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

Data - Eurostat

Author

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	1582989
CA	Calendar adjusted data, not seasonally adjusted data	1570191
NSA	Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data)	962857

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	1149052
I10	Index, 2010=100	747907
PCH_PRE	Percentage change on previous period	489578
PCH_SM	Percentage change compared to same period in previous year	468866

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	2199914
VOL_SLS	Volume of sales	1916123

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()