Code
%>%
sts_trtu_m left_join(nace_r2, by = "nace_r2") %>%
group_by(nace_r2, Nace_r2) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
Data - Eurostat
%>%
sts_trtu_m left_join(nace_r2, by = "nace_r2") %>%
group_by(nace_r2, Nace_r2) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
%>%
sts_trtu_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 |
---|---|---|
SCA | Seasonally and calendar adjusted data | 1573083 |
CA | Calendar adjusted data, not seasonally adjusted data | 1562253 |
NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 959394 |
%>%
sts_trtu_m left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
unit | Unit | Nobs |
---|---|---|
I15 | Index, 2015=100 | 1260634 |
I21 | Index, 2021=100 | 1135887 |
I10 | Index, 2010=100 | 747907 |
PCH_PRE | Percentage change on previous period | 485225 |
PCH_SM | Percentage change compared to same period in previous year | 465077 |
%>%
sts_trtu_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 |
---|---|---|
NETTUR | Net turnover | 2188062 |
VOL_SLS | Volume of sales | 1906668 |
%>%
sts_trtu_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_trtu_m group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()