MKTP.CD),
by = c("date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
ggplot(.) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme_minimal() +
scale_color_manual(values = c("#DE2910", "#003399", "#000000")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.9)) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.1),
labels = scales::percent_format(accuracy = .1)) +
xlab("") + ylab("Current Account (% of World GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
All CA
BGS_BP6_USD %>%
filter(FREQ == "A",
TIME_PERIOD == "2019") %>%
select(OBS_VALUE, REF_AREA) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, OBS_VALUE) %>%
arrange(OBS_VALUE) %>%
mutate(OBS_VALUE = round(OBS_VALUE/1000)) %>%
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 .}Balance of Payments (Bn$)
Austria, Netherlands, Denmark, Sweden
$
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("SE", "AT", "NL","DK")) %>%
year_to_date2 %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Ref_area)) +
theme_minimal() + scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
scale_x_date(breaks = seq(1950, 2020, 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("Current Account") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
Lebanon, France, Italy
$
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("LB", "FR", "IT")) %>%
year_to_date2 %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Ref_area)) +
scale_color_manual(values = c("#003399", "#000000", "#ED1C24")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = OBS_VALUE/1000, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 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") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
France, Germany, Italy
$
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("DE", "FR", "IT")) %>%
year_to_date2 %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Ref_area)) +
scale_color_manual(values = c("#003399", "#000000", "#009246")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = OBS_VALUE/1000, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 50),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account")
% of GDP
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("DE", "FR", "IT")) %>%
year_to_date2 %>%
select(date, OBS_VALUE, REF_AREA) %>%
left_join(NY.GDP.MKTP.CD %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(REF_AREA = iso2c, date, NY.GDP.MKTP.CD),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme_minimal() +
scale_color_manual(values = c("#003399", "#000000", "#009246")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.9)) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Current Account (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
% of World GDP
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("DE", "FR", "IT")) %>%
year_to_date2 %>%
select(date, OBS_VALUE, REF_AREA) %>%
left_join(NY.GDP.MKTP.CD %>%
filter(iso2c == "1W") %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(date, NY.GDP.MKTP.CD),
by = c("date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme_minimal() +
scale_color_manual(values = c("#003399", "#000000", "#009246")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.9)) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.1),
labels = scales::percent_format(accuracy = .1)) +
xlab("") + ylab("Current Account (% of World GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
Saudi Arabia, Norway
$
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("NO", "SA", "VE")) %>%
year_to_date2 %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = ifelse(REF_AREA == "VE", "Venezuela", Ref_area)) %>%
ggplot(.) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Ref_area)) +
theme_minimal() +
scale_color_manual(values = c("#EF2B2D", "#006C35", "#00247D")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
geom_image(data = . %>%
filter(date == as.Date("2012-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = OBS_VALUE/1000, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 50),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
% of GDP
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("NO", "SA", "VE")) %>%
year_to_date2 %>%
select(date, OBS_VALUE, REF_AREA) %>%
left_join(NY.GDP.MKTP.CD %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(REF_AREA = iso2c, date, NY.GDP.MKTP.CD),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = ifelse(REF_AREA == "VE", "Venezuela", Ref_area)) %>%
ggplot(.) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme_minimal() +
scale_color_manual(values = c("#EF2B2D", "#006C35", "#00247D")) +
theme(legend.title = element_blank(),
legend.position = c(0.8, 0.9)) +
geom_image(data = . %>%
filter(date == as.Date("2012-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Current Account (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
Netherlands, Greece, Spain
$
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("NL", "ES", "GR")) %>%
year_to_date2 %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Ref_area)) +
theme_minimal() +
scale_color_manual(values = c("#0D5EAF", "#AE1C28", "#FFC400")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
geom_image(data = . %>%
filter(date == as.Date("2020-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = OBS_VALUE/1000, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 50),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$ ")) +
xlab("") + ylab("Net Current Account")
% of GDP
BGS_BP6_USD %>%
filter(FREQ == "A",
REF_AREA %in% c("NL", "ES", "GR")) %>%
year_to_date2 %>%
select(date, OBS_VALUE, REF_AREA) %>%
left_join(NY.GDP.MKTP.CD %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(REF_AREA = iso2c, date, NY.GDP.MKTP.CD),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme_minimal() +
scale_color_manual(values = c("#0D5EAF", "#AE1C28", "#FFC400")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.9)) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Current Account (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")