Total financial sector liabilities, non-consolidated - annual data - tipsfs10

Data - Eurostat

Author

François Geerolf

Info

DOWNLOAD_TIME

Code 
tibble(DOWNLOAD_TIME = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/tipsfs10.RData")$mtime)) %>%
  print_table_conditional()
DOWNLOAD_TIME
2024-10-08

Last

Code 
tipsfs10 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2023 60

unit

Code 
tipsfs10 %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
MIO_NAC Million units of national currency 814
PC_GDP Percentage of gross domestic product (GDP) 814
PCH_PRE Percentage change on previous period 790

geo

Code 
tipsfs10 %>%
  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 .}

France, Germany, Italy, Spain, Netherlands

Percent

Code 
tipsfs10 %>%
  filter(geo %in% c("FR", "DE", "NL", "ES", "IT"),
         unit == "PC_GDP") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) + theme_minimal() +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 2), "-01-01")),
               labels = date_format("%y")) +
  xlab("") + ylab("Total financial sector liabilities, non-consolidated - annual data") +
  scale_y_continuous(breaks = 0.01*seq(-300, 2200, 100),
                     labels = scales::percent_format(accuracy = 1))

Change

Code 
tipsfs10 %>%
  filter(geo %in% c("FR", "DE", "NL", "ES", "IT"),
         unit == "PCH_PRE") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) + theme_minimal() +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 2), "-01-01")),
               labels = date_format("%y")) +
  xlab("") + ylab("Total financial sector liabilities, non-consolidated - annual data") +
  scale_y_continuous(breaks = 0.01*seq(-300, 2200, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0.165, linetype = "dashed")