Last observation: 2024 (N = 148)
First observation: 2005 (N = 260)
Last data update: 14 aoû 2026, 19:23. Last compile: 18 aoû 2026, 01:25
Data - Eurostat
Last observation: 2024 (N = 148)
First observation: 2005 (N = 260)
Last data update: 14 aoû 2026, 19:23. Last compile: 18 aoû 2026, 01:25
htec_sti_pers2 |>
filter(geo %in% c("FR", "DE", "IT", "ES", "PL"),
nace_r2 == "C_HTC",
unit == "HC",
prof_pos == "RSE") |>
year_to_date() |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_flags +
scale_x_date(breaks = as.Date(paste0(seq(2000, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("R&D researchers, high-tech manufacturing (head count)")
htec_sti_pers2 |>
filter(geo == "FR",
nace_r2 %in% c("C_HTC", "C_HTC_M", "C_LTC", "C_LTC_M"),
unit == "HC",
prof_pos == "RSE") |>
year_to_date() |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(2000, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("R&D researchers (head count)")
latest_y <- htec_sti_pers2 |>
filter(geo %in% c("FR", "DE", "IT", "ES", "PL", "NL", "BE", "SE"),
nace_r2 %in% c("C_HTC", "C"),
unit == "HC",
prof_pos == "RSE",
!is.na(values)) |>
summarise(m = max(time)) |>
pull(m)
htec_sti_pers2 |>
filter(geo %in% c("FR", "DE", "IT", "ES", "PL", "NL", "BE", "SE"),
nace_r2 %in% c("C_HTC", "C"),
unit == "HC",
prof_pos == "RSE",
time == latest_y) |>
select(Geo, Nace_r2, values) |>
spread(Nace_r2, values) |>
arrange(-`High-technology manufacturing`) |>
print_table_conditional()| Geo | High-technology manufacturing | Manufacturing |
|---|---|---|
| France | 66357 | 135378 |
| Germany | 47147 | 252550 |
| Italy | 15529 | NA |
| Spain | 9238 | 27636 |
| Belgium | 7920 | NA |
| Poland | 5325 | 36575 |
| Netherlands | NA | NA |
| Sweden | NA | 46451 |