tibble(DOWNLOAD_TIME = as.Date(file.info("~/Dropbox/website/data/eurostat/tec00020.RData")$mtime)) %>%
print_table_conditional()
DOWNLOAD_TIME |
---|
2023-02-14 |
%>%
tec00020 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2021 | 179 |
%>%
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 .} {
%>%
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 |
%>%
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 |
%>%
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 |
%>%
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 |
%>%
tec00020 filter(time == "2020",
== "PC_GDP") %>%
unit select_if(~ n_distinct(.) > 1) %>%
left_join(geo, by = "geo") %>%
spread(sector, values) %>%
print_table_conditional
%>%
tec00020 filter(time == "2020",
== "PC_GDP",
unit == "S13") %>%
sector select_if(~ n_distinct(.) > 1) %>%
left_join(geo, by = "geo") %>%
print_table_conditional
%>%
tec00016 bind_rows(tec00020) %>%
filter(time == "2019",
== "PC_GDP",
unit == "S13" | is.na(sector)) %>%
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 .} {