INDICATOR %>%
filter(INDICATOR %in% gsub(".Rmd$", "", list.files(pattern = "\\.Rmd$"))) %>%
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 |
| IFR_BP6_USD | Net International Investment Position (With Fund Record), US Dollars | html |
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),
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])
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),
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
# -3.6541 -0.1890 0.0849 0.3986 4.5374
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -0.129081 0.122047 -1.058 0.29206
# GGXWDG_GDP 0.005943 0.002174 2.733 0.00708 **
# ---
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#
# Residual standard error: 0.8253 on 139 degrees of freedom
# (46 observations deleted due to missingness)
# Multiple R-squared: 0.05101, Adjusted R-squared: 0.04418
# F-statistic: 7.471 on 1 and 139 DF, p-value: 0.007085GGXWDG_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),
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])
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),
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])
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),
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])
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),
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])
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 %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(6)[1:5]) +
geom_line(aes(x = date, y = OBS_VALUE, color = Ref_area)) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
scale_x_date(breaks = seq(1950, 2020, 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))
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 .}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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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))
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, 2020, 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")
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, 2020, 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)")
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, 2020, 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)")
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, 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")
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, 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")
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, 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")
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, 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")
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, 2020, 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)")
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, 2020, 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)")
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, 2020, 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")
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, 2020, 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")