Turnover and volume of sales in wholesale and retail trade - monthly data - sts_trtu_m

Data - Eurostat

nace_r2

Code
sts_trtu_m %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

s_adj

Code
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

unit

Code
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

indic_bt

Code
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

geo

Code
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 .}

time

Code
sts_trtu_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()