Last observation: 2023 (N = 276)
First observation: 2012 (N = 279)
Last data update: 02 août 2026, 08:01
Last compile: 16 sept. 2026, 20:31
Structure
Map - Cross Section
2014
Code
tgs00005 |>
filter(time == 2014,
nchar(geo) == 4) |>
right_join(europe_NUTS2, by = "geo") |>
filter(long >= -15, lat >= 33) |>
ggplot(aes(x = long, y = lat, group = group, fill = values/1000)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
breaks = seq(0, 2000, 10),
values = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 1)) +
theme_void() + theme(legend.position = c(0.25, 0.85)) +
labs(fill = "PPS GDP / person")
2015
Code
tgs00005 |>
filter(time == 2015,
nchar(geo) == 4) |>
right_join(europe_NUTS2, by = "geo") |>
filter(long >= -15, lat >= 33) |>
ggplot(aes(x = long, y = lat, group = group, fill = values/1000)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
breaks = seq(0, 2000, 10),
values = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 1)) +
theme_void() + theme(legend.position = c(0.25, 0.85)) +
labs(fill = "PPS GDP / person")
2018
Code
tgs00005 |>
filter(time == 2018,
nchar(geo) == 4) |>
right_join(europe_NUTS2, by = "geo") |>
filter(long >= -15, lat >= 33) |>
ggplot(aes(x = long, y = lat, group = group, fill = values/1000)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
breaks = seq(0, 2000, 10),
values = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 1)) +
theme_void() + theme(legend.position = c(0.25, 0.85)) +
labs(fill = "PPS GDP / person")
2019
Code
tgs00005 |>
filter(time == 2019,
nchar(geo) == 4) |>
right_join(europe_NUTS2, by = "geo") |>
filter(long >= -15, lat >= 33) |>
ggplot(aes(x = long, y = lat, group = group, fill = values/1000)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
breaks = seq(0, 2000, 10),
values = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 1)) +
theme_void() + theme(legend.position = c(0.25, 0.85)) +
labs(fill = "PPS GDP / person")
Change 2012-2022
Code
tgs00005 |>
filter(time %in% c("2012", "2022"),
nchar(geo) == 4) |>
select(-unit) |>
left_join(geo, by = "geo") |>
spread(time, values) |>
right_join(europe_NUTS2, by = "geo") |>
filter(long >= -15, lat >= 33) |>
ggplot(aes(x = long, y = lat, group = group, fill = `2022`/`2012`-1)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::percent_format(accuracy = 1),
breaks = 0.01*seq(-100, 100, 10)) +
theme_void() + theme(legend.position = c(0.25, 0.85)) +
labs(fill = "% Growth 2012-22 \n PPS GDP / person")
Change 2015-2019
Code
tgs00005 |>
filter(time %in% c("2015", "2019"),
nchar(geo) == 4) |>
select(-unit) |>
left_join(geo, by = "geo") |>
spread(time, values) |>
right_join(europe_NUTS2, by = "geo") |>
filter(long >= -15, lat >= 33) |>
ggplot(aes(x = long, y = lat, group = group, fill = `2019`/`2015`-1)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::percent_format(accuracy = 1),
breaks = 0.01*seq(-100, 100, 10)) +
theme_void() + theme(legend.position = c(0.25, 0.85)) +
labs(fill = "% Growth 2015-19 \n PPS GDP / person")
Tables: 2012, 2018, 2023, Growth
Code
tgs00005 |>
filter(time %in% c("2012", "2018", "2023")) |>
select(-unit) |>
left_join(geo, by = "geo") |>
spread(time, values) |>
mutate(`2012-23` = round(100*(`2023`/`2012`-1), 1)) |>
mutate(`2018-23` = round(100*(`2023`/`2018`-1), 1)) |>
arrange(-`2012-23`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Tables: # Of observations
Code
tgs00005 |>
select(-unit) |>
left_join(geo, by = "geo") |>
mutate(time = as.numeric(time)) |>
group_by(geo, Geo) |>
summarise(MinYear = min(time),
MaxYear = max(time),
Nobs = n()) |>
arrange(Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}