source | dataset | .html | .RData |
---|---|---|---|
imf | IFR_BP6_USD | 2025-02-27 | 2025-02-27 |
Net International Investment Position (With Fund Record), US Dollars - IFR_BP6_USD
Data - IMF - BOP
Info
LAST_COMPILE
LAST_COMPILE |
---|
2025-02-27 |
Last
TIME_PERIOD | Nobs |
---|---|
2024-Q3 | 98 |
FREQ
Code
%>%
IFR_BP6_USD group_by(FREQ) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
FREQ | Nobs |
---|---|
Q | 9358 |
A | 4139 |
REF_AREA
Code
%>%
IFR_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/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
IIPs (USD)
Largest Negative IIP in USD
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("2018", "2019", "2020")) %>%
TIME_PERIOD 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/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Largest Positive IIP in USD
Javascript
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("2016", "2019", "2013", "2010", "2007")) %>%
TIME_PERIOD left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
filter(abs(`2019`) > 10000, `2019` > 0) %>%
arrange(-`2019`) %>%
mutate_at(vars(-1, -2), funs(round(./1000))) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
png
Code
i_g("bib/imf/IFR_BP6_USD_ex1.png")
All IIP in USD
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("2018", "2019", "2020")) %>%
TIME_PERIOD left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
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/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Germany, France, Italy, Spain
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("DE", "FR", "IT", "ES")) %>%
REF_AREA %>%
year_to_date2 select(date, OBS_VALUE, REF_AREA) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(OBS_VALUE = OBS_VALUE/1000,
Counterpart_area = Ref_area) %>%
ggplot(.) + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Counterpart_area)) +
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")) +
+
add_4flags scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_y_continuous(breaks = seq(-20000, 400000, 500),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$")) +
xlab("") + ylab("Net International Investment Positions")
Germany, France, United States
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("DE", "FR", "US")) %>%
REF_AREA %>%
year_to_date2 select(date, OBS_VALUE, REF_AREA) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/1000, color = Ref_area)) +
scale_color_manual(values = c("#002395", "#000000", "#B22234")) +
geom_image(data = . %>%
filter(date == as.Date("2020-01-01")) %>%
mutate(date = as.Date("2020-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = OBS_VALUE/1000, image = image), asp = 1.5) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.4)) +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-20000, 400000, 1000),
labels = dollar_format(accuracy = 1, suffix = " Bn", prefix = "$")) +
xlab("") + ylab("Net International Investment Positions")
IIPs (% of GDP) - Nice Looking
All IIP in % of GDP
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
== "2019") %>%
TIME_PERIOD select(REF_AREA, OBS_VALUE) %>%
left_join(NY.GDP.MKTP.CD %>%
filter(year == 2019) %>%
select(REF_AREA = iso2c, NY.GDP.MKTP.CD = value),
by = c("REF_AREA")) %>%
mutate(`IIP (% of GDP)` = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, `IIP (% of GDP)`) %>%
arrange(`IIP (% of GDP)`) %>%
mutate(`IIP (% of GDP)` = round(100*`IIP (% of GDP)`)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
30 Largest Negative IIP
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
== "2019") %>%
TIME_PERIOD select(REF_AREA, OBS_VALUE) %>%
left_join(NY.GDP.MKTP.CD %>%
filter(year == 2019) %>%
select(REF_AREA = iso2c, NY.GDP.MKTP.CD = value),
by = c("REF_AREA")) %>%
mutate(`IIP (% of GDP)` = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, `IIP (% of GDP)`) %>%
arrange(`IIP (% of GDP)`) %>%
mutate(`IIP (% of GDP)` = round(100*`IIP (% of GDP)`)) %>%
head(30) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
30 Largest Positive IIP
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
== "2019") %>%
TIME_PERIOD select(REF_AREA, OBS_VALUE) %>%
left_join(NY.GDP.MKTP.CD %>%
filter(year == 2019) %>%
select(REF_AREA = iso2c, NY.GDP.MKTP.CD = value),
by = c("REF_AREA")) %>%
mutate(`IIP (% of GDP)` = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select(REF_AREA, Ref_area, `IIP (% of GDP)`) %>%
arrange(-`IIP (% of GDP)`) %>%
mutate(`IIP (% of GDP)` = round(100*`IIP (% of GDP)`)) %>%
head(30) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Philippines, Singapore, Indonesia
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("PH", "TH", "ID")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot(.) + theme_minimal() + scale_color_identity() +
geom_line(aes(x = date, y = value, color = color)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Ref_area), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
United States, Spain, Italy, Germany, France, Greece
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("US", "ES", "DE", "FR", "GR", "IT")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(Counterpart_area = Ref_area,
OBS_VALUE = value) %>%
ggplot(.) + theme_minimal() + scale_color_identity() +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
+
add_6flags scale_y_continuous(breaks = 0.01*seq(-300, 200, 20),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position\n% of GDP") +
geom_hline(yintercept = 0, linetype = "dashed")
Argentina, Chile, Uruguay, Peru
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("AR", "UY", "CL", "PE")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot(.) + theme_minimal() + scale_color_identity() +
geom_line(aes(x = date, y = value, color = color)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Ref_area), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Austria, Germany, Netherlands, Sweden
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("SE", "AT", "NL", "DE")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#ED2939", "#000000", "#21468B","#FECC00")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.85)) +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(date = as.Date("2019-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(Ref_area), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
France, Germany, Portugal, Spain, Italy
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("FR", "ES", "PT", "IT", "DE")) %>%
REF_AREA %>%
year_to_date2 filter(date >= as.Date("1980-01-01")) %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot(.) + theme_minimal() +
geom_line(aes(x = date, y = value, color = color)) +
theme(legend.position = "none") + scale_color_identity() +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Ref_area), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
France, Greece, Portugal, Spain
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("FR", "GR", "ES", "PT")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#002395", "#0D5EAF", "#006600","#C60B1E")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Ref_area), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
G7
All
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("FR", "CA", "US", "DE", "IT", "JP", "GB")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
%>%
na.omit ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#FF0000", "#002395", "#000000", "#009246", "#BC002D","#CF142B","#B22234")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(date = as.Date("2019-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
1980-
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("FR", "CA", "US", "DE", "IT", "JP", "GB")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(date >= as.Date("1980-01-01")) %>%
%>%
na.omit ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#FF0000", "#002395", "#000000", "#009246", "#BC002D","#CF142B","#B22234")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(date = as.Date("2019-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
2000-
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("FR", "CA", "US", "DE", "IT", "JP", "GB")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(date >= as.Date("2000-01-01")) %>%
%>%
na.omit ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#FF0000", "#002395", "#000000", "#009246", "#BC002D","#CF142B","#B22234")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(date = as.Date("2019-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
France, Greece, Portugal, Spain, United States
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("FR", "GR", "ES", "PT", "US")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#002395", "#0D5EAF", "#006600","#C60B1E", "#3C3B6E")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2017-01-01")) %>%
mutate(date = as.Date("2017-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
France, Germany, Greece, Netherlands, Portugal, Spain, Unted Kingdom, United States
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("FR", "DE", "GR", "ES", "PT", "US", "GB", "NL")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#002395", "#000000", "#0D5EAF", "#AE1C28", "#006600","#C60B1E","#CF142B", "#3C3B6E")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2019-01-01")) %>%
mutate(date = as.Date("2019-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Australia, New Zealand, United Kingdom, United States
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("US", "GB", "AU", "NZ")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#FF0101", "#00247D", "#CF142B","#3C3B6E")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2014-01-01")) %>%
mutate(date = as.Date("2014-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Australia, New Zealand, United Kingdom, United States, France
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("US", "GB", "AU", "NZ", "FR")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#FF0101", "#002395", "#00247D", "#CF142B","#3C3B6E")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2014-01-01")) %>%
mutate(date = as.Date("2014-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Europe, United States, Japan
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("U2", "US", "JP")) %>%
REF_AREA mutate(OBS_VALUE = OBS_VALUE*(10^(UNIT_MULT))) %>%
%>%
year_to_date2 filter(date >= as.Date("1995-01-01")) %>%
select(date, INDICATOR, OBS_VALUE, REF_AREA) %>%
mutate(REF_AREA = ifelse(REF_AREA == "U2", "XC", REF_AREA)) %>%
# Match U2 = Euro area (Member States and Institutions of the Euro Area) changing composition with XC in WDI
left_join(NY.GDP.MKTP.CD %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(REF_AREA = iso2c, date, NY.GDP.MKTP.CD = value),
by = c("REF_AREA", "date")) %>%
mutate(value = OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = case_when(REF_AREA == "XC" ~ "Europe",
~ Ref_area)) %>%
T ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#FFCC00", "#BC002D", "#3C3B6E")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2014-01-01")) %>%
mutate(date = as.Date("2014-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Europe, United States
All
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("U2", "US")) %>%
REF_AREA mutate(OBS_VALUE = OBS_VALUE*(10^(UNIT_MULT))) %>%
%>%
year_to_date2 filter(date >= as.Date("1995-01-01")) %>%
select(date, INDICATOR, OBS_VALUE, REF_AREA) %>%
mutate(REF_AREA = ifelse(REF_AREA == "U2", "XC", REF_AREA)) %>%
# Match U2 = Euro area (Member States and Institutions of the Euro Area) changing composition with XC in WDI
left_join(NY.GDP.MKTP.CD %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(REF_AREA = iso2c, date, NY.GDP.MKTP.CD = value),
by = c("REF_AREA", "date")) %>%
mutate(value = OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = case_when(REF_AREA == "XC" ~ "Europe",
~ Ref_area)) %>%
T ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#003399", "#BC002D")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2014-01-01")) %>%
mutate(date = as.Date("2014-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
All
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("U2", "US")) %>%
REF_AREA mutate(OBS_VALUE = OBS_VALUE*(10^(UNIT_MULT))) %>%
%>%
year_to_date2 filter(date >= as.Date("1999-01-01")) %>%
select(date, INDICATOR, OBS_VALUE, REF_AREA) %>%
mutate(REF_AREA = ifelse(REF_AREA == "U2", "XC", REF_AREA)) %>%
# Match U2 = Euro area (Member States and Institutions of the Euro Area) changing composition with XC in WDI
left_join(NY.GDP.MKTP.CD %>%
mutate(date = as.Date(paste0(year, "-01-01"))) %>%
select(REF_AREA = iso2c, date, NY.GDP.MKTP.CD = value),
by = c("REF_AREA", "date")) %>%
mutate(value = OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = case_when(REF_AREA == "XC" ~ "Europe",
~ Ref_area)) %>%
T ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#003399", "#BC002D")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2014-01-01")) %>%
mutate(date = as.Date("2014-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)") +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
Europe, United States
Code
%>%
IFR_BP6_USD bind_rows(NGDP_USD) %>%
filter(FREQ == "A",
%in% c("U2", "US")) %>%
REF_AREA mutate(OBS_VALUE = OBS_VALUE*(10^(UNIT_MULT))) %>%
%>%
year_to_date2 filter(date >= as.Date("1995-01-01")) %>%
select(date, INDICATOR, OBS_VALUE, REF_AREA) %>%
spread(INDICATOR, OBS_VALUE) %>%
mutate(value = IFR_BP6_USD/NGDP_USD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = case_when(REF_AREA == "U2" ~ "Europe",
~ Ref_area)) %>%
T ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#003399", "#BC002D", "#3C3B6E")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2014-01-01")) %>%
mutate(date = as.Date("2014-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
China, Europe, United States
Code
%>%
IFR_BP6_USD bind_rows(NGDP_USD) %>%
filter(FREQ == "A",
%in% c("U2", "US", "CN")) %>%
REF_AREA mutate(OBS_VALUE = OBS_VALUE*(10^(UNIT_MULT))) %>%
%>%
year_to_date2 filter(date >= as.Date("1995-01-01")) %>%
select(date, INDICATOR, OBS_VALUE, REF_AREA) %>%
spread(INDICATOR, OBS_VALUE) %>%
mutate(value = IFR_BP6_USD/NGDP_USD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(Ref_area = case_when(REF_AREA == "CN" ~ "China",
== "U2" ~ "Europe",
REF_AREA ~ Ref_area)) %>%
T ggplot(.) + theme_minimal() +
scale_color_manual(values = c("#DE2910", "#003399", "#BC002D")) +
geom_line(aes(x = date, y = value, color = Ref_area)) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2014-01-01")) %>%
mutate(date = as.Date("2014-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
aes(x = date, y = value, image = image), asp = 1.5) +
scale_y_continuous(breaks = 0.01*seq(-300, 200, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
IIPs (% of GDP)
Austria, Netherlands, Denmark, Sweden
Code
%>%
IFR_BP6_USD filter(FREQ == "A",
%in% c("SE", "AT", "NL", "DK")) %>%
REF_AREA %>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(5)[1:4]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 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(-300, 60, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Lebanon, Greece, Argentina
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("LB", "GR", "AR")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
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(-60, 60, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Germany, France, Italy
Code
%>%
IFR_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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() +
scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
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(-60, 60, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Greece, Spain, Portugal
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("GR", "ES", "PT")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
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(-300, 60, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Germany, Japan, Netherlands
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("DE", "JP", "NL")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
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(-100, 100, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Norway, Denmark, Switzerland
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("CH", "NO", "DK")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
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(-100, 300, 20),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Bahrain, Kuwait, Saudi Arabia
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("BH", "KW", "SA")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
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(-100, 300, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Australia, Hungary, Mexico
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("HU", "MX", "AU")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
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(-200, 300, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Argentina, Finland, Sweden
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("FI", "SE", "AR")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
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(-600, 300, 20),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Korea, Indonesia, Thailand
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("KR", "ID", "TH")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 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(-600, 300, 20),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Philippines, Singapore, Malaysia
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("PH", "SG", "MY")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 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(-600, 300, 20),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")
Chile, Uruguay, Turkey
Code
%>%
IFR_BP6_USD filter(`FREQ` == "A",
`REF_AREA` %in% c("CL", "UY", "TR")) %>%
%>%
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 = value),
by = c("REF_AREA", "date")) %>%
mutate(value = (10^6)*OBS_VALUE/NY.GDP.MKTP.CD) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot(.) + theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
geom_line(aes(x = date, y = value, color = Ref_area, linetype = Ref_area)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 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(-600, 300, 10),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Net International Investment Position (% of GDP)")