Median saving rate by age of the reference person - experimental statistics - icw_sr_01

Data - Eurostat

Author

François Geerolf

Info

LAST_DOWNLOAD

Code 
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/icw_sr_01.RData")$mtime)) %>%
  print_table_conditional()
LAST_DOWNLOAD
2024-11-21

LAST_COMPILE

LAST_COMPILE
2024-11-22

Last

Code 
icw_sr_01 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2020 338

age

Code 
icw_sr_01 %>%
  left_join(age, by = "age") %>%
  group_by(age, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
age Age Nobs
TOTAL Total 84
Y_LT30 Less than 30 years 84
Y30-39 From 30 to 39 years 83
Y35-44 From 35 to 44 years 83
Y40-49 From 40 to 49 years 83
Y45-54 From 45 to 54 years 83
Y50-59 From 50 to 59 years 83
Y55-64 From 55 to 64 years 83
Y60-69 From 60 to 69 years 83
Y65-74 From 65 to 74 years 83
Y_GE70 70 years or over 83
Y_GE75 75 years or over 83
Y_LT35 Less than 35 years 83
Y30-44 From 30 to 44 years 26
Y45-59 From 45 to 59 years 26
Y_GE60 60 years or over 26
UNK Unknown 15

unit

Code 
icw_sr_01 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
PC_DI Percentage of disposable income 1174

geo

Code 
icw_sr_01 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  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()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

Code 
icw_sr_01 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
time Nobs
2010 433
2015 403
2020 338

2010

Code 
icw_sr_01 %>%
  filter(time == "2010") %>%
  select(age, geo, values) %>%
  left_join(geo, by = "geo") %>%
  spread(age, values) %>%
  select(geo, Geo, TOTAL, everything()) %>%
  print_table_conditional()
geo Geo TOTAL UNK Y_GE60 Y_GE70 Y_GE75 Y_LT30 Y_LT35 Y30-39 Y30-44 Y35-44 Y40-49 Y45-54 Y45-59 Y50-59 Y55-64 Y60-69 Y65-74
AT Austria 18.0 NA 21.7 21.5 20.1 -8.0 1.1 14.9 16.1 21.3 22.8 22.5 22.2 20.7 15.8 16.0 22.1
BE Belgium 8.9 NA 3.1 -3.0 -3.4 5.9 9.8 12.8 12.8 14.0 14.1 12.1 15.2 13.5 12.7 7.2 1.7
BG Bulgaria 38.0 NA 29.5 NA NA 35.0 NA NA 40.4 NA NA NA 44.3 NA NA NA NA
CY Cyprus 12.2 NA 21.3 18.4 16.4 -0.9 2.4 3.9 0.6 2.9 4.3 9.2 13.1 19.0 22.0 20.0 23.3
CZ Czechia 23.9 NA 19.8 15.6 15.1 25.7 29.5 27.2 25.9 25.2 25.9 25.9 28.9 28.4 26.2 21.8 19.2
DE Germany 13.5 NA 8.0 7.4 7.7 4.0 11.8 19.2 18.9 20.2 19.2 17.9 17.0 15.6 11.4 8.2 7.5
DK Denmark 4.3 NA -0.9 -7.6 -10.8 -30.4 -8.2 11.0 13.2 9.3 11.1 13.2 14.9 15.9 14.9 6.8 1.7
EE Estonia 34.9 NA 37.0 36.7 36.9 26.2 33.0 35.7 36.2 31.3 32.3 35.1 32.1 34.8 34.2 37.0 38.2
EL Greece -11.2 NA 2.4 9.1 7.4 -67.6 -47.8 -34.5 -20.4 -31.5 -26.1 -21.0 -9.7 -9.8 -1.8 7.3 14.9
ES Spain 16.1 NA 25.1 22.4 22.6 -3.2 8.4 14.2 13.4 13.8 10.0 10.0 15.4 16.2 19.9 22.7 24.9
FI Finland 22.2 NA 28.0 31.0 30.5 -1.6 5.7 16.5 22.2 21.1 21.1 23.6 23.6 25.4 25.1 28.4 31.5
FR France 29.1 NA 36.2 35.6 35.3 9.2 16.3 23.5 24.2 24.7 26.2 28.2 29.8 32.1 33.9 35.1 37.2
HR Croatia 4.0 NA 0.1 -1.6 -3.1 2.7 9.0 8.5 6.3 4.6 4.2 6.2 6.6 8.6 7.5 4.7 2.9
HU Hungary 16.3 NA 16.7 17.2 17.3 7.3 13.9 17.7 17.2 16.5 15.8 16.6 15.6 16.9 15.3 15.7 18.0
IE Ireland 18.4 NA 21.9 23.6 22.0 1.6 12.7 18.3 17.6 18.0 17.7 17.6 21.1 20.1 19.6 18.8 23.6
IT Italy 26.4 NA NA 33.8 33.9 -31.6 1.7 16.3 NA 18.2 19.2 21.8 NA 27.3 30.4 35.3 37.2
LT Lithuania 22.8 NA 13.7 21.2 20.2 20.4 22.4 22.3 21.0 20.8 23.3 24.1 22.7 23.6 25.2 24.1 23.9
LU Luxembourg 28.4 NA 35.7 32.0 33.2 13.7 19.3 23.9 26.3 25.2 26.9 28.8 29.7 31.9 34.5 34.0 32.4
LV Latvia 10.5 NA 12.7 12.4 11.8 7.9 11.0 12.6 15.9 10.0 10.3 10.6 7.8 7.9 6.5 10.1 13.2
MT Malta 13.7 NA 15.1 17.5 17.0 4.6 9.4 4.7 1.4 -0.2 4.8 12.6 15.7 19.5 19.3 18.0 19.9
PL Poland 24.7 NA 24.3 22.8 23.0 20.6 24.2 25.4 25.5 24.3 25.7 27.4 26.7 26.6 24.7 24.6 24.0
PT Portugal 13.1 NA 21.7 23.7 22.0 -3.4 0.8 2.6 6.8 2.4 2.7 7.2 13.0 16.6 21.4 23.0 26.3
RO Romania -5.1 NA -3.1 -5.2 -7.0 -15.5 -8.4 -6.1 -7.3 -7.7 -8.6 -6.1 -4.2 -2.6 -1.9 -0.8 -0.4
SE Sweden 18.0 NA 20.3 11.0 8.8 -6.1 2.8 18.6 21.1 20.2 23.2 28.0 27.1 28.4 24.7 23.0 21.2
SI Slovenia 18.0 NA 20.4 19.9 17.5 -4.7 8.2 14.5 14.9 17.5 18.6 19.6 20.2 21.6 17.1 17.9 24.4
SK Slovakia 24.4 NA 19.8 16.6 15.8 22.2 25.8 26.7 25.8 26.5 27.5 29.3 28.7 29.5 26.4 22.0 18.4
UK United Kingdom 22.9 NA 22.3 22.4 21.6 12.5 19.4 26.8 25.4 24.4 24.4 24.6 25.9 24.7 23.1 22.8 23.8

Table - 2015

All

Code 
icw_sr_01 %>%
  filter(time == "2015") %>%
  select(age, geo, values) %>%
  left_join(geo, by = "geo") %>%
  spread(age, values) %>%
  arrange(Geo) %>%
  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(-`Y65-74`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Total, Y30-39, Y40-49, Y50-59, Y60-69, Y_GE70

Code 
icw_sr_01 %>%
  filter(time == "2015") %>%
  select(age, geo, values) %>%
  left_join(geo, by = "geo") %>%
  spread(age, values) %>%
  arrange(Geo) %>%
  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()) %>%
  select(1, 2, 3, 4, 9, 11, 13, 15, 5) %>%
  arrange(-`Y60-69`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Germany, France, United Kingdom, Spain

Code 
icw_sr_01 %>%
  filter(time == "2015",
         geo %in% c("UK", "FR", "DE", "ES"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 4) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2],values[4]-values[3])) %>%
               arrange(-dist, age) %>%
               head(4) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Taux d'épargne médian par âge") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.5))

Germany, France, United Kingdom

Code 
icw_sr_01 %>%
  filter(time == "2015",
         geo %in% c("UK", "FR", "DE"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.5))

Portugal, Spain, United Kingdom

Code 
icw_sr_01 %>%
  filter(time == "2015",
         geo %in% c("UK", "ES", "PT"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))

Austria, Belgium, Bulgaria

Code 
icw_sr_01 %>%
  filter(time == "2015",
         geo %in% c("AT", "BE", "BG"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))

Estonia, Spain, Croatia

Code 
icw_sr_01 %>%
  filter(time == "2015",
         geo %in% c("EE", "ES", "HR"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))

Hungary, Ireland, Lithuania

Code 
icw_sr_01 %>%
  filter(time == "2015",
         geo %in% c("HU", "IE", "LT"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) %>%
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.4))