%>%
NY.GDS.TOTL.ZS left_join(iso2c, by = "iso2c") %>%
group_by(iso2c, Iso2c) %>%
rename(value = `NY.GDS.TOTL.ZS`) %>%
mutate(value = round(value, 1)) %>%
summarise(Nobs = n(),
`Year 1` = first(year),
`Net Saving 1 (%)` = first(value),
`Year 2` = last(year),
`Net Saving 2 (%)` = last(value)) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
%>%
NY.GDS.TOTL.ZS left_join(GC.NLD.TOTL.GD.ZS, by = c("iso2c", "year")) %>%
left_join(NY.GNS.ICTR.ZS, by = c("iso2c", "year")) %>%
left_join(NE.GDI.TOTL.ZS, by = c("iso2c", "year")) %>%
rename(`Gross Domestic Saving` = NY.GDS.TOTL.ZS,
`Gross National Saving` = NY.GNS.ICTR.ZS,
`Public Saving` = GC.NLD.TOTL.GD.ZS,
`Gross Investment` = NE.GDI.TOTL.ZS) %>%
mutate(`Gross Private Saving` = `Gross Domestic Saving` - `Public Saving`) %>%
select(-`Public Saving`) %>%
gather(variable, value,- iso2c, -year) %>%
filter(iso2c %in% c("JP")) %>%
left_join(iso2c, by = "iso2c") %>%
%>%
year_to_date ggplot(.) + xlab("") + ylab("% of GDP") + theme_minimal() +
geom_line(aes(x = date, y = value/100, color = variable, linetype = variable)) +
scale_color_manual(values = viridis(5)[1:4]) +
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(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
%>%
NY.GDS.TOTL.ZS left_join(GC.NLD.TOTL.GD.ZS, by = c("iso2c", "year")) %>%
left_join(NY.GNS.ICTR.ZS, by = c("iso2c", "year")) %>%
left_join(NE.GDI.TOTL.ZS, by = c("iso2c", "year")) %>%
rename(`Gross Domestic Saving` = NY.GDS.TOTL.ZS,
`Gross National Saving` = NY.GNS.ICTR.ZS,
`Public Saving` = GC.NLD.TOTL.GD.ZS,
`Gross Investment` = NE.GDI.TOTL.ZS) %>%
mutate(`Gross Private Saving` = `Gross Domestic Saving` - `Public Saving`) %>%
select(-`Public Saving`) %>%
gather(variable, value,- iso2c, -year) %>%
filter(iso2c %in% c("DE")) %>%
left_join(iso2c, by = "iso2c") %>%
%>%
year_to_date ggplot(.) + xlab("") + ylab("% of GDP") + theme_minimal() +
geom_line(aes(x = date, y = value/100, color = variable, linetype = variable)) +
scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 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(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
%>%
NY.GDS.TOTL.ZS left_join(GC.NLD.TOTL.GD.ZS, by = c("iso2c", "year")) %>%
left_join(NY.GNS.ICTR.ZS, by = c("iso2c", "year")) %>%
left_join(NE.GDI.TOTL.ZS, by = c("iso2c", "year")) %>%
rename(`Gross Domestic Saving` = NY.GDS.TOTL.ZS,
`Gross National Saving` = NY.GNS.ICTR.ZS,
`Public Saving` = GC.NLD.TOTL.GD.ZS,
`Gross Investment` = NE.GDI.TOTL.ZS) %>%
mutate(`Gross Private Saving` = `Gross Domestic Saving` - `Public Saving`) %>%
select(-`Public Saving`) %>%
gather(variable, value,- iso2c, -year) %>%
filter(iso2c %in% c("FR")) %>%
left_join(iso2c, by = "iso2c") %>%
%>%
year_to_date ggplot(.) + xlab("") + ylab("% of GDP") + theme_minimal() +
geom_line(aes(x = date, y = value/100, color = variable, linetype = variable)) +
scale_color_manual(values = viridis(5)[1:4]) +
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(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
%>%
NY.GDS.TOTL.ZS left_join(GC.NLD.TOTL.GD.ZS, by = c("iso2c", "year")) %>%
rename(`Gross Domestic Saving` = NY.GDS.TOTL.ZS, `Public Saving` = GC.NLD.TOTL.GD.ZS) %>%
mutate(`Gross Private Saving` = `Gross Domestic Saving` + `Public Saving`) %>%
gather(variable, value,- iso2c, -year) %>%
filter(iso2c %in% c("CN")) %>%
left_join(iso2c, by = "iso2c") %>%
%>%
year_to_date ggplot(.) + xlab("") + ylab("% of GDP") + theme_minimal() +
geom_line(aes(x = date, y = value/100, color = variable, linetype = variable)) +
scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.55)) +
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, 2),
labels = scales::percent_format(accuracy = 1))
%>%
NY.GDS.TOTL.ZS filter(iso2c %in% c("CN", "FR", "DE")) %>%
left_join(iso2c, by = "iso2c") %>%
%>%
year_to_date ggplot(.) +
geom_line(aes(x = date, y = NY.GDS.TOTL.ZS/100, color = Iso2c, linetype = Iso2c)) +
theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.1, 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(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Gross domestic savings (% of GDP)")
%>%
NY.GDS.TOTL.ZS filter(iso2c %in% c("US", "GB", "ES")) %>%
left_join(iso2c, by = "iso2c") %>%
%>%
year_to_date ggplot(.) +
geom_line(aes(x = date, y = NY.GDS.TOTL.ZS/100, color = Iso2c, linetype = Iso2c)) +
theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.8, 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(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Gross domestic savings (% of GDP)")
%>%
NY.GDS.TOTL.ZS filter(iso2c %in% c("AR", "CL", "VE")) %>%
left_join(iso2c, by = "iso2c") %>%
%>%
year_to_date ggplot(.) +
geom_line(aes(x = date, y = NY.GDS.TOTL.ZS/100, color = Iso2c, linetype = Iso2c)) +
theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
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(-60, 60, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Gross domestic savings (% of GDP)")
%>%
NY.GDS.TOTL.ZS filter(iso2c %in% c("GR", "HK", "MX")) %>%
left_join(iso2c, by = "iso2c") %>%
%>%
year_to_date ggplot(.) +
geom_line(aes(x = date, y = NY.GDS.TOTL.ZS/100, color = Iso2c, linetype = Iso2c)) +
theme_minimal() + scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 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(-60, 60, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Gross domestic savings (% of GDP)")