Code
tibble(DOWNLOAD_TIME = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/tec00001.RData")$mtime)) %>%
print_table_conditional()
DOWNLOAD_TIME |
---|
2024-10-08 |
Data - Eurostat
tibble(DOWNLOAD_TIME = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/tec00001.RData")$mtime)) %>%
print_table_conditional()
DOWNLOAD_TIME |
---|
2024-10-08 |
%>%
tec00001 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2023 | 75 |
%>%
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 | 498 |
CP_EUR_HAB | Current prices, euro per capita | 470 |
%>%
tec00001 group_by(time) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
time | Nobs |
---|---|
2012 | 80 |
2013 | 82 |
2014 | 82 |
2015 | 82 |
2016 | 82 |
2017 | 82 |
2018 | 82 |
2019 | 82 |
2020 | 80 |
2021 | 80 |
2022 | 79 |
2023 | 75 |