Code
tibble(DOWNLOAD_TIME = as.Date(file.info("~/iCloud/website/data/eurostat/tec00001.RData")$mtime)) %>%
print_table_conditional()| DOWNLOAD_TIME |
|---|
| 2026-01-30 |
Data - Eurostat
tibble(DOWNLOAD_TIME = as.Date(file.info("~/iCloud/website/data/eurostat/tec00001.RData")$mtime)) %>%
print_table_conditional()| DOWNLOAD_TIME |
|---|
| 2026-01-30 |
tec00001 %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2024 | 79 |
tec00001 %>%
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 .}tec00001 %>%
left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| unit | Unit | Nobs |
|---|---|---|
| CP_MEUR | Current prices, million euro | 521 |
| CP_EUR_HAB | Current prices, euro per capita | 484 |
tec00001 %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2013 | 85 |
| 2014 | 85 |
| 2015 | 85 |
| 2016 | 85 |
| 2017 | 85 |
| 2018 | 85 |
| 2019 | 85 |
| 2020 | 83 |
| 2021 | 83 |
| 2022 | 83 |
| 2023 | 82 |
| 2024 | 79 |