Business enterprise R&D expenditure in high-tech sectors - NACE Rev. 2 - htec_sti_exp2

Data - Eurostat

nace_r2

htec_sti_exp2 %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

nace_r2	Nace_r2	Nobs
TOTAL	Total - all NACE activities	1470
C	Manufacturing	1148
C_HTC	High-technology manufacturing	1132
C_HTC_M	Medium high-technology manufacturing	1112
G-N	Services of the business economy	1082
C_LTC	Low-technology manufacturing	1052
C_LTC_M	Medium low-technology manufacturing	998

unit

htec_sti_exp2 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

unit	Unit	Nobs
MIO_EUR	Million euro	3997
PC_TOT	Percentage of total	3997

geo

htec_sti_exp2 %>%
  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

htec_sti_exp2 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()

time	Nobs
2022	362
2021	520
2020	498
2019	576
2018	496
2017	576
2016	492
2015	540
2014	438
2013	514
2012	456
2011	524
2010	464
2009	496
2008	422
2007	282
2006	182
2005	156