Median saving rate by income quintile - experimental statistics - icw_sr_03
Data - Eurostat
Info
Last observation: Annual: 2020 (N = 174)
First observation: Annual: 2010 (N = 162)
Last data update: 24 jul 2026, 01:57. Last compile: 24 jul 2026, 01:57
Structure
Tables
2010
Code
icw_sr_03 %>%
filter(time == "2010") %>%
select(quant_inc, geo, Geo, values) %>%
spread(quant_inc, values) %>%
select(geo, Geo, TOTAL, everything()) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
arrange(-`TOTAL`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}2015
Code
icw_sr_03 %>%
filter(time == "2015") %>%
select(quant_inc, geo, Geo, values) %>%
spread(quant_inc, values) %>%
select(geo, Geo, TOTAL, everything()) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
arrange(-`TOTAL`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Latest
Code
latest_y <- icw_sr_03 %>%
filter(!is.na(values)) %>%
summarise(m = max(time)) %>%
pull(m)
icw_sr_03 %>%
filter(time == latest_y) %>%
select(quant_inc, geo, Geo, values) %>%
spread(quant_inc, values) %>%
select(geo, Geo, TOTAL, everything()) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
arrange(-`TOTAL`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Germany, France, Sweden
Code
icw_sr_03 %>%
filter(time == "2015",
geo %in% c("SE", "FR", "DE")) %>%
mutate(quantile = substr(quant_inc, 3, 3) %>% as.numeric) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = quantile, y = values/100, color = color)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 3) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/100, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Median saving rate by income quintile") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
scale_x_continuous(breaks = seq(0, 5, 1))
Portugal, Spain, United Kingdom
Code
icw_sr_03 %>%
filter(time == "2015",
geo %in% c("UK", "ES", "PT")) %>%
mutate(quantile = substr(quant_inc, 3, 3) %>% as.numeric) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = quantile, y = values/100, color = color)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 5) %>%
mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/100, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Median saving rate by income quintile") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
scale_x_continuous(breaks = seq(0, 5, 1))
Evolution over Time: France, Germany, Spain, Portugal
Code
icw_sr_03 %>%
filter(quant_inc == "TOTAL",
geo %in% c("FR", "DE", "ES", "PT")) %>%
year_to_date %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_4flags +
scale_x_date(breaks = as.Date(paste0(seq(2005, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Median saving rate, overall population") +
scale_y_continuous(breaks = 0.01*seq(0, 50, 5),
labels = percent_format(accuracy = 1))