Financial balance sheets - nasa_10_f_bs

Data - Eurostat

Author

unit

Code

nasa_10_f_bs %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

unit	Unit	Nobs
MIO_NAC	Million units of national currency	3076709
MIO_EUR	Million euro	3017667
PC_GDP	Percentage of gross domestic product (GDP)	2999441
PCH_PRE	Percentage change on previous period	1866812
PC_GADI	NA	882

co_nco

Code

nasa_10_f_bs %>%
  left_join(co_nco, by = "co_nco") %>%
  group_by(co_nco, Co_nco) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

co_nco	Co_nco	Nobs
NCO	Non-consolidated	5601769
CO	Consolidated	5359742

sector

Code

nasa_10_f_bs %>%
  left_join(sector, by = "sector") %>%
  group_by(sector, Sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

finpos

Code

nasa_10_f_bs %>%
  left_join(finpos, by = "finpos") %>%
  group_by(finpos, Finpos) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

finpos	Finpos	Nobs
ASS	Assets	5491067
LIAB	Liabilities	5470444

na_item

Code

load_data("eurostat/na_item.RData")
nasa_10_f_bs %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

geo

Code

nasa_10_f_bs %>%
  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 .}

Financial Net Worth - BF90

Table

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE"),
         time == "2019", 
         co_nco == "CO",
         na_item == "BF90",
         unit == "PC_GDP") %>%
  left_join(geo, by = "geo") %>%
  left_join(sector, by = "sector") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../icon/flag/vsmall/', Geo, '.png" alt="Flag">')) %>%
  select(Geo, sector, Sector, values) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE"),
         sector %in% c("S1", "S11", "S12", "S13", "S14_S15", "S2"),
         time == "2019", 
         co_nco == "CO",
         na_item == "BF90",
         unit == "PC_GDP") %>%
  left_join(geo, by = "geo") %>%
  left_join(sector, by = "sector") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../icon/flag/vsmall/', Geo, '.png" alt="Flag">')) %>%
  mutate(icon = paste0('<img src="../../icon/sector/vsmall/', sector, '.png" alt="All">')) %>%
  select(icon, Geo, sector, Sector, values) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Graphs

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE"),
         sector %in% c("S11", "S12", "S13", "S14_S15"),
         co_nco == "CO",
         na_item == "BF90",
         unit == "PC_GDP") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  left_join(sector, by = "sector") %>%
  year_to_date %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo, linetype = Sector)) + 
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_flags +
  geom_image(data = tibble(date = rep(as.Date("2020-01-01"), 6),
                           value = c(1.6, 0.1, -0.2, -0.5, -0.75, -.95),
                           image = c("../../icon/sector/vsmall/S14_S15.png",
                                     "../../icon/sector/vsmall/S12.png",
                                     "../../icon/sector/vsmall/S13.png",
                                     "../../icon/sector/vsmall/S11.png",
                                     "../../icon/sector/vsmall/S13.png",
                                     "../../icon/sector/vsmall/S11.png")),
             aes(x = date, y = value, image = image), asp = 1.5) +
  scale_color_manual(values = c("#0055a4", "#000000")) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, 20),
                     labels = percent_format()) +
  theme(legend.position = "none")

2018, France, Germany, Italy, United Kingdom

All Items

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE", "IT", "UK"),
         time == "2019", 
         sector == "S1",
         co_nco == "CO",
         finpos == "ASS",
         unit == "PC_GDP") %>%
  left_join(na_item, by = "na_item") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../icon/flag/vsmall/', Geo, '.png" alt="Flag">')) %>%
  select(Geo, na_item, Na_item, values) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Non-financial corporations

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE", "IT", "UK"),
         time == "2019", 
         sector == "S11",
         co_nco == "CO",
         finpos == "ASS",
         unit == "PC_GDP") %>%
  left_join(na_item, by = "na_item") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../icon/flag/vsmall/', Geo, '.png" alt="Flag">')) %>%
  select(Geo, na_item, Na_item, values) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Financial corporations

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE", "IT", "UK"),
         time == "2019", 
         sector == "S12",
         co_nco == "CO",
         finpos == "ASS",
         unit == "PC_GDP") %>%
  left_join(na_item, by = "na_item") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../icon/flag/vsmall/', Geo, '.png" alt="Flag">')) %>%
  select(Geo, na_item, Na_item, values) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

General government

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE", "IT", "UK"),
         time == "2019", 
         sector == "S13",
         co_nco == "CO",
         finpos == "ASS",
         unit == "PC_GDP") %>%
  left_join(na_item, by = "na_item") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../icon/flag/vsmall/', Geo, '.png" alt="Flag">')) %>%
  select(Geo, na_item, Na_item, values) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Households

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE", "IT", "UK"),
         time == "2019", 
         sector == "S14_S15",
         co_nco == "CO",
         finpos == "ASS",
         unit == "PC_GDP") %>%
  left_join(na_item, by = "na_item") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../icon/flag/vsmall/', Geo, '.png" alt="Flag">')) %>%
  select(Geo, na_item, Na_item, values) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Rest of the World

Code

nasa_10_f_bs %>%
  filter(geo %in% c("FR", "DE", "IT", "UK"),
         time == "2019", 
         sector == "S2",
         co_nco == "CO",
         finpos == "ASS",
         unit == "PC_GDP") %>%
  left_join(na_item, by = "na_item") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../icon/flag/vsmall/', Geo, '.png" alt="Flag">')) %>%
  select(Geo, na_item, Na_item, values) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}