Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/sts_cobp_m.RData")$mtime)) %>%
print_table_conditional()| LAST_DOWNLOAD |
|---|
| 2026-01-30 |
Data - Eurostat
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/sts_cobp_m.RData")$mtime)) %>%
print_table_conditional()| LAST_DOWNLOAD |
|---|
| 2026-01-30 |
| LAST_COMPILE |
|---|
| 2026-01-31 |
sts_cobp_m %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2025M12 | 91 |
sts_cobp_m %>%
left_join(s_adj, by = "s_adj") %>%
group_by(s_adj, S_adj) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| s_adj | S_adj | Nobs |
|---|---|---|
| NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 214601 |
| SCA | Seasonally and calendar adjusted data | 205437 |
sts_cobp_m %>%
left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| unit | Unit | Nobs |
|---|---|---|
| I21 | Index, 2021=100 | 145142 |
| I15 | Index, 2015=100 | 132840 |
| PCH_SM | Percentage change compared to same period in previous year | 71884 |
| PCH_PRE | Percentage change on previous period | 70172 |
sts_cobp_m %>%
left_join(indic_bt, by = "indic_bt") %>%
group_by(indic_bt, Indic_bt) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| indic_bt | Indic_bt | Nobs |
|---|---|---|
| BPRM_SQM | Building permits - m2 of useful floor area | 302074 |
| BPRM_DW | Building permits - number of dwellings | 117964 |
sts_cobp_m %>%
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 .}sts_cobp_m %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()