~/data/eurostat/

Info

DOWNLOAD_TIME

tibble(DOWNLOAD_TIME = as.Date(file.info("~/Dropbox/website/data/eurostat/tec00020.RData")$mtime)) %>%
  print_table_conditional()
DOWNLOAD_TIME
2023-02-14

Last

tec00020 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2021 179

geo

tec00020 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  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 .}

na_item

tec00020 %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
na_item Na_item Nobs
D2REC Taxes on production and imports, receivable 2126

unit

tec00020 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
PC_GDP Percentage of gross domestic product (GDP) 2126

sector

tec00020 %>%
  left_join(sector, by = "sector") %>%
  group_by(sector, Sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
sector Sector Nobs
S13 General government 396
S1311 Central government 396
S1312 State government 396
S1313 Local government 396
S1314 Social security funds 396
S212 Institutions of the EU 146

time

tec00020 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()
time Nobs
2010 177
2011 177
2012 177
2013 177
2014 177
2015 177
2016 177
2017 177
2018 177
2019 177
2020 177
2021 179

2020

All

tec00020 %>%
  filter(time == "2020",
         unit == "PC_GDP") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  spread(sector, values) %>%
  print_table_conditional

General Government - S13

tec00020 %>%
  filter(time == "2020",
         unit == "PC_GDP",
         sector == "S13") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  print_table_conditional

Compare tec00020

tec00016 %>%
  bind_rows(tec00020) %>%
  filter(time == "2019",
         unit == "PC_GDP",
         sector == "S13" | is.na(sector)) %>%
  select(-sector) %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(na_item, by = "na_item") %>%
  select(-na_item) %>%
  left_join(geo, by = "geo")  %>%
  spread(Na_item, values) %>%
  arrange(-`Taxes on production and imports less subsidies`) %>%
  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 .}