Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/lc_an_cost_r2.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2024-10-08 |
Data - Eurostat
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/lc_an_cost_r2.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2024-10-08 |
LAST_COMPILE |
---|
2024-11-22 |
%>%
lc_an_cost_r2 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2011 | 6483 |
%>%
lc_an_cost_r2 left_join(indic_lc, by = "indic_lc") %>%
group_by(indic_lc, Indic_lc) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
indic_lc | Indic_lc | Nobs |
---|---|---|
LC_H | Labour cost per hour | 14241 |
LC_M | Labour cost per month | 13737 |
%>%
lc_an_cost_r2 left_join(currency, by = "currency") %>%
group_by(currency, Currency) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
currency | Currency | Nobs |
---|---|---|
EUR | Euro | 9326 |
NAC | National currency | 9326 |
PPS | Purchasing Power Standard | 9326 |
%>%
lc_an_cost_r2 left_join(sizeclas, by = "sizeclas") %>%
group_by(sizeclas, Sizeclas) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
sizeclas | Sizeclas | Nobs |
---|---|---|
GE10 | 10 employees or more | 18081 |
TOTAL | Total | 9897 |
%>%
lc_an_cost_r2 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 |
---|---|---|
F | Construction | 1002 |
G | Wholesale and retail trade; repair of motor vehicles and motorcycles | 1002 |
H | Transportation and storage | 1002 |
J | Information and communication | 1002 |
K | Financial and insurance activities | 1002 |
C | Manufacturing | 993 |
B-E | Industry (except construction) | 984 |
B-F | Industry and construction | 984 |
B-N | Business economy | 984 |
E | Water supply; sewerage, waste management and remediation activities | 984 |
G-J | Wholesale and retail trade; transport; accommodation and food service activities; information and communication | 984 |
G-N | Services of the business economy | 984 |
I | Accommodation and food service activities | 984 |
K-N | Financial and insurance activities; real estate activities; professional, scientific and technical activities; administrative and support service activities | 984 |
M | Professional, scientific and technical activities | 984 |
N | Administrative and support service activities | 984 |
P-S | Education; human health and social work activities; arts, entertainment and recreation; other service activities | 984 |
Q | Human health and social work activities | 984 |
R | Arts, entertainment and recreation | 984 |
S | Other service activities | 984 |
B | Mining and quarrying | 966 |
D | Electricity, gas, steam and air conditioning supply | 966 |
L | Real estate activities | 966 |
P | Education | 966 |
B-S_X_O | Industry, construction and services (except public administration, defense, compulsory social security) | 942 |
G-S_X_O | Services (except public administration, defense, compulsory social security, activities of households as employers and extra-territorial organisations and bodies) | 918 |
B-S | Industry, construction and services (except activities of households as employers and extra-territorial organisations and bodies) | 843 |
O | Public administration and defence; compulsory social security | 831 |
O-S | Public administration and defence; compulsory social security; education; human health and social work activities; arts, entertainment and recreation; other service activities | 801 |
%>%
lc_an_cost_r2 left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
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 .} {
%>%
lc_an_cost_r2 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()
time | Nobs |
---|---|
2011 | 6483 |
2010 | 6519 |
2009 | 6693 |
2008 | 5259 |
2007 | 486 |
2006 | 486 |
2005 | 486 |
2004 | 174 |
2003 | 174 |
2002 | 174 |
2001 | 174 |
2000 | 174 |
1999 | 174 |
1998 | 174 |
1997 | 174 |
1996 | 174 |