Average full time adjusted salary per employee

Data - Eurostat

Author

Info

source	dataset	Title	.html	.rData
eurostat	namq_10_pe	Population and employment	2026-01-29	2026-01-29
eurostat	nama_10_fte	Average full time adjusted salary per employee	2026-01-29	2026-01-29

LAST_COMPILE

LAST_COMPILE
2026-01-31

Last

Code

nama_10_fte %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()

time	Nobs
2024	56

freq

Code

nama_10_fte %>%
  left_join(freq, by = "freq") %>%
  group_by(freq, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

freq	Freq	Nobs
A	Annual	1560

unit

Code

nama_10_fte %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

unit	Unit	Nobs
EUR	Euro	780
NAC	National currency	780

geo

Code

nama_10_fte %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

time

Code

nama_10_fte %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Population Table

English

Code

nama_10_fte %>%
  filter(time %in% c(max(time), "2019", "2009", "1999", "1989"),
         unit == "EUR") %>%
  select(geo, time, values) %>%
  mutate(values = round(values/1000, 1)) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  arrange(- `2019`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

French

Code

load_data("eurostat/geo_fr.RData")
nama_10_fte %>%
  filter(time %in% c(max(time), "2019", "2009", "1999", "1989"),
         unit == "EUR") %>%
  select(geo, time, values) %>%
  mutate(values = round(values/1000, 1)) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  arrange(- `2019`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Countries

Poland, Germany, Hungary

Code

load_data("eurostat/geo.RData")
nama_10_fte %>%
  filter(geo %in% c("PL", "DE", "HU"),
         unit == "EUR") %>%
  select(geo, time, values) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  ggplot + geom_line(aes(x = date, y = values/1000, color = Geo)) +
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(-30, 150, 10))

France, Italy, Spain

Code

nama_10_fte %>%
  filter(geo %in% c("FR", "IT", "ES"),
         unit == "EUR") %>%
  select(geo, time, values) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  ggplot + geom_line(aes(x = date, y = values/1000, color = Geo)) +
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(-30, 150, 10))