Turnover in services - monthly data - sts_setu_m

Data - Eurostat

Author

nace_r2

Code

sts_setu_m %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

s_adj

Code

sts_setu_m %>%
  left_join(s_adj, by = "s_adj") %>%
  group_by(s_adj, S_adj) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

s_adj	S_adj	Nobs
SCA	Seasonally and calendar adjusted data	759996
CA	Calendar adjusted data, not seasonally adjusted data	751777
NSA	Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data)	476182

unit

Code

sts_setu_m %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

unit	Unit	Nobs
I15	Index, 2015=100	648837
I21	Index, 2021=100	581021
I10	Index, 2010=100	308757
PCH_PRE	Percentage change on previous period	231827
PCH_SM	Percentage change compared to same period in previous year	217513

indic_bt

Code

sts_setu_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	1908304
VOL_SLS	Volume of sales	79651

geo

Code

sts_setu_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 .}

time

Code

sts_setu_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}