Business surveys - NACE Rev. 2 activity - Retail sale - monthly data - ei_bsrt_m_r2

Data - Eurostat

Author

Info

LAST_DOWNLOAD

Code

tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/ei_bsrt_m_r2.RData")$mtime)) %>%
  print_table_conditional()

LAST_DOWNLOAD
2024-11-21

LAST_COMPILE

LAST_COMPILE
2024-11-22

Last

Code

ei_bsrt_m_r2 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()

time	Nobs
2024M10	434

indic

Code

ei_bsrt_m_r2 %>%
  left_join(indic, by = "indic") %>%
  group_by(indic, Indic) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

indic	Indic	Nobs
BS-RPBS	Business activity (sales) development over the past 3 months	22050
BS-RAS	Volume of stocks currently hold	22014
BS-RCI	Retail confidence indicator	22014
BS-REBS	Business activity expectations over the next 3 months	22014
BS-ROP	Expectations of the number of orders placed with suppliers over the next 3 months	21886
BS-REM	Employment expectations over the next 3 months	21782
BS-RPE	Price expectations over the next 3 months	17588

s_adj

Code

ei_bsrt_m_r2 %>%
  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
SA	Seasonally adjusted data, not calendar adjusted data	74748
NSA	Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data)	74600

geo

Code

ei_bsrt_m_r2 %>%
  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

ei_bsrt_m_r2 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}